I recently gave a talk on mixed models and confounding factors which is a long time interest of our research group at a workshop which is part of the Evolutionary Biology and the Theory of Computing program which was held at the Simons Institute on the UC Berkeley Campus. The talk was held on February 21st. This talk spans many years of work in our group including work by Hyun Min Kang (now at Michigan), Noah Zaitlen (now at UCSF), and Jimmie Ye (now at Harvard) as well as a sneak peak at very recent work by Joanne Joo, Jae-Hoon Sul and Buhm Han.
The video of the talk is available here and is also on our YouTube Channel ZarlabUCLA.
The papers which are covered in the talk include the EMMA, EMMAX and ICE papers published in 2008 as well as a very new paper that should be coming out soon. The key papers from the talk are:
Kang, Hyun Min; Sul, Jae Hoon ; Service, Susan K; Zaitlen, Noah A; Kong, Sit-Yee Y; Freimer, Nelson B; Sabatti, Chiara ; Eskin, Eleazar Variance component model to account for sample structure in genome-wide association studies. Journal Article In: Nat Genet, 42 (4), pp. 348-54, 2010, ISSN: 1546-1718. @article{Kang:NatGenet:2010, title = {Variance component model to account for sample structure in genome-wide association studies.}, author = { Hyun Min Kang and Jae Hoon Sul and Susan K. Service and Noah A. Zaitlen and Sit-Yee Y. Kong and Nelson B. Freimer and Chiara Sabatti and Eleazar Eskin}, url = {http://dx.doi.org/10.1038/ng.548}, issn = {1546-1718}, year = {2010}, date = {2010-01-01}, journal = {Nat Genet}, volume = {42}, number = {4}, pages = {348-54}, address = {United States}, organization = {Center for Statistical Genetics, Department of Biostatistics, University of Michigan, Ann Arbor, Michigan, USA.}, abstract = {Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches, such as efficient mixed-model association (EMMA), can correct for a wide range of sample structures by explicitly accounting for pairwise relatedness between individuals, using high-density markers to model the phenotype distribution; but such approaches are computationally impractical. We report here a variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours. We apply this method to two human GWAS data sets, performing association analysis for ten quantitative traits from the Northern Finland Birth Cohort and seven common diseases from the Wellcome Trust Case Control Consortium. We find that EMMAX outperforms both principal component analysis and genomic control in correcting for sample structure.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Although genome-wide association studies (GWASs) have identified numerous loci associated with complex traits, imprecise modeling of the genetic relatedness within study samples may cause substantial inflation of test statistics and possibly spurious associations. Variance component approaches, such as efficient mixed-model association (EMMA), can correct for a wide range of sample structures by explicitly accounting for pairwise relatedness between individuals, using high-density markers to model the phenotype distribution; but such approaches are computationally impractical. We report here a variance component approach implemented in publicly available software, EMMA eXpedited (EMMAX), that reduces the computational time for analyzing large GWAS data sets from years to hours. We apply this method to two human GWAS data sets, performing association analysis for ten quantitative traits from the Northern Finland Birth Cohort and seven common diseases from the Wellcome Trust Case Control Consortium. We find that EMMAX outperforms both principal component analysis and genomic control in correcting for sample structure. |
Kang, Hyun Min; Zaitlen, Noah A; Wade, Claire M; Kirby, Andrew ; Heckerman, David ; Daly, Mark J; Eskin, Eleazar Efficient control of population structure in model organism association mapping. Journal Article In: Genetics, 178 (3), pp. 1709-23, 2008, ISSN: 0016-6731. @article{Kang:Genetics:2008, title = {Efficient control of population structure in model organism association mapping.}, author = { Hyun Min Kang and Noah A. Zaitlen and Claire M. Wade and Andrew Kirby and David Heckerman and Mark J. Daly and Eleazar Eskin}, url = {http://dx.doi.org/10.1534/genetics.107.080101}, issn = {0016-6731}, year = {2008}, date = {2008-01-01}, journal = {Genetics}, volume = {178}, number = {3}, pages = {1709-23}, address = {United States}, organization = {Department of Computer Science, University of California, Los Angeles, California 90095-1596, USA.}, abstract = {Genomewide association mapping in model organisms such as inbred mouse strains is a promising approach for the identification of risk factors related to human diseases. However, genetic association studies in inbred model organisms are confronted by the problem of complex population structure among strains. This induces inflated false positive rates, which cannot be corrected using standard approaches applied in human association studies such as genomic control or structured association. Recent studies demonstrated that mixed models successfully correct for the genetic relatedness in association mapping in maize and Arabidopsis panel data sets. However, the currently available mixed-model methods suffer from computational inefficiency. In this article, we propose a new method, efficient mixed-model association (EMMA), which corrects for population structure and genetic relatedness in model organism association mapping. Our method takes advantage of the specific nature of the optimization problem in applying mixed models for association mapping, which allows us to substantially increase the computational speed and reliability of the results. We applied EMMA to in silico whole-genome association mapping of inbred mouse strains involving hundreds of thousands of SNPs, in addition to Arabidopsis and maize data sets. We also performed extensive simulation studies to estimate the statistical power of EMMA under various SNP effects, varying degrees of population structure, and differing numbers of multiple measurements per strain. Despite the limited power of inbred mouse association mapping due to the limited number of available inbred strains, we are able to identify significantly associated SNPs, which fall into known QTL or genes identified through previous studies while avoiding an inflation of false positives. An R package implementation and webserver of our EMMA method are publicly available.