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研究揭示人类疾病中蛋白质编码变异的遗传关联
2022-02-27 14:13
来源：小柯机器人

美国Biogen公司Heiko Runz、Benjamin B. Sun等研究人员合作揭示人类疾病中蛋白质编码变体的遗传关联。相关论文于2022年2月23日在线发表在《自然》杂志上。

研究人员将392,814名英国生物银行参与者的全外显子组测序与260,405名FinnGen参与者（共653,219人）的推算基因型相结合，对蛋白质编码等位基因频率谱中的744个疾病终点进行了关联元分析，弥补了常见和罕见变体研究之间的差距。研究人员确定了975个关联，其中三分之一以上是以前没有报道的。研究人员证明了以前被认为是导致单基因疾病突变的群体水平相关性，将全基因组关联研究（GWAS）关联映射到可能的致病基因上，解释疾病机制，并系统地将疾病关联与117种生物标志物和临床阶段的药物目标水平联系起来。
 
结合两个人口生物库的测序和基因分型，使研究人员能够受益于检测和解释疾病关联的更大力量，通过复制验证研究结果，并提出罕见遗传变异的医学可操作性。这项研究提供了一个蛋白质编码变异体关联的概要，以便今后对疾病生物学和药物发现进行深入了解。
 
据介绍，GWAS已经确定了数千个与人类疾病风险有关的遗传变异。然而，到目前为止，GWAS在识别罕见和低频等位基因谱的关联方面仍然能力不足，并且缺乏追踪基础基因因果机制的分辨率。
 
附：英文原文
 
Title: Genetic associations of protein-coding variants in human disease

Author: Sun, Benjamin B., Kurki, Mitja I., Foley, Christopher N., Mechakra, Asma, Chen, Chia-Yen, Marshall, Eric, Wilk, Jemma B., Chahine, Mohamed, Chevalier, Philippe, Christ, Georges, Palotie, Aarno, Daly, Mark J., Runz, Heiko

Issue&Volume: 2022-02-23

Abstract: Genome-wide association studies (GWAS) have identified thousands of genetic variants linked to the risk of human disease. However, GWAS have so far remained largely underpowered in relation to identifying associations in the rare and low-frequency allelic spectrum and have lacked the resolution to trace causal mechanisms to underlying genes1. Here we combined whole-exome sequencing in 392,814 UK Biobank participants with imputed genotypes from 260,405 FinnGen participants (653,219 total individuals) to conduct association meta-analyses for 744 disease endpoints across the protein-coding allelic frequency spectrum, bridging the gap between common and rare variant studies. We identified 975 associations, with more than one-third being previously unreported. We demonstrate population-level relevance for mutations previously ascribed to causing single-gene disorders, map GWAS associations to likely causal genes, explain disease mechanisms, and systematically relate disease associations to levels of 117 biomarkers and clinical-stage drug targets. Combining sequencing and genotyping in two population biobanks enabled us to benefit from increased power to detect and explain disease associations, validate findings through replication and propose medical actionability for rare genetic variants. Our study provides a compendium of protein-coding variant associations for future insights into disease biology and drug discovery. A meta-analysis combining whole-exome sequencing data from UK Biobank participants and imputed genotypes from FinnGen participants enables identification of genetic associations with human disease in the rare and low-frequency allelic spectrum

DOI: 10.1038/s41586-022-04394-w

Source: https://www.nature.com/articles/s41586-022-04394-w

Nature：《自然》，创刊于1869年。隶属于施普林格·自然出版集团，最新IF：69.504
官方网址：http://www.nature.com/
投稿链接：http://www.nature.com/authors/submit_manuscript.html


本期文章：《自然》：Online/在线发表

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