A Robust and Powerful Set-Valued Approach to Rare Variant Association Analyses of Secondary Traits in Case-Control Sequencing Studies
A Robust and Powerful Set-Valued Approach to Rare Variant Association Analyses of Secondary Traits in Case-Control Sequencing Studies
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病例对照测序研究中次要性状稀有变异关联分析的稳健且强大的集值方法
DOI：
10.1534/genetics.116.192377
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发表时间：
2016-12
期刊：
影响因子：
3.3
通讯作者：
Yue W
中科院分区：
文献类型：
--
作者：
Kang G;Bi W;Zhang H;Pounds S;Cheng C;Shete S;Zou F;Zhao Y;Zhang J;Yue W
In many case-control designs of genome-wide association (GWAS) or next generation sequencing (NGS) studies, extensive data on secondary traits that may correlate and share the common genetic variants with the primary disease are available. Investigating these secondary traits can provide critical insights into the disease etiology or pathology, and enhance the GWAS or NGS results. Methods based on logistic regression (LG) were developed for this purpose. However, for the identification of rare variants (RVs), certain inadequacies in the LG models and algorithmic instability can cause severely inflated type I error, and significant loss of power, when the two traits are correlated and the RV is associated with the disease, especially at stringent significance levels. To address this issue, we propose a novel set-valued (SV) method that models a binary trait by dichotomization of an underlying continuous variable, and incorporate this into the genetic association model as a critical component. Extensive simulations and an analysis of seven secondary traits in a GWAS of benign ethnic neutropenia show that the SV method consistently controls type I error well at stringent significance levels, has larger power than the LG-based methods, and is robust in performance to effect pattern of the genetic variant (risk or protective), rare or common variants, rare or common diseases, and trait distributions. Because of the SV method’s striking and profound advantage, we strongly recommend the SV method be employed instead of the LG-based methods for secondary traits analyses in case-control sequencing studies.
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影响因子：
64.8
作者：
通讯作者：
--
DOI：
10.1161/circgenetics.113.000350
发表时间：
2014-06
期刊：
Circulation. Cardiovascular genetics
影响因子：
--
作者：
Lin H;Wang M;Brody JA;Bis JC;Dupuis J;Lumley T;McKnight B;Rice KM;Sitlani CM;Reid JG;Bressler J;Liu X;Davis BC;Johnson AD;O'Donnell CJ;Kovar CL;Dinh H;Wu Y;Newsham I;Chen H;Broka A;DeStefano AL;Gupta M;Lunetta KL;Liu CT;White CC;Xing C;Zhou Y;Benjamin EJ;Schnabel RB;Heckbert SR;Psaty BM;Muzny DM;Cupples LA;Morrison AC;Boerwinkle E
通讯作者：
Boerwinkle E
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影响因子：
30.8
作者：
Kathiresan, Sekar;Melander, Olle;Guiducci, Candace;Surti, Aarti;Burtt, Noel P.;Rieder, Mark J.;Cooper, Gregory M.;Roos, Charlotta;Voight, Benjamin F.;Havulinna, Aki S.;Wahlstrand, Bjorn;Hedner, Thomas;Corella, Dolores;Tai, E. Shyong;Ordovas, Jose M.;Berglund, Goran;Vartiainen, Erkki;Jousilahti, Pekka;Hedblad, Bo;Taskinen, Marja-Riitta;Newton-Cheh, Christopher;Salomaa, Veikko;Peltonen, Leena;Groop, Leif;Altshuler, David M.;Orho-Melander, Marju
通讯作者：
Orho-Melander, Marju
影响因子：
9.8
作者：
Reiner, Alex P.;Beleza, Sandra;Tang, Hua
通讯作者：
Tang, Hua
DOI：
--
发表时间：
2015
期刊：
--
影响因子：
--
作者：
R. Do;N. Stitziel;H. Won;A. Jørgensen;S. Duga;P. Merlini;Adam Kiezun;M. Farrall;A. Goel
通讯作者：
R. Do;N. Stitziel;H. Won;A. Jørgensen;S. Duga;P. Merlini;Adam Kiezun;M. Farrall;A. Goel