Our group publishes papers presenting new methodologies, describing the results of studies that use our software, and reviewing current topics in the field of Bioinformatics. Scroll down or click here for a complete list of papers produced by our lab. Since 2013, we write blog posts summarizing new research papers and review articles:
GWAS
- Fine Mapping Causal Variants and Allelic Heterogeneity
- Widespread Allelic Heterogeneity in Complex Traits
- Selection in Europeans on Fatty Acid Desaturases Associated with Dietary Changes
- Incorporating prior information into association studies
- Characterization of Expression Quantitative Trait Loci in Pedigrees from Colombia and Costa Rica Ascertained for Bipolar Disorder
- Simultaneous modeling of disease status and clinical phenotypes to increase power in GWAS
- Efficient and accurate multiple-phenotype regression method for high dimensional data considering population structure
- Review Article: Population Structure in Genetic Studies: Confounding Factors and Mixed Models
- Colocalization of GWAS and eQTL Signals Detects Target Genes
- Chromosome conformation elucidates regulatory relationships in developing human brain
Mouse Genetics
- Review Article: The Hybrid Mouse Diversity Panel
- Genes, Environments and Meta-Analysis
- Review Article: Mixed Models and Population Structure
- Identifying Genes Involved in Blood Cell Traits
- Genes, Diet, and Body Weight (in Mice)
- Review Article: Mouse Genetics
Population Structure
- Efficient and accurate multiple-phenotype regression method for high dimensional data considering population structure
- Review Article: Population Structure in Genetic Studies: Confounding Factors and Mixed Models
- Accounting for Population Structure in Gene-by-Environment Interactions in Genome-Wide Association Studies Using Mixed Models
- Multiple testing correction in linear mixed models
- Identification of causal genes for complex traits (CAVIAR-gene)
- Accurate viral population assembly from ultra-deep sequencing data
- GRAT: Speeding up Expression Quantitative Trail Loci (eQTL) Studies
- Correcting Population Structure using Mixed Models Webcast
- Mixed models can correct for population structure for genomic regions under selection
Review Articles
- Review Article: Population Structure in Genetic Studies: Confounding Factors and Mixed Models
- Review Article: The Hybrid Mouse Diversity Panel
- Review Article: GWAS and Missing Heritability
- Review Article: Mixed Models and Population Structure
- Review Article: Mouse Genetics
Publications
Allele Specific Expression Association Priors Association Study Methods Causal Inference Causal Inference Biology cochlear function exome sequencing Expression QTLs Fine Mapping genome-wide association studies Haplotype Phasing Haplotyping from Sequences Heritability HMDP Imputation Meta-Analysis Mixed Models Motif Finding Mouse Genetics Multiple Phenotypes Multiple Testing Population Genetics Population Structure Methods Rare Variants RNA sequencing Sequence Assembly Sequencing with Pools Structural Variation unmapped reads Virus Genomics;Virus Assembly
2014 |
Hormozdiari, Farhad; Kostem, Emrah ; Kang, Eun Yong ; Pasaniuc, Bogdan ; Eskin, Eleazar Identifying causal variants at Loci with multiple signals of association. Journal Article Genetics, 198 (2), pp. 497-508, 2014, ISSN: 1943-2631. Abstract | Links | BibTeX | Tags: Fine Mapping @article{Hormozdiari:Genetics:2014, title = {Identifying causal variants at Loci with multiple signals of association.}, author = { Farhad Hormozdiari and Emrah Kostem and Eun Yong Kang and Bogdan Pasaniuc and Eleazar Eskin}, url = {http://dx.doi.org/10.1534/genetics.114.167908}, issn = {1943-2631}, year = {2014}, date = {2014-01-01}, journal = {Genetics}, volume = {198}, number = {2}, pages = {497-508}, address = {United States}, abstract = {Although genome-wide association studies have successfully identified thousands of risk loci for complex traits, only a handful of the biologically causal variants, responsible for association at these loci, have been successfully identified. Current statistical methods for identifying causal variants at risk loci either use the strength of the association signal in an iterative conditioning framework or estimate probabilities for variants to be causal. A main drawback of existing methods is that they rely on the simplifying assumption of a single causal variant at each risk locus, which is typically invalid at many risk loci. In this work, we propose a new statistical framework that allows for the possibility of an arbitrary number of causal variants when estimating the posterior probability of a variant being causal. A direct benefit of our approach is that we predict a set of variants for each locus that under reasonable assumptions will contain all of the true causal variants with a high confidence level (e.g., 95%) even when the locus contains multiple causal variants. We use simulations to show that our approach provides 20-50% improvement in our ability to identify the causal variants compared to the existing methods at loci harboring multiple causal variants. We validate our approach using empirical data from an expression QTL study of CHI3L2 to identify new causal variants that affect gene expression at this locus. CAVIAR is publicly available online at http://genetics.cs.ucla.edu/caviar/}, keywords = {Fine Mapping}, pubstate = {published}, tppubtype = {article} } Although genome-wide association studies have successfully identified thousands of risk loci for complex traits, only a handful of the biologically causal variants, responsible for association at these loci, have been successfully identified. Current statistical methods for identifying causal variants at risk loci either use the strength of the association signal in an iterative conditioning framework or estimate probabilities for variants to be causal. A main drawback of existing methods is that they rely on the simplifying assumption of a single causal variant at each risk locus, which is typically invalid at many risk loci. In this work, we propose a new statistical framework that allows for the possibility of an arbitrary number of causal variants when estimating the posterior probability of a variant being causal. A direct benefit of our approach is that we predict a set of variants for each locus that under reasonable assumptions will contain all of the true causal variants with a high confidence level (e.g., 95%) even when the locus contains multiple causal variants. We use simulations to show that our approach provides 20-50% improvement in our ability to identify the causal variants compared to the existing methods at loci harboring multiple causal variants. We validate our approach using empirical data from an expression QTL study of CHI3L2 to identify new causal variants that affect gene expression at this locus. CAVIAR is publicly available online at http://genetics.cs.ucla.edu/caviar/ |
Kichaev, Gleb; Yang, Wen-Yun Y; Lindstrom, Sara ; Hormozdiari, Farhad ; Eskin, Eleazar ; Price, Alkes L; Kraft, Peter ; Pasaniuc, Bogdan Integrating functional data to prioritize causal variants in statistical fine-mapping studies. Journal Article PLoS Genet, 10 (10), pp. e1004722, 2014, ISSN: 1553-7404. Abstract | Links | BibTeX | Tags: Fine Mapping @article{Kichaev:PlosGenet:2014b, title = {Integrating functional data to prioritize causal variants in statistical fine-mapping studies.}, author = { Gleb Kichaev and Wen-Yun Y. Yang and Sara Lindstrom and Farhad Hormozdiari and Eleazar Eskin and Alkes L. Price and Peter Kraft and Bogdan Pasaniuc}, url = {http://dx.doi.org/10.1371/journal.pgen.1004722}, issn = {1553-7404}, year = {2014}, date = {2014-01-01}, journal = {PLoS Genet}, volume = {10}, number = {10}, pages = {e1004722}, address = {United States}, abstract = {Standard statistical approaches for prioritization of variants for functional testing in fine-mapping studies either use marginal association statistics or estimate posterior probabilities for variants to be causal under simplifying assumptions. Here, we present a probabilistic framework that integrates association strength with functional genomic annotation data to improve accuracy in selecting plausible causal variants for functional validation. A key feature of our approach is that it empirically estimates the contribution of each functional annotation to the trait of interest directly from summary association statistics while allowing for multiple causal variants at any risk locus. We devise efficient algorithms that estimate the parameters of our model across all risk loci to further increase performance. Using simulations starting from the 1000 Genomes data, we find that our framework consistently outperforms the current state-of-the-art fine-mapping methods, reducing the number of variants that need to be selected to capture 90% of the causal variants from an average of 13.3 to 10.4 SNPs per locus (as compared to the next-best performing strategy). Furthermore, we introduce a cost-to-benefit optimization framework for determining the number of variants to be followed up in functional assays and assess its performance using real and simulation data. We validate our findings using a large scale meta-analysis of four blood lipids traits and find that the relative probability for causality is increased for variants in exons and transcription start sites and decreased in repressed genomic regions at the risk loci of these traits. Using these highly predictive, trait-specific functional annotations, we estimate causality probabilities across all traits and variants, reducing the size of the 90% confidence set from an average of 17.5 to 13.5 variants per locus in this data}, keywords = {Fine Mapping}, pubstate = {published}, tppubtype = {article} } Standard statistical approaches for prioritization of variants for functional testing in fine-mapping studies either use marginal association statistics or estimate posterior probabilities for variants to be causal under simplifying assumptions. Here, we present a probabilistic framework that integrates association strength with functional genomic annotation data to improve accuracy in selecting plausible causal variants for functional validation. A key feature of our approach is that it empirically estimates the contribution of each functional annotation to the trait of interest directly from summary association statistics while allowing for multiple causal variants at any risk locus. We devise efficient algorithms that estimate the parameters of our model across all risk loci to further increase performance. Using simulations starting from the 1000 Genomes data, we find that our framework consistently outperforms the current state-of-the-art fine-mapping methods, reducing the number of variants that need to be selected to capture 90% of the causal variants from an average of 13.3 to 10.4 SNPs per locus (as compared to the next-best performing strategy). Furthermore, we introduce a cost-to-benefit optimization framework for determining the number of variants to be followed up in functional assays and assess its performance using real and simulation data. We validate our findings using a large scale meta-analysis of four blood lipids traits and find that the relative probability for causality is increased for variants in exons and transcription start sites and decreased in repressed genomic regions at the risk loci of these traits. Using these highly predictive, trait-specific functional annotations, we estimate causality probabilities across all traits and variants, reducing the size of the 90% confidence set from an average of 17.5 to 13.5 variants per locus in this data |
Hormozdiari, Farhad; Eskin, Eleazar Memory efficient assembly of human genome. Journal Article J Bioinform Comput Biol, pp. 1550008, 2014, ISSN: 1757-6334. Abstract | Links | BibTeX | Tags: Sequence Assembly @article{Hormozdiari:JBioinformComputBiol:2014, title = {Memory efficient assembly of human genome.}, author = {Farhad Hormozdiari and Eleazar Eskin}, url = {http://dx.doi.org/10.1142/S0219720015500080}, issn = {1757-6334}, year = {2014}, date = {2014-01-01}, journal = {J Bioinform Comput Biol}, pages = {1550008}, abstract = {The ability to detect the genetic variations between two individuals is an essential component for genetic studies. In these studies, obtaining the genome sequence of both individuals is the first step toward variation detection problem. The emergence of high-throughput sequencing (HTS) technology has made DNA sequencing practical, and is widely used by diagnosticians to increase their knowledge about the casual factor in genetic related diseases. As HTS advances, more data are generated every day than the amount that scientists can process. Genome assembly is one of the existing methods to tackle the variation detection problem. The de Bruijn graph formulation of the assembly problem is widely used in the field. Furthermore, it is the only method which can assemble any genome in linear time. However, it requires an enormous amount of memory in order to assemble any mammalian size genome. The high demands of sequencing more individuals and the urge to assemble them are the driving forces for a memory efficient assembler. In this work, we propose a novel method which builds the de Bruijn graph while consuming lower memory. Moreover, our proposed method can reduce the memory usage by 37% compared to the existing methods. In addition, we used a real data set (chromosome 17 of A/J strain) to illustrate the performance of our method}, keywords = {Sequence Assembly}, pubstate = {published}, tppubtype = {article} } The ability to detect the genetic variations between two individuals is an essential component for genetic studies. In these studies, obtaining the genome sequence of both individuals is the first step toward variation detection problem. The emergence of high-throughput sequencing (HTS) technology has made DNA sequencing practical, and is widely used by diagnosticians to increase their knowledge about the casual factor in genetic related diseases. As HTS advances, more data are generated every day than the amount that scientists can process. Genome assembly is one of the existing methods to tackle the variation detection problem. The de Bruijn graph formulation of the assembly problem is widely used in the field. Furthermore, it is the only method which can assemble any genome in linear time. However, it requires an enormous amount of memory in order to assemble any mammalian size genome. The high demands of sequencing more individuals and the urge to assemble them are the driving forces for a memory efficient assembler. In this work, we propose a novel method which builds the de Bruijn graph while consuming lower memory. Moreover, our proposed method can reduce the memory usage by 37% compared to the existing methods. In addition, we used a real data set (chromosome 17 of A/J strain) to illustrate the performance of our method |
Yang, Wen-Yun Y; Platt, Alexander; Chiang, Charleston Wen-Kai; Eskin, Eleazar; Novembre, John; Pasaniuc, Bogdan Spatial localization of recent ancestors for admixed individuals. Journal Article G3 (Bethesda), 4 (12), pp. 2505-18, 2014, ISSN: 2160-1836. Abstract | Links | BibTeX | Tags: Spatial Population Structure @article{Yang:G3:2014, title = {Spatial localization of recent ancestors for admixed individuals.}, author = { Wen-Yun Y. Yang and Alexander Platt and Charleston Wen-Kai Chiang and Eleazar Eskin and John Novembre and Bogdan Pasaniuc}, url = {http://dx.doi.org/10.1534/g3.114.014274}, issn = {2160-1836}, year = {2014}, date = {2014-01-01}, journal = {G3 (Bethesda)}, volume = {4}, number = {12}, pages = {2505-18}, address = {United States}, abstract = {Ancestry analysis from genetic data plays a critical role in studies of human disease and evolution. Recent work has introduced explicit models for the geographic distribution of genetic variation and has shown that such explicit models yield superior accuracy in ancestry inference over nonmodel-based methods. Here we extend such work to introduce a method that models admixture between ancestors from multiple sources across a geographic continuum. We devise efficient algorithms based on hidden Markov models to localize on a map the recent ancestors (e.g., grandparents) of admixed individuals, joint with assigning ancestry at each locus in the genome. We validate our methods by using empirical data from individuals with mixed European ancestry from the Population Reference Sample study and show that our approach is able to localize their recent ancestors within an average of 470 km of the reported locations of their grandparents. Furthermore, simulations from real Population Reference Sample genotype data show that our method attains high accuracy in localizing recent ancestors of admixed individuals in Europe (an average of 550 km from their true location for localization of two ancestries in Europe, four generations ago). We explore the limits of ancestry localization under our approach and find that performance decreases as the number of distinct ancestries and generations since admixture increases. Finally, we build a map of expected localization accuracy across admixed individuals according to the location of origin within Europe of their ancestors}, keywords = {Spatial Population Structure}, pubstate = {published}, tppubtype = {article} } Ancestry analysis from genetic data plays a critical role in studies of human disease and evolution. Recent work has introduced explicit models for the geographic distribution of genetic variation and has shown that such explicit models yield superior accuracy in ancestry inference over nonmodel-based methods. Here we extend such work to introduce a method that models admixture between ancestors from multiple sources across a geographic continuum. We devise efficient algorithms based on hidden Markov models to localize on a map the recent ancestors (e.g., grandparents) of admixed individuals, joint with assigning ancestry at each locus in the genome. We validate our methods by using empirical data from individuals with mixed European ancestry from the Population Reference Sample study and show that our approach is able to localize their recent ancestors within an average of 470 km of the reported locations of their grandparents. Furthermore, simulations from real Population Reference Sample genotype data show that our method attains high accuracy in localizing recent ancestors of admixed individuals in Europe (an average of 550 km from their true location for localization of two ancestries in Europe, four generations ago). We explore the limits of ancestry localization under our approach and find that performance decreases as the number of distinct ancestries and generations since admixture increases. Finally, we build a map of expected localization accuracy across admixed individuals according to the location of origin within Europe of their ancestors |
2013 |
Wang, Zhanyong; Hormozdiari, Farhad ; Yang, Wen-Yun Y; Halperin, Eran ; Eskin, Eleazar CNVeM: Copy Number Variation Detection Using Uncertainty of Read Mapping. Journal Article J Comput Biol, 2013, ISSN: 1557-8666. Abstract | Links | BibTeX | Tags: Sequencing Next Generation, Structural Variation @article{Wang:JComputBiol:2013, title = {CNVeM: Copy Number Variation Detection Using Uncertainty of Read Mapping.}, author = { Zhanyong Wang and Farhad Hormozdiari and Wen-Yun Y. Yang and Eran Halperin and Eleazar Eskin}, url = {http://dx.doi.org/10.1089/cmb.2012.0258}, issn = {1557-8666}, year = {2013}, date = {2013-01-01}, journal = {J Comput Biol}, organization = {1 Computer Science Department, University of California Los Angeles , Los Angeles, CA.}, abstract = {Abstract Copy number variations (CNVs) are widely known to be an important mediator for diseases and traits. The development of high-throughput sequencing (HTS) technologies has provided great opportunities to identify CNV regions in mammalian genomes. In a typical experiment, millions of short reads obtained from a genome of interest are mapped to a reference genome. The mapping information can be used to identify CNV regions. One important challenge in analyzing the mapping information is the large fraction of reads that can be mapped to multiple positions. Most existing methods either only consider reads that can be uniquely mapped to the reference genome or randomly place a read to one of its mapping positions. Therefore, these methods have low power to detect CNVs located within repeated sequences. In this study, we propose a probabilistic model, CNVeM, that utilizes the inherent uncertainty of read mapping. We use maximum likelihood to estimate locations and copy numbers of copied regions and implement an expectation-maximization (EM) algorithm. One important contribution of our model is that we can distinguish between regions in the reference genome that differ from each other by as little as 0.1%. As our model aims to predict the copy number of each nucleotide, we can predict the CNV boundaries with high resolution. We apply our method to simulated datasets and achieve higher accuracy compared to CNVnator. Moreover, we apply our method to real data from which we detected known CNVs. To our knowledge, this is the first attempt to predict CNVs at nucleotide resolution and to utilize uncertainty of read mapping}, keywords = {Sequencing Next Generation, Structural Variation}, pubstate = {published}, tppubtype = {article} } Abstract Copy number variations (CNVs) are widely known to be an important mediator for diseases and traits. The development of high-throughput sequencing (HTS) technologies has provided great opportunities to identify CNV regions in mammalian genomes. In a typical experiment, millions of short reads obtained from a genome of interest are mapped to a reference genome. The mapping information can be used to identify CNV regions. One important challenge in analyzing the mapping information is the large fraction of reads that can be mapped to multiple positions. Most existing methods either only consider reads that can be uniquely mapped to the reference genome or randomly place a read to one of its mapping positions. Therefore, these methods have low power to detect CNVs located within repeated sequences. In this study, we propose a probabilistic model, CNVeM, that utilizes the inherent uncertainty of read mapping. We use maximum likelihood to estimate locations and copy numbers of copied regions and implement an expectation-maximization (EM) algorithm. One important contribution of our model is that we can distinguish between regions in the reference genome that differ from each other by as little as 0.1%. As our model aims to predict the copy number of each nucleotide, we can predict the CNV boundaries with high resolution. We apply our method to simulated datasets and achieve higher accuracy compared to CNVnator. Moreover, we apply our method to real data from which we detected known CNVs. To our knowledge, this is the first attempt to predict CNVs at nucleotide resolution and to utilize uncertainty of read mapping |
