Scalable and Robust Regression Methods for Phenome-Wide Association Analysis on Large-Scale Biobank Data.
Scalable and Robust Regression Methods for Phenome-Wide Association Analysis on Large-Scale Biobank Data.
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DOI：
10.3389/fgene.2021.682638
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发表时间：
2021
影响因子：
3.7
通讯作者：
Lee S
中科院分区：
文献类型：
--
作者：
Bi W;Lee S
随着基因分型技术和电子健康记录（EHR）的进步，大型生物库已经成为在全基因组甚至全表型范围内识别新的遗传关联和基因-环境相互作用的重要资源。迄今为止，已经对生物库数据进行了几项全表型关联研究（PheWAS），这些研究为人类遗传学和生物学的许多方面提供了全面的见解。虽然鼓舞人心，但PheWAS在大规模生物库数据上遇到了新的挑战，包括计算负担，不平衡的表型分布和遗传关系。在本文中，我们首先讨论这些新的挑战及其对数据分析的潜在影响。然后，我们总结了GWAS和PheWAS中可扩展和健壮的方法。这篇综述可作为遗传学家、流行病学家和其他医学研究人员在大规模生物库数据分析中识别与健康相关表型相关的遗传变异的实用指南。同时，它还可以帮助统计人员全面了解当前技术工具的发展。
With the advances in genotyping technologies and electronic health records (EHRs), large biobanks have been great resources to identify novel genetic associations and gene-environment interactions on a genome-wide and even a phenome-wide scale. To date, several phenome-wide association studies (PheWAS) have been performed on biobank data, which provides comprehensive insights into many aspects of human genetics and biology. Although inspiring, PheWAS on large-scale biobank data encounters new challenges including computational burden, unbalanced phenotypic distribution, and genetic relationship. In this paper, we first discuss these new challenges and their potential impact on data analysis. Then, we summarize approaches that are scalable and robust in GWAS and PheWAS. This review can serve as a practical guide for geneticists, epidemiologists, and other medical researchers to identify genetic variations associated with health-related phenotypes in large-scale biobank data analysis. Meanwhile, it can also help statisticians to gain a comprehensive and up-to-date understanding of the current technical tool development.
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影响因子：
64.8
作者：
Bycroft C;Freeman C;Petkova D;Band G;Elliott LT;Sharp K;Motyer A;Vukcevic D;Delaneau O;O'Connell J;Cortes A;Welsh S;Young A;Effingham M;McVean G;Leslie S;Allen N;Donnelly P;Marchini J
通讯作者：
Marchini J
DOI：
10.1056/nejmsr1809937
发表时间：
2019-08-15
期刊：
The New England journal of medicine
影响因子：
--
作者：
All of Us Research Program Investigators;Denny JC;Rutter JL;Goldstein DB;Philippakis A;Smoller JW;Jenkins G;Dishman E
通讯作者：
Dishman E
影响因子：
9.2
作者：
Chang CC;Chow CC;Tellier LC;Vattikuti S;Purcell SM;Lee JJ
通讯作者：
Lee JJ
影响因子：
9.8
作者：
Bi, Wenjian;Zhao, Zhangchen;Lee, Seunggeun
通讯作者：
Lee, Seunggeun
影响因子：
9.8
作者：
Chen, Han;Huffman, Jennifer E.;Lin, Xihong
通讯作者：
Lin, Xihong