Department of Training, Research and Innovation, University Center for Primary Care and Public Health, Lausanne 1010, Switzerland.
Swiss Institute of Bioinformatics, Lausanne 1015, Switzerland.
Bioinformatics. 2020 Aug 1;36(15):4374-4376. doi: 10.1093/bioinformatics/btaa549.
Increasing sample size is not the only strategy to improve discovery in Genome Wide Association Studies (GWASs) and we propose here an approach that leverages published studies of related traits to improve inference. Our Bayesian GWAS method derives informative prior effects by leveraging GWASs of related risk factors and their causal effect estimates on the focal trait using multivariable Mendelian randomization. These prior effects are combined with the observed effects to yield Bayes Factors, posterior and direct effects. The approach not only increases power, but also has the potential to dissect direct and indirect biological mechanisms.
bGWAS package is freely available under a GPL-2 License, and can be accessed, alongside with user guides and tutorials, from https://github.com/n-mounier/bGWAS.
Supplementary data are available at Bioinformatics online.
增加样本量并不是提高全基因组关联研究（GWAS）发现能力的唯一策略，我们在这里提出了一种利用相关性状已发表研究来改善推断的方法。我们的贝叶斯 GWAS 方法通过利用多变量孟德尔随机化研究相关风险因素及其对焦点性状的因果效应估计，从 GWAS 中获取信息丰富的先验效应。这些先验效应与观察到的效应相结合，产生贝叶斯因子、后验和直接效应。该方法不仅可以提高功效，还有可能剖析直接和间接的生物学机制。
bGWAS 包在 GPL-2 许可证下免费提供，可通过 https://github.com/n-mounier/bGWAS 访问，同时还提供用户指南和教程。
补充数据可在生物信息学在线获取。
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