Rare-variant collapsing analyses for complex traits: guidelines and applications

被引:0
|
作者
Gundula Povysil
Slavé Petrovski
Joseph Hostyk
Vimla Aggarwal
Andrew S. Allen
David B. Goldstein
机构
[1] Institute for Genomic Medicine,Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D
[2] Columbia University Irving Medical Center,Department of Medicine
[3] Columbia University,Department of Biostatistics and Bioinformatics
[4] AstraZeneca,undefined
[5] The University of Melbourne,undefined
[6] Austin Health and Royal Melbourne Hospital,undefined
[7] Duke University,undefined
来源
Nature Reviews Genetics | 2019年 / 20卷
关键词
D O I
暂无
中图分类号
学科分类号
摘要
The first phase of genome-wide association studies (GWAS) assessed the role of common variation in human disease. Advances optimizing and economizing high-throughput sequencing have enabled a second phase of association studies that assess the contribution of rare variation to complex disease in all protein-coding genes. Unlike the early microarray-based studies, sequencing-based studies catalogue the full range of genetic variation, including the evolutionarily youngest forms. Although the experience with common variants helped establish relevant standards for genome-wide studies, the analysis of rare variation introduces several challenges that require novel analysis approaches.
引用
收藏
页码:747 / 759
页数:12
相关论文
共 50 条
  • [31] Bayesian model comparison for rare-variant association studies
    Venkataraman, Guhan Ram
    DeBoever, Christopher
    Tanigawa, Yosuke
    Aguirre, Matthew
    Ioannidis, Alexander G.
    Mostafavi, Hakhamanesh
    Spencer, Chris C. A.
    Poterba, Timothy
    Bustamante, Carlos D.
    Daly, Mark J.
    Pirinen, Matti
    Rivas, Manuel A.
    AMERICAN JOURNAL OF HUMAN GENETICS, 2021, 108 (12) : 2354 - 2367
  • [32] Bayesian Collapsing Model for Rare Variant Detection
    He, Liang
    Ripatti, Samuli
    Pitkaniemi, Janne
    GENETIC EPIDEMIOLOGY, 2012, 36 (07) : 763 - 763
  • [33] Rare Variant Collapsing Test with Variable Binning
    Drichel, D.
    Lacour, A.
    Herold, C.
    Schueller, V.
    Vaitsiakhovich, T.
    Becker, T.
    HUMAN HEREDITY, 2015, 79 (01) : 33 - 34
  • [34] Incorporating external information to improve sparse signal detection in rare-variant gene-set-based analyses
    Zhang, Mengqi
    Gelfman, Sahar
    McCarthy, Janice
    Harms, Matthew B.
    Moreno, Cristiane A. M.
    Goldstein, David B.
    Allen, Andrew S.
    GENETIC EPIDEMIOLOGY, 2020, 44 (04) : 330 - 338
  • [35] Rare Variant Intensity Estimation for Genetic Mapping of Complex Traits
    Jhuang, Jing-Rong
    Wei, Wan-Yu
    Lin, Yin-Chun
    Guo, Chao-Yu
    Yang, Hsin-Chou
    GENETIC EPIDEMIOLOGY, 2024, 48 (07) : 362 - 363
  • [36] A Bayesian hierarchically structured prior for rare-variant association testing
    Yang, Yi
    Basu, Saonli
    Zhang, Lin
    GENETIC EPIDEMIOLOGY, 2021, 45 (04) : 413 - 424
  • [37] Improving power for rare-variant tests by integrating external controls
    Lee, Seunggeun
    Kim, Sehee
    Fuchsberger, Christian
    GENETIC EPIDEMIOLOGY, 2017, 41 (07) : 610 - 619
  • [38] Rare-Variant Association Analysis: Study Designs and Statistical Tests
    Lee, Seunggeung
    Abecasis, Goncalo R.
    Boehnke, Michael
    Lin, Xihong
    AMERICAN JOURNAL OF HUMAN GENETICS, 2014, 95 (01) : 5 - 23
  • [39] A Statistical Approach for Rare-Variant Association Testing in Affected Sibships
    Epstein, Michael P.
    Duncan, Richard
    Ware, Erin B.
    Jhun, Min A.
    Bielak, Lawrence F.
    Zhao, Wei
    Smith, Jennifer A.
    Peyser, Patricia A.
    Kardia, Sharon L. R.
    Satten, Glen A.
    AMERICAN JOURNAL OF HUMAN GENETICS, 2015, 96 (04) : 543 - 554
  • [40] Rare-Variant Studies to Complement Genome-Wide Association Studies
    Sazonovs, A.
    Barrett, J. C.
    ANNUAL REVIEW OF GENOMICS AND HUMAN GENETICS, VOL 19, 2018, 19 : 97 - 112