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 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.

Figure 1. Illustration of liability threshold model: simulated T2D example.

The posterior mean of ε for low-BMI and high-BMI cases is the expected value of ε given that it exceeds c(t−)+m. High-BMI cases have a lower posterior mean relative to low-BMI cases since they require a smaller contribution from genetics to exceed the threshold in the liability threshold model.

Figure 2. Power calculations for LogR, G+GxE, and LT approaches in simulated data.

For each statistic we display power to attain P<5×10⁻⁸ based on 1,000,000 simulations of 3000 cases and 3000 controls, for various effect sizes γ. The increase in power (ratio of y-axis values) for LT versus LogR is 22.8% for γ = 0.1, and 23.0% when computing average power across all values of γ. For γ = 0 the power was 5.0% for all statistics when the P-value threshold is 0.05. G+GxE performs worse due to an extra degree of freedom.