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

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Supplement to “bayesian Large-scale Multiple Regression with Summary Statistics from Genome-wide Association Studies”

A Theoretical derivation of RSS . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 A.1 Proofs of propositions in main text . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 A.1.1 Proof of Proposition 2.1 . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 2 A.1.2 Proof of Proposition 2.2 . . . . . . . . . . . . . . ....

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Bayesian Large-scale Multiple Regression with Summary Statistics from Genome-wide Association Studies.

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 req...

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We consider applying Bayesian Variable Selection Regression, or BVSR, to genome-wide association studies, and similar large-scale regression problems. Currently, typical genome-wide association studies measure hundreds of thousands, or millions, of genetic variants (SNPs), in thousands or tens of thousands of individuals, and attempt to identify regions harboring SNPs that affect some phenotype...

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Analysis of genome-wide association data by large-scale Bayesian logistic regression

Single-locus analysis is often used to analyze genome-wide association (GWA) data, but such analysis is subject to severe multiple comparisons adjustment. Multivariate logistic regression is proposed to fit a multi-locus model for case-control data. However, when the sample size is much smaller than the number of single-nucleotide polymorphisms (SNPs) or when correlation among SNPs is high, tra...

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Meta-analysis of single trait for multiple cohorts has been used for increasing statistical power in genome-wide association studies (GWASs). Although hundreds of variants have been identified by GWAS, these variants only explain a small fraction of phenotypic variation. Cross-phenotype association analysis (CPASSOC) can further improve statistical power by searching for variants that contribut...

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ژورنال

عنوان ژورنال: The Annals of Applied Statistics

سال: 2017

ISSN: 1932-6157

DOI: 10.1214/17-aoas1046