Farhad Hormozdiari successfully defended his thesis,”Statistical Methods to Understand the Genetic Architecture of Complex Traits,” on Tuesday, May 17, 2016 in Boelter 4760. His talk, which is posted on our YouTube channel ZarlabUCLA, discusses methods for applying CAVIAR to understand the underlying mechanism of GWAS risk loci, introduces eCAVIAR, a statistical method capable of computing the probability that the same variant is responsible for both the GWAS and eQTL signal, while accounting for complex LD structure, and proposes an approach called phenotype imputation that allows GWAS computation on a phenotype that is difficult to collect.




In addition to his forthcoming paper on phenotype imputation, more details about Farhad’s research are available in the following papers:

Hormozdiari, Farhad; Kichaev, Gleb; Yang, Wen-Yun; Pasaniuc, Bogdan; Eskin, Eleazar (2015): Identification of causal genes for complex traits.. In: Bioinformatics, 31 (12), pp. i206-i213, 2015, ISSN: 1367-4811. (Type: Journal Article | Abstract | Links | BibTeX)
Hormozdiari, Farhad; Joo, Jong Wha; Wadia, Akshay; Guan, Feng; Ostrosky, Rafail; Sahai, Amit; Eskin, Eleazar (2014): Privacy preserving protocol for detecting genetic relatives using rare variants.. In: Bioinformatics, 30 (12), pp. i204-i211, 2014, ISSN: 1367-4811. (Type: Journal Article | Abstract | Links | BibTeX)
Hormozdiari, Farhad; Kostem, Emrah; Kang, Eun Yong; Pasaniuc, Bogdan; Eskin, Eleazar (2014): Identifying causal variants at Loci with multiple signals of association.. In: Genetics, 198 (2), pp. 497-508, 2014, ISSN: 1943-2631. (Type: Journal Article | Abstract | Links | BibTeX)
Eskin, Itamar; Hormozdiari, Farhad; Conde, Lucia; Riby, Jacques; Skibola, Chris; Eskin, Eleazar; Halperin, Eran (2013): eALPS: Estimating Abundance Levels in Pooled Sequencing Using Available Genotyping Data.. In: J Comput Biol, 2013, ISSN: 1557-8666. (Type: Journal Article | Abstract | Links | BibTeX)

Our group recently developed the Read Origin Protocol (ROP) method to discover the source of all reads in an RNA-seq experiment. Reads originate from complex RNA molecules, recombinant antibodies and microbial communities. ROP accounts for 98.8% of all reads across poly(A) and ribo-depletion protocols, compared to 83.8% by conventional reference-based protocols. We find that the vast majority of unmapped reads are human in origin and originate from diverse sources, including repetitive elements, non-co-linear elements or recombined B and T cell receptors (BCR/TCR). In addition to human RNA, a large number of reads were microbial in origin, often occurring in sufficient numbers to study the taxonomic composition of microbial communities.




The majority of RNA-Seq analyses begin by mapping each experimentally produced sequence (i.e., read) to a set of annotated reference sequences for the organism of interest. For both biological and technical reasons, a significant fraction of reads remains unmapped. Our study is the first that systematically accounts for almost all reads in RNA-seq studies. We demonstrate the value of analyzing unmapped reads present in the RNA-seq data to better understand the functional mechanisms underlying the connection between immune system, microbiome, human gene expression, and disease etiology.

We applied our method to to RNA-seq data from 53 asthmatic cases and 33 controls collected from three tissues, using both poly(A) selection and ribo-depletion libraries. Using the ROP pipeline we show that immune profiles of asthmatic individuals are significantly different from the controls with decreased T-cell/B-cell receptor diversity and that immune diversity is inversely correlated with microbial load. This case study highlights the potential for novel discoveries without additional TCR/BCR or microbiome sequencing when the information in RNA-seq data is fully leveraged by incorporating the analysis of unmapped reads.

The ROP can not only help researchers make the best use of sequencing data, but will also enable additional scientific questions to be answered with no additional cost. For example, one can now interrogate additional features of the immune system without additional expensive TCR/BCR sequencing. The ‘dumpster diving’ profile of unmapped reads output by our method is not limited to RNA-Seq technology and may be applied to whole-exome and whole-genome sequencing. We anticipate that ‘dumpster diving’ profiling will find broad future applications in studies involving different tissue and disease types.

This project was led by Serghei Mangul and involved Harry Yang (Taegyun), both of whom developed the protocol as open source software. This was a joint project with the Noah Zaitlen group (http://zaitlenlab.ucsf.edu/) at University of California, San Francisco.

