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CARMA是一种新的用于在全基因组关联元分析中进行精细定位的贝叶斯模型
2023-05-18 10:31

美国纽约哥伦比亚大学Iuliana Ionita-Laza团队近期取得重要工作进展。他们研究提出了CARMA工具,这是一种新的用于在全基因组关联元分析中进行精细定位的贝叶斯模型。相关研究成果2023年5月11日在线发表于《自然—遗传学》杂志上。

据介绍,精细定位通常用于识别全基因组重要基因座的假定因果变异。

研究人员提出了一种用于精细映射的贝叶斯模型,该模型与现有方法相比具有几个优势,包括对效应大小的先验分布的灵活规范、汇总统计和函数注释的联合建模,以及在荟萃分析中解释汇总统计和外部联系不平衡之间的差异。通过仿真,研究人员将性能与常用的精细映射方法进行了比较,并表明所提出的模型在包括功能注释时具有更高的功率和更低的错误发现率(FDR),并且在元分析中对可信集具有更高功率、更低的FDR和更高的覆盖率。研究人员将其应用于阿尔茨海默病全基因组关联研究的荟萃分析来进一步说明了这一方法,在该研究中,研究人员优先考虑假定的因果变异和基因。

附:英文原文

Title: CARMA is a new Bayesian model for fine-mapping in genome-wide association meta-analyses

Author: Yang, Zikun, Wang, Chen, Liu, Linxi, Khan, Atlas, Lee, Annie, Vardarajan, Badri, Mayeux, Richard, Kiryluk, Krzysztof, Ionita-Laza, Iuliana

Issue&Volume: 2023-05-11

Abstract: Fine-mapping is commonly used to identify putative causal variants at genome-wide significant loci. Here we propose a Bayesian model for fine-mapping that has several advantages over existing methods, including flexible specification of the prior distribution of effect sizes, joint modeling of summary statistics and functional annotations and accounting for discrepancies between summary statistics and external linkage disequilibrium in meta-analyses. Using simulations, we compare performance with commonly used fine-mapping methods and show that the proposed model has higher power and lower false discovery rate (FDR) when including functional annotations, and higher power, lower FDR and higher coverage for credible sets in meta-analyses. We further illustrate our approach by applying it to a meta-analysis of Alzheimer’s disease genome-wide association studies where we prioritize putatively causal variants and genes.

DOI: 10.1038/s41588-023-01392-0

Source: https://www.nature.com/articles/s41588-023-01392-0

Nature Genetics:《自然—遗传学》,创刊于1992年。隶属于施普林格·自然出版集团,最新IF:41.307
官方网址:https://www.nature.com/ng/
投稿链接:https://mts-ng.nature.com/cgi-bin/main.plex


本期文章:《自然—遗传学》:Online/在线发表

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