Research Unit of Mathematical Sciences, University of Oulu, Oulu, P.O.Box 8000, FI-90014, Finland.
Research Unit of Population Health, University of Oulu, Oulu, Finland.
Bioinformatics. 2023 Jul 1;39(7). doi: 10.1093/bioinformatics/btad396.
Genome-wide association studies (GWAS) have been successful in identifying genomic loci associated with complex traits. Genetic fine-mapping aims to detect independent causal variants from the GWAS-identified loci, adjusting for linkage disequilibrium patterns.
We present "FiniMOM" (fine-mapping using a product inverse-moment prior), a novel Bayesian fine-mapping method for summarized genetic associations. For causal effects, the method uses a nonlocal inverse-moment prior, which is a natural prior distribution to model non-null effects in finite samples. A beta-binomial prior is set for the number of causal variants, with a parameterization that can be used to control for potential misspecifications in the linkage disequilibrium reference. The results of simulations studies aimed to mimic a typical GWAS on circulating protein levels show improved credible set coverage and power of the proposed method over current state-of-the-art fine-mapping method SuSiE, especially in the case of multiple causal variants within a locus.
全基因组关联研究（GWAS）已成功鉴定出与复杂性状相关的基因组位点。遗传精细映射旨在从 GWAS 鉴定的基因座中检测独立的因果变异，同时调整连锁不平衡模式。
我们提出了“FiniMOM”（使用乘积逆矩先验进行精细映射），这是一种用于汇总遗传关联的新型贝叶斯精细映射方法。对于因果效应，该方法使用非局部逆矩先验，这是一种自然的先验分布，可以在有限样本中对非零效应进行建模。对于因果变异的数量设置了贝塔二项式先验，并进行参数化，以便可以控制连锁不平衡参考中的潜在指定错误。旨在模拟循环蛋白水平的典型 GWAS 的模拟研究结果表明，与当前最先进的精细映射方法 SuSiE 相比，所提出的方法在可信集覆盖和功效方面都有所提高，尤其是在一个基因座内存在多个因果变异的情况下。
Zotero 插件