ROP is available at https://sergheimangul.wordpress.com/rop/.

The article is available at: http://biorxiv.org/content/early/2016/05/13/053041.

The full citation to our paper is:

Mangul, Serghei; Yang, Harry Taegyun; Strauli, Nicolas; Gruhl, Franziska; Daley, Timothy; Christenson, Stephanie; Andersen, Agata Wesolowska; Spreafico, Roberto; Rios, Cydney; Eng, Celeste; Smith, Andrew; Hernandez, Ryan; Ophoff, Roel; Santana, Jose Rodriguez; Woodruff, Prescott; Burchard, Esteban; Seibold, Max; Shifman, Sagiv; Eskin, Eleazar; Zaitlen, Noah (2016): Dumpster diving in RNA-sequencing to find the source of every last read. In: BioRxiv, 2016. (Type: Journal Article | Links | BibTeX)
Our group recently published a new paper on multiple testing applied to genetic studies with population structure.  This project was led by Jong Wha (Joanne) Joo and also involved Farhad Hormozdiari.  The project was joint with Buhm Han’s group.  The approach built upon Buhm Han’s previous work SLIDE (Han et al. 2009; Han and Eskin 2012).
Genome-wide association studies (GWAS) have discovered many variants that are associated with complex traits in the human genome. In GWAS, researchers collect both phenotypic information and genetic information on variants spread through the genome from a population. In order to identify the set of variants associated with a trait of interest, we assess correlations between the phenotype and the genetic information at each variant, which we call the genotype. GWAS are now routinely performed on tens of thousands of individuals—and millions of genetic variants.
GWAS methodology must address specific problems that are tied to this exceptionally large scale of analysis. One major challenge in GWAS is multiple hypothesis testing. In routine analyses, the significance of hypothesis testing is assessed using the p value as a per-marker threshold. However, GWAS involves computing up to millions of statistical tests in a single study. When using traditional association study techniques, multiple hypothesis testing can generate false positives or spurious associations, and p value threshold for significance must be adjusted to control the overall false positive rate.
Several approaches are useful in correcting these potential pitfalls, including Bonferroni correction and permutation test.
Recently, researchers have accepted the linear mixed model (LMM) as standard practice for performing GWAS. The LMM can address two important challenges in GWAS: population structure and insufficient power. Population structure refers to the complex relatedness structure among individuals, which can drive errors in data reporting such as false positives. In many cases, LMM approaches can increase the statistical power and avoid generating false positives by explicitly modeling the population structure’s genetic relationships. Nonetheless, multiple hypothesis testing with LMM approaches may generate some errors of association. Unfortunately, the current approaches for multiple hypothesis testing correction cannot be applied to LMM.  This is because population structure actually affects the correlation structure of the statistics as we show in the paper.
To address this issue, we developed the first gold standard approach for multiple hypothesis testing correction in LMM. This method, called multiple testing in transformed space (MultiTrans), can efficiently correct for multiple testing in LMM approaches. MultiTrans is a parametric bootstrapping resampling approach that is the equivalent of the permutation test. Specifically, our approach samples randomized null phenotypes from the distribution fitted by LMM.
Straightforward parametric bootstrapping where phenotypes are sampled is prohibitively computationally expensive.  MultiTrans instead utilizes   a Multivariate Normal Distribution to directly samples the association statistics.  The figure shows an overview of our methodology.
The full citation to our paper is:
Joo, Jong Wha; Hormozdiari, Farhad; Han, Buhm; Eskin, Eleazar (2016): Multiple testing correction in linear mixed models.. In: Genome Biol, 17 (1), pp. 62, 2016, ISSN: 1474-760X. (Type: Journal Article | Abstract | Links | BibTeX)
Multiple hypothesis testing is an essential step in GWAS analysis. The correct per-marker threshold differs as a function of species, marker densities, genetic relatedness, and trait heritability—and no previous multiple testing correction methods can comprehensively account for these factors. The method we developed to address this issue, MultiTrans, is an efficient and accurate multiple testing correction approach for LMM. Our method (a) performs a unique transformation of genotype data to account for actual genetic relatedness and heritability under LMM approaches, and (b) efficiently utilizes the multivariate normal distribution. Using MultiTrans, we accurately estimated per-marker thresholds in mouse, yeast, and human datasets—while reducing computation time from months to hours.
Jong Wha (Joanne) Joo successfully defended her thesis,”Design of efficient and accurate statistical approaches to correct for confounding effects in genetic association studies,” on Friday, December 4, 2015 in Boelter 4760.  Her talk, which is posted on our YouTube channel ZarlabUCLA, discusses using a mixed model analysis (GAMMA) to efficiently analyzes large numbers of phenotypes while simultaneously considering population structure, an expression quantitative trait loci (eQTL) mapping tool to eliminate spurious hotspots while retaining genuine regulatory hotspots, and a multiple testing correction method (slideLMM) for linear mixed models.
More details about her research are available in the three papers she discusses:
Joo, Jong Wha; Hormozdiari, Farhad; Han, Buhm; Eskin, Eleazar (2016): Multiple testing correction in linear mixed models.. In: Genome Biol, 17 (1), pp. 62, 2016, ISSN: 1474-760X. (Type: Journal Article | Abstract | Links | BibTeX)
Joo, Jong Wha; Kang, Eun Yong; Org, Elin; Furlotte, Nick; Parks, Brian; Lusis, Aldons; Eskin, Eleazar (2015): Efficient and Accurate Multiple-Phenotypes Regression Method for High Dimensional Data Considering Population Structure. In: Research in Computational Molecular Biology, pp. 136-153, Springer International Publishing, 2015. (Type: Book Chapter | Abstract | Links | BibTeX)
Joo, Jong Wha; Sul, Jae Hoon; Han, Buhm; Ye, Chun; Eskin, Eleazar (2014): Effectively identifying regulatory hotspots while capturing expression heterogeneity in gene expression studies.. In: Genome Biol, 15 (4), pp. R61, 2014, ISSN: 1465-6914. (Type: Journal Article | Abstract | Links | BibTeX)

