A fast algorithm for Bayesian multi-locus model in genome-wide association studies

Genome-wide association studies (GWAS) have identified a large amount of single-nucleotide polymorphisms (SNPs) associated with complex traits. A recently developed linear mixed model for estimating heritability by simultaneously fitting all SNPs suggests that common variants can explain a substanti...

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Vydáno v:	Molecular genetics and genomics : MGG Ročník 292; číslo 4; s. 923 - 934
Hlavní autoři:	Duan, Weiwei, Zhao, Yang, Wei, Yongyue, Yang, Sheng, Bai, Jianling, Shen, Sipeng, Du, Mulong, Huang, Lihong, Hu, Zhibin, Chen, Feng
Médium:	Journal Article
Jazyk:	angličtina
Vydáno:	Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2017 Springer Nature B.V
Témata:	Algorithms Animal Genetics and Genomics Bayes Theorem Bayesian analysis Biochemistry Biomedical and Life Sciences Computational Biology - methods Computer Simulation Data processing Genome-wide association studies genome-wide association study Genome-Wide Association Study - methods Genomes Heritability Human Genetics Humans Life Sciences Lung cancer lung neoplasms Markov chain Markov Chains Mathematical models Methods Paper Microbial Genetics and Genomics Models, Genetic Monte Carlo Method Plant Genetics and Genomics Polymorphism Polymorphism, Single Nucleotide - genetics Quantitative Trait, Heritable Single-nucleotide polymorphism statistical models Bayesian adaptive lasso Genome-wide association studies Multi-locus model Variational inference Variable selection
ISSN:	1617-4615, 1617-4623, 1617-4623
On-line přístup:	Získat plný text
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Popis
Shrnutí:	Genome-wide association studies (GWAS) have identified a large amount of single-nucleotide polymorphisms (SNPs) associated with complex traits. A recently developed linear mixed model for estimating heritability by simultaneously fitting all SNPs suggests that common variants can explain a substantial fraction of heritability, which hints at the low power of single variant analysis typically used in GWAS. Consequently, many multi-locus shrinkage models have been proposed under a Bayesian framework. However, most use Markov Chain Monte Carlo (MCMC) algorithm, which are time-consuming and challenging to apply to GWAS data. Here, we propose a fast algorithm of Bayesian adaptive lasso using variational inference (BAL-VI). Extensive simulations and real data analysis indicate that our model outperforms the well-known Bayesian lasso and Bayesian adaptive lasso models in accuracy and speed. BAL-VI can complete a simultaneous analysis of a lung cancer GWAS data with ~3400 subjects and ~570,000 SNPs in about half a day.
Bibliografie:	ObjectType-Article-1 SourceType-Scholarly Journals-1 ObjectType-Feature-2 content type line 14 content type line 23
ISSN:	1617-4615 1617-4623 1617-4623
DOI:	10.1007/s00438-017-1322-4