Eskin, Itamar; Hormozdiari, Farhad ; Conde, Lucia ; Riby, Jacques ; Skibola, Chris ; Eskin, Eleazar ; Halperin, Eran eALPS: Estimating Abundance Levels in Pooled Sequencing Using Available Genotyping Data Conference Research in Computational Molecular Biology, Tel-Aviv University Springer Berlin Heidelberg, 2013. Abstract | Links | BibTeX | Tags: Sequencing with Pools @conference{Eskin:ResearchInComputationalMolecularBiology:2013, title = {eALPS: Estimating Abundance Levels in Pooled Sequencing Using Available Genotyping Data}, author = { Itamar Eskin and Farhad Hormozdiari and Lucia Conde and Jacques Riby and Chris Skibola and Eleazar Eskin and Eran Halperin}, url = {http://dx.doi.org/10.1007/978-3-642-37195-0_4}, year = {2013}, date = {2013-01-01}, booktitle = {Research in Computational Molecular Biology}, pages = {32-44}, publisher = {Springer Berlin Heidelberg}, organization = {Tel-Aviv University}, abstract = {The recent advances in high-throughput sequencing technologies bring the potential of a better characterization of the genetic variation in humans and other organisms. In many occasions, either by design or by necessity, the sequencing procedure is performed on a pool of DNA samples with different abundances, where the abundance of each sample is unknown. Such a scenario is naturally occurring in the case of metagenomics analysis where a pool of bacteria is sequenced, or in the case of population studies involving DNA pools by design. Particularly, various pooling designs were recently suggested that can identify carriers of rare alleles in large cohorts, dramatically reducing the cost of such large-scale sequencing projects. A fundamental problem with such approaches for population studies is that the uncertainly of DNA proportions from different individuals in the pools might lead to spurious associations. Fortunately, it is often the case that the genotype data of at least some of the individuals in the pool is known. Here, we propose a method (eALPS) that uses the genotype data in conjunction with the pooled sequence data in order to accurately estimate the proportions of the samples in the pool, even in cases where not all individuals in the pool were genotyped (eALPS-LD). Using real data from a sequencing pooling study of Non-Hodgkins Lymphoma, we demonstrate that the estimation of the proportions is crucial, since otherwise there is a risk for false discoveries. Additionally, we demonstrate that our approach is also applicable to the problem of quantification of species in metagenomics samples (eALPS-BCR), and is particularly suitable for metagenomic quantification of closely-related species.}, keywords = {Sequencing with Pools}, pubstate = {published}, tppubtype = {conference} } The recent advances in high-throughput sequencing technologies bring the potential of a better characterization of the genetic variation in humans and other organisms. In many occasions, either by design or by necessity, the sequencing procedure is performed on a pool of DNA samples with different abundances, where the abundance of each sample is unknown. Such a scenario is naturally occurring in the case of metagenomics analysis where a pool of bacteria is sequenced, or in the case of population studies involving DNA pools by design. Particularly, various pooling designs were recently suggested that can identify carriers of rare alleles in large cohorts, dramatically reducing the cost of such large-scale sequencing projects. A fundamental problem with such approaches for population studies is that the uncertainly of DNA proportions from different individuals in the pools might lead to spurious associations. Fortunately, it is often the case that the genotype data of at least some of the individuals in the pool is known. Here, we propose a method (eALPS) that uses the genotype data in conjunction with the pooled sequence data in order to accurately estimate the proportions of the samples in the pool, even in cases where not all individuals in the pool were genotyped (eALPS-LD). Using real data from a sequencing pooling study of Non-Hodgkins Lymphoma, we demonstrate that the estimation of the proportions is crucial, since otherwise there is a risk for false discoveries. Additionally, we demonstrate that our approach is also applicable to the problem of quantification of species in metagenomics samples (eALPS-BCR), and is particularly suitable for metagenomic quantification of closely-related species. |
Kostem, Emrah; Eskin, Eleazar Efficiently Identifying Significant Associations in Genome-Wide Association Studies Conference Research in Computational Molecular Biology, University of California Springer Berlin Heidelberg, 2013. Abstract | Links | BibTeX | Tags: Association Study Methods, Expression QTLs @conference{Kostem:ResearchInComputationalMolecularBiology:201, title = {Efficiently Identifying Significant Associations in Genome-Wide Association Studies}, author = { Emrah Kostem and Eleazar Eskin}, url = {http://dx.doi.org/10.1007/978-3-642-37195-0_10}, year = {2013}, date = {2013-01-01}, booktitle = {Research in Computational Molecular Biology}, pages = {118-131}, publisher = {Springer Berlin Heidelberg}, organization = {University of California}, abstract = {Over the past several years, genome wide association studies (GWAS) have implicated hundreds of genes in common disease. More recently, the GWAS approach has been utilized to identify regions of the genome which harbor variation affecting gene expression or expression quantitative trait loci (eQTLs). Unlike GWAS applied to clinical traits where only a handful of phenotypes are analyzed per study, in (eQTL) studies, tens of thousands of gene expression levels are measured and the GWAS approach is applied to each gene expression level. This leads to computing billions of statistical tests and requires substantial computational resources, particularly when applying novel statistical methods such as mixed-models. We introduce a novel two-stage testing procedure that identifies all of the significant associations more efficiently than testing all the SNPs. In the first-stage a small number of informative SNPs, or proxies, across the genome are tested. Based on their observed associations, our approach locates the regions which may contain significant SNPs and only tests additional SNPs from those regions. We show through simulations and analysis of real GWAS datasets that the proposed two-stage procedure increases the computational speed by a factor of 10. Additionally, efficient implementation of our software increases the computational speed relative to state of the art testing approaches by a factor of 75.}, keywords = {Association Study Methods, Expression QTLs}, pubstate = {published}, tppubtype = {conference} } Over the past several years, genome wide association studies (GWAS) have implicated hundreds of genes in common disease. More recently, the GWAS approach has been utilized to identify regions of the genome which harbor variation affecting gene expression or expression quantitative trait loci (eQTLs). Unlike GWAS applied to clinical traits where only a handful of phenotypes are analyzed per study, in (eQTL) studies, tens of thousands of gene expression levels are measured and the GWAS approach is applied to each gene expression level. This leads to computing billions of statistical tests and requires substantial computational resources, particularly when applying novel statistical methods such as mixed-models. We introduce a novel two-stage testing procedure that identifies all of the significant associations more efficiently than testing all the SNPs. In the first-stage a small number of informative SNPs, or proxies, across the genome are tested. Based on their observed associations, our approach locates the regions which may contain significant SNPs and only tests additional SNPs from those regions. We show through simulations and analysis of real GWAS datasets that the proposed two-stage procedure increases the computational speed by a factor of 10. Additionally, efficient implementation of our software increases the computational speed relative to state of the art testing approaches by a factor of 75. |
Leikauf, George D; Concel, Vincent J; Bein, Kiflai ; Liu, Pengyuan ; Berndt, Annerose ; Martin, Timothy M; Ganguly, Koustav ; Jang, An-Soo S; Brant, Kelly A; Jr, Richard Dopico A; Upadhyay, Swapna ; Cario, Clinton L; Di, Yp Peter ; Vuga, Louis J; Kostem, Emrah ; Eskin, Eleazar ; You, Ming ; Kaminski, Naftali ; Prows, Daniel R; Knoell, Daren L; Fabisiak, James P Functional Genomic Assessment of Phosgene-induced Acute Lung Injury in Mice. Journal Article Am J Respir Cell Mol Biol, 2013, ISSN: 1535-4989. Abstract | Links | BibTeX | Tags: Mouse Genetics @article{Leikauf:AmJRespirCellMolBiol:2013, title = {Functional Genomic Assessment of Phosgene-induced Acute Lung Injury in Mice.}, author = { George D. Leikauf and Vincent J. Concel and Kiflai Bein and Pengyuan Liu and Annerose Berndt and Timothy M. Martin and Koustav Ganguly and An-Soo S. Jang and Kelly A. Brant and Richard A. Dopico Jr and Swapna Upadhyay and Clinton L. Cario and Yp Peter Di and Louis J. Vuga and Emrah Kostem and Eleazar Eskin and Ming You and Naftali Kaminski and Daniel R. Prows and Daren L. Knoell and James P. Fabisiak}, url = {http://dx.doi.org/10.1165/rcmb.2012-0337OC}, issn = {1535-4989}, year = {2013}, date = {2013-01-01}, journal = {Am J Respir Cell Mol Biol}, organization = {University of Pittsburgh, Graduate School of Public Health, Department of Environmental and Occupational Health, Pittsburgh, Pennsylvania, United States ; gleikauf@pitt.edu.}, abstract = {In this study, a genetically-diverse panel of 43 mouse strains was exposed to phosgene and genome-wide association mapping performed employing a high density SNP assembly. Transcriptomic analysis was also used to improve the genetic resolution in the identification of genetic determinants of phosgene-induced acute lung injury. We prioritized the identified genes based on whether the encoded protein was previously associated with lung injury or contained a nonsynonymous SNP within a functional domain. In addition, candidates were selected that contained a promoter SNP that could alter a putative transcription factor binding site and had variable expression by transcriptomic analyses. The latter 2 criteria also required that 10% of mice carried the minor allele and that this allele could account for 10% of the phenotypic difference noted between the strains at the phenotypic extremes. This integrative functional approach revealed 14 candidate genes that included Atp1a1, Alox5, Galnt11, Hrh1, Mbd4, Phactr2, Plxnd1, Ptprt, Reln, and Zfand4, which had significant SNP associations, and Itga9, Man1a2, Mapk14, and Vwf, which had suggestive SNP associations. Of the genes with significant SNP associations, Atp1a1, Alox5, Plxnd1, Ptprt, and Zfand4 are particularly noteworthy and could be associated with acute lung injury in several ways. Using a competitive electrophoretic mobility shift analysis, Atp1a1 promoter (rs215053185) oligonucleotide containing the minor G-allele formed a major distinct faster-migrating complex. In addition, a gene with a suggestive SNP association, Itga9, is linked to TGFB1-signaling, which previously has been associated with the susceptibility to acute lung injury in mice}, keywords = {Mouse Genetics}, pubstate = {published}, tppubtype = {article} } In this study, a genetically-diverse panel of 43 mouse strains was exposed to phosgene and genome-wide association mapping performed employing a high density SNP assembly. Transcriptomic analysis was also used to improve the genetic resolution in the identification of genetic determinants of phosgene-induced acute lung injury. We prioritized the identified genes based on whether the encoded protein was previously associated with lung injury or contained a nonsynonymous SNP within a functional domain. In addition, candidates were selected that contained a promoter SNP that could alter a putative transcription factor binding site and had variable expression by transcriptomic analyses. The latter 2 criteria also required that 10% of mice carried the minor allele and that this allele could account for 10% of the phenotypic difference noted between the strains at the phenotypic extremes. This integrative functional approach revealed 14 candidate genes that included Atp1a1, Alox5, Galnt11, Hrh1, Mbd4, Phactr2, Plxnd1, Ptprt, Reln, and Zfand4, which had significant SNP associations, and Itga9, Man1a2, Mapk14, and Vwf, which had suggestive SNP associations. Of the genes with significant SNP associations, Atp1a1, Alox5, Plxnd1, Ptprt, and Zfand4 are particularly noteworthy and could be associated with acute lung injury in several ways. Using a competitive electrophoretic mobility shift analysis, Atp1a1 promoter (rs215053185) oligonucleotide containing the minor G-allele formed a major distinct faster-migrating complex. In addition, a gene with a suggestive SNP association, Itga9, is linked to TGFB1-signaling, which previously has been associated with the susceptibility to acute lung injury in mice |
Parks, Brian W; Nam, Elizabeth ; Org, Elin ; Kostem, Emrah ; Norheim, Frode ; Hui, Simon T; Pan, Calvin ; Civelek, Mete ; Rau, Christoph D; Bennett, Brian J; Mehrabian, Margarete ; Ursell, Luke K; He, Aiqing ; Castellani, Lawrence W; Zinker, Bradley ; Kirby, Mark ; Drake, Thomas A; Drevon, Christian A; Knight, Rob ; Gargalovic, Peter ; Kirchgessner, Todd ; Eskin, Eleazar ; Lusis, Aldons J Genetic control of obesity and gut microbiota composition in response to high-fat, high-sucrose diet in mice. Journal Article Cell Metab, 17 (1), pp. 141-52, 2013, ISSN: 1932-7420. Abstract | Links | BibTeX | Tags: HMDP, Mouse Genetics @article{Parks:CellMetab:2013, title = {Genetic control of obesity and gut microbiota composition in response to high-fat, high-sucrose diet in mice.}, author = { Brian W. Parks and Elizabeth Nam and Elin Org and Emrah Kostem and Frode Norheim and Simon T. Hui and Calvin Pan and Mete Civelek and Christoph D. Rau and Brian J. Bennett and Margarete Mehrabian and Luke K. Ursell and Aiqing He and Lawrence W. Castellani and Bradley Zinker and Mark Kirby and Thomas A. Drake and Christian A. Drevon and Rob Knight and Peter Gargalovic and Todd Kirchgessner and Eleazar Eskin and Aldons J. Lusis}, url = {http://dx.doi.org/10.1016/j.cmet.2012.12.007}, issn = {1932-7420}, year = {2013}, date = {2013-01-01}, journal = {Cell Metab}, volume = {17}, number = {1}, pages = {141-52}, address = {United States}, organization = {Department of Medicine/Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA 90095, USA. Electronic address: bparks@mednet.ucla.edu.}, abstract = {Obesity is a highly heritable disease driven by complex interactions between genetic and environmental factors. Human genome-wide association studies (GWAS) have identified a number of loci contributing to obesity; however, a major limitation of these studies is the inability to assess environmental interactions common to obesity. Using a systems genetics approach, we measured obesity traits, global gene expression, and gut microbiota composition in response to a high-fat/high-sucrose (HF/HS) diet of more than 100 inbred strains of mice. Here we show that HF/HS feeding promotes robust, strain-specific changes in obesity that are not accounted for by food intake and provide evidence for a genetically determined set point for obesity. GWAS analysis identified 11 genome-wide significant loci associated with obesity traits, several of which overlap with loci identified in human studies. We also show strong relationships between genotype and gut microbiota plasticity during HF/HS feeding and identify gut microbial phylotypes associated with obesity}, keywords = {HMDP, Mouse Genetics}, pubstate = {published}, tppubtype = {article} } Obesity is a highly heritable disease driven by complex interactions between genetic and environmental factors. Human genome-wide association studies (GWAS) have identified a number of loci contributing to obesity; however, a major limitation of these studies is the inability to assess environmental interactions common to obesity. Using a systems genetics approach, we measured obesity traits, global gene expression, and gut microbiota composition in response to a high-fat/high-sucrose (HF/HS) diet of more than 100 inbred strains of mice. Here we show that HF/HS feeding promotes robust, strain-specific changes in obesity that are not accounted for by food intake and provide evidence for a genetically determined set point for obesity. GWAS analysis identified 11 genome-wide significant loci associated with obesity traits, several of which overlap with loci identified in human studies. We also show strong relationships between genotype and gut microbiota plasticity during HF/HS feeding and identify gut microbial phylotypes associated with obesity |
Parrish, Nathaniel; Sudakov, Benjamin ; Eskin, Eleazar Genome reassembly with high-throughput sequencing data Journal Article BMC Genomics, 14 (Suppl 1), pp. S8, 2013, ISSN: 1471-2164. Abstract | Links | BibTeX | Tags: Sequence Assembly @article{Parrish:BmcGenomics:2013, title = {Genome reassembly with high-throughput sequencing data}, author = { Nathaniel Parrish and Benjamin Sudakov and Eleazar Eskin}, url = {http://dx.doi.org/10.1186/1471-2164-14-S1-S8}, issn = {1471-2164}, year = {2013}, date = {2013-01-01}, journal = {BMC Genomics}, volume = {14}, number = {Suppl 1}, pages = {S8}, publisher = {BioMed Central Ltd}, address = {England}, abstract = {MOTIVATION: Recent studies in genomics have highlighted the significance of structural variation in determining individual variation. Current methods for identifying structural variation, however, are predominantly focused on either assembling whole genomes from scratch, or identifying the relatively small changes between a genome and a reference sequence. While significant progress has been made in recent years on both de novo assembly and resequencing (read mapping) methods, few attempts have been made to bridge the gap between them. RESULTS: In this paper, we present a computational method for incorporating a reference sequence into an assembly algorithm. We propose a novel graph construction that builds upon the well-known de Bruijn graph to incorporate the reference, and describe a simple algorithm, based on iterative message passing, which uses this information to significantly improve assembly results. We validate our method by applying it to a series of 5 Mb simulation genomes derived from both mammalian and bacterial references. The results of applying our method to this simulation data are presented along with a discussion of the benefits and drawbacks of this technique}, keywords = {Sequence Assembly}, pubstate = {published}, tppubtype = {article} } MOTIVATION: Recent studies in genomics have highlighted the significance of structural variation in determining individual variation. Current methods for identifying structural variation, however, are predominantly focused on either assembling whole genomes from scratch, or identifying the relatively small changes between a genome and a reference sequence. While significant progress has been made in recent years on both de novo assembly and resequencing (read mapping) methods, few attempts have been made to bridge the gap between them. RESULTS: In this paper, we present a computational method for incorporating a reference sequence into an assembly algorithm. We propose a novel graph construction that builds upon the well-known de Bruijn graph to incorporate the reference, and describe a simple algorithm, based on iterative message passing, which uses this information to significantly improve assembly results. We validate our method by applying it to a series of 5 Mb simulation genomes derived from both mammalian and bacterial references. The results of applying our method to this simulation data are presented along with a discussion of the benefits and drawbacks of this technique |
He, Dan; Han, Buhm ; Eskin, Eleazar Hap-seq: An Optimal Algorithm for Haplotype Phasing with Imputation Using Sequencing Data. Journal Article J Comput Biol, 20 (2), pp. 80-92, 2013, ISSN: 1557-8666. Abstract | Links | BibTeX | Tags: Haplotyping from Sequences, Imputation @article{He:JComputBiol:2013, title = {Hap-seq: An Optimal Algorithm for Haplotype Phasing with Imputation Using Sequencing Data.}, author = { Dan He and Buhm Han and Eleazar Eskin}, url = {http://dx.doi.org/10.1089/cmb.2012.0091}, issn = {1557-8666}, year = {2013}, date = {2013-01-01}, journal = {J Comput Biol}, volume = {20}, number = {2}, pages = {80-92}, address = {United States}, organization = {1 IBM T.J. Watson Research , Yorktown Heights, NY.}, abstract = {Abstract Inference of haplotypes, or the sequence of alleles along each chromosome, is a fundamental problem in genetics and is important for many analyses, including admixture mapping, identifying regions of identity by descent, and imputation. Traditionally, haplotypes are inferred from genotype data obtained from microarrays using information on population haplotype frequencies inferred from either a large sample of genotyped individuals or a reference dataset such as the HapMap. Since the availability of large reference datasets, modern approaches for haplotype phasing along these lines are closely related to imputation methods. When applied to data obtained from sequencing studies, a straightforward way to obtain haplotypes is to first infer genotypes from the sequence data and then apply an imputation method. However, this approach does not take into account that alleles on the same sequence read originate from the same chromosome. Haplotype assembly approaches take advantage of this insight and predict haplotypes by assigning the reads to chromosomes in such a way that minimizes the number of conflicts between the reads and the predicted haplotypes. Unfortunately, assembly approaches require very high sequencing coverage and are usually not able to fully reconstruct the haplotypes. In this work, we present a novel approach, Hap-seq, which is simultaneously an imputation and assembly method that combines information from a reference dataset with the information from the reads using a likelihood framework. Our method applies a dynamic programming algorithm to identify the predicted haplotype, which maximizes the joint likelihood of the haplotype with respect to the reference dataset and the haplotype with respect to the observed reads. We show that our method requires only low sequencing coverage and can reconstruct haplotypes containing both common and rare alleles with higher accuracy compared to the state-of-the-art imputation methods}, keywords = {Haplotyping from Sequences, Imputation}, pubstate = {published}, tppubtype = {article} } Abstract Inference of haplotypes, or the sequence of alleles along each chromosome, is a fundamental problem in genetics and is important for many analyses, including admixture mapping, identifying regions of identity by descent, and imputation. Traditionally, haplotypes are inferred from genotype data obtained from microarrays using information on population haplotype frequencies inferred from either a large sample of genotyped individuals or a reference dataset such as the HapMap. Since the availability of large reference datasets, modern approaches for haplotype phasing along these lines are closely related to imputation methods. When applied to data obtained from sequencing studies, a straightforward way to obtain haplotypes is to first infer genotypes from the sequence data and then apply an imputation method. However, this approach does not take into account that alleles on the same sequence read originate from the same chromosome. Haplotype assembly approaches take advantage of this insight and predict haplotypes by assigning the reads to chromosomes in such a way that minimizes the number of conflicts between the reads and the predicted haplotypes. Unfortunately, assembly approaches require very high sequencing coverage and are usually not able to fully reconstruct the haplotypes. In this work, we present a novel approach, Hap-seq, which is simultaneously an imputation and assembly method that combines information from a reference dataset with the information from the reads using a likelihood framework. Our method applies a dynamic programming algorithm to identify the predicted haplotype, which maximizes the joint likelihood of the haplotype with respect to the reference dataset and the haplotype with respect to the observed reads. We show that our method requires only low sequencing coverage and can reconstruct haplotypes containing both common and rare alleles with higher accuracy compared to the state-of-the-art imputation methods |
