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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Veröffentlicht in:Molecular genetics and genomics : MGG Jg. 292; H. 4; S. 923 - 934
Hauptverfasser: Duan, Weiwei, Zhao, Yang, Wei, Yongyue, Yang, Sheng, Bai, Jianling, Shen, Sipeng, Du, Mulong, Huang, Lihong, Hu, Zhibin, Chen, Feng
Format: Journal Article
Sprache:Englisch
Veröffentlicht: Berlin/Heidelberg Springer Berlin Heidelberg 01.08.2017
Springer Nature B.V
Schlagworte:
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
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Zusammenfassung: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.
Bibliographie:ObjectType-Article-1
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ISSN:1617-4615
1617-4623
1617-4623
DOI:10.1007/s00438-017-1322-4