Informed conditioning on clinical covariates increases power in case-control association studies.
Informed conditioning on clinical covariates increases power in case-control association studies.
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DOI：
10.1371/journal.pgen.1003032
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发表时间：
2012
期刊：
影响因子：
4.5
通讯作者：
Price AL
中科院分区：
文献类型：
--
作者：
Zaitlen N;Lindström S;Pasaniuc B;Cornelis M;Genovese G;Pollack S;Barton A;Bickeböller H;Bowden DW;Eyre S;Freedman BI;Friedman DJ;Field JK;Groop L;Haugen A;Heinrich J;Henderson BE;Hicks PJ;Hocking LJ;Kolonel LN;Landi MT;Langefeld CD;Le Marchand L;Meister M;Morgan AW;Raji OY;Risch A;Rosenberger A;Scherf D;Steer S;Walshaw M;Waters KM;Wilson AG;Wordsworth P;Zienolddiny S;Tchetgen ET;Haiman C;Hunter DJ;Plenge RM;Worthington J;Christiani DC;Schaumberg DA;Chasman DI;Altshuler D;Voight B;Kraft P;Patterson N;Price AL
Genetic case-control association studies often include data on clinical covariates, such as body mass index (BMI), smoking status, or age, that may modify the underlying genetic risk of case or control samples. For example, in type 2 diabetes, odds ratios for established variants estimated from low–BMI cases are larger than those estimated from high–BMI cases. An unanswered question is how to use this information to maximize statistical power in case-control studies that ascertain individuals on the basis of phenotype (case-control ascertainment) or phenotype and clinical covariates (case-control-covariate ascertainment). While current approaches improve power in studies with random ascertainment, they often lose power under case-control ascertainment and fail to capture available power increases under case-control-covariate ascertainment. We show that an informed conditioning approach, based on the liability threshold model with parameters informed by external epidemiological information, fully accounts for disease prevalence and non-random ascertainment of phenotype as well as covariates and provides a substantial increase in power while maintaining a properly controlled false-positive rate. Our method outperforms standard case-control association tests with or without covariates, tests of gene x covariate interaction, and previously proposed tests for dealing with covariates in ascertained data, with especially large improvements in the case of case-control-covariate ascertainment. We investigate empirical case-control studies of type 2 diabetes, prostate cancer, lung cancer, breast cancer, rheumatoid arthritis, age-related macular degeneration, and end-stage kidney disease over a total of 89,726 samples. In these datasets, informed conditioning outperforms logistic regression for 115 of the 157 known associated variants investigated (P-value = 1×10−9). The improvement varied across diseases with a 16% median increase in χ2 test statistics and a commensurate increase in power. This suggests that applying our method to existing and future association studies of these diseases may identify novel disease loci. This work describes a new methodology for analyzing genome-wide case-control association studies of diseases with strong correlations to clinical covariates, such as age in prostate cancer and body mass index in type 2 diabetes. Currently, researchers either ignore these clinical covariates or apply approaches that ignore the disease's prevalence and the study's ascertainment strategy. We take an alternative approach, leveraging external prevalence information from the epidemiological literature and constructing a statistic based on the classic liability threshold model of disease. Our approach not only improves the power of studies that ascertain individuals randomly or based on the disease phenotype, but also improves the power of studies that ascertain individuals based on both the disease phenotype and clinical covariates. We apply our statistic to seven datasets over six different diseases and a variety of clinical covariates. We found that there was a substantial improvement in test statistics relative to current approaches at known associated variants. This suggests that novel loci may be identified by applying our method to existing and future association studies of these diseases.
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DOI：
10.1126/science.1193032
发表时间：
2010-08-13
期刊：
Science (New York, N.Y.)
影响因子：
--
作者：
Genovese G;Friedman DJ;Ross MD;Lecordier L;Uzureau P;Freedman BI;Bowden DW;Langefeld CD;Oleksyk TK;Uscinski Knob AL;Bernhardy AJ;Hicks PJ;Nelson GW;Vanhollebeke B;Winkler CA;Kopp JB;Pays E;Pollak MR
通讯作者：
Pollak MR
多阶段全基因组关联研究确定了七个前列腺癌易感位点
影响因子：
30.8
作者：
通讯作者：
--
影响因子：
30.8
作者：
Imielinski, Marcin;Baldassano, Robert N.;Griffiths, Anne;Russell, Richard K.;Annese, Vito;Dubinsky, Marla;Kugathasan, Subra;Bradfield, Jonathan P.;Walters, Thomas D.;Sleiman, Patrick;Kim, Cecilia E.;Muise, Aleixo;Wang, Kai;Glessner, Joseph T.;Saeed, Shehzad;Zhang, Haitao;Frackelton, Edward C.;Hou, Cuiping;Flory, James H.;Otieno, George;Chiavacci, Rosetta M.;Grundmeier, Robert;Castro, Massimo;Latiano, Anna;Dallapiccola, Bruno;Stempak, Joanne;Abrams, Debra J.;Taylor, Kent;McGovern, Dermot;Heyman, Melvin B.;Ferry, George D.;Kirschner, Barbara;Lee, Jessica;Essers, Jonah;Grand, Richard;Stephens, Michael;Levine, Arie;Piccoli, David;Van Limbergen, Johan;Cucchiara, Salvatore;Monos, Dimitri S.;Guthery, Stephen L.;Denson, Lee;Wilson, David C.;Grant, Struan F. A.;Daly, Mark;Silverberg, Mark S.;Satsangi, Jack;Hakonarson, Hakon
通讯作者：
Hakonarson, Hakon
DOI：
10.1073/pnas.0812824106
发表时间：
2009-03-10
影响因子：
11.1
作者：
Kryukov, Gregory V.;Shpunt, Alexander;Sunyaev, Shamil R.
通讯作者：
Sunyaev, Shamil R.
影响因子：
--
作者：
Ellis, Katrina L.;Pilbrow, Anna P.;Cameron, Vicky A.
通讯作者：
Cameron, Vicky A.