Davis, Richard C; van Nas, Atila ; Bennett, Brian ; Orozco, Luz ; Pan, Calvin ; Rau, Christoph D; Eskin, Eleazar ; Lusis, Aldons J Genome-wide association mapping of blood cell traits in mice. Journal Article Mamm Genome, 2013, ISSN: 1432-1777. Abstract | Links | BibTeX | Tags: HMDP, Mouse Genetics @article{Davis:MammGenome:2013, title = {Genome-wide association mapping of blood cell traits in mice.}, author = { Richard C. Davis and Atila van Nas and Brian Bennett and Luz Orozco and Calvin Pan and Christoph D. Rau and Eleazar Eskin and Aldons J. Lusis}, url = {http://dx.doi.org/10.1007/s00335-013-9448-0}, issn = {1432-1777}, year = {2013}, date = {2013-01-01}, journal = {Mamm Genome}, organization = {Department of Medicine/Division of Cardiology, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.}, abstract = {Genetic variations in blood cell parameters can impact clinical traits. We report here the mapping of blood cell traits in a panel of 100 inbred strains of mice of the Hybrid Mouse Diversity Panel (HMDP) using genome-wide association (GWA). We replicated a locus previously identified in using linkage analysis in several genetic crosses for mean corpuscular volume (MCV) and a number of other red blood cell traits on distal chromosome 7. Our peak for SNP association to MCV occurred in a linkage disequilibrium (LD) block spanning from 109.38 to 111.75Mb that includes Hbb-b1, the likely causal gene. Altogether, we identified five loci controlling red blood cell traits (on chromosomes 1, 7, 11, 12, and 16), and four of these correspond to loci for red blood cell traits reported in a recent human GWA study. For white blood cells, including granulocytes, monocytes, and lymphocytes, a total of six significant loci were identified on chromosomes 1, 6, 8, 11, 12, and 15. An average of ten candidate genes were found at each locus and those were prioritized by examining functional variants in the HMDP such as missense and expression variants. These results provide intermediate phenotypes and candidate loci for genetic studies of atherosclerosis and cancer as well as inflammatory and immune disorders in mice}, keywords = {HMDP, Mouse Genetics}, pubstate = {published}, tppubtype = {article} } Genetic variations in blood cell parameters can impact clinical traits. We report here the mapping of blood cell traits in a panel of 100 inbred strains of mice of the Hybrid Mouse Diversity Panel (HMDP) using genome-wide association (GWA). We replicated a locus previously identified in using linkage analysis in several genetic crosses for mean corpuscular volume (MCV) and a number of other red blood cell traits on distal chromosome 7. Our peak for SNP association to MCV occurred in a linkage disequilibrium (LD) block spanning from 109.38 to 111.75Mb that includes Hbb-b1, the likely causal gene. Altogether, we identified five loci controlling red blood cell traits (on chromosomes 1, 7, 11, 12, and 16), and four of these correspond to loci for red blood cell traits reported in a recent human GWA study. For white blood cells, including granulocytes, monocytes, and lymphocytes, a total of six significant loci were identified on chromosomes 1, 6, 8, 11, 12, and 15. An average of ten candidate genes were found at each locus and those were prioritized by examining functional variants in the HMDP such as missense and expression variants. These results provide intermediate phenotypes and candidate loci for genetic studies of atherosclerosis and cancer as well as inflammatory and immune disorders in mice |
Luykx, J J; Bakker, S C; Lentjes, E; Neeleman, M; Strengman, E; Mentink, L; Deyoung, J; de Jong, S; Sul, J H; Eskin, E; van Eijk, K; van Setten, J; Buizer-Voskamp, J E; Cantor, R M; Lu, A; van Amerongen, M; van Dongen, E P A; Keijzers, P; Kappen, T; Borgdorff, P; Bruins, P; Derks, E M; Kahn, R S; Ophoff, R A Genome-wide association study of monoamine metabolite levels in human cerebrospinal fluid. Journal Article Mol Psychiatry, 2013, ISSN: 1476-5578. Abstract | Links | BibTeX | Tags: @article{Luykx:MolPsychiatry:2013, title = {Genome-wide association study of monoamine metabolite levels in human cerebrospinal fluid.}, author = { J. J. Luykx and S. C. Bakker and E. Lentjes and M. Neeleman and E. Strengman and L. Mentink and J. Deyoung and S. de Jong and J. H. Sul and E. Eskin and K. van Eijk and J. van Setten and J. E. Buizer-Voskamp and R. M. Cantor and A. Lu and M. van Amerongen and E. P. A. van Dongen and P. Keijzers and T. Kappen and P. Borgdorff and P. Bruins and E. M. Derks and R. S. Kahn and R. A. Ophoff}, url = {http://dx.doi.org/10.1038/mp.2012.183}, issn = {1476-5578}, year = {2013}, date = {2013-01-01}, journal = {Mol Psychiatry}, organization = {1] Department of Psychiatry, Rudolf Magnus Institute of Neuroscience, University Medical Center Utrecht, Utrecht, The Netherlands [2] Department of Medical Genetics, University Medical Center Utrecht,}, abstract = {Studying genetic determinants of intermediate phenotypes is a powerful tool to increase our understanding of genotype-phenotype correlations. Metabolic traits pertinent to the central nervous system (CNS) constitute a potentially informative target for genetic studies of intermediate phenotypes as their genetic underpinnings may elucidate etiological mechanisms. We therefore conducted a genome-wide association study (GWAS) of monoamine metabolite (MM) levels in cerebrospinal fluid (CSF) of 414 human subjects from the general population. In a linear model correcting for covariates, we identified one locus associated with MMs at a genome-wide significant level (standardized $beta$=0.32}, keywords = {}, pubstate = {published}, tppubtype = {article} } Studying genetic determinants of intermediate phenotypes is a powerful tool to increase our understanding of genotype-phenotype correlations. Metabolic traits pertinent to the central nervous system (CNS) constitute a potentially informative target for genetic studies of intermediate phenotypes as their genetic underpinnings may elucidate etiological mechanisms. We therefore conducted a genome-wide association study (GWAS) of monoamine metabolite (MM) levels in cerebrospinal fluid (CSF) of 414 human subjects from the general population. In a linear model correcting for covariates, we identified one locus associated with MMs at a genome-wide significant level (standardized $beta$=0.32 |
Kostem, Emrah; Eskin, Eleazar Improving the accuracy and efficiency of partitioning heritability into the contributions of genomic regions. Journal Article Am J Hum Genet, 92 (4), pp. 558-64, 2013, ISSN: 1537-6605. Abstract | Links | BibTeX | Tags: Heritability, Mixed Models @article{Kostem:AmJHumGenet:2013, title = {Improving the accuracy and efficiency of partitioning heritability into the contributions of genomic regions.}, author = { Emrah Kostem and Eleazar Eskin}, url = {http://dx.doi.org/10.1016/j.ajhg.2013.03.010}, issn = {1537-6605}, year = {2013}, date = {2013-01-01}, journal = {Am J Hum Genet}, volume = {92}, number = {4}, pages = {558-64}, address = {United States}, organization = {Department of Computer Science, University of California, Los Angeles, Los Angeles, CA 90095, USA. Electronic address: ekostem@cs.ucla.edu.}, abstract = {Quantifying heritability, the amount of genetic contribution in a complex trait, has been of fundamental interest to geneticists for decades. Recently, partitioning the heritability accounted for by common variants into the contributions of genomic regions has received a lot of attention given its important applications for understanding the genetic architecture of complex traits. Current methods partition the total heritability by jointly estimating the contributions of all regions. However, these methods are computationally intractable and can be inaccurate when the number of regions is large. In this paper, we present an alternative approach that partitions the total heritability into the contributions of an arbitrary number of regions. We demonstrate by using simulations that our approach is more accurate and computationally efficient than current approaches. Using a data set from a genome-wide association study on human height, we demonstrate the utility of our method by estimating the heritability contributions of chromosomes and subchromosomal regions}, keywords = {Heritability, Mixed Models}, pubstate = {published}, tppubtype = {article} } Quantifying heritability, the amount of genetic contribution in a complex trait, has been of fundamental interest to geneticists for decades. Recently, partitioning the heritability accounted for by common variants into the contributions of genomic regions has received a lot of attention given its important applications for understanding the genetic architecture of complex traits. Current methods partition the total heritability by jointly estimating the contributions of all regions. However, these methods are computationally intractable and can be inaccurate when the number of regions is large. In this paper, we present an alternative approach that partitions the total heritability into the contributions of an arbitrary number of regions. We demonstrate by using simulations that our approach is more accurate and computationally efficient than current approaches. Using a data set from a genome-wide association study on human height, we demonstrate the utility of our method by estimating the heritability contributions of chromosomes and subchromosomal regions |
He, Dan; Wang, Zhanyong ; Han, Buhm ; Parida, Laxmi ; Eskin, Eleazar IPED: Inheritance Path Based Pedigree Reconstruction Algorithm Using Genotype Data Conference Research in Computational Molecular Biology, University of California Los Angeles Springer Berlin Heidelberg, 2013. Abstract | Links | BibTeX | Tags: Pedigree Inference @conference{He:ResearchInComputationalMolecularBiology:2013, title = {IPED: Inheritance Path Based Pedigree Reconstruction Algorithm Using Genotype Data}, author = { Dan He and Zhanyong Wang and Buhm Han and Laxmi Parida and Eleazar Eskin}, url = {http://dx.doi.org/10.1007/978-3-642-37195-0_7}, year = {2013}, date = {2013-01-01}, booktitle = {Research in Computational Molecular Biology}, pages = {75-87}, publisher = {Springer Berlin Heidelberg}, organization = {University of California Los Angeles}, abstract = {The problem of inference of family trees, or pedigree reconstruction, for a group of individuals is a fundamental problem in genetics. Various methods have been proposed to automate the process of pedigree reconstruction given the genotypes or haplotypes of a set of individuals. Current methods, unfortunately, are very time consuming and inaccurate for complicated pedigrees such as pedigrees with inbreeding. In this work, we propose an efficient algorithm which is able to reconstruct large pedigrees with reasonable accuracy. Our algorithm reconstructs the pedigrees generation by generation backwards in time from the extant generation. We predict the relationships between individuals in the same generation using an inheritance path based approach implemented using an efficient dynamic programming algorithm. Experiments show that our algorithm runs in linear time with respect to the number of reconstructed generations and therefore it can reconstruct pedigrees which have a large number of generations. Indeed it is the first practical method for reconstruction of large pedigrees from genotype data.}, keywords = {Pedigree Inference}, pubstate = {published}, tppubtype = {conference} } The problem of inference of family trees, or pedigree reconstruction, for a group of individuals is a fundamental problem in genetics. Various methods have been proposed to automate the process of pedigree reconstruction given the genotypes or haplotypes of a set of individuals. Current methods, unfortunately, are very time consuming and inaccurate for complicated pedigrees such as pedigrees with inbreeding. In this work, we propose an efficient algorithm which is able to reconstruct large pedigrees with reasonable accuracy. Our algorithm reconstructs the pedigrees generation by generation backwards in time from the extant generation. We predict the relationships between individuals in the same generation using an inheritance path based approach implemented using an efficient dynamic programming algorithm. Experiments show that our algorithm runs in linear time with respect to the number of reconstructed generations and therefore it can reconstruct pedigrees which have a large number of generations. Indeed it is the first practical method for reconstruction of large pedigrees from genotype data. |
Lagarrigue, Sandrine; Hormozdiari, Farhad ; Martin, Lisa Joy ; Lecerf, Frédéric ; Hasin, Yehudit ; Rau, Christoph ; Hagopian, Raffi ; Xiao, Yu ; Yan, Jun ; Drake, Thomas A; Ghazalpour, Anatole ; Eskin, Eleazar ; Lusis, Aldons J Limited RNA Editing in Exons of Mouse Liver and Adipose Tissue. Journal Article Genetics, 2013, ISSN: 1943-2631. Abstract | Links | BibTeX | Tags: RNAseq @article{Lagarrigue:Genetics:2013, title = {Limited RNA Editing in Exons of Mouse Liver and Adipose Tissue.}, author = { Sandrine Lagarrigue and Farhad Hormozdiari and Lisa Joy Martin and Frédéric Lecerf and Yehudit Hasin and Christoph Rau and Raffi Hagopian and Yu Xiao and Jun Yan and Thomas A. Drake and Anatole Ghazalpour and Eleazar Eskin and Aldons J. Lusis}, url = {http://dx.doi.org/10.1534/genetics.112.149054}, issn = {1943-2631}, year = {2013}, date = {2013-01-01}, journal = {Genetics}, organization = {Agrocampus Ouest Department of Animal Sciences;}, abstract = {Several studies have investigated RNA-DNA differences (RDD), presumably due to RNA editing, with conflicting results. We report a rigorous analysis of RDD in exonic regions in mice, taking into account critical biases in RNA-Seq analysis. Using deep-sequenced F1 reciprocal inbred mice, we mapped 40 million RNA-Seq reads per liver sample and 180 million reads per adipose sample. We found 7,300 apparent hepatic RDDs using a multiple-site mapping procedure, compared with 293 RDD found using a unique-site mapping procedure. After filtering for repeat sequence, splice junction proximity, undirectional strand and extremity read bias, 63 RDD remained. In adipose tissue unique-site mapping identified 1,667 RDD, and after applying the same four filters, 188 RDDs remained. In both tissues, the filtering procedure increased the proportion of canonical (A-to-I and C-to-U) editing events. The genomic DNA of 12 RDD sites among the potential 63 hepatic RDD was tested by Sanger sequencing, three of which proved to be due to an unreferenced SNP. We validated seven liver RDD with Sequenom technology, including two non-canonical, Gm5424 C-to-I(G) and Pisd I(G)-to-A RDD. Differences in diet, sex, or genetic background had very modest effects on RDD occurrence. Only a small number of apparent RDD sites overlapped between liver and adipose, indicating a high degree of tissue specificity. Our findings underscore the importance of properly filtering for bias in RNA-Seq investigations, including the necessity of confirming the DNA sequence to eliminate unreferenced SNPs. Based on our results, we conclude that RNA editing is likely limited to hundreds of events in exonic RNA in liver and adipose}, keywords = {RNAseq}, pubstate = {published}, tppubtype = {article} } Several studies have investigated RNA-DNA differences (RDD), presumably due to RNA editing, with conflicting results. We report a rigorous analysis of RDD in exonic regions in mice, taking into account critical biases in RNA-Seq analysis. Using deep-sequenced F1 reciprocal inbred mice, we mapped 40 million RNA-Seq reads per liver sample and 180 million reads per adipose sample. We found 7,300 apparent hepatic RDDs using a multiple-site mapping procedure, compared with 293 RDD found using a unique-site mapping procedure. After filtering for repeat sequence, splice junction proximity, undirectional strand and extremity read bias, 63 RDD remained. In adipose tissue unique-site mapping identified 1,667 RDD, and after applying the same four filters, 188 RDDs remained. In both tissues, the filtering procedure increased the proportion of canonical (A-to-I and C-to-U) editing events. The genomic DNA of 12 RDD sites among the potential 63 hepatic RDD was tested by Sanger sequencing, three of which proved to be due to an unreferenced SNP. We validated seven liver RDD with Sequenom technology, including two non-canonical, Gm5424 C-to-I(G) and Pisd I(G)-to-A RDD. Differences in diet, sex, or genetic background had very modest effects on RDD occurrence. Only a small number of apparent RDD sites overlapped between liver and adipose, indicating a high degree of tissue specificity. Our findings underscore the importance of properly filtering for bias in RNA-Seq investigations, including the necessity of confirming the DNA sequence to eliminate unreferenced SNPs. Based on our results, we conclude that RNA editing is likely limited to hundreds of events in exonic RNA in liver and adipose |
Sul, Jae Hoon; Eskin, Eleazar Mixed models can correct for population structure for genomic regions under selection. Journal Article Nat Rev Genet, 2013, ISSN: 1471-0064. Links | BibTeX | Tags: Mixed Models, Population Structure Methods @article{Sul:NatRevGenet:2013, title = {Mixed models can correct for population structure for genomic regions under selection.}, author = { Jae Hoon Sul and Eleazar Eskin}, url = {http://dx.doi.org/10.1038/nrg2813-c1}, issn = {1471-0064}, year = {2013}, date = {2013-01-01}, journal = {Nat Rev Genet}, organization = {Computer Science Department, University of California, Los Angeles, California 90095, USA.}, keywords = {Mixed Models, Population Structure Methods}, pubstate = {published}, tppubtype = {article} } |
Navon, Oron; Sul, Jae Hoon ; Han, Buhm ; Conde, Lucia ; Bracci, Paige ; Riby, Jacques ; Skibola, Christine F; Eskin, Eleazar ; Halperin, Eran Rare Variant Association Testing Under Low-Coverage Sequencing. Journal Article Genetics, 2013, ISSN: 1943-2631. Abstract | Links | BibTeX | Tags: Rare Variants, Sequencing with Pools @article{Navon:Genetics:2013, title = {Rare Variant Association Testing Under Low-Coverage Sequencing.}, author = { Oron Navon and Jae Hoon Sul and Buhm Han and Lucia Conde and Paige Bracci and Jacques Riby and Christine F. Skibola and Eleazar Eskin and Eran Halperin}, url = {http://dx.doi.org/10.1534/genetics.113.150169}, issn = {1943-2631}, year = {2013}, date = {2013-01-01}, journal = {Genetics}, organization = {Tel-Aviv University;}, abstract = {Deep sequencing technologies enable the study of the effects of rare variants in disease risk. While methods have been developed to increase statistical power for detection of such effects, detecting subtle associations requires studies with hundreds or thousands of individuals, which is prohibitively costly. Recently, low coverage sequencing has been shown to effectively reduce the cost of genome-wide association studies using current sequencing technologies. However, current methods for disease association testing on rare variants cannot be applied directly to low coverage sequencing data, as they require individual genotype data, which may not be called correctly due to low coverage and inherent sequencing errors. In this paper, we propose two novel methods for detecting association of rare SNPs with disease risk, using low coverage, error-prone sequencing. We show by simulation that our methods outperform previous methods under both low and high coverage sequencing, and under different disease architectures. We use real data and simulation studies to demonstrate that to maximize the power to detect associations for a fixed budget, it is desirable to include more samples while lowering coverage, and perform an analysis using our suggested methods}, keywords = {Rare Variants, Sequencing with Pools}, pubstate = {published}, tppubtype = {article} } Deep sequencing technologies enable the study of the effects of rare variants in disease risk. While methods have been developed to increase statistical power for detection of such effects, detecting subtle associations requires studies with hundreds or thousands of individuals, which is prohibitively costly. Recently, low coverage sequencing has been shown to effectively reduce the cost of genome-wide association studies using current sequencing technologies. However, current methods for disease association testing on rare variants cannot be applied directly to low coverage sequencing data, as they require individual genotype data, which may not be called correctly due to low coverage and inherent sequencing errors. In this paper, we propose two novel methods for detecting association of rare SNPs with disease risk, using low coverage, error-prone sequencing. We show by simulation that our methods outperform previous methods under both low and high coverage sequencing, and under different disease architectures. We use real data and simulation studies to demonstrate that to maximize the power to detect associations for a fixed budget, it is desirable to include more samples while lowering coverage, and perform an analysis using our suggested methods |
Sul, Jae Hoon; Han, Buhm ; Ye, Chun ; Choi, Ted ; Eskin, Eleazar Effectively Identifying eQTLs from Multiple Tissues by Combining Mixed Model and Meta-analytic Approaches Journal Article PLoS Genet, 9 (6), pp. e1003491, 2013, ISSN: 1553-7404. Abstract | Links | BibTeX | Tags: Expression QTLs, Meta-Analysis, Mixed Models, Multiple Phenotypes @article{10.1371/journal.pgen.1003491, title = {Effectively Identifying eQTLs from Multiple Tissues by Combining Mixed Model and Meta-analytic Approaches}, author = { Jae Hoon Sul and Buhm Han and Chun Ye and Ted Choi and Eleazar Eskin}, url = {http://dx.doi.org/10.1371%2Fjournal.pgen.1003491}, issn = {1553-7404}, year = {2013}, date = {2013-01-01}, journal = {PLoS Genet}, volume = {9}, number = {6}, pages = {e1003491}, publisher = {Public Library of Science}, address = {United States}, abstract = {Author Summary The combination of gene expression and genetic variation data has enabled the identification of genetic variants that affect gene expression levels. It has been shown that some variants influence gene expression in only one tissue while others influence gene expression in multiple tissues. However, an analysis of multiple tissue data using traditional statistical methods typically fails to identify those variants that affect multiple tissues because each tissue is treated independently and due to low statistical power, the effect in a given tissue may be missed. Building on recent advances in statistical methods for meta-analysis and mixed models, we present a novel method that combines information from multiple tissues to identify genetic variation that affects multiple tissues. We show that our method detects more genetic variation that influences multiple tissues than traditional statistical methods both on simulated and real data.}, keywords = {Expression QTLs, Meta-Analysis, Mixed Models, Multiple Phenotypes}, pubstate = {published}, tppubtype = {article} } Author Summary The combination of gene expression and genetic variation data has enabled the identification of genetic variants that affect gene expression levels. It has been shown that some variants influence gene expression in only one tissue while others influence gene expression in multiple tissues. However, an analysis of multiple tissue data using traditional statistical methods typically fails to identify those variants that affect multiple tissues because each tissue is treated independently and due to low statistical power, the effect in a given tissue may be missed. Building on recent advances in statistical methods for meta-analysis and mixed models, we present a novel method that combines information from multiple tissues to identify genetic variation that affects multiple tissues. We show that our method detects more genetic variation that influences multiple tissues than traditional statistical methods both on simulated and real data. |
