Informed conditioning on clinical covariates increases power in case-control association studies.
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-...
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doaj-708d0c6242ad40ada417d4c7a6f438142020-11-25T00:08:40ZengPublic Library of Science (PLoS)PLoS Genetics1553-73901553-74042012-01-01811e100303210.1371/journal.pgen.1003032Informed conditioning on clinical covariates increases power in case-control association studies.Noah ZaitlenSara LindströmBogdan PasaniucMarilyn CornelisGiulio GenoveseSamuela PollackAnne BartonHeike BickeböllerDonald W BowdenSteve EyreBarry I FreedmanDavid J FriedmanJohn K FieldLeif GroopAage HaugenJoachim HeinrichBrian E HendersonPamela J HicksLynne J HockingLaurence N KolonelMaria Teresa LandiCarl D LangefeldLoic Le MarchandMichael MeisterAnn W MorganOlaide Y RajiAngela RischAlbert RosenbergerDavid ScherfSophia SteerMartin WalshawKevin M WatersAnthony G WilsonPaul WordsworthShanbeh ZienolddinyEric Tchetgen TchetgenChristopher HaimanDavid J HunterRobert M PlengeJane WorthingtonDavid C ChristianiDebra A SchaumbergDaniel I ChasmanDavid AltshulerBenjamin VoightPeter KraftNick PattersonAlkes L PriceGenetic 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.http://europepmc.org/articles/PMC3493452?pdf=render |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
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 |
spellingShingle |
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 Informed conditioning on clinical covariates increases power in case-control association studies. PLoS Genetics |
author_facet |
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 |
author_sort |
Noah Zaitlen |
title |
Informed conditioning on clinical covariates increases power in case-control association studies. |
title_short |
Informed conditioning on clinical covariates increases power in case-control association studies. |
title_full |
Informed conditioning on clinical covariates increases power in case-control association studies. |
title_fullStr |
Informed conditioning on clinical covariates increases power in case-control association studies. |
title_full_unstemmed |
Informed conditioning on clinical covariates increases power in case-control association studies. |
title_sort |
informed conditioning on clinical covariates increases power in case-control association studies. |
publisher |
Public Library of Science (PLoS) |
series |
PLoS Genetics |
issn |
1553-7390 1553-7404 |
publishDate |
2012-01-01 |
description |
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. |
url |
http://europepmc.org/articles/PMC3493452?pdf=render |
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