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More powerful genetic association testing via a new statistical framework for integrative genomics
Author(s) -
Zhao Sihai D.,
Cai T. Tony,
Li Hongzhe
Publication year - 2014
Publication title -
biometrics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.298
H-Index - 130
eISSN - 1541-0420
pISSN - 0006-341X
DOI - 10.1111/biom.12206
Subject(s) - association (psychology) , genomics , genome wide association study , genetic association , computational biology , computer science , statistical genetics , data science , biology , genetics , psychology , genome , genotype , single nucleotide polymorphism , gene , psychotherapist
Summary Integrative genomics offers a promising approach to more powerful genetic association studies. The hope is that combining outcome and genotype data with other types of genomic information can lead to more powerful SNP detection. We present a new association test based on a statistical model that explicitly assumes that genetic variations affect the outcome through perturbing gene expression levels. It is shown analytically that the proposed approach can have more power to detect SNPs that are associated with the outcome through transcriptional regulation, compared to tests using the outcome and genotype data alone, and simulations show that our method is relatively robust to misspecification. We also provide a strategy for applying our approach to high‐dimensional genomic data. We use this strategy to identify a potentially new association between a SNP and a yeast cell's response to the natural product tomatidine, which standard association analysis did not detect.