Yang, Wen-Yun Y; Hormozdiari, Farhad ; Wang, Zhanyong ; He, Dan ; Pasaniuc, Bogdan ; Eskin, Eleazar Leveraging Multi-SNP Reads from Sequencing Data for Haplotype Inference. Journal Article Bioinformatics, 2013, ISSN: 1367-4811. Abstract | Links | BibTeX | Tags: Haplotype Phasing, Haplotyping from Sequences, Imputation @article{Yang:Bioinformatics:2013, title = {Leveraging Multi-SNP Reads from Sequencing Data for Haplotype Inference.}, author = { Wen-Yun Y. Yang and Farhad Hormozdiari and Zhanyong Wang and Dan He and Bogdan Pasaniuc and Eleazar Eskin}, url = {http://dx.doi.org/10.1093/bioinformatics/btt386}, issn = {1367-4811}, year = {2013}, date = {2013-01-01}, journal = {Bioinformatics}, organization = {Department of Computer Science,University of California, Los Angeles.}, abstract = {MOTIVATION: Haplotypes, defined as the sequence of alleles on one chromosome, are crucial for many genetic analyses. Since experimental determination of haplotypes is extremely expensive, haplotypes are traditionally inferred using computational approaches from genotype data, i.e. the mixture of the genetic information from both haplotypes. Best performing approaches for haplotype inference rely on Hidden Markov Models (HMMs), with the underlying assumption that the haplotypes of a given individual can be represented as a mosaic of segments from other haplotypes in the same population. Such algorithms utilize this model to predict the most likely haplotypes that explain the observed genotype data conditional on reference panel of haplotypes. With rapid advances in short read sequencing technologies, sequencing is quickly establishing as a powerful approach for collecting genetic variation information. As opposed to traditional genotyping-array technologies that independently calls genotypes at polymorphic sites, short read sequencing often collects haplotypic information; a read spanning more than one polymorphic locus (multi-SNP read) contains information on the haplotype from which the read originates. However, this information is generally ignored in existing approaches for haplotype phasing and genotype-calling from short read data. RESULTS: In this paper, we propose a novel framework for haplotype inference from short read sequencing that leverages multi-SNP reads together with a reference panel of haplotypes. The basis of our approach is a new probabilistic model that finds the most likely haplotype segments from the reference panel to explain the short read sequencing data for a given individual. We devised an efficient sampling method within a probabilistic model to achieve superior performance than existing methods. Using simulated sequencing reads from real individual genotypes in the HapMap data and the 1000 Genomes projects, we show that our method is highly accurate and computationally efficient. Our haplotype predictions improve accuracy over the basic haplotype copying model by around 20% with comparable computational time, and over another recently proposed approach Hap-SeqX by around 10% with significantly reduced computational time and memory usage. AVAILABILITY: Publicly available software is available at http://genetics.cs.ucla.edu/harsh CONTACT: bpasaniuc@mednet.ucla.edu; eeskin@cs.ucla.edu}, keywords = {Haplotype Phasing, Haplotyping from Sequences, Imputation}, pubstate = {published}, tppubtype = {article} } MOTIVATION: Haplotypes, defined as the sequence of alleles on one chromosome, are crucial for many genetic analyses. Since experimental determination of haplotypes is extremely expensive, haplotypes are traditionally inferred using computational approaches from genotype data, i.e. the mixture of the genetic information from both haplotypes. Best performing approaches for haplotype inference rely on Hidden Markov Models (HMMs), with the underlying assumption that the haplotypes of a given individual can be represented as a mosaic of segments from other haplotypes in the same population. Such algorithms utilize this model to predict the most likely haplotypes that explain the observed genotype data conditional on reference panel of haplotypes. With rapid advances in short read sequencing technologies, sequencing is quickly establishing as a powerful approach for collecting genetic variation information. As opposed to traditional genotyping-array technologies that independently calls genotypes at polymorphic sites, short read sequencing often collects haplotypic information; a read spanning more than one polymorphic locus (multi-SNP read) contains information on the haplotype from which the read originates. However, this information is generally ignored in existing approaches for haplotype phasing and genotype-calling from short read data. RESULTS: In this paper, we propose a novel framework for haplotype inference from short read sequencing that leverages multi-SNP reads together with a reference panel of haplotypes. The basis of our approach is a new probabilistic model that finds the most likely haplotype segments from the reference panel to explain the short read sequencing data for a given individual. We devised an efficient sampling method within a probabilistic model to achieve superior performance than existing methods. Using simulated sequencing reads from real individual genotypes in the HapMap data and the 1000 Genomes projects, we show that our method is highly accurate and computationally efficient. Our haplotype predictions improve accuracy over the basic haplotype copying model by around 20% with comparable computational time, and over another recently proposed approach Hap-SeqX by around 10% with significantly reduced computational time and memory usage. AVAILABILITY: Publicly available software is available at http://genetics.cs.ucla.edu/harsh CONTACT: bpasaniuc@mednet.ucla.edu; eeskin@cs.ucla.edu |
Lagarrigue, Sandrine; Martin, Lisa J; Hormozdiari, Farhad; Roux, Pierre-François F; Pan, Calvin; van Nas, Atila; Demeure, Olivier; Cantor, Rita; Ghazalpour, Anatole; Eskin, Eleazar; Lusis, Aldons J Analysis of Allele Specific Expression in Mouse Liver by RNA-Seq: A Comparison with Journal Article Genetics, 2013, ISSN: 1943-2631. Abstract | Links | BibTeX | Tags: Allele Specific Expression @article{Lagarrigue:Genetics:2013b, title = {Analysis of Allele Specific Expression in Mouse Liver by RNA-Seq: A Comparison with }, author = {Sandrine Lagarrigue and Lisa J. Martin and Farhad Hormozdiari and Pierre-François F. Roux and Calvin Pan and Atila van Nas and Olivier Demeure and Rita Cantor and Anatole Ghazalpour and Eleazar Eskin and Aldons J. Lusis}, url = {http://dx.doi.org/10.1534/genetics.113.153882}, issn = {1943-2631}, year = {2013}, date = {2013-01-01}, journal = {Genetics}, organization = {INRA;}, abstract = {We report an analysis of allele specific expression (ASE) and parent-of-origin expression in adult mouse liver using next generation sequencing (RNA-Seq) of reciprocal crosses of heterozygous F1 mice from the parental strains C57BL/6J and DBA/2J. We found a 60% overlap between genes exhibiting ASE and putative cis-acting expression quantitative trait loci (cis-eQTL) identified in an intercross between the same strains. We discuss the various biological and technical factors that contribute to the differences. We also identify genes exhibiting parental imprinting and complex expression patterns. Our study demonstrates the importance of biological replicates to limit the number of false positives with RNA-Seq data}, keywords = {Allele Specific Expression}, pubstate = {published}, tppubtype = {article} } We report an analysis of allele specific expression (ASE) and parent-of-origin expression in adult mouse liver using next generation sequencing (RNA-Seq) of reciprocal crosses of heterozygous F1 mice from the parental strains C57BL/6J and DBA/2J. We found a 60% overlap between genes exhibiting ASE and putative cis-acting expression quantitative trait loci (cis-eQTL) identified in an intercross between the same strains. We discuss the various biological and technical factors that contribute to the differences. We also identify genes exhibiting parental imprinting and complex expression patterns. Our study demonstrates the importance of biological replicates to limit the number of false positives with RNA-Seq data |
Eskin, Itamar; Hormozdiari, Farhad; Conde, Lucia; Riby, Jacques; Skibola, Chris; Eskin, Eleazar; Halperin, Eran eALPS: Estimating Abundance Levels in Pooled Sequencing Using Available Genotyping Data. Journal Article J Comput Biol, 2013, ISSN: 1557-8666. Abstract | Links | BibTeX | Tags: Sequencing with Pools @article{Eskin:JComputBiol:2013, title = {eALPS: Estimating Abundance Levels in Pooled Sequencing Using Available Genotyping Data.}, author = {Itamar Eskin and Farhad Hormozdiari and Lucia Conde and Jacques Riby and Chris Skibola and Eleazar Eskin and Eran Halperin}, url = {http://dx.doi.org/10.1089/cmb.2013.0105}, issn = {1557-8666}, year = {2013}, date = {2013-01-01}, journal = {J Comput Biol}, organization = {1 The Blavatnik School of Computer Science, Tel-Aviv University , Tel Aviv, Israel .}, abstract = {Abstract The recent advances in high-throughput sequencing technologies bring the potential of a better characterization of the genetic variation in humans and other organisms. In many occasions, either by design or by necessity, the sequencing procedure is performed on a pool of DNA samples with different abundances, where the abundance of each sample is unknown. Such a scenario is naturally occurring in the case of metagenomics analysis where a pool of bacteria is sequenced, or in the case of population studies involving DNA pools by design. Particularly, various pooling designs were recently suggested that can identify carriers of rare alleles in large cohorts, dramatically reducing the cost of such large-scale sequencing projects. A fundamental problem with such approaches for population studies is that the uncertainty of DNA proportions from different individuals in the pools might lead to spurious associations. Fortunately, it is often the case that the genotype data of at least some of the individuals in the pool is known. Here, we propose a method (eALPS) that uses the genotype data in conjunction with the pooled sequence data in order to accurately estimate the proportions of the samples in the pool, even in cases where not all individuals in the pool were genotyped (eALPS-LD). Using real data from a sequencing pooling study of non-Hodgkin's lymphoma, we demonstrate that the estimation of the proportions is crucial, since otherwise there is a risk for false discoveries. Additionally, we demonstrate that our approach is also applicable to the problem of quantification of species in metagenomics samples (eALPS-BCR) and is particularly suitable for metagenomic quantification of closely related species}, keywords = {Sequencing with Pools}, pubstate = {published}, tppubtype = {article} } Abstract The recent advances in high-throughput sequencing technologies bring the potential of a better characterization of the genetic variation in humans and other organisms. In many occasions, either by design or by necessity, the sequencing procedure is performed on a pool of DNA samples with different abundances, where the abundance of each sample is unknown. Such a scenario is naturally occurring in the case of metagenomics analysis where a pool of bacteria is sequenced, or in the case of population studies involving DNA pools by design. Particularly, various pooling designs were recently suggested that can identify carriers of rare alleles in large cohorts, dramatically reducing the cost of such large-scale sequencing projects. A fundamental problem with such approaches for population studies is that the uncertainty of DNA proportions from different individuals in the pools might lead to spurious associations. Fortunately, it is often the case that the genotype data of at least some of the individuals in the pool is known. Here, we propose a method (eALPS) that uses the genotype data in conjunction with the pooled sequence data in order to accurately estimate the proportions of the samples in the pool, even in cases where not all individuals in the pool were genotyped (eALPS-LD). Using real data from a sequencing pooling study of non-Hodgkin's lymphoma, we demonstrate that the estimation of the proportions is crucial, since otherwise there is a risk for false discoveries. Additionally, we demonstrate that our approach is also applicable to the problem of quantification of species in metagenomics samples (eALPS-BCR) and is particularly suitable for metagenomic quantification of closely related species |
Kostem, Emrah; Eskin, Eleazar Efficiently Identifying Significant Associations in Genome-wide Association Studies. Journal Article J Comput Biol, 20 (10), pp. 817-30, 2013, ISSN: 1557-8666. Abstract | Links | BibTeX | Tags: Association Study Methods, Expression QTLs @article{Kostem:JComputBiol:2013, title = {Efficiently Identifying Significant Associations in Genome-wide Association Studies.}, author = {Emrah Kostem and Eleazar Eskin}, url = {http://dx.doi.org/10.1089/cmb.2013.0087}, issn = {1557-8666}, year = {2013}, date = {2013-01-01}, journal = {J Comput Biol}, volume = {20}, number = {10}, pages = {817-30}, address = {United States}, organization = {1 Computer Science Department, University of California , Los Angeles, California.}, abstract = {Abstract Over the past several years, genome-wide association studies (GWAS) have implicated hundreds of genes in common disease. More recently, the GWAS approach has been utilized to identify regions of the genome that harbor variation affecting gene expression or expression quantitative trait loci (eQTLs). Unlike GWAS applied to clinical traits, where only a handful of phenotypes are analyzed per study, in eQTL studies, tens of thousands of gene expression levels are measured, and the GWAS approach is applied to each gene expression level. This leads to computing billions of statistical tests and requires substantial computational resources, particularly when applying novel statistical methods such as mixed models. We introduce a novel two-stage testing procedure that identifies all of the significant associations more efficiently than testing all the single nucleotide polymorphisms (SNPs). In the first stage, a small number of informative SNPs, or proxies, across the genome are tested. Based on their observed associations, our approach locates the regions that may contain significant SNPs and only tests additional SNPs from those regions. We show through simulations and analysis of real GWAS datasets that the proposed two-stage procedure increases the computational speed by a factor of 10. Additionally, efficient implementation of our software increases the computational speed relative to the state-of-the-art testing approaches by a factor of 75}, keywords = {Association Study Methods, Expression QTLs}, pubstate = {published}, tppubtype = {article} } Abstract Over the past several years, genome-wide association studies (GWAS) have implicated hundreds of genes in common disease. More recently, the GWAS approach has been utilized to identify regions of the genome that harbor variation affecting gene expression or expression quantitative trait loci (eQTLs). Unlike GWAS applied to clinical traits, where only a handful of phenotypes are analyzed per study, in eQTL studies, tens of thousands of gene expression levels are measured, and the GWAS approach is applied to each gene expression level. This leads to computing billions of statistical tests and requires substantial computational resources, particularly when applying novel statistical methods such as mixed models. We introduce a novel two-stage testing procedure that identifies all of the significant associations more efficiently than testing all the single nucleotide polymorphisms (SNPs). In the first stage, a small number of informative SNPs, or proxies, across the genome are tested. Based on their observed associations, our approach locates the regions that may contain significant SNPs and only tests additional SNPs from those regions. We show through simulations and analysis of real GWAS datasets that the proposed two-stage procedure increases the computational speed by a factor of 10. Additionally, efficient implementation of our software increases the computational speed relative to the state-of-the-art testing approaches by a factor of 75 |
He, Dan; Wang, Zhanyong; Han, Buhm; Parida, Laxmi; Eskin, Eleazar IPED: Inheritance Path-based Pedigree Reconstruction Algorithm Using Genotype Data. Journal Article J Comput Biol, 20 (10), pp. 780-91, 2013, ISSN: 1557-8666. Abstract | Links | BibTeX | Tags: Pedigree Inference @article{He:JComputBiol:2013b, title = {IPED: Inheritance Path-based Pedigree Reconstruction Algorithm Using Genotype Data.}, author = {Dan He and Zhanyong Wang and Buhm Han and Laxmi Parida and Eleazar Eskin}, url = {http://dx.doi.org/10.1089/cmb.2013.0080}, issn = {1557-8666}, year = {2013}, date = {2013-01-01}, journal = {J Comput Biol}, volume = {20}, number = {10}, pages = {780-91}, address = {United States}, organization = {1 IBM T.J. Watson Research , Yorktown Heights, New York.}, abstract = {Abstract The problem of inference of family trees, or pedigree reconstruction, for a group of individuals is a fundamental problem in genetics. Various methods have been proposed to automate the process of pedigree reconstruction given the genotypes or haplotypes of a set of individuals. Current methods, unfortunately, are very time-consuming and inaccurate for complicated pedigrees, such as pedigrees with inbreeding. In this work, we propose an efficient algorithm that is able to reconstruct large pedigrees with reasonable accuracy. Our algorithm reconstructs the pedigrees generation by generation, backward in time from the extant generation. We predict the relationships between individuals in the same generation using an inheritance path-based approach implemented with an efficient dynamic programming algorithm. Experiments show that our algorithm runs in linear time with respect to the number of reconstructed generations, and therefore, it can reconstruct pedigrees that have a large number of generations. Indeed it is the first practical method for reconstruction of large pedigrees from genotype data}, keywords = {Pedigree Inference}, pubstate = {published}, tppubtype = {article} } Abstract The problem of inference of family trees, or pedigree reconstruction, for a group of individuals is a fundamental problem in genetics. Various methods have been proposed to automate the process of pedigree reconstruction given the genotypes or haplotypes of a set of individuals. Current methods, unfortunately, are very time-consuming and inaccurate for complicated pedigrees, such as pedigrees with inbreeding. In this work, we propose an efficient algorithm that is able to reconstruct large pedigrees with reasonable accuracy. Our algorithm reconstructs the pedigrees generation by generation, backward in time from the extant generation. We predict the relationships between individuals in the same generation using an inheritance path-based approach implemented with an efficient dynamic programming algorithm. Experiments show that our algorithm runs in linear time with respect to the number of reconstructed generations, and therefore, it can reconstruct pedigrees that have a large number of generations. Indeed it is the first practical method for reconstruction of large pedigrees from genotype data |
Marsden, Clare Diana; Lee, Yoosook; Kreppel, Katharina; Weakley, Allison; Cornel, Anthony; Ferguson, Heather M; Eskin, Eleazar; Lanzaro, Gregory C Diversity, Differentiation and Linkage Disequilibrium: Prospects for Association Mapping in the Malaria Vector, Anopheles arabiensis. Journal Article G3 (Bethesda), 2013, ISSN: 2160-1836. Abstract | Links | BibTeX | Tags: association mapping, population structure @article{Marsden:G3:2013, title = {Diversity, Differentiation and Linkage Disequilibrium: Prospects for Association Mapping in the Malaria Vector, Anopheles arabiensis.}, author = { Clare Diana Marsden and Yoosook Lee and Katharina Kreppel and Allison Weakley and Anthony Cornel and Heather M. Ferguson and Eleazar Eskin and Gregory C. Lanzaro}, url = {http://dx.doi.org/10.1534/g3.113.008326}, issn = {2160-1836}, year = {2013}, date = {2013-01-01}, journal = {G3 (Bethesda)}, organization = {University of California, Davis.}, abstract = {Association mapping is a widely applied method for elucidating the genetic basis of phenotypic traits. However, factors such as linkage disequilibrium and levels of genetic diversity influence the power and resolution of this approach. Moreover, the presence of population sub-division among samples can result in spurious associations if not accounted for. As such it is useful to have a detailed understanding of these factors prior to conducting association mapping experiments. Here we conducted whole genome sequencing on 24 specimens of the malaria mosquito vector, Anopheles arabiensis, to further understanding of patterns of genetic diversity, population sub-division and linkage disequilibrium in this species. We found high levels of genetic diversity within the An. arabiensis genome, with ~800,000 high confidence single nucleotide polymorphisms detected. However, levels of nucleotide diversity varied significantly both within and between chromosomes. We observed lower diversity on the X chromosome, within some inversions, and near centromeres. Population structure was absent at the local scale (Kilombero Valley, Tanzania) but detected between distant populations (Cameroon vs. Tanzania) where differentiation was largely restricted to certain autosomal chromosomal inversions such as 2Rb. Overall, linkage disequilibrium within An. arabiensis decayed very rapidly (within 200bp) across all chromosomes. However, elevated linkage disequilibrium was observed within some inversions, suggesting that recombination is reduced in those regions. The overall low levels of linkage disequilibrium suggests that association studies in this taxon will be very challenging for all but variants of large effect, and will require large sample sizes}, keywords = {association mapping, population structure}, pubstate = {published}, tppubtype = {article} } Association mapping is a widely applied method for elucidating the genetic basis of phenotypic traits. However, factors such as linkage disequilibrium and levels of genetic diversity influence the power and resolution of this approach. Moreover, the presence of population sub-division among samples can result in spurious associations if not accounted for. As such it is useful to have a detailed understanding of these factors prior to conducting association mapping experiments. Here we conducted whole genome sequencing on 24 specimens of the malaria mosquito vector, Anopheles arabiensis, to further understanding of patterns of genetic diversity, population sub-division and linkage disequilibrium in this species. We found high levels of genetic diversity within the An. arabiensis genome, with ~800,000 high confidence single nucleotide polymorphisms detected. However, levels of nucleotide diversity varied significantly both within and between chromosomes. We observed lower diversity on the X chromosome, within some inversions, and near centromeres. Population structure was absent at the local scale (Kilombero Valley, Tanzania) but detected between distant populations (Cameroon vs. Tanzania) where differentiation was largely restricted to certain autosomal chromosomal inversions such as 2Rb. Overall, linkage disequilibrium within An. arabiensis decayed very rapidly (within 200bp) across all chromosomes. However, elevated linkage disequilibrium was observed within some inversions, suggesting that recombination is reduced in those regions. The overall low levels of linkage disequilibrium suggests that association studies in this taxon will be very challenging for all but variants of large effect, and will require large sample sizes |
