Mar 4, 2016

Bayesian large-scale multiple regression with summary statistics from genome-wide association studies

BioRxiv : the Preprint Server for Biology
Xiang Zhu, Matthew Stephens

Abstract

Bayesian methods for large-scale multiple regression provide attractive approaches to the analysis of genome-wide association studies (GWAS). For example, they can estimate heritability of complex traits, allowing for both polygenic and sparse models; and by incorporating external genomic data into the priors they can increase power and yield new biological insights. However, these methods require access to individual genotypes and phenotypes, which are often not easily available. Here we provide a framework for performing these analyses without individual-level data. Specifically, we introduce a "Regression with Summary Statistics" (RSS) likelihood, which relates the multiple regression coefficients to univariate regression results that are often easily available. The RSS likelihood requires estimates of correlations among covariates (SNPs), which also can be obtained from public databases. We perform Bayesian multiple regression analysis by combining the RSS likelihood with previously-proposed prior distributions, sampling posteriors by Markov chain Monte Carlo. In a wide range of simulations RSS performs similarly to analyses using the individual data, both for estimating heritability and detecting associations. We apply RSS...Continue Reading

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Mentioned in this Paper

Computer Software
Genome-Wide Association Study
Genome
Genomics
Simulation
Regression Analysis
Analysis
Disease Regression
Eichsfeld Type Congenital Muscular Dystrophy
Single Nucleotide Polymorphism

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