ipam-logoDear Colleagues,

I am happy to announce the UCLA Computational Genomics Summer Institute, which is a new National Institutes of Health funded program at UCLA jointly hosted with the Institute of Pure and Applied Mathematics (IPAM). The program will take place each summer for one month. The dates for 2016 are July 18th – August 12th.

The program focuses on providing training in methodology development for genomics. We hope that it will be of interest to researchers at all levels. Our program builds upon a successful program hosted by IPAM in 2011 on “Mathematical and Computational Approaches in High Throughput Biology.” IPAM is a national math institute funded by the National Science Foundation.

The program consists of two parts. The first part (July 18th – July 22nd) is the Short Program which is in the format of a short course consisting of lectures from leading researchers in computational genomics. The short program is appropriate for researchers at all levels including both researchers actively involved in methodology development as well as other researchers who want to incorporate a methodology development aspect to their research program.

The second part (July 21st – August 12th) is the Long Program which is a continuation of the Short Program. The program is in the style of a typical long program hosted at IPAM where participants have opportunity to interact and collaborate with each other as well as the leading researchers who will serve as program faculty. The program is targeted toward senior trainees such as senior students or post-docs through established researchers.

Researchers at all levels — students, post-docs, staff researchers, as well as junior and senior faculty — are encouraged to participate in the program. Funding is available to support faculty and participant costs during the program. Because space is limited in the program, we are requiring interested participants and potential program faculty to apply as soon as possible.

Application materials are available on the program website (http://computationalgenomics.bioinformatics.ucla.edu). For questions about the program, interested individuals should email uclacgsi@gmail.com.

The UCLA CGSI Organizing Committee
Eleazar Eskin, UCLA, CGSI Director
Russel Caflisch, UCLA. IPAM Director
Eran Halperin, Tel Aviv University
John Novembre, University of Chicago
Ben Raphael, Brown University

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cacm-coverA couple of years ago I was asked to write a review article on the progress of my field (computational genetics) targeted toward computer scientists. My article “Discovering Genes Involved in Disease and the Mystery of Missing Heritability” was just published on the cover of the Communications of the ACM. This article is written to be an introduction to the field as well as describe the rapid progress over the past decade in terms of the discovery of large number of variants involved in common human diseases. The article is written assuming no background in biology and is designed to be accessible to researchers and students outside the field. I hope that it will encourage other computational researchers to get involved in genetics.  The journal also made a video highlighting this article which is available here:

Discovering Genes Involved in Disease and the Mystery of Missing Heritability from CACM on Vimeo.

