ArticleMolecular genetics and genomics : MGG2017
A fast algorithm for Bayesian multi-locus model in genome-wide association studies.
Article in Molecular genetics and genomics : MGG, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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Who cites it
4 citing papers in PubMed.
- A genome-wide survey of interaction between rice andBioengineered · 2021Article
- Evaluating the Potential of Younger Cases and Older Controls Cohorts to Improve Discovery Power in Genome-Wide Association Studies of Late-Onset Diseases.Journal of personalized medicine · 2019Article
- Genetic variants in SLC22A3 contribute to the susceptibility to colorectal cancer.International journal of cancer · 2019Article
- Bayesian variable selection for parametric survival model with applications to cancer omics data.Human genomics · 2018Article
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Authors and funding
10 authors.
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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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