Bayesian model comparison in genetic association analysis: linear mixed modeling and SNP set testing.

Xiaoquan Wen

Abstract read

In one paragraph

Article in Biostatistics (Oxford, England), 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 papers.

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2citing papers in PubMed

–field-weighted citation impact

1 · What the graph read from it

What it found

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3 · Its place in the literature

Who cites it

2 citing papers in PubMed.

A review of kernel methods for genetic association studies.Genetic epidemiology · 2019
Review
Inference on the Genetic Basis of Eye and Skin Color in an Admixed Population via Bayesian Linear Mixed Models.Genetics · 2017
Article

4 · The record

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5 · Who and what money

Authors and funding

1 author.

Xiaoquan WenDepartment of Biostatistics, University of Michigan, Ann Arbor, MI 48109, USA.

Funding

Computational and statistical models for human geneticsR01HG007022 · NHGRI · UNIVERSITY OF MICHIGAN AT ANN ARBOR · PI ABECASIS, GONCALO · 2012 to 2016

$3.0M

Statistical analysis of gene expression quantitative trait loci (eQTL)R01MH101825 · NIMH · UNIVERSITY OF CHICAGO · PI STEPHENS, MATTHEW · 2013 to 2015

$1.1M

NHGRI NIH HHS R01-HG007022NIMH NIH HHS R01 MH101825NIMH NIH HHS R01-MH101825

6 · The paper itself

Abstract

We consider the problems of hypothesis testing and model comparison under a flexible Bayesian linear regression model whose formulation is closely connected with the linear mixed effect model and the parametric models for Single Nucleotide Polymorphism (SNP) set analysis in genetic association studies. We derive a class of analytic approximate Bayes factors and illustrate their connections with a variety of frequentist test statistics, including the Wald statistic and the variance component score statistic. Taking advantage of Bayesian model averaging and hierarchical modeling, we demonstrate some distinct advantages and flexibilities in the approaches utilizing the derived Bayes factors in the context of genetic association studies. We demonstrate our proposed methods using real or simulated numerical examples in applications of single SNP association testing, multi-locus fine-mapping and SNP set association testing.

Indexed as

Bayes TheoremModels, StatisticalPolymorphism, Single NucleotideArabidopsisGenetic Association StudiesLinear ModelsBayes factorGenetic associationLinear mixed modelModel comparisonSNP set analysis

Identifiers

PMID25796429

DOI10.1093/biostatistics/kxv009

PMCPMC4570575

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.