A fast algorithm for Bayesian multi-locus model in genome-wide association studies |
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| Authors: | Weiwei?Duan Email author" target="_blank">Feng?Chen |
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| Institution: | 1.Department of Biostatistics, School of Public Health,Nanjing Medical University,Nanjing,China;2.The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health,Nanjing Medical University,Nanjing,China;3.Joint Laboratory of Health and Environmental Risk Assessment (HERA),Nanjing Medical University School of Public Health/Harvard School of Public Health,Nanjing,China;4.Key Laboratory of Biomedical Big Data,Nanjing Medical University,Nanjing,China;5.Department of Epidemiology, School of Public Health,Nanjing Medical University,Nanjing,China;6.Section of Clinical Epidemiology,?Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Cancer Center,Nanjing Medical University,Nanjing,China |
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| 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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