单核苷酸多态性
生物
SNP公司
遗传学
遗传建筑学
疾病
基因
遗传力
等位基因
遗传关联
计算生物学
全基因组关联研究
SNP基因分型
数量性状位点
基因型
医学
内科学
作者
Steven Gazal,Omer Weissbrod,Farhad Hormozdiari,Kushal K. Dey,Joseph Nasser,Karthik A. Jagadeesh,Daniel J. Weiner,Huwenbo Shi,Charles P. Fulco,Luke J. O’Connor,Bogdan Paşaniuc,Jesse M. Engreitz,Alkes L. Price
出处
期刊:Nature Genetics
[Springer Nature]
日期:2022-06-01
卷期号:54 (6): 827-836
被引量:72
标识
DOI:10.1038/s41588-022-01087-y
摘要
Disease-associated single-nucleotide polymorphisms (SNPs) generally do not implicate target genes, as most disease SNPs are regulatory. Many SNP-to-gene (S2G) linking strategies have been developed to link regulatory SNPs to the genes that they regulate in cis. Here, we developed a heritability-based framework for evaluating and combining different S2G strategies to optimize their informativeness for common disease risk. Our optimal combined S2G strategy (cS2G) included seven constituent S2G strategies and achieved a precision of 0.75 and a recall of 0.33, more than doubling the recall of any individual strategy. We applied cS2G to fine-mapping results for 49 UK Biobank diseases/traits to predict 5,095 causal SNP–gene-disease triplets (with S2G-derived functional interpretation) with high confidence. We further applied cS2G to provide an empirical assessment of disease omnigenicity; we determined that the top 1% of genes explained roughly half of the SNP heritability linked to all genes and that gene-level architectures vary with variant allele frequency. A heritability-based framework for evaluation of SNP-to-gene linking methods is used to construct an optimal, combined approach and applied to 49 traits. Analysis of trait omnigenicity suggests gene-level architecture varies depending on variant frequency.
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