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Exome sequencing and analysis of 454,787 UK Biobank participants
Nature ( IF 64.8 ) Pub Date : 2021-10-18 , DOI: 10.1038/s41586-021-04103-z
Joshua D Backman 1 , Alexander H Li 1 , Anthony Marcketta 1 , Dylan Sun 1 , Joelle Mbatchou 1 , Michael D Kessler 1 , Christian Benner 1 , Daren Liu 1 , Adam E Locke 1 , Suganthi Balasubramanian 1 , Ashish Yadav 1 , Nilanjana Banerjee 1 , Christopher E Gillies 1 , Amy Damask 1 , Simon Liu 1 , Xiaodong Bai 1 , Alicia Hawes 1 , Evan Maxwell 1 , Lauren Gurski 1 , Kyoko Watanabe 1 , Jack A Kosmicki 1 , Veera Rajagopal 1 , Jason Mighty 1 , , , Marcus Jones 1 , Lyndon Mitnaul 1 , Eli Stahl 1 , Giovanni Coppola 1 , Eric Jorgenson 1 , Lukas Habegger 1 , William J Salerno 1 , Alan R Shuldiner 1 , Luca A Lotta 1 , John D Overton 1 , Michael N Cantor 1 , Jeffrey G Reid 1 , George Yancopoulos 1 , Hyun M Kang 1 , Jonathan Marchini 1 , Aris Baras 1 , Gonçalo R Abecasis 1 , Manuel A R Ferreira 1
Affiliation  

A major goal in human genetics is to use natural variation to understand the phenotypic consequences of altering each protein-coding gene in the genome. Here we used exome sequencing1 to explore protein-altering variants and their consequences in 454,787 participants in the UK Biobank study2. We identified 12 million coding variants, including around 1 million loss-of-function and around 1.8 million deleterious missense variants. When these were tested for association with 3,994 health-related traits, we found 564 genes with trait associations at P ≤ 2.18 × 10−11. Rare variant associations were enriched in loci from genome-wide association studies (GWAS), but most (91%) were independent of common variant signals. We discovered several risk-increasing associations with traits related to liver disease, eye disease and cancer, among others, as well as risk-lowering associations for hypertension (SLC9A3R2), diabetes (MAP3K15, FAM234A) and asthma (SLC27A3). Six genes were associated with brain imaging phenotypes, including two involved in neural development (GBE1, PLD1). Of the signals available and powered for replication in an independent cohort, 81% were confirmed; furthermore, association signals were generally consistent across individuals of European, Asian and African ancestry. We illustrate the ability of exome sequencing to identify gene–trait associations, elucidate gene function and pinpoint effector genes that underlie GWAS signals at scale.



中文翻译:

454,787 名英国生物银行参与者的外显子组测序和分析

人类遗传学的一个主要目标是利用自然变异来了解改变基因组中每个蛋白质编码基因的表型后果。在这里,我们使用外显子组测序1在英国生物银行研究2的 454,787 名参与者中探索蛋白质改变变异体及其后果。我们确定了 1200 万个编码变体,包括约 100 万个功能丧失和约 180 万个有害的错义变体。当测试这些基因与 3,994 种健康相关性状的关联时,我们发现 564 种基因在P  ≤ 2.18 × 10 -11时具有性状关联. 来自全基因组关联研究 (GWAS) 的基因座丰富了罕见变异关联,但大多数 (91%) 独立于常见变异信号。我们发现了与肝病、眼病和癌症等相关特征的几种风险增加关联,以及与高血压 ( SLC9A3R2 )、糖尿病 ( MAP3K15FAM234A ) 和哮喘 ( SLC27A3 ) 相关的风险降低关联。六个基因与脑成像表型相关,其中两个与神经发育有关(GBE1PLD1). 在独立队列中可获得和支持复制的信号中,81% 得到确认;此外,关联信号在欧洲、亚洲和非洲血统的个体之间通常是一致的。我们说明了外显子组测序识别基因-性状关联、阐明基因功能和精确定位大规模 GWAS 信号基础的效应基因的能力。

更新日期:2021-10-18
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