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

作者：Duan, Weiwei; Zhao, Yang; Wei, Yongyue; Yang, Sheng; Bai, Jianling; Shen, Sipeng; Du, Mulong; Huang, Lihong; Hu, Zhibin; Chen, Feng^*

来源：Molecular Genetics and Genomics, 2017, 292(4): 923-934.

DOI：10.1007/s00438-017-1322-4

• Genome-wide association studies
• Multi-locus model
• Bayesian adaptive lasso
• Variational inference
• Variable selection

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 (BALVI). 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 similar to 3400 subjects and similar to 570,000 SNPs in about half a day.

• 出版日期2017-8
• 单位南京医科大学