Efficient and flexible Integration of variant characteristics in rare variant association studies using integrated nested Laplace approximation

by Hana Susak, Laura Serra-Saurina, German Demidov, Raquel Rabionet, Laura Dom ènech, Mattia Bosio, Francesc Muyas, Xavier Estivill, Geòrgia Escaramís, Stephan Ossowski Rare variants are thought to play an important role in the etiology of complex diseases and may explain a significant fraction of the missing heritability in genetic disease studies. Next-generation sequencing facilitates the association of rare variants in coding or regulatory regions with comple x diseases in large cohorts at genome-wide scale. However, rare variant association studies (RVAS) still lack power when cohorts are small to medium-sized and if genetic variation explains a small fraction of phenotypic variance. Here we present a novel Bayesian rare variant Association Test using I ntegrated Nested Laplace Approximation (BATI). Unlike existing RVAS tests, BATI allows integration of individual or variant-specific features as covariates, while efficiently performing inference based on full model estimation. We demonstrate that BATI outperforms established RVAS methods on realist ic, semi-synthetic whole-exome sequencing cohorts, especially when using meaningful biological context, such as functional annotation. We show that BATI achieves power above 70% in scenarios in which competing tests fail to identify risk genes, e.g. when risk variants in sum explain less than 0.5% o f phenotypic variance. We have integrated BATI, together with five existing RVAS tests in the ‘Rare Variant Genome Wide Ass...
Source: PLoS Computational Biology - Category: Biology Authors: Source Type: research