Over the past few years genome-wide association studies (GWASs) have identified numerous genes associated with common human diseases. In these studies, genetic variation in thousands of individuals is collected and correlated with the disease status in these individuals. A challenging aspect of GWAS is that the collected individuals are related to each other by differing degrees. This can lead to spurious associations which are genes that appear to be associated with the disease, but in fact are an artifact of the relatedness between individuals. Several methods have been proposed to address this problem and are implemented in publicly available software packages. Environmental factors often interact with genetic variation to increase risk of disease. Identifying these interactions, referrd to as gene-by-environment (GxE) interactions, is now a major focus of research in both human studies and model organism studies. Discovering GxE interactions can provide insight into disease pathways, an understanding of the effect of environmental factors in disease, better risk prediction and personalized therapies. Model organisms such as mouse are ideal environments for studying GxE interactions because environmental exposures can be carefully controlled. Unfortunately, for the same reasons that relatedness can cause spurious associations in association studies, relatedness can cause spurious gene-by- environment interactions. In this proposal we propose to develop methodology that corrects for relatedness in studies that search for gene-by-environment interactions. The results of our project will be a set of methods that are can detect gene-by-environment interactions consistently even when the individuals in the study are related. These methods can then be widely used by many researchers involved in studies to discovery gene-by-environment interactions. We will apply our developed methods to the Minnesota Center for Twin and Family Research (MCTFR) data to investigate how gene-environment interplay influences the development of substance abuse (SA) and to mouse genetic studies investigating the genetic factors which influence response to high fat diet and susceptibility to heart failure. We will make implement our methods available to the research community through publicly available software packages and webserver resources.

Public Health Relevance

An individual's predisposition to disease depends on both genetic factors and environmental factors as well as the interaction of these genetic and environmental factors. Relatively little is known about these interactions and few of them have been discovered. This proposal is relevant to human health because it develops novel methods for discovering these types of interactions which can provide insight into the factors which contribute to disease leading to directions for new treatments and therapies.

National Institute of Health (NIH)
National Institute of Environmental Health Sciences (NIEHS)
Research Project (R01)
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Genomics, Computational Biology and Technology Study Section (GCAT)
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Mcallister, Kimberly A
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University of California Los Angeles
Biostatistics & Other Math Sci
Schools of Engineering
Los Angeles
United States
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Hormozdiari, Farhad; van de Bunt, Martijn; Segrè, Ayellet V et al. (2016) Colocalization of GWAS and eQTL Signals Detects Target Genes. Am J Hum Genet 99:1245-1260
Schweiger, Regev; Kaufman, Shachar; Laaksonen, Reijo et al. (2016) Fast and Accurate Construction of Confidence Intervals for Heritability. Am J Hum Genet 98:1181-92
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Lavinsky, Joel; Ge, Marshall; Crow, Amanda L et al. (2016) The Genetic Architecture of Noise-Induced Hearing Loss: Evidence for a Gene-by-Environment Interaction. G3 (Bethesda) 6:3219-3228
Hormozdiari, Farhad; Kang, Eun Yong; Bilow, Michael et al. (2016) Imputing Phenotypes for Genome-wide Association Studies. Am J Hum Genet 99:89-103
Sul, Jae Hoon; Bilow, Michael; Yang, Wen-Yun et al. (2016) Accounting for Population Structure in Gene-by-Environment Interactions in Genome-Wide Association Studies Using Mixed Models. PLoS Genet 12:e1005849
Kang, Eun Yong; Martin, Lisa; Mangul, Serghei et al. (2016) Discovering SNPs Regulating Human Gene Expression Using Allele Specific Expression from RNA-Seq Data. Genetics :
Joo, Jong Wha J; Kang, Eun Yong; Org, Elin et al. (2016) Efficient and Accurate Multiple-Phenotype Regression Method for High Dimensional Data Considering Population Structure. Genetics 204:1379-1390
Joo, Jong Wha J; Hormozdiari, Farhad; Han, Buhm et al. (2016) Multiple testing correction in linear mixed models. Genome Biol 17:62
Wang, Zhanyong; Sul, Jae Hoon; Snir, Sagi et al. (2015) Gene-Gene Interactions Detection Using a Two-stage Model. J Comput Biol 22:563-76

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