Han, Buhm; Kang, Eun Yong ; Raychaudhuri, Soumya ; de Bakker, Paul I W; Eskin, Eleazar Fast Pairwise IBD Association Testing in Genome-wide Association Studies. Journal Article Bioinformatics, 2013, ISSN: 1367-4811. Abstract | Links | BibTeX | Tags: IBD Detection and Association @article{Han:Bioinformatics:2013, title = {Fast Pairwise IBD Association Testing in Genome-wide Association Studies.}, author = { Buhm Han and Eun Yong Kang and Soumya Raychaudhuri and Paul I. W. de Bakker and Eleazar Eskin}, url = {http://dx.doi.org/10.1093/bioinformatics/btt609}, issn = {1367-4811}, year = {2013}, date = {2013-01-01}, journal = {Bioinformatics}, organization = {Division of Genetics, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachusetts, USA Division of Rheumatology, Brigham and Women's Hospital, Harvard Medical School, Boston, Massachus}, abstract = {MOTIVATION: Recently, investigators have proposed state-of-the-art Identity-by-descent (IBD) mapping methods to detect IBD segments between purportedly unrelated individuals. The IBD information can then be used for association testing in genetic association studies. One approach for this IBD association testing strategy is to test for excessive IBD between pairs of cases ("pairwise method"). However, this approach is inefficient because it requires a large number of permutations. Moreover, a limited number of permutations define a lower bound for p-values which makes fine-mapping associated regions difficult, because in practice a much larger genomic region is implicated than the region that is actually associated. RESULTS: In this paper, we introduce a new pairwise method Fast-Pairwise. Fast-Pairwise utilizes importance sampling to improve efficiency and enable approximation of extremely small p-values. Fast-pairwise takes only days to complete a genome-wide scan. In the application to the WTCCC type 1 diabetes data, Fast-Pairwise successfully fine-maps a known HLA gene which is known to cause the disease. AVAILABILITY: Fast-Pairwise is publicly available at http://genetics.cs.ucla.edu/graphibd. CONTACT: eeskin@cs.ucla.edu}, keywords = {IBD Detection and Association}, pubstate = {published}, tppubtype = {article} } MOTIVATION: Recently, investigators have proposed state-of-the-art Identity-by-descent (IBD) mapping methods to detect IBD segments between purportedly unrelated individuals. The IBD information can then be used for association testing in genetic association studies. One approach for this IBD association testing strategy is to test for excessive IBD between pairs of cases ("pairwise method"). However, this approach is inefficient because it requires a large number of permutations. Moreover, a limited number of permutations define a lower bound for p-values which makes fine-mapping associated regions difficult, because in practice a much larger genomic region is implicated than the region that is actually associated. RESULTS: In this paper, we introduce a new pairwise method Fast-Pairwise. Fast-Pairwise utilizes importance sampling to improve efficiency and enable approximation of extremely small p-values. Fast-pairwise takes only days to complete a genome-wide scan. In the application to the WTCCC type 1 diabetes data, Fast-Pairwise successfully fine-maps a known HLA gene which is known to cause the disease. AVAILABILITY: Fast-Pairwise is publicly available at http://genetics.cs.ucla.edu/graphibd. CONTACT: eeskin@cs.ucla.edu |
2012 |
He, Dan; Han, Buhm ; Eskin, Eleazar Hap-seq: An Optimal Algorithm for Haplotype Phasing with Imputation Using Sequencing Data Conference Research in Computational Molecular Biology, Univ. of California Springer Berlin Heidelberg, 2012. Abstract | Links | BibTeX | Tags: Haplotyping from Sequences @conference{He:ResearchInComputationalMolecularBiology:2012, title = {Hap-seq: An Optimal Algorithm for Haplotype Phasing with Imputation Using Sequencing Data}, author = { Dan He and Buhm Han and Eleazar Eskin}, url = {http://dx.doi.org/10.1007/978-3-642-29627-7_8}, year = {2012}, date = {2012-01-01}, booktitle = {Research in Computational Molecular Biology}, pages = {64-78}, publisher = {Springer Berlin Heidelberg}, organization = {Univ. of California}, abstract = {Inference of haplotypes, or the sequence of alleles along each chromosome, is a fundamental problem in genetics and is important for many analyses including admixture mapping, identifying regions of identity by descent and imputation. Traditionally, haplotypes were inferred from genotype data obtained from microarrays utilizing information on population haplotype frequencies inferred from either a large sample of genotyped individuals or a reference dataset such as the HapMap. Since the availability of large reference datasets, modern approaches for haplotype phasing along these lines are closely related to imputation methods. When applied to data obtained from sequencing studies, a straightforward way to obtain haplotypes is to first infer genotypes from the sequence data and then apply an imputation method. However, this approach does not take into account that alleles on the same sequence read originate from the same chromosome. Haplotype assembly approaches take advantage of this insight and predict haplotypes by assigning the reads to chromosomes in such a way that minimizes the number of conflicts between the reads and the predicted haplotypes. Unfortunately, assembly approaches require very high sequencing coverage and are usually not able to fully reconstruct the haplotypes. In this work, we present a novel approach, Hap-seq, which is simultaneously an imputation and assembly method which combines information from a reference dataset with the information from the reads using a likelihood framework. Our method applies a dynamic programming algorithm to identify the predicted haplotype which maximizes the joint likelihood of the haplotype with respect to the reference dataset and the haplotype with respect to the observed reads. We show that our method requires only low sequencing coverage and can reconstruct haplotypes containing both common and rare alleles with higher accuracy compared to the state-of-the-art imputation methods.}, keywords = {Haplotyping from Sequences}, pubstate = {published}, tppubtype = {conference} } Inference of haplotypes, or the sequence of alleles along each chromosome, is a fundamental problem in genetics and is important for many analyses including admixture mapping, identifying regions of identity by descent and imputation. Traditionally, haplotypes were inferred from genotype data obtained from microarrays utilizing information on population haplotype frequencies inferred from either a large sample of genotyped individuals or a reference dataset such as the HapMap. Since the availability of large reference datasets, modern approaches for haplotype phasing along these lines are closely related to imputation methods. When applied to data obtained from sequencing studies, a straightforward way to obtain haplotypes is to first infer genotypes from the sequence data and then apply an imputation method. However, this approach does not take into account that alleles on the same sequence read originate from the same chromosome. Haplotype assembly approaches take advantage of this insight and predict haplotypes by assigning the reads to chromosomes in such a way that minimizes the number of conflicts between the reads and the predicted haplotypes. Unfortunately, assembly approaches require very high sequencing coverage and are usually not able to fully reconstruct the haplotypes. In this work, we present a novel approach, Hap-seq, which is simultaneously an imputation and assembly method which combines information from a reference dataset with the information from the reads using a likelihood framework. Our method applies a dynamic programming algorithm to identify the predicted haplotype which maximizes the joint likelihood of the haplotype with respect to the reference dataset and the haplotype with respect to the observed reads. We show that our method requires only low sequencing coverage and can reconstruct haplotypes containing both common and rare alleles with higher accuracy compared to the state-of-the-art imputation methods. |
Weiss, James N; Karma, Alain ; Maclellan, Robb W; Deng, Mario ; Rau, Christoph D; Rees, Colin M; Wang, Jessica ; Wisniewski, Nicholas ; Eskin, Eleazar ; Horvath, Steve ; Qu, Zhilin ; Wang, Yibin ; Lusis, Aldons J "Good enough solutions" and the genetics of complex diseases. Journal Article Circ Res, 111 (4), pp. 493-504, 2012, ISSN: 1524-4571. Abstract | Links | BibTeX | Tags: @article{Weiss:CircRes:2012, title = {"Good enough solutions" and the genetics of complex diseases.}, author = { James N. Weiss and Alain Karma and W. Robb Maclellan and Mario Deng and Christoph D. Rau and Colin M. Rees and Jessica Wang and Nicholas Wisniewski and Eleazar Eskin and Steve Horvath and Zhilin Qu and Yibin Wang and Aldons J. Lusis}, url = {http://dx.doi.org/10.1161/CIRCRESAHA.112.269084}, issn = {1524-4571}, year = {2012}, date = {2012-01-01}, journal = {Circ Res}, volume = {111}, number = {4}, pages = {493-504}, address = {United States}, organization = {Division of Cardiology, 3641 MRL Bldg, David Geffen School of Medicine at UCLA, Los Angeles, CA 90095. jweiss@mednet.ucla.edu.}, abstract = {In this Emerging Science Review, we discuss a systems genetics strategy, which we call gene module association study (GMAS), as a novel approach complementing genome-wide association studies (GWAS), to understand complex diseases by focusing on how genes work together in groups rather than singly. The first step is to characterize phenotypic differences among a genetically diverse population. The second step is to use gene expression microarray (or other high-throughput) data from the population to construct gene coexpression networks. Coexpression analysis typically groups 20 000 genes into 20 to 30 modules containing tens to hundreds of genes, whose aggregate behavior can be represented by the module's "eigengene." The third step is to correlate expression patterns with phenotype, as in GWAS, only applied to eigengenes instead of single nucleotide polymorphisms. The goal of the GMAS approach is to identify groups of coregulated genes that explain complex traits from a systems perspective. From an evolutionary standpoint, we hypothesize that variability in eigengene patterns reflects the "good enough solution" concept, that biological systems are sufficiently complex so that many possible combinations of the same elements (in this case eigengenes) can produce an equivalent output, that is, a "good enough solution" to accomplish normal biological functions. However, when faced with environmental stresses, some "good enough solutions" adapt better than others, explaining individual variability to disease and drug susceptibility. If validated, GMAS may imply that common polygenic diseases are related as much to group interactions between normal genes, as to multiple gene mutations.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In this Emerging Science Review, we discuss a systems genetics strategy, which we call gene module association study (GMAS), as a novel approach complementing genome-wide association studies (GWAS), to understand complex diseases by focusing on how genes work together in groups rather than singly. The first step is to characterize phenotypic differences among a genetically diverse population. The second step is to use gene expression microarray (or other high-throughput) data from the population to construct gene coexpression networks. Coexpression analysis typically groups 20 000 genes into 20 to 30 modules containing tens to hundreds of genes, whose aggregate behavior can be represented by the module's "eigengene." The third step is to correlate expression patterns with phenotype, as in GWAS, only applied to eigengenes instead of single nucleotide polymorphisms. The goal of the GMAS approach is to identify groups of coregulated genes that explain complex traits from a systems perspective. From an evolutionary standpoint, we hypothesize that variability in eigengene patterns reflects the "good enough solution" concept, that biological systems are sufficiently complex so that many possible combinations of the same elements (in this case eigengenes) can produce an equivalent output, that is, a "good enough solution" to accomplish normal biological functions. However, when faced with environmental stresses, some "good enough solutions" adapt better than others, explaining individual variability to disease and drug susceptibility. If validated, GMAS may imply that common polygenic diseases are related as much to group interactions between normal genes, as to multiple gene mutations. |
Yang, Wen-Yun Y; Novembre, John ; Eskin, Eleazar ; Halperin, Eran A model-based approach for analysis of spatial structure in genetic data. Journal Article Nat Genet, 44 (6), pp. 725-31, 2012, ISSN: 1546-1718. Abstract | Links | BibTeX | Tags: Population Genetics, Population Structure Methods @article{Yang:NatGenet:2012, title = {A model-based approach for analysis of spatial structure in genetic data.}, author = { Wen-Yun Y. Yang and John Novembre and Eleazar Eskin and Eran Halperin}, url = {http://dx.doi.org/10.1038/ng.2285}, issn = {1546-1718}, year = {2012}, date = {2012-01-01}, journal = {Nat Genet}, volume = {44}, number = {6}, pages = {725-31}, address = {United States}, organization = {1] Interdepartmental Program in Bioinformatics, University of California, Los Angeles, California, USA. [2] Department of Computer Science, University of California, Los Angeles, California, USA.}, abstract = {Characterizing genetic diversity within and between populations has broad applications in studies of human disease and evolution. We propose a new approach, spatial ancestry analysis, for the modeling of genotypes in two- or three-dimensional space. In spatial ancestry analysis (SPA), we explicitly model the spatial distribution of each SNP by assigning an allele frequency as a continuous function in geographic space. We show that the explicit modeling of the allele frequency allows individuals to be localized on the map on the basis of their genetic information alone. We apply our SPA method to a European and a worldwide population genetic variation data set and identify SNPs showing large gradients in allele frequency, and we suggest these as candidate regions under selection. These regions include SNPs in the well-characterized LCT region, as well as at loci including FOXP2, OCA2 and LRP1B.}, keywords = {Population Genetics, Population Structure Methods}, pubstate = {published}, tppubtype = {article} } Characterizing genetic diversity within and between populations has broad applications in studies of human disease and evolution. We propose a new approach, spatial ancestry analysis, for the modeling of genotypes in two- or three-dimensional space. In spatial ancestry analysis (SPA), we explicitly model the spatial distribution of each SNP by assigning an allele frequency as a continuous function in geographic space. We show that the explicit modeling of the allele frequency allows individuals to be localized on the map on the basis of their genetic information alone. We apply our SPA method to a European and a worldwide population genetic variation data set and identify SNPs showing large gradients in allele frequency, and we suggest these as candidate regions under selection. These regions include SNPs in the well-characterized LCT region, as well as at loci including FOXP2, OCA2 and LRP1B. |
Wang, Zhanyong; Hormozdiari, Farhad ; Yang, Wen-Yun - Y; Halperin, Eran ; Eskin, Eleazar CNVeM: Copy Number Variation Detection Using Uncertainty of Read Mapping Conference Research in Computational Molecular Biology, University of California Springer Berlin Heidelberg, 2012. Abstract | Links | BibTeX | Tags: Structural Variation @conference{Wang:ResearchInComputationalMolecularBiology:2012, title = {CNVeM: Copy Number Variation Detection Using Uncertainty of Read Mapping}, author = { Zhanyong Wang and Farhad Hormozdiari and Wen-Yun -. Y. Yang and Eran Halperin and Eleazar Eskin}, url = {http://dx.doi.org/10.1007/978-3-642-29627-7_34}, year = {2012}, date = {2012-01-01}, booktitle = {Research in Computational Molecular Biology}, pages = {326-340}, publisher = {Springer Berlin Heidelberg}, organization = {University of California}, abstract = {Copy number variations (CNVs) are widely known to be an important mediator for diseases and traits. The development of high-throughput sequencing (HTS) technologies has provided great opportunities to identify CNV regions in mammalian genomes. In a typical experiment, millions of short reads obtained from a genome of interest are mapped to a reference genome. The mapping information can be used to identify CNV regions. One important challenge in analyzing the mapping information is the large fraction of reads that can be mapped to multiple positions. Most existing methods either only consider reads that can be uniquely mapped to the reference genome, or randomly place a read to one of its mapping positions. Therefore, these methods have low power to detect CNVs located within repeated sequences. In this study, we propose a probabilistic model, CNVeM, that utilizes the inherent uncertainty of read mapping. We use maximum likelihood to estimate locations and copy numbers of copied regions, and implement an expectation-maximization (EM) algorithm. One important contribution of our model is that we can distinguish between regions in the reference genome that differ from each other by as little as 0.1%. As our model aims to predict the copy number of each nucleotide, we can predict the CNV boundaries with high resolution. We apply our method to simulated datasets and achieve higher accuracy compared to CNVnator. Moreover, we apply our method to real data from which we detected known CNVs. To our knowledge, this is the first attempt to predict CNVs at nucleotide resolution, and to utilize uncertainty of read mapping.}, keywords = {Structural Variation}, pubstate = {published}, tppubtype = {conference} } Copy number variations (CNVs) are widely known to be an important mediator for diseases and traits. The development of high-throughput sequencing (HTS) technologies has provided great opportunities to identify CNV regions in mammalian genomes. In a typical experiment, millions of short reads obtained from a genome of interest are mapped to a reference genome. The mapping information can be used to identify CNV regions. One important challenge in analyzing the mapping information is the large fraction of reads that can be mapped to multiple positions. Most existing methods either only consider reads that can be uniquely mapped to the reference genome, or randomly place a read to one of its mapping positions. Therefore, these methods have low power to detect CNVs located within repeated sequences. In this study, we propose a probabilistic model, CNVeM, that utilizes the inherent uncertainty of read mapping. We use maximum likelihood to estimate locations and copy numbers of copied regions, and implement an expectation-maximization (EM) algorithm. One important contribution of our model is that we can distinguish between regions in the reference genome that differ from each other by as little as 0.1%. As our model aims to predict the copy number of each nucleotide, we can predict the CNV boundaries with high resolution. We apply our method to simulated datasets and achieve higher accuracy compared to CNVnator. Moreover, we apply our method to real data from which we detected known CNVs. To our knowledge, this is the first attempt to predict CNVs at nucleotide resolution, and to utilize uncertainty of read mapping. |
Hormozdiariy, Farhad; Wang, Zhanyong ; Yang, Wen-Yun - Y; Eskin, Eleazar Efficient genotyping of individuals using overlapping pool sequencing and imputation Conference 2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR), IEEE, 2012, ISBN: 978-1-4673-5051-8. Abstract | Links | BibTeX | Tags: Sequencing with Pools @conference{Hormozdiariy:2012ConferenceRecordOfTheFortySixth:2, title = {Efficient genotyping of individuals using overlapping pool sequencing and imputation}, author = { Farhad Hormozdiariy and Zhanyong Wang and Wen-Yun -. Y. Yang and Eleazar Eskin}, url = {http://dx.doi.org/10.1109/ACSSC.2012.6489173}, isbn = {978-1-4673-5051-8}, year = {2012}, date = {2012-01-01}, booktitle = {2012 Conference Record of the Forty Sixth Asilomar Conference on Signals, Systems and Computers (ASILOMAR)}, pages = {1023-1027}, publisher = {IEEE}, abstract = {Abstract Next generation sequencing technologies are rapidly decreasing the cost of obtaining genetic information. The cost for utilizing one of these technologies consists of a sample preparation step and a sequencing step of the prepared sample. The dramatic increase in the efficiency of the sequencing technology makes the costs of the sequencing step negligible for small target regions. Thus the main remaining cost is the sample preparation step. Using overlapping sequencing pools where samples are mixed together into pools which are prepared and sequenced together has been shown to reduce the cost significantly for collecting information on genetic variants which only occur in a few of the samples. These methods utilize ideas from compressed sensing. In this paper, we extend this approach to utilize additional information from reference genetic variation datasets which provide the correlation structure between genetic variants. Utilizing this information, we can significantly increase the efficiency of overlapping pool sequencing.}, keywords = {Sequencing with Pools}, pubstate = {published}, tppubtype = {conference} } Abstract Next generation sequencing technologies are rapidly decreasing the cost of obtaining genetic information. The cost for utilizing one of these technologies consists of a sample preparation step and a sequencing step of the prepared sample. The dramatic increase in the efficiency of the sequencing technology makes the costs of the sequencing step negligible for small target regions. Thus the main remaining cost is the sample preparation step. Using overlapping sequencing pools where samples are mixed together into pools which are prepared and sequenced together has been shown to reduce the cost significantly for collecting information on genetic variants which only occur in a few of the samples. These methods utilize ideas from compressed sensing. In this paper, we extend this approach to utilize additional information from reference genetic variation datasets which provide the correlation structure between genetic variants. Utilizing this information, we can significantly increase the efficiency of overlapping pool sequencing. |
