Epistasis Analysis for Quantitative Trait with Next-generation Sequencing Data. F. Zhang, E. Boerwinkle, M. Xiong University of Texas School of Public Health.
The critical barrier in interaction analysis for rare variants is that most traditional statistical methods for testing interaction were originally designed for testing the interaction between common variants and are difficult to be applied to rare variants for their prohibitive computational time and low power. The great challenges for successful detection of interactions with next-generation sequencing (NGS) data are (1) lack of methods for interaction analysis with rare variants, (2) suffering from severe multiple testing, and (3) heavy computations. To meet these challenges, we shift the paradigm of interaction analysis between two loci to interaction analysis between two sets of loci or genomic regions and collectively test interaction between all possible pairs of SNPs within two genome regions. In other words, we take a genome region as a basic unit of interaction analysis and use high dimensional data reduction and functional data analysis techniques to develop a novel functional regression model to collectively test interaction between all possible pairs of SNPs within two genome regions. By intensive simulations, we demonstrate that the functional regression models for interaction analysis of the quantitative trait has the correct type 1 error rates and much higher power to detect interaction than the current pair-wise interaction analysis. The proposed method was applied to exome sequence data from the NHLBIs Exome Sequencing Project (ESP) and CHARGE-S study. We discovered 27 pairs of genes showing significant interactions after applying the Bonferroni correction (P-values <4.58E-10) in ESP and 11 of them were replicated in CHARGE-S study.
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