High-throughput inference of pairwise coalescence times identifies signals of selection and enriched disease heritability.

High-throughput inference of pairwise coalescence times identifies signals of selection and enriched disease heritability.
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DOI:
10.1038/s41588-018-0177-x
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发表时间:
2018-09
期刊:
影响因子:
30.8
通讯作者:
Price AL
Price AL
中科院分区:
生物学1区
文献类型:
--
作者:
Palamara PF;Terhorst J;Song YS;Price AL

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人们对重建人口历史的兴趣推动了从全基因组序列数据中估计特定于基因座的成对结合时间的方法的发展。在这里,我们介绍了一种强大的新方法,ASMC,它可以仅使用SNP阵列数据来估计合并时间,并且比以前的方法快几个数量级。我们应用ASMC在来自英国生物库的113,851个英国阶段性样本中检测到最近的正选择,并检测到12个全基因组显著信号,其中包括6个新的基因座。我们还应用ASMC对498名荷兰人的测序数据进行了测序,以检测更深层次的背景选择。我们在对20个独立疾病和复杂性状的分析中发现,在一组广泛的功能注释(包括其他背景选择注释)的条件下,分层LD Score回归在高背景选择区域发现了很强的遗传力丰富。这些结果强调了背景选择对复杂性状遗传结构的广泛影响。
Interest in reconstructing demographic histories has motivated the development of methods to estimate locus-specific pairwise coalescence times from whole-genome sequence data. Here we introduce a powerful new method, ASMC, that can estimate coalescence times using only SNP array data, and is orders of magnitude faster than previous approaches. We applied ASMC to detect recent positive selection in 113,851 phased British samples from the UK Biobank, and detected 12 genome-wide significant signals, including 6 novel loci. We also applied ASMC to sequencing data from 498 Dutch individuals to detect background selection at deeper time scales. We detected strong heritability enrichment in regions of high background selection in an analysis of 20 independent diseases and complex traits using stratified LD score regression, conditioned on a broad set of functional annotations (including other background selection annotations). These results underscore the widespread effects of background selection on the genetic architecture of complex traits.
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