B0968
Title: Efficient estimation of SNP heritability using Gaussian predictive process in large scale cohort studies
Authors: Saonli Basu - University of Minnesota (United States) [presenting]
Souvik Seal - University of Minnesota (United States)
Abhirup Datta - Johns Hopkins University (United States)
Abstract: For decades, Linear Mixed Model (LMM) has been the most popular tool for estimating heritability in twin and family studies. Recently, with the advent of high throughput genetic data, there is quite a bit of interest to estimate heritability by using a high-dimensional Genetic Relationship Matrix (GRM) constructed from genome-wide SNP data on distantly related individuals. Fitting such an LMM in large scale cohort studies, however, is tremendously challenging due to high dimensional linear algebraic operations. We simplify the LMM unifying the concepts of Genetic Coalescence and Gaussian Predictive Process modeling, greatly alleviating the computational burden. The method, named as PredLMM, has much better computational complexity than most of the existing packages and thus, provides an efficient alternative of estimating heritability in large scale cohort study. We illustrate our approach with extensive simulation studies and use PredLMM to estimate the heritability of multiple quantitative traits from the UK Biobank cohort.