Flint, Jonathan; Eskin, Eleazar Genome-wide association studies in mice Journal Article Nature Reviews Genetics, 13 (11), pp. 807-17, 2012, ISSN: 1471-0064. Abstract | Links | BibTeX | Tags: Mouse Genetics @article{Flint:NatureReviewsGenetics:2012, title = {Genome-wide association studies in mice}, author = { Jonathan Flint and Eleazar Eskin}, url = {http://dx.doi.org/10.1038/nrg3335}, issn = {1471-0064}, year = {2012}, date = {2012-01-01}, journal = {Nature Reviews Genetics}, volume = {13}, number = {11}, pages = {807-17}, publisher = {Nature Publishing Group}, address = {England}, abstract = {Genome-wide association studies (GWASs) have transformed the field of human genetics and have led to the discovery of hundreds of genes that are implicated in human disease. The technological advances that drove this revolution are now poised to transform genetic studies in model organisms, including mice. However, the design of GWASs in mouse strains is fundamentally different from the design of human GWASs, creating new challenges and opportunities. This Review gives an overview of the novel study designs for mouse GWASs, which dramatically improve both the statistical power and resolution compared to classical gene-mapping approaches.}, keywords = {Mouse Genetics}, pubstate = {published}, tppubtype = {article} } Genome-wide association studies (GWASs) have transformed the field of human genetics and have led to the discovery of hundreds of genes that are implicated in human disease. The technological advances that drove this revolution are now poised to transform genetic studies in model organisms, including mice. However, the design of GWASs in mouse strains is fundamentally different from the design of human GWASs, creating new challenges and opportunities. This Review gives an overview of the novel study designs for mouse GWASs, which dramatically improve both the statistical power and resolution compared to classical gene-mapping approaches. |
Scharf, J M; Yu, D; Mathews, C A; Neale, B M; Stewart, S E; Fagerness, J A; Evans, P; Gamazon, E; Edlund, C K; Service, S K; Tikhomirov, A; Osiecki, L; Illmann, C; Pluzhnikov, A; Konkashbaev, A; Davis, L K; Han, B; Crane, J; Moorjani, P; Crenshaw, A T; Parkin, M A; Reus, V I; Lowe, T L; Rangel-Lugo, M; Chouinard, S; Dion, Y; Girard, S; Cath, D C; Smit, J H; King, R A; Fernandez, T V; Leckman, J F; Kidd, K K; Kidd, J R; Pakstis, A J; State, M W; Herrera, L D; Romero, R; Fournier, E; Sandor, P; Barr, C L; Phan, N; Gross-Tsur, V; Benarroc, F Genome-wide association study of Tourette's syndrome. Journal Article Mol Psychiatry, 2012, ISSN: 1476-5578. Abstract | Links | BibTeX | Tags: @article{Scharf:MolPsychiatry:2012, title = {Genome-wide association study of Tourette's syndrome.}, author = {J M Scharf and D Yu and C A Mathews and B M Neale and S E Stewart and J A Fagerness and P Evans and E Gamazon and C K Edlund and S K Service and A Tikhomirov and L Osiecki and C Illmann and A Pluzhnikov and A Konkashbaev and L K Davis and B Han and J Crane and P Moorjani and A T Crenshaw and M A Parkin and V I Reus and T L Lowe and M Rangel-Lugo and S Chouinard and Y Dion and S Girard and D C Cath and J H Smit and R A King and T V Fernandez and J F Leckman and K K Kidd and J R Kidd and A J Pakstis and M W State and L D Herrera and R Romero and E Fournier and P Sandor and C L Barr and N Phan and V Gross-Tsur and F Benarroc}, url = {http://dx.doi.org/10.1038/mp.2012.69}, issn = {1476-5578}, year = {2012}, date = {2012-01-01}, journal = {Mol Psychiatry}, organization = {1] Psychiatric and Neurodevelopmental Genetics Unit, Center for Human Genetics Research, Boston, MA, USA [2] Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA [3] Movement Diso}, abstract = {Tourette's syndrome (TS) is a developmental disorder that has one of the highest familial recurrence rates among neuropsychiatric diseases with complex inheritance. However, the identification of definitive TS susceptibility genes remains elusive. Here, we report the first genome-wide association study (GWAS) of TS in 1285 cases and 4964 ancestry-matched controls of European ancestry, including two European-derived population isolates, Ashkenazi Jews from North America and Israel and French Canadians from Quebec, Canada. In a primary meta-analysis of GWAS data from these European ancestry samples, no markers achieved a genome-wide threshold of significance (P<5 $times$ 10(-8)); the top signal was found in rs7868992 on chromosome 9q32 within COL27A1 (P=1.85 $times$ 10(-6)). A secondary analysis including an additional 211 cases and 285 controls from two closely related Latin American population isolates from the Central Valley of Costa Rica and Antioquia, Colombia also identified rs7868992 as the top signal (P=3.6 $times$ 10(-7) for the combined sample of 1496 cases and 5249 controls following imputation with 1000 Genomes data). This study lays the groundwork for the eventual identification of common TS susceptibility variants in larger cohorts and helps to provide a more complete understanding of the full genetic architecture of this disorder.Molecular Psychiatry advance online publication, 14 August 2012; doi:10.1038/mp.2012.69.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Tourette's syndrome (TS) is a developmental disorder that has one of the highest familial recurrence rates among neuropsychiatric diseases with complex inheritance. However, the identification of definitive TS susceptibility genes remains elusive. Here, we report the first genome-wide association study (GWAS) of TS in 1285 cases and 4964 ancestry-matched controls of European ancestry, including two European-derived population isolates, Ashkenazi Jews from North America and Israel and French Canadians from Quebec, Canada. In a primary meta-analysis of GWAS data from these European ancestry samples, no markers achieved a genome-wide threshold of significance (P<5 $times$ 10(-8)); the top signal was found in rs7868992 on chromosome 9q32 within COL27A1 (P=1.85 $times$ 10(-6)). A secondary analysis including an additional 211 cases and 285 controls from two closely related Latin American population isolates from the Central Valley of Costa Rica and Antioquia, Colombia also identified rs7868992 as the top signal (P=3.6 $times$ 10(-7) for the combined sample of 1496 cases and 5249 controls following imputation with 1000 Genomes data). This study lays the groundwork for the eventual identification of common TS susceptibility variants in larger cohorts and helps to provide a more complete understanding of the full genetic architecture of this disorder.Molecular Psychiatry advance online publication, 14 August 2012; doi:10.1038/mp.2012.69. |
He, Dan; Eskin, Eleazar Hap-seqX: Expedite Algorithm for Haplotype Phasing with Imputation using Sequence Data. Journal Article Gene, 2012, ISSN: 1879-0038. Abstract | Links | BibTeX | Tags: Haplotyping from Sequences, Imputation @article{He:Gene:2012, title = {Hap-seqX: Expedite Algorithm for Haplotype Phasing with Imputation using Sequence Data.}, author = { Dan He and Eleazar Eskin}, url = {http://dx.doi.org/10.1016/j.gene.2012.11.093}, issn = {1879-0038}, year = {2012}, date = {2012-01-01}, journal = {Gene}, organization = {IBM T.J. Watson Research, Yorktown Heights, NY, USA. Electronic address: dhe@us.ibm.com.}, abstract = {Haplotype phasing is one of the most important problems in population genetics as haplotypes can be used to estimate the relatedness of individuals and to impute genotype information which is a commonly performed analysis when searching for variants involved in disease. The problem of haplotype phasing has been well studied. Methodologies for haplotype inference from sequencing data either combine a set of reference haplotypes and collected genotypes using a hidden Markov model or assemble haplotypes by overlapping sequencing reads. A recent algorithm Hap-seq considers using both sequencing data and reference haplotypes and it is a hybrid of a dynamic programming algorithm and an Hidden Markov Model (HMM), which is shown to be optimal. However, the algorithm requires extremely large amount of memory which is not practical for whole genome datasets. The current algorithm requires saving intermediate results to disk and reads back these results when needed, which significantly affects the practicality of the algorithm. In this work, we proposed the expedited version of the algorithm Hap-seqX, which addressed the memory issue by using a posterior probability to select the records that should be saved in memory. We show that Hap-seqX can save all intermediate results in memory and improves the execution time of the algorithm dramatically. Utilizing the strategy, Hap-seqX is able to predict haplotypes from whole genome sequencing data}, keywords = {Haplotyping from Sequences, Imputation}, pubstate = {published}, tppubtype = {article} } Haplotype phasing is one of the most important problems in population genetics as haplotypes can be used to estimate the relatedness of individuals and to impute genotype information which is a commonly performed analysis when searching for variants involved in disease. The problem of haplotype phasing has been well studied. Methodologies for haplotype inference from sequencing data either combine a set of reference haplotypes and collected genotypes using a hidden Markov model or assemble haplotypes by overlapping sequencing reads. A recent algorithm Hap-seq considers using both sequencing data and reference haplotypes and it is a hybrid of a dynamic programming algorithm and an Hidden Markov Model (HMM), which is shown to be optimal. However, the algorithm requires extremely large amount of memory which is not practical for whole genome datasets. The current algorithm requires saving intermediate results to disk and reads back these results when needed, which significantly affects the practicality of the algorithm. In this work, we proposed the expedited version of the algorithm Hap-seqX, which addressed the memory issue by using a posterior probability to select the records that should be saved in memory. We show that Hap-seqX can save all intermediate results in memory and improves the execution time of the algorithm dramatically. Utilizing the strategy, Hap-seqX is able to predict haplotypes from whole genome sequencing data |
Bennett, Brian J; Orozco, Luz ; Kostem, Emrah ; Erbilgin, Ayca ; Dallinga, Marchien ; Neuhaus, Isaac ; Guan, Bo ; Wang, Xuping ; Eskin, Eleazar ; Lusis, Aldons J High-Resolution Association Mapping of Atherosclerosis Loci in Mice. Journal Article Arterioscler Thromb Vasc Biol, 2012, ISSN: 1524-4636. Abstract | Links | BibTeX | Tags: HMDP, Mouse Genetics @article{Bennett:ArteriosclerThrombVascBiol:2012, title = {High-Resolution Association Mapping of Atherosclerosis Loci in Mice.}, author = { Brian J. Bennett and Luz Orozco and Emrah Kostem and Ayca Erbilgin and Marchien Dallinga and Isaac Neuhaus and Bo Guan and Xuping Wang and Eleazar Eskin and Aldons J. Lusis}, url = {http://dx.doi.org/10.1161/ATVBAHA.112.253864}, issn = {1524-4636}, year = {2012}, date = {2012-01-01}, journal = {Arterioscler Thromb Vasc Biol}, organization = {Department of Genetics, University of North Carolina, Chapel Hill, NC.}, abstract = {OBJECTIVE: The purpose of this study was to fine map previously identified quantitative trait loci affecting atherosclerosis in mice using association analysis. METHODS AND RESULTS: We recently showed that high-resolution association analysis using common inbred strains of mice is feasible if corrected for population structure. To use this approach for atherosclerosis, which requires a sensitizing mutation, we bred human apolipoprotein B-100 transgenic mice with 22 different inbred strains to produce F1 heterozygotes. Mice carrying the dominant transgene were tested for association with high-density single nucleotide polymorphism maps. Here, we focus on high-resolution mapping of the previously described atherosclerosis 30 locus on chromosome 1. Compared with the previous linkage analysis, association improved the resolution of the atherosclerosis 30 locus by more than an order of magnitude. Using expression quantitative trait locus analysis, we identified one of the genes in the region, desmin, as a strong candidate. CONCLUSIONS: Our high-resolution mapping approach accurately identifies and fine maps known atherosclerosis quantitative trait loci. These results suggest that high-resolution genome-wide association analysis for atherosclerosis is feasible in mice.}, keywords = {HMDP, Mouse Genetics}, pubstate = {published}, tppubtype = {article} } OBJECTIVE: The purpose of this study was to fine map previously identified quantitative trait loci affecting atherosclerosis in mice using association analysis. METHODS AND RESULTS: We recently showed that high-resolution association analysis using common inbred strains of mice is feasible if corrected for population structure. To use this approach for atherosclerosis, which requires a sensitizing mutation, we bred human apolipoprotein B-100 transgenic mice with 22 different inbred strains to produce F1 heterozygotes. Mice carrying the dominant transgene were tested for association with high-density single nucleotide polymorphism maps. Here, we focus on high-resolution mapping of the previously described atherosclerosis 30 locus on chromosome 1. Compared with the previous linkage analysis, association improved the resolution of the atherosclerosis 30 locus by more than an order of magnitude. Using expression quantitative trait locus analysis, we identified one of the genes in the region, desmin, as a strong candidate. CONCLUSIONS: Our high-resolution mapping approach accurately identifies and fine maps known atherosclerosis quantitative trait loci. These results suggest that high-resolution genome-wide association analysis for atherosclerosis is feasible in mice. |
Friese, Ryan S; Ye, Chun ; Nievergelt, Caroline M; Schork, Andrew J; Mahapatra, Nitish R; Rao, Fangwen ; Napolitan, Philip S; Waalen, Jill ; Schmid-Schönbein, Geert W; Eskin, Eleazar ; O'Connor, Daniel T Circ Cardiovasc Genet, 2012, ISSN: 1942-3268. Abstract | Links | BibTeX | Tags: @article{Friese:CircCardiovascGenet:2012, title = {Integrated Computational and Experimental Analysis of the Neuroendocrine Transcriptome in Genetic Hypertension Identifies Novel Control Points for the Cardio-Metabolic Syndrome.}, author = { Ryan S. Friese and Chun Ye and Caroline M. Nievergelt and Andrew J. Schork and Nitish R. Mahapatra and Fangwen Rao and Philip S. Napolitan and Jill Waalen and Geert W. Schmid-Schönbein and Eleazar Eskin and Daniel T. O'Connor}, url = {http://dx.doi.org/10.1161/CIRCGENETICS.111.962415}, issn = {1942-3268}, year = {2012}, date = {2012-01-01}, journal = {Circ Cardiovasc Genet}, organization = {1 University of California at San Diego, San Diego, CA;}, abstract = {BACKGROUND: -Essential hypertension, a common complex disease, displays substantial genetic influence. Contemporary methods to dissect the genetic basis of complex diseases, such as the genome-wide association study, are powerful, yet a large gap exists betweens the fraction of population trait variance explained by such associations and total disease heritability. METHODS AND RESULTS: -We developed a novel, integrative method (combining animal models, transcriptomics, bioinformatics, molecular biology, and trait-extreme phenotypes) to identify candidate genes for essential hypertension and the metabolic syndrome. We first undertook transcriptome profiling on adrenal glands from blood pressure extreme mouse strains: the hypertensive BPH and hypotensive BPL. Microarray data clustering revealed a striking pattern of global underexpression of intermediary metabolism transcripts in BPH. The MITRA algorithm identified a conserved motif in the transcriptional regulatory regions of the underexpressed metabolic genes, and we then hypothesized that regulation through this motif contributed to the global underexpression. Luciferase reporter assays demonstrated transcriptional activity of the motif, via transcription factors HOXA3, SRY, and YY1. We finally hypothesized that genetic variation at HOXA3, SRY, and YY1 might predict blood pressure and other metabolic syndrome traits in humans. Tagging variants for each locus were associated with BP in a human population BP extreme sample, with the most extensive associations for YY1 tagging SNP rs11625658, on SBP, DBP, BMI, and fasting glucose. Meta-analysis extended the YY1 results into two additional large population samples, with significant effects preserved on DBP, BMI, and fasting glucose. CONCLUSIONS: -The results outline an innovative, systematic approach to the genetic pathogenesis of complex cardiovascular disease traits, and point to transcription factor YY1 as a potential candidate gene involved in essential hypertension and the cardio-metabolic syndrome.}, keywords = {}, pubstate = {published}, tppubtype = {article} } BACKGROUND: -Essential hypertension, a common complex disease, displays substantial genetic influence. Contemporary methods to dissect the genetic basis of complex diseases, such as the genome-wide association study, are powerful, yet a large gap exists betweens the fraction of population trait variance explained by such associations and total disease heritability. METHODS AND RESULTS: -We developed a novel, integrative method (combining animal models, transcriptomics, bioinformatics, molecular biology, and trait-extreme phenotypes) to identify candidate genes for essential hypertension and the metabolic syndrome. We first undertook transcriptome profiling on adrenal glands from blood pressure extreme mouse strains: the hypertensive BPH and hypotensive BPL. Microarray data clustering revealed a striking pattern of global underexpression of intermediary metabolism transcripts in BPH. The MITRA algorithm identified a conserved motif in the transcriptional regulatory regions of the underexpressed metabolic genes, and we then hypothesized that regulation through this motif contributed to the global underexpression. Luciferase reporter assays demonstrated transcriptional activity of the motif, via transcription factors HOXA3, SRY, and YY1. We finally hypothesized that genetic variation at HOXA3, SRY, and YY1 might predict blood pressure and other metabolic syndrome traits in humans. Tagging variants for each locus were associated with BP in a human population BP extreme sample, with the most extensive associations for YY1 tagging SNP rs11625658, on SBP, DBP, BMI, and fasting glucose. Meta-analysis extended the YY1 results into two additional large population samples, with significant effects preserved on DBP, BMI, and fasting glucose. CONCLUSIONS: -The results outline an innovative, systematic approach to the genetic pathogenesis of complex cardiovascular disease traits, and point to transcription factor YY1 as a potential candidate gene involved in essential hypertension and the cardio-metabolic syndrome. |
Ghazalpour, Anatole; Rau, Christoph D; Farber, Charles R; Bennett, Brian J; Orozco, Luz D; van Nas, Atila; Pan, Calvin; Allayee, Hooman; Beaven, Simon W; Civelek, Mete; Davis, Richard C; Drake, Thomas A; Friedman, Rick A; Furlotte, Nick; Hui, Simon T; Jentsch, David J; Kostem, Emrah; Kang, Hyun Min; Kang, Eun Yong; Joo, Jong Wha; Korshunov, Vyacheslav A; Laughlin, Rick E; Martin, Lisa J; Ohmen, Jeffrey D; Parks, Brian W; Pellegrini, Matteo; Reue, Karen; Smith, Desmond J; Tetradis, Sotirios; Wang, Jessica; Wang, Yibin; N, James Hybrid mouse diversity panel: a panel of inbred mouse strains suitable for analysis of complex genetic traits. Journal Article Mamm Genome, 2012, ISSN: 1432-1777. Abstract | Links | BibTeX | Tags: HMDP, Mouse Genetics @article{Ghazalpour:MammGenome:2012, title = {Hybrid mouse diversity panel: a panel of inbred mouse strains suitable for analysis of complex genetic traits.}, author = {Anatole Ghazalpour and Christoph D Rau and Charles R Farber and Brian J Bennett and Luz D Orozco and Atila van Nas and Calvin Pan and Hooman Allayee and Simon W Beaven and Mete Civelek and Richard C Davis and Thomas A Drake and Rick A Friedman and Nick Furlotte and Simon T Hui and J David Jentsch and Emrah Kostem and Hyun Min Kang and Eun Yong Kang and Jong Wha Joo and Vyacheslav A Korshunov and Rick E Laughlin and Lisa J Martin and Jeffrey D Ohmen and Brian W Parks and Matteo Pellegrini and Karen Reue and Desmond J Smith and Sotirios Tetradis and Jessica Wang and Yibin Wang and James N}, url = {http://dx.doi.org/10.1007/s00335-012-9411-5}, issn = {1432-1777}, year = {2012}, date = {2012-01-01}, journal = {Mamm Genome}, organization = {Department of Medicine, David Geffen School of Medicine, University of California, Los Angeles, CA, USA.}, abstract = {We have developed an association-based approach using classical inbred strains of mice in which we correct for population structure, which is very extensive in mice, using an efficient mixed-model algorithm. Our approach includes inbred parental strains as well as recombinant inbred strains in order to capture loci with effect sizes typical of complex traits in mice (in the range of 5% of total trait variance). Over the last few years, we have typed the hybrid mouse diversity panel (HMDP) strains for a variety of clinical traits as well as intermediate phenotypes and have shown that the HMDP has sufficient power to map genes for highly complex traits with resolution that is in most cases less than a megabase. In this essay, we review our experience with the HMDP, describe various ongoing projects, and discuss how the HMDP may fit into the larger picture of common diseases and different approaches.}, keywords = {HMDP, Mouse Genetics}, pubstate = {published}, tppubtype = {article} } We have developed an association-based approach using classical inbred strains of mice in which we correct for population structure, which is very extensive in mice, using an efficient mixed-model algorithm. Our approach includes inbred parental strains as well as recombinant inbred strains in order to capture loci with effect sizes typical of complex traits in mice (in the range of 5% of total trait variance). Over the last few years, we have typed the hybrid mouse diversity panel (HMDP) strains for a variety of clinical traits as well as intermediate phenotypes and have shown that the HMDP has sufficient power to map genes for highly complex traits with resolution that is in most cases less than a megabase. In this essay, we review our experience with the HMDP, describe various ongoing projects, and discuss how the HMDP may fit into the larger picture of common diseases and different approaches. |
