ArticlePLoS genetics2012
Informed conditioning on clinical covariates increases power in case-control association studies.
Article in PLoS genetics, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 54 papers, 3 of them syntheses that pooled it.
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Who cites it
54 citing papers in PubMed, 3 syntheses or guidelines pooled it.
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- ADuLT: An efficient and robust time-to-event GWAS.Nature communications · 2023Article
- Incorporating family history of disease improves polygenic risk scores in diverse populations.Cell genomics · 2022Article
- Genetic association of anthropometric traits with type 2 diabetes in ethnically endogamous Sindhi families.PloS one · 2021Article
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- Genetic Associations in Four Decades of Multienvironment Trials Reveal Agronomic Trait Evolution in Common Bean.Genetics · 2020Article
- Liability threshold modeling of case-control status and family history of disease increases association power.Nature genetics · 2020Article
- Efficient Estimation and Applications of Cross-Validated Genetic Predictions to Polygenic Risk Scores and Linear Mixed Models.Journal of computational biology : a journal of computational molecular cell biology · 2020Article
- Quantifying between-cohort and between-sex genetic heterogeneity in major depressive disorder.American journal of medical genetics. Part B, Neuropsychiatric genetics : the official publication of the International Society of Psychiatric Genetics · 2019Article
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Authors and funding
48 authors.
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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.
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