Leveraging information between multiple population groups and traits improves fine-mapping resolution.
Leveraging information between multiple population groups and traits improves fine-mapping resolution.
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DOI：
10.1038/s41467-023-43159-5
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发表时间：
2023-11-10
影响因子：
16.6
通讯作者：
Asimit, Jennifer L
中科院分区：
文献类型：
--
作者：
Zhou, Feng;Soremekun, Opeyemi;Chikowore, Tinashe;Fatumo, Segun;Barroso, Ines;Morris, Andrew P;Asimit, Jennifer L
统计精细定位有助于查明潜在遗传关联信号的可能因果变异。它的分辨率可以提高（i）利用性状之间的信息;（ii）利用不同群体之间的连锁不平衡结构的差异。使用关联汇总统计，MGflashfm联合精细映射来自多个性状和群体的信号; MGfm使用类似的框架来分别分析每个性状。我们还提供了一种实用的方法来精细映射与样本外的参考面板。在模拟研究中，我们表明MGflashfm和MGfm是校准良好的，PP > 0.80的因果变异的平均比例高于0.75（MGflashfm）和0.70（MGfm）。在我们对五个人群中的四种脂质性状的分析中，MGflashfm给出了比MGfm减少10.5%的99%可信集合的中位数。MGflashfm和MGfm只需要汇总级别的数据，这使得它们在无法共享个人级别数据的联盟工作中成为非常有用的精细映射工具。统计精细映射有助于查明潜在遗传关联信号的可能因果变异，并且可以通过使用多祖先数据集来增强。在这里，作者介绍了MGflashfm，这是一种精细映射方法，用于在多个性状和人群中精确定位可能的因果变异。
Statistical fine-mapping helps to pinpoint likely causal variants underlying genetic association signals. Its resolution can be improved by (i) leveraging information between traits; and (ii) exploiting differences in linkage disequilibrium structure between diverse population groups. Using association summary statistics, MGflashfm jointly fine-maps signals from multiple traits and population groups; MGfm uses an analogous framework to analyse each trait separately. We also provide a practical approach to fine-mapping with out-of-sample reference panels. In simulation studies we show that MGflashfm and MGfm are well-calibrated and that the mean proportion of causal variants with PP > 0.80 is above 0.75 (MGflashfm) and 0.70 (MGfm). In our analysis of four lipids traits across five population groups, MGflashfm gives a median 99% credible set reduction of 10.5% over MGfm. MGflashfm and MGfm only require summary level data, making them very useful fine-mapping tools in consortia efforts where individual-level data cannot be shared. Statistical fine-mapping helps to pinpoint likely causal variants underlying genetic association signals, and can be enhanced by using multi-ancestry datasets. Here, the authors introduce MGflashfm, a fine-mapping method for pinpointing likely causal variants amongst multiple traits and population groups.