Darnell, Gregory; Duong, Dat ; Han, Buhm ; Eskin, Eleazar Incorporating prior information into association studies. Journal Article Bioinformatics, 28 (12), pp. i147-i153, 2012, ISSN: 1367-4811. Abstract | Links | BibTeX | Tags: Association Priors, Association Study Methods @article{Darnell:Bioinformatics:2012, title = {Incorporating prior information into association studies.}, author = { Gregory Darnell and Dat Duong and Buhm Han and Eleazar Eskin}, url = {http://dx.doi.org/10.1093/bioinformatics/bts235}, issn = {1367-4811}, year = {2012}, date = {2012-01-01}, journal = {Bioinformatics}, volume = {28}, number = {12}, pages = {i147-i153}, address = {England}, organization = {Department of Computer Science, University of California, Los Angeles, CA 90095, Department of Statistics, University of California, Berkeley, CA 94720 and Department of Human Genetics, University of }, abstract = {SUMMARY: Recent technological developments in measuring genetic variation have ushered in an era of genome-wide association studies which have discovered many genes involved in human disease. Current methods to perform association studies collect genetic information and compare the frequency of variants in individuals with and without the disease. Standard approaches do not take into account any information on whether or not a given variant is likely to have an effect on the disease. We propose a novel method for computing an association statistic which takes into account prior information. Our method improves both power and resolution by 8% and 27%, respectively, over traditional methods for performing association studies when applied to simulations using the HapMap data. Advantages of our method are that it is as simple to apply to association studies as standard methods, the results of the method are interpretable as the method reports p-values, and the method is optimal in its use of prior information in regards to statistical power. AVAILABILITY: The method presented herein is available at http://masa.cs.ucla.edu CONTACT: eeskin@cs.ucla.edu.}, keywords = {Association Priors, Association Study Methods}, pubstate = {published}, tppubtype = {article} } SUMMARY: Recent technological developments in measuring genetic variation have ushered in an era of genome-wide association studies which have discovered many genes involved in human disease. Current methods to perform association studies collect genetic information and compare the frequency of variants in individuals with and without the disease. Standard approaches do not take into account any information on whether or not a given variant is likely to have an effect on the disease. We propose a novel method for computing an association statistic which takes into account prior information. Our method improves both power and resolution by 8% and 27%, respectively, over traditional methods for performing association studies when applied to simulations using the HapMap data. Advantages of our method are that it is as simple to apply to association studies as standard methods, the results of the method are interpretable as the method reports p-values, and the method is optimal in its use of prior information in regards to statistical power. AVAILABILITY: The method presented herein is available at http://masa.cs.ucla.edu CONTACT: eeskin@cs.ucla.edu. |
Listgarten, Jennifer; Lippert, Christoph ; Kadie, Carl M; Davidson, Robert I; Eskin, Eleazar ; Heckerman, David Improved linear mixed models for genome-wide association studies. Journal Article Nat Methods, 9 (6), pp. 525-6, 2012, ISSN: 1548-7105. Links | BibTeX | Tags: Mixed Models, Population Structure Methods @article{Listgarten:NatMethods:2012, title = {Improved linear mixed models for genome-wide association studies.}, author = { Jennifer Listgarten and Christoph Lippert and Carl M. Kadie and Robert I. Davidson and Eleazar Eskin and David Heckerman}, url = {http://dx.doi.org/10.1038/nmeth.2037}, issn = {1548-7105}, year = {2012}, date = {2012-01-01}, journal = {Nat Methods}, volume = {9}, number = {6}, pages = {525-6}, address = {United States}, organization = {1] Microsoft Research, Los Angeles, California, USA. [2].}, keywords = {Mixed Models, Population Structure Methods}, pubstate = {published}, tppubtype = {article} } |
Hersch, Micha; Peter, Bastian ; Kang, Hyun Min ; Schüpfer, Fanny ; Abriel, Hugues ; Pedrazzini, Thierry ; Eskin, Eleazar ; Beckmann, Jacques S; Bergmann, Sven ; Maurer, Fabienne Mapping genetic variants associated with Beta-adrenergic responses in inbred mice. Journal Article PLoS One, 7 (7), pp. e41032, 2012, ISSN: 1932-6203. Abstract | Links | BibTeX | Tags: Mouse Genetics @article{Hersch:PlosOne:2012, title = {Mapping genetic variants associated with Beta-adrenergic responses in inbred mice.}, author = { Micha Hersch and Bastian Peter and Hyun Min Kang and Fanny Schüpfer and Hugues Abriel and Thierry Pedrazzini and Eleazar Eskin and Jacques S. Beckmann and Sven Bergmann and Fabienne Maurer}, url = {http://dx.doi.org/10.1371/journal.pone.0041032}, issn = {1932-6203}, year = {2012}, date = {2012-01-01}, journal = {PLoS One}, volume = {7}, number = {7}, pages = {e41032}, address = {United States}, organization = {Department of Medical Genetics, University of Lausanne, Lausanne, Switzerland.}, abstract = {$beta$-blockers and $beta$-agonists are primarily used to treat cardiovascular diseases. Inter-individual variability in response to both drug classes is well recognized, yet the identity and relative contribution of the genetic players involved are poorly understood. This work is the first genome-wide association study (GWAS) addressing the values and susceptibility of cardiovascular-related traits to a selective $beta$(1)-blocker, Atenolol (ate), and a $beta$-agonist, Isoproterenol (iso). The phenotypic dataset consisted of 27 highly heritable traits, each measured across 22 inbred mouse strains and four pharmacological conditions. The genotypic panel comprised 79922 informative SNPs of the mouse HapMap resource. Associations were mapped by Efficient Mixed Model Association (EMMA), a method that corrects for the population structure and genetic relatedness of the various strains. A total of 205 separate genome-wide scans were analyzed. The most significant hits include three candidate loci related to cardiac and body weight, three loci for electrocardiographic (ECG) values, two loci for the susceptibility of atrial weight index to iso, four loci for the susceptibility of systolic blood pressure (SBP) to perturbations of the $beta$-adrenergic system, and one locus for the responsiveness of QTc (p<10(-8)). An additional 60 loci were suggestive for one or the other of the 27 traits, while 46 others were suggestive for one or the other drug effects (p<10(-6)). Most hits tagged unexpected regions, yet at least two loci for the susceptibility of SBP to $beta$-adrenergic drugs pointed at members of the hypothalamic-pituitary-thyroid axis. Loci for cardiac-related traits were preferentially enriched in genes expressed in the heart, while 23% of the testable loci were replicated with datasets of the Mouse Phenome Database (MPD). Altogether these data and validation tests indicate that the mapped loci are relevant to the traits and responses studied.}, keywords = {Mouse Genetics}, pubstate = {published}, tppubtype = {article} } $beta$-blockers and $beta$-agonists are primarily used to treat cardiovascular diseases. Inter-individual variability in response to both drug classes is well recognized, yet the identity and relative contribution of the genetic players involved are poorly understood. This work is the first genome-wide association study (GWAS) addressing the values and susceptibility of cardiovascular-related traits to a selective $beta$(1)-blocker, Atenolol (ate), and a $beta$-agonist, Isoproterenol (iso). The phenotypic dataset consisted of 27 highly heritable traits, each measured across 22 inbred mouse strains and four pharmacological conditions. The genotypic panel comprised 79922 informative SNPs of the mouse HapMap resource. Associations were mapped by Efficient Mixed Model Association (EMMA), a method that corrects for the population structure and genetic relatedness of the various strains. A total of 205 separate genome-wide scans were analyzed. The most significant hits include three candidate loci related to cardiac and body weight, three loci for electrocardiographic (ECG) values, two loci for the susceptibility of atrial weight index to iso, four loci for the susceptibility of systolic blood pressure (SBP) to perturbations of the $beta$-adrenergic system, and one locus for the responsiveness of QTc (p<10(-8)). An additional 60 loci were suggestive for one or the other of the 27 traits, while 46 others were suggestive for one or the other drug effects (p<10(-6)). Most hits tagged unexpected regions, yet at least two loci for the susceptibility of SBP to $beta$-adrenergic drugs pointed at members of the hypothalamic-pituitary-thyroid axis. Loci for cardiac-related traits were preferentially enriched in genes expressed in the heart, while 23% of the testable loci were replicated with datasets of the Mouse Phenome Database (MPD). Altogether these data and validation tests indicate that the mapped loci are relevant to the traits and responses studied. |
Calabrese, Gina; Bennett, Brian J; Orozco, Luz ; Kang, Hyun M; Eskin, Eleazar ; Dombret, Carlos ; Backer, Olivier De ; Lusis, Aldons J; Farber, Charles R Systems genetic analysis of osteoblast-lineage cells. Journal Article PLoS Genet, 8 (12), pp. e1003150, 2012, ISSN: 1553-7404. Abstract | Links | BibTeX | Tags: HMDP, Mouse Genetics @article{Calabrese:PlosGenet:2012, title = {Systems genetic analysis of osteoblast-lineage cells.}, author = { Gina Calabrese and Brian J. Bennett and Luz Orozco and Hyun M. Kang and Eleazar Eskin and Carlos Dombret and Olivier De Backer and Aldons J. Lusis and Charles R. Farber}, url = {http://dx.doi.org/10.1371/journal.pgen.1003150}, issn = {1553-7404}, year = {2012}, date = {2012-01-01}, journal = {PLoS Genet}, volume = {8}, number = {12}, pages = {e1003150}, address = {United States}, organization = {Center for Public Health Genomics, University of Virginia, Charlottesville, Virginia, United States of America.}, abstract = {The osteoblast-lineage consists of cells at various stages of maturation that are essential for skeletal development, growth, and maintenance. Over the past decade, many of the signaling cascades that regulate this lineage have been elucidated; however, little is known of the networks that coordinate, modulate, and transmit these signals. Here, we identify a gene network specific to the osteoblast-lineage through the reconstruction of a bone co-expression network using microarray profiles collected on 96 Hybrid Mouse Diversity Panel (HMDP) inbred strains. Of the 21 modules that comprised the bone network, module 9 (M9) contained genes that were highly correlated with prototypical osteoblast maker genes and were more highly expressed in osteoblasts relative to other bone cells. In addition, the M9 contained many of the key genes that define the osteoblast-lineage, which together suggested that it was specific to this lineage. To use the M9 to identify novel osteoblast genes and highlight its biological relevance, we knocked-down the expression of its two most connected "hub" genes, Maged1 and Pard6g. Their perturbation altered both osteoblast proliferation and differentiation. Furthermore, we demonstrated the mice deficient in Maged1 had decreased bone mineral density (BMD). It was also discovered that a local expression quantitative trait locus (eQTL) regulating the Wnt signaling antagonist Sfrp1 was a key driver of the M9. We also show that the M9 is associated with BMD in the HMDP and is enriched for genes implicated in the regulation of human BMD through genome-wide association studies. In conclusion, we have identified a physiologically relevant gene network and used it to discover novel genes and regulatory mechanisms involved in the function of osteoblast-lineage cells. Our results highlight the power of harnessing natural genetic variation to generate co-expression networks that can be used to gain insight into the function of specific cell-types}, keywords = {HMDP, Mouse Genetics}, pubstate = {published}, tppubtype = {article} } The osteoblast-lineage consists of cells at various stages of maturation that are essential for skeletal development, growth, and maintenance. Over the past decade, many of the signaling cascades that regulate this lineage have been elucidated; however, little is known of the networks that coordinate, modulate, and transmit these signals. Here, we identify a gene network specific to the osteoblast-lineage through the reconstruction of a bone co-expression network using microarray profiles collected on 96 Hybrid Mouse Diversity Panel (HMDP) inbred strains. Of the 21 modules that comprised the bone network, module 9 (M9) contained genes that were highly correlated with prototypical osteoblast maker genes and were more highly expressed in osteoblasts relative to other bone cells. In addition, the M9 contained many of the key genes that define the osteoblast-lineage, which together suggested that it was specific to this lineage. To use the M9 to identify novel osteoblast genes and highlight its biological relevance, we knocked-down the expression of its two most connected "hub" genes, Maged1 and Pard6g. Their perturbation altered both osteoblast proliferation and differentiation. Furthermore, we demonstrated the mice deficient in Maged1 had decreased bone mineral density (BMD). It was also discovered that a local expression quantitative trait locus (eQTL) regulating the Wnt signaling antagonist Sfrp1 was a key driver of the M9. We also show that the M9 is associated with BMD in the HMDP and is enriched for genes implicated in the regulation of human BMD through genome-wide association studies. In conclusion, we have identified a physiologically relevant gene network and used it to discover novel genes and regulatory mechanisms involved in the function of osteoblast-lineage cells. Our results highlight the power of harnessing natural genetic variation to generate co-expression networks that can be used to gain insight into the function of specific cell-types |
Han, Buhm; Eskin, Eleazar Interpreting meta-analyses of genome-wide association studies. Journal Article PLoS Genet, 8 (3), pp. e1002555, 2012, ISSN: 1553-7404. Abstract | Links | BibTeX | Tags: Meta-Analysis @article{Han:PlosGenet:2012, title = {Interpreting meta-analyses of genome-wide association studies.}, author = { Buhm Han and Eleazar Eskin}, url = {http://dx.doi.org/10.1371/journal.pgen.1002555}, issn = {1553-7404}, year = {2012}, date = {2012-01-01}, journal = {PLoS Genet}, volume = {8}, number = {3}, pages = {e1002555}, address = {United States}, organization = {Department of Computer Science, University of California Los Angeles, Los Angeles, California, United States of America.}, abstract = {Meta-analysis is an increasingly popular tool for combining multiple genome-wide association studies in a single analysis to identify associations with small effect sizes. The effect sizes between studies in a meta-analysis may differ and these differences, or heterogeneity, can be caused by many factors. If heterogeneity is observed in the results of a meta-analysis, interpreting the cause of heterogeneity is important because the correct interpretation can lead to a better understanding of the disease and a more effective design of a replication study. However, interpreting heterogeneous results is difficult. The standard approach of examining the association p-values of the studies does not effectively predict if the effect exists in each study. In this paper, we propose a framework facilitating the interpretation of the results of a meta-analysis. Our framework is based on a new statistic representing the posterior probability that the effect exists in each study, which is estimated utilizing cross-study information. Simulations and application to the real data show that our framework can effectively segregate the studies predicted to have an effect, the studies predicted to not have an effect, and the ambiguous studies that are underpowered. In addition to helping interpretation, the new framework also allows us to develop a new association testing procedure taking into account the existence of effect.}, keywords = {Meta-Analysis}, pubstate = {published}, tppubtype = {article} } Meta-analysis is an increasingly popular tool for combining multiple genome-wide association studies in a single analysis to identify associations with small effect sizes. The effect sizes between studies in a meta-analysis may differ and these differences, or heterogeneity, can be caused by many factors. If heterogeneity is observed in the results of a meta-analysis, interpreting the cause of heterogeneity is important because the correct interpretation can lead to a better understanding of the disease and a more effective design of a replication study. However, interpreting heterogeneous results is difficult. The standard approach of examining the association p-values of the studies does not effectively predict if the effect exists in each study. In this paper, we propose a framework facilitating the interpretation of the results of a meta-analysis. Our framework is based on a new statistic representing the posterior probability that the effect exists in each study, which is estimated utilizing cross-study information. Simulations and application to the real data show that our framework can effectively segregate the studies predicted to have an effect, the studies predicted to not have an effect, and the ambiguous studies that are underpowered. In addition to helping interpretation, the new framework also allows us to develop a new association testing procedure taking into account the existence of effect. |
Miller, Michael B; Basu, Saonli ; Cunningham, Julie ; Eskin, Eleazar ; Malone, Steven M; Oetting, William S; Schork, Nicholas ; Sul, Jae Hoon ; Iacono, William G; McGue, Matt The Minnesota Center for Twin and Family Research genome-wide association study. Journal Article Twin Res Hum Genet, 15 (6), pp. 767-74, 2012, ISSN: 1832-4274. Abstract | Links | BibTeX | Tags: @article{Miller:TwinResHumGenet:2012, title = {The Minnesota Center for Twin and Family Research genome-wide association study.}, author = { Michael B. Miller and Saonli Basu and Julie Cunningham and Eleazar Eskin and Steven M. Malone and William S. Oetting and Nicholas Schork and Jae Hoon Sul and William G. Iacono and Matt McGue}, url = {http://dx.doi.org/10.1017/thg.2012.62}, issn = {1832-4274}, year = {2012}, date = {2012-01-01}, journal = {Twin Res Hum Genet}, volume = {15}, number = {6}, pages = {767-74}, address = {England}, organization = {Department of Psychology, University of Minnesota, Minneapolis, MN, USA. mbmiller@umn.edu}, abstract = {As part of the Genes, Environment and Development Initiative, the Minnesota Center for Twin and Family Research (MCTFR) undertook a genome-wide association study, which we describe here. A total of 8,405 research participants, clustered in four-member families, have been successfully genotyped on 527,829 single nucleotide polymorphism (SNP) markers using lllumina's Human660W-Ouad array. Quality control screening of samples and markers as well as SNP imputation procedures are described. We also describe methods for ancestry control and how the familial clustering of the MCTFR sample can be accounted for in the analysis using a Rapid Feasible Generalized Least Squares algorithm. The rich longitudinal MCTFR assessments provide numerous opportunities for collaboration}, keywords = {}, pubstate = {published}, tppubtype = {article} } As part of the Genes, Environment and Development Initiative, the Minnesota Center for Twin and Family Research (MCTFR) undertook a genome-wide association study, which we describe here. A total of 8,405 research participants, clustered in four-member families, have been successfully genotyped on 527,829 single nucleotide polymorphism (SNP) markers using lllumina's Human660W-Ouad array. Quality control screening of samples and markers as well as SNP imputation procedures are described. We also describe methods for ancestry control and how the familial clustering of the MCTFR sample can be accounted for in the analysis using a Rapid Feasible Generalized Least Squares algorithm. The rich longitudinal MCTFR assessments provide numerous opportunities for collaboration |
Furlotte, Nicholas A; Kang, Eun Yong; Nas, Atila Van; Farber, Charles R; Lusis, Aldons J; Eskin, Eleazar Increasing Association Mapping Power and Resolution in Mouse Genetic Studies Through the Use of Meta-analysis for Structured Populations. Journal Article Genetics, 191 (3), pp. 959-67, 2012, ISSN: 1943-2631. Abstract | Links | BibTeX | Tags: Meta-Analysis, Meta-Analysis Grant, Mouse Genetics @article{Furlotte:Genetics:2012, title = {Increasing Association Mapping Power and Resolution in Mouse Genetic Studies Through the Use of Meta-analysis for Structured Populations.}, author = { Nicholas A. Furlotte and Eun Yong Kang and Atila Van Nas and Charles R. Farber and Aldons J. Lusis and Eleazar Eskin}, url = {http://dx.doi.org/10.1534/genetics.112.140277}, issn = {1943-2631}, year = {2012}, date = {2012-01-01}, journal = {Genetics}, volume = {191}, number = {3}, pages = {959-67}, address = {United States}, organization = {University of California, Los Angeles;}, abstract = {Genetic studies in mouse models have played an integral role in the discovery of the mechanisms underlying many human diseases. The primary mode of discovery has been the application of linkage analysis to mouse crosses. This approach results in high power to identify regions that affect traits, but in low resolution, making it difficult to identify the precise genomic location harboring the causal variant. Recently, a panel of mice referred to as the hybrid mouse diversity panel (HMDP) has been developed to overcome this problem. However, power in this panel is limited by the availability of inbred strains. Previous studies have suggested combining results across multiple panels as a means to increase power, but the methods employed may not be well suited for structured populations, such as the HMDP. In this paper, we introduce a meta-analysis based method that may be used to combine HMDP studies with F2 cross studies to gain power, while increasing resolution. Due to the drastically different genetic structure of F2s and the HMDP, the best way to combine two studies for a given SNP depends on the strain distribution pattern in each study. We show that combining results, while accounting for these patterns, leads to increased power and resolution. Using our method to map bone mineral density, we find that two previously implicated loci are replicated with increased significance and that the size of the associated is decreased. We also map HDL cholesterol and show a dramatic increase in the significance of a previously identified result.}, keywords = {Meta-Analysis, Meta-Analysis Grant, Mouse Genetics}, pubstate = {published}, tppubtype = {article} } Genetic studies in mouse models have played an integral role in the discovery of the mechanisms underlying many human diseases. The primary mode of discovery has been the application of linkage analysis to mouse crosses. This approach results in high power to identify regions that affect traits, but in low resolution, making it difficult to identify the precise genomic location harboring the causal variant. Recently, a panel of mice referred to as the hybrid mouse diversity panel (HMDP) has been developed to overcome this problem. However, power in this panel is limited by the availability of inbred strains. Previous studies have suggested combining results across multiple panels as a means to increase power, but the methods employed may not be well suited for structured populations, such as the HMDP. In this paper, we introduce a meta-analysis based method that may be used to combine HMDP studies with F2 cross studies to gain power, while increasing resolution. Due to the drastically different genetic structure of F2s and the HMDP, the best way to combine two studies for a given SNP depends on the strain distribution pattern in each study. We show that combining results, while accounting for these patterns, leads to increased power and resolution. Using our method to map bone mineral density, we find that two previously implicated loci are replicated with increased significance and that the size of the associated is decreased. We also map HDL cholesterol and show a dramatic increase in the significance of a previously identified result. |
2011 |
