Informed Conditioning on Clinical Covariates Increases Power in Case-Control Association Studies

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 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.

Accurate liability estimation improves power in ascertained case-control studies

“…9,10 . However, the ratio of the means is often dominated by excessively large test statistics, whereas the mean of the ratios assigns equal importance to all variants.…”

“…LEAP bears similarities to several recent methods for estimating the portion of the liability that is explained by a small set of explanatory variables 9,10 . Unlike these methods, LEAP estimates liabilities using the entire genome (Supplementary Note).…”

“…LEAP bears similarities to several recently developed methods for estimating the portion of the liability that is explained by a given set of explanatory variables 9,10 . These methods are designed to prevent power loss in ascertained case-control studies using covariates, which arises due to induced correlations between tested and conditioned variables, as discussed above.…”

“…41 , we included the top five principal components, associated with the five largest eigenvalues, as fixed effects under this selection method. For both methods, we evaluated subsets of sizes [1,5,10,15,20,30,40,50,75, 100, 250, 500, 750, 1000, 2000, 3000, 4000, 5000, 6000, 7000, 8000, 9000, 10000, 20000, 30000, 40000, 50000, all SNPs].…”

“…This induced correlation takes place because cases of rare diseases often carry high dosages of multiple risk variables 10,[34][35][36] . Furthermore, when fitting covariates as fixed effects, the analysis is no longer independent of the ascertainment scheme.…”