The full citation to the article is:
Eskin, Eleazar (2015): Discovering Genes Involved in Disease and the Mystery of Missing Heritability. In: Commun. ACM, 58 (10), pp. 80-87, 2015, ISSN: 0001-0782. (Type: Journal Article | Abstract | Links | BibTeX)

Although genome-wide association studies (GWAS) have identified thousands of variants associated with common diseases and complex traits, only a handful of these variants are validated to be causal. We consider ‘causal variants’ as variants which are responsible for the association signal at a locus. As opposed to association studies that benefit from linkage disequilibrium (LD), the main challenge in identifying causal variants at associated loci lies in distinguishing among the many closely correlated variants due to LD. This is particularly important for model organisms such as inbred mice, where LD extends much further than in human populations, resulting in large stretches of the genome with significantly associated variants. Furthermore, these model organisms are highly structured and require correction for population structure to remove potential spurious associations.

In our recently published work, we propose CAVIAR-Gene (CAusal Variants Identification in Associated Regions), a novel method that is able to operate across large LD regions of the genome while also correcting for population structure. A key feature of our approach is that it provides as output a minimally sized set of genes that captures the genes which harbor causal variants with probability q. Through extensive simulations, we demonstrate that our method not only speeds up computation, but also have an average of 10% higher recall rate compared with the existing approaches. We validate our method using a real mouse high-density lipoprotein data (HDL) and show that CAVIAR-Gene is able to identify Apoa2 (a gene known to harbor causal variants for HDL), while reducing the number of genes that need to be tested for functionality by a factor of 2.

In the context of association studies, the genetic variants which are responsible for the association signal at a locus are referred to in the genetics literature as the ‘causal variants.’ Causal variants have biological effect on the phenotype.

CAVIAR-Gene provides better ranking of the causal genes for Outbred, F2, and HMDP datasets. Panels a and b illustrate the results for Outbred genotypes for case where we have one causal and two causal genes, respectively. Panels c and d illustrate the results for F2 genotypes for case where we have one causal and two causal genes, respectively. Panels e and f illustrate the results for Outbred genotypes for case where we have one causal and two causal genes, respectively.

CAVIAR-Gene provides better ranking of the causal genes for Outbred, F2, and HMDP datasets. Panels a and b illustrate the results for Outbred genotypes for case where we have one causal and two causal genes, respectively. Panels c and d illustrate the results for F2 genotypes for case where we have one causal and two causal genes, respectively. Panels e and f illustrate the results for Outbred genotypes for case where we have one causal and two causal genes, respectively.

Generally, variants can be categorized into three main groups. The first group is the causal variants which have a biological effect on the phenotype and are responsible for the association signal. The second group is the variants which are statistically associated with the phenotype due to LD with a causal variant. Even though association tests for these variants may be statistically significant, under our definition, they are not causal variants. The third group is the variants which are not statistically associated with the phenotype and are not causal.

CAVIAR-Gene is a statistical method for fine mapping that addresses two main limitations of existing methods. First, as opposed to existing approaches that focus on individual variants, we propose to search only over the space of gene combinations that explain the statistical association signal, and thus drastically reduce runtime. Second, CAVIAR-Gene extends existing framework for fine mapping to account for population structure. The output of our approach is a minimal set of genes that will contain the true casual gene at a pre-specified significance level.  The output of our approach is a minimal set of genes that will contain the true casual gene at a pre-specified significance level. This gene set together with its individual gene probability of causality provides a natural way of prioritizing genes for functional testing (e.g. knockout strategies) in model organisms. Through extensive simulations, we demonstrate that CAVIAR-Gene is superior to existing methodologies, requiring the smallest set of genes to follow-up in order to capture the true causal gene(s).

Building off our previous work with CAVIAR,  CAVIAR-Gene takes as input the marginal statistics for each variant at a locus, an LD matrix consisting of pairwise Pearson correlations computed between the genotypes of a pair of genetic variants, a partitioning of the set of variants in a locus into genes, and the kinship matrix which indicates the genetic similarity between each pair of individuals. Marginal statistics are computed using methods that correct for population structure.  We consider a variant to be causal when the variant is responsible for the association signal at a locus and aim to discriminate these variants from ones that are correlated due to LD.

In model organisms, the large stretches of LD regions result in a large number of variants associated in each region, thus making CAVIAR computationally

infeasible. Instead of producing a rho causal set of SNPs, CAVIAR-gene detects a ‘q causal gene set’ which is a set of genes in the locus that will contain the actual causal genes with probability of at least q.

