Informed Conditioning on Clinical Covariates Increases Power in Case-Control Association Studies
Year: 2012
Type: article
Abstract: 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 χ(2) 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.
Source: PLoS Genetics
Authors: Noah Zaitlen, Sara Lindström, Bogdan Pasaniuc, Marilyn Cornelis, Giulio Genovese, Samuela Pollack, Anne Barton, Heike Bickeböller, Donald W. Bowden, Steve Eyre, Barry I. Freedman, David J. Friedman, John K. Field, Leif Groop, Aage Haugen, Joachim Heinrich, Brian E. Henderson, Pamela J. Hicks, Lynne J. Hocking, Laurence N. Kolonel, Maria Teresa Landi, Carl D. Langefeld, Loic Le Marchand, Michael Meister, Ann W. Morgan, Olaide Y. Raji, Angela Risch, Albert Rosenberger, David Scherf, Sophia Steer, Martin Walshaw, Kevin M. Waters, Anthony G. Wilson, Paul Wordsworth, Shanbeh Zienolddiny, Eric Tchetgen Tchetgen, Christopher Haiman, David J. Hunter, Robert M. Plenge, Jane Worthington, David C. Christiani, Debra A. Schaumberg, Daniel I. Chasman, David Altshuler, Benjamin Voight, Peter Kraft, Nick Patterson, Alkes L. Price
Institutions: Broad Institute, Harvard University, Massachusetts Institute of Technology, Brigham and Women's Hospital, Beth Israel Deaconess Medical Center, Manchester Academic Health Science Centre, University of Manchester, Universitätsmedizin Göttingen, University of Göttingen, Wake Forest University Health Sciences, Wake Forest University, University of Liverpool, Lund University, National Institute of Occupational Health, Helmholtz Munich, University of Southern California, University of Aberdeen, University of Hawaiʻi at Mānoa, University of Hawaii System, National Institutes of Health, Division of Cancer Epidemiology and Genetics, University Hospital Heidelberg, German Center for Lung Research, NIHR Leeds Musculoskeletal Biomedical Research Unit, German Cancer Research Center, National Health Service, King's College Hospital NHS Foundation Trust, King's College Hospital, Liverpool Heart and Chest Hospital, University of Sheffield, Nuffield Orthopaedic Centre, NIHR Oxford Biomedical Research Centre, Massachusetts General Hospital
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $3,043
Funders: Donald W. Reynolds Foundation, Amgen, Fondation Leducq, Versus Arthritis, National Institutes of Health, Manchester Biomedical Research Centre, National Heart, Lung, and Blood Institute, National Human Genome Research Institute, National Cancer Institute, National Institute of Environmental Health Sciences
Awards: G1148307873, G1420790711, G1528515809, G229684361, Molecular and Genetic Analysis of Lung Cancer Survival, G2642285025, G2736363316, G2919159421, Liability threshold modeling of genes and environment in case-control studies, Pharmacogenomics and Risk of Cardiovascular Disease, G3219722387, Characterizing Genetic Susceptibility to Breast and Prostate Cancer: The BPC3., G395156118, Leveraging functional data to predict disease risk in multi-ethnic populations, Pharmacogenomics and Risk of Cardiovascular Disease, Characterizing Genetic Susceptibility to Breast and Prostate Cancer; the BPC3, Transdisciplinary Research in Cancer of the Lung (TRICL), G4996234081, G5009335695, G5021959070, G552178769, G558986044, Semiparametric Methods for Gene-environment Interaction, Characterizing Genetic Susceptibility to Breast and Prostate Cancer; the BPC3, TRIAL OF ASPIRIN AND VITAMIN E IN WOMEN, Characterizing Genetic Susceptibility to Breast and Prostate Cancer; the BPC3, Women's Health Study: Continued Follow-up, G7962241407, G8013581208, G9000058182, G9000332794, Graduate Training in Biostatistics
OpenAlex ID: W2146289468