It is intuitive that the genetic risk for human disease depends on the environment, or that the effect of an exposure in disease is not identical across human populations of different genetic backgrounds. This concept is known as gene-by-environment interaction (GxE) and it is hypothesized that disease risk can be better explained by identifying GxE. Despite the importance in understanding GxE in human disease, there have been few studies that have documented the concept. There are a number of explanations for few-recorded GxE. First, there a few ways to measure standardized indicators of the environment (unlike single nucleotide polymorphisms [SNPs]). When GxE are investigated, environmental factors are selected without sufficient evidence of their prior association in disease traits. Second, investigating GxE requires large sample sizes to identify interactions between individual SNPs and environmental factors. The problem is exacerbated when accounting for multiple tests of millions of SNPs with small main effects. Using current day methods and unstandardized environmental data, it is difficult to collect evidence for interactions between millions of specific SNPs and environmental factors. It is now possible to detect GxE in complex disease traits that contribute to significant disease burden, such as body mass index (BMI) and blood pressure (BP), by developing new methods in quantitative genetics and leveraging existing methods in environmental exposure bioinformatics. This project has four aims to achieve this goal. First, the investigators will develop and validate genome-wide polygenic prediction scores to summarize the contribution of all common SNPs in BMI and BP. The investigators will develop and validate the scores in preexisting genome-wide association study (GWAS) consortia data. In the second aim, the investigators will standardize environmental variables from 7 independent cohort studies deposited in the Database of Genotypes and Phenotypes (dbGaP) to build a large cohort of N ~ 30K for GxE testing. Third, the investigators will develop methods to detect and validate GxE between polygenic trait scores and specific environmental factors selected from Environment-Wide Association Studies (EWAS) in BMI and BP with the combined dbGaP cohorts. Fourth, the investigators will estimate the proportion of variation in BMI and BP due to GxE interaction. The methods proposed in the R21 provide a new paradigm for GxE estimation by taking advantage of all SNPs on the genome while considering a larger number of environmental factors with robust support from EWAS. This will lead to a more complete picture of variability ascribed to genes and environment in complex traits of highest disease burden. If successful, the methods will enable the rapid documentation of reproducible GxE, a need in the human genetics and environmental health fields.
Complex disease is influenced by the interaction between environmental and genetic factors. The aims of this project are to develop new quantitative methods to find, validate, and summarize interactions between genetic factors across the entire genome and multiple environmental factors in two complex risk factors for cardiovascular disease, blood pressure and body mass index. A better understanding of the interaction between multiple environmental and genome-wide genetic factors will help improve efforts to better understand the causation diseases of highest public health priority.
|Xue, Angli; Wu, Yang; Zhu, Zhihong et al. (2018) Genome-wide association analyses identify 143 risk variants and putative regulatory mechanisms for type 2 diabetes. Nat Commun 9:2941|
|Chung, Ming Kei; Kannan, Kurunthachalam; Louis, Germaine M et al. (2018) Toward Capturing the Exposome: Exposure Biomarker Variability and Coexposure Patterns in the Shared Environment. Environ Sci Technol 52:8801-8810|
|Wu, Yang; Zeng, Jian; Zhang, Futao et al. (2018) Integrative analysis of omics summary data reveals putative mechanisms underlying complex traits. Nat Commun 9:918|
|Tangirala, Sivateja; Patel, Chirag J (2018) Integrated Analysis of Gene Expression Differences in Twins Discordant for Disease and Binary Phenotypes. Sci Rep 8:17|
|Brown, Adam S; Patel, Chirag J (2018) A review of validation strategies for computational drug repositioning. Brief Bioinform 19:174-177|
|Brown, Adam S; Rasooly, Danielle; Patel, Chirag J (2018) Leveraging Population-Based Clinical Quantitative Phenotyping for Drug Repositioning. CPT Pharmacometrics Syst Pharmacol 7:124-129|
|Sagers, Jessica E; Brown, Adam S; Vasilijic, Sasa et al. (2018) Computational repositioning and preclinical validation of mifepristone for human vestibular schwannoma. Sci Rep 8:5437|
|Luber, Jacob M; Tierney, Braden T; Cofer, Evan M et al. (2018) Aether: leveraging linear programming for optimal cloud computing in genomics. Bioinformatics 34:1565-1567|
|Wilson, Ander; Zigler, Corwin M; Patel, Chirag J et al. (2018) Model-averaged confounder adjustment for estimating multivariate exposure effects with linear regression. Biometrics 74:1034-1044|
|Brown, Adam S; Patel, Chirag J (2017) MeSHDD: Literature-based drug-drug similarity for drug repositioning. J Am Med Inform Assoc 24:614-618|
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