Title |
A fast algorithm for Bayesian multi-locus model in genome-wide association studies
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Published in |
Molecular Genetics and Genomics, May 2017
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DOI | 10.1007/s00438-017-1322-4 |
Pubmed ID | |
Authors |
Weiwei Duan, Yang Zhao, Yongyue Wei, Sheng Yang, Jianling Bai, Sipeng Shen, Mulong Du, Lihong Huang, Zhibin Hu, Feng Chen |
Abstract |
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. |
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Unknown | 1 | 100% |
Demographic breakdown
Type | Count | As % |
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Members of the public | 1 | 100% |
Mendeley readers
Geographical breakdown
Country | Count | As % |
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Unknown | 36 | 100% |
Demographic breakdown
Readers by professional status | Count | As % |
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Student > Master | 10 | 28% |
Student > Ph. D. Student | 7 | 19% |
Researcher | 4 | 11% |
Professor > Associate Professor | 2 | 6% |
Other | 2 | 6% |
Other | 4 | 11% |
Unknown | 7 | 19% |
Readers by discipline | Count | As % |
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Agricultural and Biological Sciences | 9 | 25% |
Biochemistry, Genetics and Molecular Biology | 7 | 19% |
Computer Science | 5 | 14% |
Nursing and Health Professions | 2 | 6% |
Medicine and Dentistry | 2 | 6% |
Other | 3 | 8% |
Unknown | 8 | 22% |