Genome-wide rare variant analysis for thousands of phenotypes in over 70,000 exomes from two cohorts
Genome-wide rare variant analysis for thousands of phenotypes in over 70,000 exomes from two cohorts
复制标题
DOI：
10.1038/s41467-020-14288-y
复制
发表时间：
2020-01-28
影响因子：
16.6
通讯作者：
Washington, Nicole L.
中科院分区：
文献类型：
--
作者：
Cirulli, Elizabeth T.;White, Simon;Washington, Nicole L.
了解罕见变异的影响对了解人类健康至关重要。我们分析了来自UK Biobank的49960名外显子组测序个体中4264种表型的罕见（MAF < 0.1%）变异，以及在Helix进行外显子组+测序的21866名健康内内华达州项目（HNP）队列成员中的1934种表型（1821种与UK Biobank重叠）。在使用我们的罕见变异定制方法来减少测试统计膨胀后，我们在我们的两个队列的荟萃分析中确定了64个具有统计学意义的基于基因的关联，并且仅在一个队列中发现了37个表型相关。单基因对我们的研究结果做出了重大贡献，而且绝大多数的关联无法用基因分型芯片识别。我们的结果可在web应用程序（https://ukb.research.helix.com）中进行交互式浏览。这一综合分析说明了未选择种群的大型、深度表型队列与NGS数据相结合的生物学价值。
Understanding the impact of rare variants is essential to understanding human health. We analyze rare (MAF < 0.1%) variants against 4264 phenotypes in 49,960 exome-sequenced individuals from the UK Biobank and 1934 phenotypes (1821 overlapping with UK Biobank) in 21,866 members of the Healthy Nevada Project (HNP) cohort who underwent Exome + sequencing at Helix. After using our rare-variant-tailored methodology to reduce test statistic inflation, we identify 64 statistically significant gene-based associations in our meta-analysis of the two cohorts and 37 for phenotypes available in only one cohort. Singletons make significant contributions to our results, and the vast majority of the associations could not have been identified with a genotyping chip. Our results are available for interactive browsing in a webapp (https://ukb.research.helix.com). This comprehensive analysis illustrates the biological value of large, deeply phenotyped cohorts of unselected populations coupled with NGS data.