Bayesian Variable Selection in Searching for Additive and Dominant Effects in Genome-Wide Data

Although complex diseases and traits are thought to have multifactorial genetic basis, the common methods in genome-wide association analyses test each variant for association independent of the others. This computational simplification may lead to reduced power to identify variants with small effec...

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Main Authors:	Peltola, Tomi, Marttinen, Pekka, Jula, Antti, Salomaa, Veikko, Perola, Markus, Vehtari, Aki
Format:	Online
Language:	English
Published:	Public Library of Science 2012
Online Access:	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3250410/

id	pubmed-3250410
recordtype	oai_dc
spelling	pubmed-32504102012-01-10 Bayesian Variable Selection in Searching for Additive and Dominant Effects in Genome-Wide Data Peltola, Tomi Marttinen, Pekka Jula, Antti Salomaa, Veikko Perola, Markus Vehtari, Aki Research Article Although complex diseases and traits are thought to have multifactorial genetic basis, the common methods in genome-wide association analyses test each variant for association independent of the others. This computational simplification may lead to reduced power to identify variants with small effect sizes and requires correcting for multiple hypothesis tests with complex relationships. However, advances in computational methods and increase in computational resources are enabling the computation of models that adhere more closely to the theory of multifactorial inheritance. Here, a Bayesian variable selection and model averaging approach is formulated for searching for additive and dominant genetic effects. The approach considers simultaneously all available variants for inclusion as predictors in a linear genotype-phenotype mapping and averages over the uncertainty in the variable selection. This leads to naturally interpretable summary quantities on the significances of the variants and their contribution to the genetic basis of the studied trait. We first characterize the behavior of the approach in simulations. The results indicate a gain in the causal variant identification performance when additive and dominant variation are simulated, with a negligible loss of power in purely additive case. An application to the analysis of high- and low-density lipoprotein cholesterol levels in a dataset of 3895 Finns is then presented, demonstrating the feasibility of the approach at the current scale of single-nucleotide polymorphism data. We describe a Markov chain Monte Carlo algorithm for the computation and give suggestions on the specification of prior parameters using commonly available prior information. An open-source software implementing the method is available at http://www.lce.hut.fi/research/mm/bmagwa/ and https://github.com/to-mi/. Public Library of Science 2012-01-03 /pmc/articles/PMC3250410/ /pubmed/22235263 http://dx.doi.org/10.1371/journal.pone.0029115 Text en Peltola et al. http://creativecommons.org/licenses/by/4.0/ This is an open-access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are properly credited.
repository_type	Open Access Journal
institution_category	Foreign Institution
institution	US National Center for Biotechnology Information
building	NCBI PubMed
collection	Online Access
language	English
format	Online
author	Peltola, Tomi Marttinen, Pekka Jula, Antti Salomaa, Veikko Perola, Markus Vehtari, Aki
spellingShingle	Peltola, Tomi Marttinen, Pekka Jula, Antti Salomaa, Veikko Perola, Markus Vehtari, Aki Bayesian Variable Selection in Searching for Additive and Dominant Effects in Genome-Wide Data
author_facet	Peltola, Tomi Marttinen, Pekka Jula, Antti Salomaa, Veikko Perola, Markus Vehtari, Aki
author_sort	Peltola, Tomi
title	Bayesian Variable Selection in Searching for Additive and Dominant Effects in Genome-Wide Data
title_short	Bayesian Variable Selection in Searching for Additive and Dominant Effects in Genome-Wide Data
title_full	Bayesian Variable Selection in Searching for Additive and Dominant Effects in Genome-Wide Data
title_fullStr	Bayesian Variable Selection in Searching for Additive and Dominant Effects in Genome-Wide Data
title_full_unstemmed	Bayesian Variable Selection in Searching for Additive and Dominant Effects in Genome-Wide Data
title_sort	bayesian variable selection in searching for additive and dominant effects in genome-wide data
description	Although complex diseases and traits are thought to have multifactorial genetic basis, the common methods in genome-wide association analyses test each variant for association independent of the others. This computational simplification may lead to reduced power to identify variants with small effect sizes and requires correcting for multiple hypothesis tests with complex relationships. However, advances in computational methods and increase in computational resources are enabling the computation of models that adhere more closely to the theory of multifactorial inheritance. Here, a Bayesian variable selection and model averaging approach is formulated for searching for additive and dominant genetic effects. The approach considers simultaneously all available variants for inclusion as predictors in a linear genotype-phenotype mapping and averages over the uncertainty in the variable selection. This leads to naturally interpretable summary quantities on the significances of the variants and their contribution to the genetic basis of the studied trait. We first characterize the behavior of the approach in simulations. The results indicate a gain in the causal variant identification performance when additive and dominant variation are simulated, with a negligible loss of power in purely additive case. An application to the analysis of high- and low-density lipoprotein cholesterol levels in a dataset of 3895 Finns is then presented, demonstrating the feasibility of the approach at the current scale of single-nucleotide polymorphism data. We describe a Markov chain Monte Carlo algorithm for the computation and give suggestions on the specification of prior parameters using commonly available prior information. An open-source software implementing the method is available at http://www.lce.hut.fi/research/mm/bmagwa/ and https://github.com/to-mi/.
publisher	Public Library of Science
publishDate	2012
url	https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3250410/
_version_	1611497924354637824

Bayesian Variable Selection in Searching for Additive and Dominant Effects in Genome-Wide Data

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