PhD candidate in Biostatistics, Souvik Seal, will present:

“Efficient SNP-based Heritability Estimation using Gaussian Predictive Process in Large Scale Cohort Studies”

PhD Adviser: Saonli Basu

Abstract: For decades, Linear Mixed Model (LMM) has been the most popular tool for estimating heritability in twin and family studies. Recently, the classical LMM that uses known pedigree relationships has been significantly augmented by using a high-dimensional Genetic Relationship Matrix (GRM) constructed using genome-wide SNP data on distantly related individuals. Fitting such a LMM in large scale cohort studies (e.g UK Biobank, Precision Medicine cohort, Millions Veterans Program), 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. Our method, named as PredLMM, has much better computational complexity than most of the existing packages and thus, is particularly usable on any large scale cohort study. Along with extensive simulation studies, we use PredLMM to estimate the heritability of multiple quantitative traits from the UK Biobank cohort.