}, keywords = {}, pubstate = {published}, tppubtype = {article} } Genomewide association mapping in model organisms such as inbred mouse strains is a promising approach for the identification of risk factors related to human diseases. However, genetic association studies in inbred model organisms are confronted by the problem of complex population structure among strains. This induces inflated false positive rates, which cannot be corrected using standard approaches applied in human association studies such as genomic control or structured association. Recent studies demonstrated that mixed models successfully correct for the genetic relatedness in association mapping in maize and Arabidopsis panel data sets. However, the currently available mixed-model methods suffer from computational inefficiency. In this article, we propose a new method, efficient mixed-model association (EMMA), which corrects for population structure and genetic relatedness in model organism association mapping. Our method takes advantage of the specific nature of the optimization problem in applying mixed models for association mapping, which allows us to substantially increase the computational speed and reliability of the results. We applied EMMA to in silico whole-genome association mapping of inbred mouse strains involving hundreds of thousands of SNPs, in addition to Arabidopsis and maize data sets. We also performed extensive simulation studies to estimate the statistical power of EMMA under various SNP effects, varying degrees of population structure, and differing numbers of multiple measurements per strain. Despite the limited power of inbred mouse association mapping due to the limited number of available inbred strains, we are able to identify significantly associated SNPs, which fall into known QTL or genes identified through previous studies while avoiding an inflation of false positives. An R package implementation and webserver of our EMMA method are publicly available. |
Kang, Hyun Min; Ye, Chun ; Eskin, Eleazar Accurate discovery of expression quantitative trait loci under confounding from spurious and genuine regulatory hotspots. Journal Article In: Genetics, 180 (4), pp. 1909-25, 2008, ISSN: 0016-6731. @article{Kang:Genetics:2008b, title = {Accurate discovery of expression quantitative trait loci under confounding from spurious and genuine regulatory hotspots.}, author = { Hyun Min Kang and Chun Ye and Eleazar Eskin}, url = {http://dx.doi.org/10.1534/genetics.108.094201}, issn = {0016-6731}, year = {2008}, date = {2008-01-01}, journal = {Genetics}, volume = {180}, number = {4}, pages = {1909-25}, address = {United States}, organization = {Department of Human Genetics, University of California, Los Angeles, California 90095, USA.}, abstract = {In genomewide mapping of expression quantitative trait loci (eQTL), it is widely believed that thousands of genes are trans-regulated by a small number of genomic regions called "regulatory hotspots," resulting in "trans-regulatory bands" in an eQTL map. As several recent studies have demonstrated, technical confounding factors such as batch effects can complicate eQTL analysis by causing many spurious associations including spurious regulatory hotspots. Yet little is understood about how these technical confounding factors affect eQTL analyses and how to correct for these factors. Our analysis of data sets with biological replicates suggests that it is this intersample correlation structure inherent in expression data that leads to spurious associations between genetic loci and a large number of transcripts inducing spurious regulatory hotspots. We propose a statistical method that corrects for the spurious associations caused by complex intersample correlation of expression measurements in eQTL mapping. Applying our intersample correlation emended (ICE) eQTL mapping method to mouse, yeast, and human identifies many more cis associations while eliminating most of the spurious trans associations. The concordances of cis and trans associations have consistently increased between different replicates, tissues, and populations, demonstrating the higher accuracy of our method to identify real genetic effects.}, keywords = {}, pubstate = {published}, tppubtype = {article} } In genomewide mapping of expression quantitative trait loci (eQTL), it is widely believed that thousands of genes are trans-regulated by a small number of genomic regions called "regulatory hotspots," resulting in "trans-regulatory bands" in an eQTL map. As several recent studies have demonstrated, technical confounding factors such as batch effects can complicate eQTL analysis by causing many spurious associations including spurious regulatory hotspots. Yet little is understood about how these technical confounding factors affect eQTL analyses and how to correct for these factors. Our analysis of data sets with biological replicates suggests that it is this intersample correlation structure inherent in expression data that leads to spurious associations between genetic loci and a large number of transcripts inducing spurious regulatory hotspots. We propose a statistical method that corrects for the spurious associations caused by complex intersample correlation of expression measurements in eQTL mapping. Applying our intersample correlation emended (ICE) eQTL mapping method to mouse, yeast, and human identifies many more cis associations while eliminating most of the spurious trans associations. The concordances of cis and trans associations have consistently increased between different replicates, tissues, and populations, demonstrating the higher accuracy of our method to identify real genetic effects. |