For further details of our new method, CAVIAR-gene, view our full paper here:

Studies carried out over the last decade have revealed that gut microbiota contribute to a variety of common disorders, including obesity and diabetes (Musso et al. 2011), colitis (Devkota et al. 2012), atherosclerosis (Wang et al. 2011), rheumatoid arthritis (Vaahtovuo et al. 2008), and cancer (Yoshimoto et al. 2013). The evidence for metabolic interactions is particularly strong, as a large body of data now supports the conclusion that gut microbiota influence the energy harvest from dietary components, particularly complex carbohydrates, and that metabolites such as the short chain fatty acids produced by gut bacteria can perturb metabolic traits, including adiposity and insulin resistance (Turnbaugh et al. 2006; Backhed et al. 2007; Wen et al. 2008; Turnbaugh et al. 2009; Ridaura et al. 2013).

Gut microbiota communities are assembled by generation, influenced by maternal seeding, environmental factors, host genetics and age, resulting in substantial variations in composition among individuals in human populations (Eckburg et al. 2005; Costello et al. 2009; Huttenhower and Consortium 2012; Goodrich et al. 2014). Most experimental studies of host-gut microbiota interactions have employed large perturbations, such as comparisons of germ-free versus conventional mice, and the significance of common variations in gut microbiota composition for disease susceptibility is still poorly understood. Furthermore, while studies with germ-free mice have clearly implicated microbiota in clinically relevant traits, it has proven difficult to identify the responsible taxa of bacteria.

We now report a population-based analysis of host-gut microbiota interactions in the mouse. One of the issues we explore is the role of host genetics. Although some evidence is consistent with significant heritability of gut microbiota composition, the extent to which the host controls microbiota composition under controlled environmental conditions is unclear. We also examine the role of common variations in gut microbiota in metabolic traits such as obesity and insulin resistance. We performed our study using a resource termed the Hybrid Mouse Diversity Panel (HMDP), consisting of about 100 inbred strains of  mice that have been either sequenced or subjected to high density genotyping (Bennett et al. 2010). The resource has several advantages for genetic analysis as compared to traditional genetic crosses. First, it allows high resolution mapping by association rather than linkage analysis, and it has now been used for the identification of a number of novel genes underlying complex traits (Farber et al. 2011; Lavinsky et al. 2015; Parks et al. 2015; Rau et al. 2015). Second, since the strains are permanent the data from separate studies can be integrated, allowing the development of large, publically available databases of physiological and molecular traits relevant to a variety of clinical disorders (systems.genetics.ucla.edu and phenome.jax.org). Third, the panel is ideal for examining gene-by-environment interactions, since it is possible to examine individuals of a particular genotype under a variety of conditions (Orozco et al. 2012; Parks et al. 2013).

Genetics provides a potentially powerful approach to dissect host-gut microbiota interactions. Using a SNP-based approach with a linear mixed model we estimated the heritability of microbiota composition. We conclude that in a controlled environment the genetic background accounts for a significant fraction of abundance of most common microbiota.The mice were previously studied for response to a high fat, high sucrose diet, and we hypothesized that the dietary response was determined in part by gut microbiota composition. We tested this using a cross-fostering strategy in which a strain showing a modest response, SWR, was seeded with microbiota from a strain showing a strong response, AxB19. Consistent with a role of microbiota in dietary response, the cross-fostered SWR pups exhibited a significantly increased response in weight gain. To examine specific microbiota contributing to the response, we identified various genera whose abundance correlated with dietary response. In an effort to further understand host-microbiota interactions, we mapped loci controlling microbiota composition and prioritized candidate genes. Our publically available data provide a resource for future studies.

In our study, we concluded:

– In a total of 599 mice, 75% of them abundantly exhibited the same 17 genera

– These 17 genera accounted for 68% of reads

– Consistent with previous studies, changing diet drastically changes gut microbiota composition, and these shifts are strongly dependent on the genetic background of the mice

– Gut microbiota contribute to dietary responsiveness

– Several gut microbiota (known and novel to this study) contribute to obesity and metabolic phenotypes

– seven genome-wide significant loci (P < 4 x 10-6) were found to be associated with common genera

– We were able to estimated the heritability by using a linear mixed model approach andassuming an additive effect based on the proportion of phenotype variance accounted for by genetic relationships among the strains.

We began our study with the hypothesis that the dietary response was dictated in part by differences in gut microbiota. We showed that different inbred strains of mice differ strikingly in the composition of gut microbiota and provided evidence that the variation is determined in part by the host genetic background. Consistent with our hypothesis, we showed that cross-fostering between two strains of mice affected dietary response to the high fat, high sucrose diet. By correlating microbiota composition with dietary response among the HMDP inbred strains, we were able to identify several candidate microbiota influencing dietary response.

