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Your Environment. Your Health.

PCA-BASED SELECTION SCANS IN VERY LARGE SAMPLES

Export to Word (http://www.niehs.nih.gov//portfolio/index.cfm/portfolio/grantdetail/grant_number/R03ES027902/format/word)
Principal Investigator: Price, Alkes L
Institute Receiving Award Harvard School Of Public Health
Location Boston, MA
Grant Number R03ES027902
Funding Organization National Institute of Environmental Health Sciences
Award Funding Period 01 Apr 2018 to 31 Mar 2021
DESCRIPTION (provided by applicant): Detecting signals of selection can provide biological insights into adaptations that have shaped human history. Genetic variants and phenotypes that are highly differentiated between closely related subpopulations (historically viewed as a source of confounding due to population stratification), can enable the detection of natural selection on functionally important genes, the result of interaction between genes and environment. This approach can detect either selection on individual genetic variants, or polygenic selection reflecting the combined impact of environmental stimuli on many genetic variants that influence a trait. In either case, closely related subpopulations and very large samples are required in order for the approach to have sufficient power. The resulting insights are complementary to those obtained from GWAS, but a challenge is that it is often unclear how to select subpopulations to compare. Here, we propose to address this challenge by analyzing population differentiation along axes of variation inferred using principal components analysis (PCA). We will apply this approach to identify genetic variants with unusual differentiation along top PCs, and to detect polygenic selection on phenotypes with unusual differentiation along top PCs in their genetic values. Our research will be driven by large empirical data sets, with a total of >700,000 samples with genetic data and rich phenotype data. Our development of methods to detect the action of natural selection on environmental stimuli will serve to elucidate the connection between genes and environment in human disease.
Science Code(s)/Area of Science(s) Primary: 07 - Human Genetics/Gene X Environment Interaction
Publications See publications associated with this Grant.
Program Officer Kimberly Mcallister
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