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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Autores principales:	Tomi Peltola, Pekka Marttinen, Antti Jula, Veikko Salomaa, Markus Perola, Aki Vehtari
Formato:	article
Lenguaje:	EN
Publicado:	Public Library of Science (PLoS) 2012
Materias:	Medicine R Science Q
Acceso en línea:	https://doaj.org/article/8b80db146b424272a4dd3ff560ccf6b8
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spelling	oai:doaj.org-article:8b80db146b424272a4dd3ff560ccf6b82021-11-18T07:31:11ZBayesian variable selection in searching for additive and dominant effects in genome-wide data.1932-620310.1371/journal.pone.0029115https://doaj.org/article/8b80db146b424272a4dd3ff560ccf6b82012-01-01T00:00:00Zhttps://www.ncbi.nlm.nih.gov/pmc/articles/pmid/22235263/?tool=EBIhttps://doaj.org/toc/1932-6203Although 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/.Tomi PeltolaPekka MarttinenAntti JulaVeikko SalomaaMarkus PerolaAki VehtariPublic Library of Science (PLoS)articleMedicineRScienceQENPLoS ONE, Vol 7, Iss 1, p e29115 (2012)
institution	DOAJ
collection	DOAJ
language	EN
topic	Medicine R Science Q
spellingShingle	Medicine R Science Q Tomi Peltola Pekka Marttinen Antti Jula Veikko Salomaa Markus Perola Aki Vehtari 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/.
format	article
author	Tomi Peltola Pekka Marttinen Antti Jula Veikko Salomaa Markus Perola Aki Vehtari
author_facet	Tomi Peltola Pekka Marttinen Antti Jula Veikko Salomaa Markus Perola Aki Vehtari
author_sort	Tomi Peltola
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.
publisher	Public Library of Science (PLoS)
publishDate	2012
url	https://doaj.org/article/8b80db146b424272a4dd3ff560ccf6b8
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Bayesian variable selection in searching for additive and dominant effects in genome-wide data.

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