For all the details of our research and our methods, read our paper here.

Recently Zarlab hosted the first-ever Undergraduate Bioinformatics Speaker Series. Our lab has been steadily growing as our undergraduate research program becomes more robust, and we decided it was time we gave the undergrads an outlet of their own. Recently, the Computational Genetics Student Group (CGSG) was formed to serve the research, networking and extracurricular educational needs of the bioinformatics students (and those potentially interested in bioinformatics) at UCLA.

For our first event, we chose to explore the field of forensics and learn how bioinformatics and statistics can be used to solve crimes by analyzing DNA. Associate professor Kirk Lohmueller and Jill Licht, senior criminalist with the LA County Sheriff’s Department, gave insights into murder investigations where they served as expert witnesses. Kirk spoke about how the case was overthrown by the judge due to overlooking key forensic evidence. At the second trial, Kirk was able to testify to a potential second suspect whose blood was found at the crime scene. However, even with the additional DNA evidence, the jury still convicted the primary suspect based on a child’s eye witness account!

Jill was able to provide stories of what the day-to-day life of a forensic biologist is like. At least one week every month, she has to remain alert and ready to drive to the scene of a crime 24-hours a day. Sometimes she’ll get the call at 2 a.m. and have to drive an hour to get to the location. She explained how the Los Angeles Police Department only has jurisdiction in the city of Los Angeles, but the sheriff’s department oversees the rest of LA County. That means she could be called to anywhere from Pasadena to Long Beach. For someone who is squeamish at the sight of blood, Jill says she is able to handle it at work. The ultimate goal is to determine the story behind the scene, and she must stay focused in order to do her best work at the scene. Could you handle working with blood and brains?

If you are interested in this and future talks, leave us a comment below.

Over the past few years, genome-wide association studies (GWAS) have been used to find genetic variants that are involved in disease and other traits by testing for correlations between these traits and genetic variants across the genome. A typical GWAS examines the correlation of a single phenotype and each genotype one at a time. Recently, large amounts of genomic data such as expression data have been collected from GWAS cohorts. This data often contains thousands of phenotypes per individual. The standard approach to analyze this type of data is to perform a GWAS on each phenotype individually, a single-phenotype analysis.

A major flaw of the analysis strategy of analyzing phenotypes independently is that this strategy is underpowered. For example, unmeasured aspects of complex biological networks, such as protein mediators, could be captured with many phenotypes together that might be missed with a single phenotype or a few phenotypes. Previous methods are based on the assumption that the phenotypes of the individuals are independently and identically distributed (i.i.d.). Unfortunately, as has been shown in GWAS studies, this assumption is not valid due to a phenomenon referred to as population structure.

As we recently presented at the RECOMB 2015 conference, we propose a method called GAMMA (Generalized Analysis of Molecular variance for Mixed model Analysis) that efficiently analyzes large numbers of phenotypes while simultaneously considering population structure. Recently, the linear mixed model (LMM) has become a popular approach for GWAS, as it can correct for population structure. The LMM incorporates genetic similarities between all pairs of individuals, known as the kinship, into their model and corrects for population structure.

In figure 3 of our paper (shown above), we apply GAMMA to yeast data and compare to another popular method MDMR. The x-axis corresponds to SNP locations and the y-axis corresponds to gene locations. The y-axis corresponds to -log10 of p value. Blue stars above each plot show putative hotspots that were reported in a previous study for the yeast data.

In figure 3 of our paper (shown above), we apply GAMMA to yeast data and compare to another popular method MDMR. The x-axis corresponds to SNP locations and the y-axis corresponds to gene locations. The y-axis corresponds to -log10 of p value. Blue stars above each plot show putative hotspots that were reported in a previous study for the yeast data.

Unlike the traditional univariate analysis that tests an association between each phenotype and each genotype, our goal is to identify SNPs that are associated with multiple phenotypes. However, in GWAS, it has been widely known that genetic relatedness, referred to as population structure, complicates the analysis by creating spurious associations. The linear model does not account for population structure and assuming the linear model may induce many false positive identifications. Moreover, this could cause even more significant problem in multiple-phenotype analysis because the bias accumulates for each phenotype as their test statistics are summed over the phenotypes (See details in Material and Methods.). Recently, the linear mixed model has emerged as a powerful tool for GWAS as it could correct for population structure.

For more information on GAMMA, check out our full paper here:

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