He, Dan; Zaitlen, Noah ; Pasaniuc, Bogdan ; Eskin, Eleazar ; Halperin, Eran Genotyping common and rare variation using overlapping pool sequencing. Journal Article BMC Bioinformatics, 12 Suppl 6 , pp. S2, 2011, ISSN: 1471-2105. Abstract | Links | BibTeX | Tags: Sequencing with Pools @article{He:BmcBioinformatics:2011, title = {Genotyping common and rare variation using overlapping pool sequencing.}, author = { Dan He and Noah Zaitlen and Bogdan Pasaniuc and Eleazar Eskin and Eran Halperin}, url = {http://dx.doi.org/10.1186/1471-2105-12-S6-S2}, issn = {1471-2105}, year = {2011}, date = {2011-01-01}, journal = {BMC Bioinformatics}, volume = {12 Suppl 6}, pages = {S2}, address = {England}, organization = {Department of Computer Science, University of California, Los Angeles, CA 90095, USA.}, abstract = {BACKGROUND: Recent advances in sequencing technologies set the stage for large, population based studies, in which the ANA or RNA of thousands of individuals will be sequenced. Currently, however, such studies are still infeasible using a straightforward sequencing approach; as a result, recently a few multiplexing schemes have been suggested, in which a small number of ANA pools are sequenced, and the results are then deconvoluted using compressed sensing or similar approaches. These methods, however, are limited to the detection of rare variants. RESULTS: In this paper we provide a new algorithm for the deconvolution of DNA pools multiplexing schemes. The presented algorithm utilizes a likelihood model and linear programming. The approach allows for the addition of external data, particularly imputation data, resulting in a flexible environment that is suitable for different applications. CONCLUSIONS: Particularly, we demonstrate that both low and high allele frequency SNPs can be accurately genotyped when the DNA pooling scheme is performed in conjunction with microarray genotyping and imputation. Additionally, we demonstrate the use of our framework for the detection of cancer fusion genes from RNA sequences.}, keywords = {Sequencing with Pools}, pubstate = {published}, tppubtype = {article} } BACKGROUND: Recent advances in sequencing technologies set the stage for large, population based studies, in which the ANA or RNA of thousands of individuals will be sequenced. Currently, however, such studies are still infeasible using a straightforward sequencing approach; as a result, recently a few multiplexing schemes have been suggested, in which a small number of ANA pools are sequenced, and the results are then deconvoluted using compressed sensing or similar approaches. These methods, however, are limited to the detection of rare variants. RESULTS: In this paper we provide a new algorithm for the deconvolution of DNA pools multiplexing schemes. The presented algorithm utilizes a likelihood model and linear programming. The approach allows for the addition of external data, particularly imputation data, resulting in a flexible environment that is suitable for different applications. CONCLUSIONS: Particularly, we demonstrate that both low and high allele frequency SNPs can be accurately genotyped when the DNA pooling scheme is performed in conjunction with microarray genotyping and imputation. Additionally, we demonstrate the use of our framework for the detection of cancer fusion genes from RNA sequences. |
Dan He Farhad Hormozdiari, Nicholas Furlotte ; Eskin, Eleazar Efficient Algorithms for Tandem Copy Number Variation Reconstruction in Repeat-rich Regions. Journal Article Bioinformatics, 2011, ISSN: 1367-4811. Abstract | Links | BibTeX | Tags: Structural Variation @article{He:Bioinformatics:2011, title = {Efficient Algorithms for Tandem Copy Number Variation Reconstruction in Repeat-rich Regions.}, author = { Dan He, Farhad Hormozdiari, Nicholas Furlotte, and Eleazar Eskin}, url = {http://dx.doi.org/10.1093/bioinformatics/btr169}, issn = {1367-4811}, year = {2011}, date = {2011-01-01}, journal = {Bioinformatics}, organization = {Department of Computer Science, University of California Los Angeles.}, abstract = {MOTIVATION: Structural variations and in particular Copy Number Variations (CNV) have dramatic effects of disease and traits. Technologies for identifying CNVs have been an active area of research for over 10 years. The current generation of high-throughput sequencing techniques presents new opportunities for identification of CNVs. Methods that utilize these technologies map sequencing reads to a reference genome and look for signatures which might indicate the presence of a CNV. These methods work well when CNVs lie within unique genomic regions. However, the problem of CNV identification and reconstruction becomes much more challenging when CNVs are in repeat-rich regions, due to the multiple mapping positions of the reads. RESULTS: In this study, we propose an efficient algorithm to handle these multi-mapping reads such that the CNVs can be reconstructed with high accuracy even for repeat-rich regions. To our knowledge, this is the first attempt to both identify and reconstruct CNVs in repeat-rich regions. Our experiments show that our method is not only computationally efficient but also accurate. CONTACT: eeskin@cs.ucla.edu.}, keywords = {Structural Variation}, pubstate = {published}, tppubtype = {article} } MOTIVATION: Structural variations and in particular Copy Number Variations (CNV) have dramatic effects of disease and traits. Technologies for identifying CNVs have been an active area of research for over 10 years. The current generation of high-throughput sequencing techniques presents new opportunities for identification of CNVs. Methods that utilize these technologies map sequencing reads to a reference genome and look for signatures which might indicate the presence of a CNV. These methods work well when CNVs lie within unique genomic regions. However, the problem of CNV identification and reconstruction becomes much more challenging when CNVs are in repeat-rich regions, due to the multiple mapping positions of the reads. RESULTS: In this study, we propose an efficient algorithm to handle these multi-mapping reads such that the CNVs can be reconstructed with high accuracy even for repeat-rich regions. To our knowledge, this is the first attempt to both identify and reconstruct CNVs in repeat-rich regions. Our experiments show that our method is not only computationally efficient but also accurate. CONTACT: eeskin@cs.ucla.edu. |
Parrish, Nathaniel; Hormozdiari, Farhad ; Eskin, Eleazar Assembly of non-unique insertion content using next-generation sequencing. Journal Article BMC Bioinformatics, 12 Suppl 6 , pp. S3, 2011, ISSN: 1471-2105. Abstract | Links | BibTeX | Tags: Sequence Assembly, Structural Variation @article{Parrish:BmcBioinformatics:2011, title = {Assembly of non-unique insertion content using next-generation sequencing.}, author = { Nathaniel Parrish and Farhad Hormozdiari and Eleazar Eskin}, url = {http://dx.doi.org/10.1186/1471-2105-12-S6-S3}, issn = {1471-2105}, year = {2011}, date = {2011-01-01}, journal = {BMC Bioinformatics}, volume = {12 Suppl 6}, pages = {S3}, address = {England}, organization = {Department of Computer Science, University of California Los Angeles, CA, USA.}, abstract = {Recent studies in genomics have highlighted the significance of sequence insertions in determining individual variation. Efforts to discover the content of these sequence insertions have been limited to short insertions and long unique insertions. Much of the inserted sequence in the typical human genome, however, is a mixture of repeated and unique sequence. Current methods are designed to assemble only unique sequence insertions, using reads that do not map to the reference. These methods are not able to assemble repeated sequence insertions, as the reads will map to the reference in a different locus.In this paper, we present a computational method for discovering the content of sequence insertions that are unique, repeated, or a combination of the two. Our method analyzes the read mappings and depth of coverage of paired-end reads to identify reads that originated from inserted sequence. We demonstrate the process of assembling these reads to characterize the insertion content. Our method is based on the idea of segment extension, which progressively extends segments of known content using paired-end reads. We apply our method in simulation to discover the content of inserted sequences in a modified mouse chromosome and show that our method produces reliable results at 40x coverage.}, keywords = {Sequence Assembly, Structural Variation}, pubstate = {published}, tppubtype = {article} } Recent studies in genomics have highlighted the significance of sequence insertions in determining individual variation. Efforts to discover the content of these sequence insertions have been limited to short insertions and long unique insertions. Much of the inserted sequence in the typical human genome, however, is a mixture of repeated and unique sequence. Current methods are designed to assemble only unique sequence insertions, using reads that do not map to the reference. These methods are not able to assemble repeated sequence insertions, as the reads will map to the reference in a different locus.In this paper, we present a computational method for discovering the content of sequence insertions that are unique, repeated, or a combination of the two. Our method analyzes the read mappings and depth of coverage of paired-end reads to identify reads that originated from inserted sequence. We demonstrate the process of assembling these reads to characterize the insertion content. Our method is based on the idea of segment extension, which progressively extends segments of known content using paired-end reads. We apply our method in simulation to discover the content of inserted sequences in a modified mouse chromosome and show that our method produces reliable results at 40x coverage. |
Ghazalpour, Anatole; Bennett, Brian; Petyuk, Vladislav A; Orozco, Luz; Hagopian, Raffi; Mungrue, Imran N; Farber, Charles R; Sinsheimer, Janet; Kang, Hyun M; Furlotte, Nicholas; Park, Christopher C; Wen, Ping-Zi; Brewer, Heather; Weitz, Karl; Camp, David G; Pan, Calvin; Yordanova, Roumyana; Neuhaus, Isaac; Tilford, Charles; Siemers, Nathan; Gargalovic, Peter; Eskin, Eleazar; Kirchgessner, Todd; Smith, Desmond J; Smith, Richard D; Lusis, Aldons J Comparative analysis of proteome and transcriptome variation in mouse. Journal Article PLoS Genet, 7 (6), pp. e1001393, 2011, ISSN: 1553-7404. Abstract | Links | BibTeX | Tags: HMDP, Mouse Genetics @article{Ghazalpour:PlosGenet:2011, title = {Comparative analysis of proteome and transcriptome variation in mouse.}, author = {Anatole Ghazalpour and Brian Bennett and Vladislav A Petyuk and Luz Orozco and Raffi Hagopian and Imran N Mungrue and Charles R Farber and Janet Sinsheimer and Hyun M Kang and Nicholas Furlotte and Christopher C Park and Ping-Zi Wen and Heather Brewer and Karl Weitz and David G Camp and Calvin Pan and Roumyana Yordanova and Isaac Neuhaus and Charles Tilford and Nathan Siemers and Peter Gargalovic and Eleazar Eskin and Todd Kirchgessner and Desmond J Smith and Richard D Smith and Aldons J Lusis}, url = {http://dx.doi.org/10.1371/journal.pgen.1001393}, issn = {1553-7404}, year = {2011}, date = {2011-01-01}, journal = {PLoS Genet}, volume = {7}, number = {6}, pages = {e1001393}, address = {United States}, organization = {Department of Medicine/Division of Cardiology, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States of America.}, abstract = {The relationships between the levels of transcripts and the levels of the proteins they encode have not been examined comprehensively in mammals, although previous work in plants and yeast suggest a surprisingly modest correlation. We have examined this issue using a genetic approach in which natural variations were used to perturb both transcript levels and protein levels among inbred strains of mice. We quantified over 5,000 peptides and over 22,000 transcripts in livers of 97 inbred and recombinant inbred strains and focused on the 7,185 most heritable transcripts and 486 most reliable proteins. The transcript levels were quantified by microarray analysis in three replicates and the proteins were quantified by Liquid Chromatography-Mass Spectrometry using O(18)-reference-based isotope labeling approach. We show that the levels of transcripts and proteins correlate significantly for only about half of the genes tested, with an average correlation of 0.27, and the correlations of transcripts and proteins varied depending on the cellular location and biological function of the gene. We examined technical and biological factors that could contribute to the modest correlation. For example, differential splicing clearly affects the analyses for certain genes; but, based on deep sequencing, this does not substantially contribute to the overall estimate of the correlation. We also employed genome-wide association analyses to map loci controlling both transcript and protein levels. Surprisingly, little overlap was observed between the protein- and transcript-mapped loci. We have typed numerous clinically relevant traits among the strains, including adiposity, lipoprotein levels, and tissue parameters. Using correlation analysis, we found that a low number of clinical trait relationships are preserved between the protein and mRNA gene products and that the majority of such relationships are specific to either the protein levels or transcript levels. Surprisingly, transcript levels were more strongly correlated with clinical traits than protein levels. In light of the widespread use of high-throughput technologies in both clinical and basic research, the results presented have practical as well as basic implications.}, keywords = {HMDP, Mouse Genetics}, pubstate = {published}, tppubtype = {article} } The relationships between the levels of transcripts and the levels of the proteins they encode have not been examined comprehensively in mammals, although previous work in plants and yeast suggest a surprisingly modest correlation. We have examined this issue using a genetic approach in which natural variations were used to perturb both transcript levels and protein levels among inbred strains of mice. We quantified over 5,000 peptides and over 22,000 transcripts in livers of 97 inbred and recombinant inbred strains and focused on the 7,185 most heritable transcripts and 486 most reliable proteins. The transcript levels were quantified by microarray analysis in three replicates and the proteins were quantified by Liquid Chromatography-Mass Spectrometry using O(18)-reference-based isotope labeling approach. We show that the levels of transcripts and proteins correlate significantly for only about half of the genes tested, with an average correlation of 0.27, and the correlations of transcripts and proteins varied depending on the cellular location and biological function of the gene. We examined technical and biological factors that could contribute to the modest correlation. For example, differential splicing clearly affects the analyses for certain genes; but, based on deep sequencing, this does not substantially contribute to the overall estimate of the correlation. We also employed genome-wide association analyses to map loci controlling both transcript and protein levels. Surprisingly, little overlap was observed between the protein- and transcript-mapped loci. We have typed numerous clinically relevant traits among the strains, including adiposity, lipoprotein levels, and tissue parameters. Using correlation analysis, we found that a low number of clinical trait relationships are preserved between the protein and mRNA gene products and that the majority of such relationships are specific to either the protein levels or transcript levels. Surprisingly, transcript levels were more strongly correlated with clinical traits than protein levels. In light of the widespread use of high-throughput technologies in both clinical and basic research, the results presented have practical as well as basic implications. |
Sul, Jae Hoon; Han, Buhm ; He, Dan ; Eskin, Eleazar An Optimal Weighted Aggregated Association Test for Identification of Rare Variants Involved in Common Diseases. Journal Article Genetics, 188 (1), pp. 181-188, 2011, ISSN: 1943-2631. Abstract | Links | BibTeX | Tags: Rare Variants @article{Sul:Genetics:2011, title = {An Optimal Weighted Aggregated Association Test for Identification of Rare Variants Involved in Common Diseases.}, author = { Jae Hoon Sul and Buhm Han and Dan He and Eleazar Eskin}, url = {http://dx.doi.org/10.1534/genetics.110.125070}, issn = {1943-2631}, year = {2011}, date = {2011-01-01}, journal = {Genetics}, volume = {188}, number = {1}, pages = {181-188}, organization = {University of California, Los Angeles.}, abstract = {The advent of next generation sequencing technologies allows one to discover nearly all rare variants in a genomic region of interest. This technological development increases the need for an effective statistical method for testing the aggregated effect of rare variants in a gene on disease susceptibility. The idea behind this approach is that if a certain gene is involved in a disease, many rare variants within the gene will disrupt the function of the gene and are associated with the disease. In this paper, we present Rare variant Weighted Aggregate Statistic (RWAS), a method that groups rare variants and computes a weighted sum of differences between case and control mutation counts. We show that our method outperforms the groupwise association test of Madsen and Browning in the disease-risk model which assumes that each variant makes an equally small contribution to disease-risk. In addition, we can incorporate prior information into our method of which variants are likely causal. By using simulated data and real mutation screening data of the susceptibility gene for ataxia telangiectasia, we demonstrate that prior information has a substantial influence on the statistical power of association studies. Our method is publicly available at http://genetics.cs.ucla.edu/rarevariants.}, keywords = {Rare Variants}, pubstate = {published}, tppubtype = {article} } The advent of next generation sequencing technologies allows one to discover nearly all rare variants in a genomic region of interest. This technological development increases the need for an effective statistical method for testing the aggregated effect of rare variants in a gene on disease susceptibility. The idea behind this approach is that if a certain gene is involved in a disease, many rare variants within the gene will disrupt the function of the gene and are associated with the disease. In this paper, we present Rare variant Weighted Aggregate Statistic (RWAS), a method that groups rare variants and computes a weighted sum of differences between case and control mutation counts. We show that our method outperforms the groupwise association test of Madsen and Browning in the disease-risk model which assumes that each variant makes an equally small contribution to disease-risk. In addition, we can incorporate prior information into our method of which variants are likely causal. By using simulated data and real mutation screening data of the susceptibility gene for ataxia telangiectasia, we demonstrate that prior information has a substantial influence on the statistical power of association studies. Our method is publicly available at http://genetics.cs.ucla.edu/rarevariants. |
Park, Christopher C; Gale, Greg D; de Jong, Simone ; Ghazalpour, Anatole ; Bennett, Brian J; Farber, Charles R; Langfelder, Peter ; Lin, Andy ; Khan, Arshad H; Eskin, Eleazar ; Horvath, Steve ; Lusis, Aldons J; Ophoff, Roel A; Smith, Desmond J Gene networks associated with conditional fear in mice identified using a systems genetics approach. Journal Article BMC Syst Biol, 5 , pp. 43, 2011, ISSN: 1752-0509. Abstract | Links | BibTeX | Tags: HMDP, Mouse Genetics @article{Park:BmcSystBiol:2011, title = {Gene networks associated with conditional fear in mice identified using a systems genetics approach.}, author = { Christopher C. Park and Greg D. Gale and Simone de Jong and Anatole Ghazalpour and Brian J. Bennett and Charles R. Farber and Peter Langfelder and Andy Lin and Arshad H. Khan and Eleazar Eskin and Steve Horvath and Aldons J. Lusis and Roel A. Ophoff and Desmond J. Smith}, url = {http://dx.doi.org/10.1186/1752-0509-5-43}, issn = {1752-0509}, year = {2011}, date = {2011-01-01}, journal = {BMC Syst Biol}, volume = {5}, pages = {43}, address = {England}, organization = {Department of Molecular and Medical Pharmacology, David Geffen School of Medicine, University of California, Los Angeles, CA 90095, USA. DSmith@mednet.ucla.edu.}, abstract = {UNLABELLED: ABSTRACT: BACKGROUND: Our understanding of the genetic basis of learning and memory remains shrouded in mystery. To explore the genetic networks governing the biology of conditional fear, we used a systems genetics approach to analyze a hybrid mouse diversity panel (HMDP) with high mapping resolution. RESULTS: A total of 27 behavioral quantitative trait loci were mapped with a false discovery rate of 5%. By integrating fear phenotypes, transcript profiling data from hippocampus and striatum and also genotype information, two gene co-expression networks correlated with context-dependent immobility were identified. We prioritized the key markers and genes in these pathways using intramodular connectivity measures and structural equation modeling. Highly connected genes in the context fear modules included Psmd6, Ube2a and Usp33, suggesting an important role for ubiquitination in learning and memory. In addition, we surveyed the architecture of brain transcript regulation and demonstrated preservation of gene co-expression modules in hippocampus and striatum, while also highlighting important differences. Rps15a, Kif3a, Stard7, 6330503K22RIK, and Plvap were among the individual genes whose transcript abundance were strongly associated with fear phenotypes. CONCLUSION: Application of our multi-faceted mapping strategy permits an increasingly detailed characterization of the genetic networks underlying behavior.}, keywords = {HMDP, Mouse Genetics}, pubstate = {published}, tppubtype = {article} } UNLABELLED: ABSTRACT: BACKGROUND: Our understanding of the genetic basis of learning and memory remains shrouded in mystery. To explore the genetic networks governing the biology of conditional fear, we used a systems genetics approach to analyze a hybrid mouse diversity panel (HMDP) with high mapping resolution. RESULTS: A total of 27 behavioral quantitative trait loci were mapped with a false discovery rate of 5%. By integrating fear phenotypes, transcript profiling data from hippocampus and striatum and also genotype information, two gene co-expression networks correlated with context-dependent immobility were identified. We prioritized the key markers and genes in these pathways using intramodular connectivity measures and structural equation modeling. Highly connected genes in the context fear modules included Psmd6, Ube2a and Usp33, suggesting an important role for ubiquitination in learning and memory. In addition, we surveyed the architecture of brain transcript regulation and demonstrated preservation of gene co-expression modules in hippocampus and striatum, while also highlighting important differences. Rps15a, Kif3a, Stard7, 6330503K22RIK, and Plvap were among the individual genes whose transcript abundance were strongly associated with fear phenotypes. CONCLUSION: Application of our multi-faceted mapping strategy permits an increasingly detailed characterization of the genetic networks underlying behavior. |