针对协变量校正二值 GWAS 易感尺度分析的条件患病率校正
Conditional-prevalence correction for liability-scale analyses of covariate-adjusted binary GWAS
在疾病 GWAS 中校正可遗传协变量会引入对撞偏倚，产生虚假遗传关联；为在代表连续疾病易感性的易感尺度上解释校正后的关联，需要从 log odds 做恰当转换。研究在高斯易感模型下针对小遗传效应推导了该转换，发现排除经校正协变量通路的直接效应与对撞偏倚成分共享同一转换因子，并刻画了该因子如何随协变量取值间疾病风险变化而变化，推导还扩展到病例对照抽样。模拟中，该转换降低了仅按疾病患病率缩放所带来的效应量膨胀。结果将现有的对撞偏倚校正与直接易感效应估计联系起来，并澄清了人群与病例对照研究中协变量校正关联的解释。
来源：PLOS Genetics · journals.plos.org
by Siyi Chen
Adjustment for heritable covariates in genome-wide association studies of disease can induce collider bias, creating spurious genetic associations. Following bias correction, interpretation on the liability scale, which represents continuous disease susceptibility, requires an appropriate conversion from log odds. We derive this conversion under a Gaussian liability model for small genetic effects. The direct effect, excluding pathways through the adjusted covariates, and the collider-bias component share a common conversion factor. We characterize how this factor depends on variation in disease risk across covariate values and extend the derivation to case-control sampling. In simulations, the proposed conversion reduced the effect-size inflation associated with scaling based solely on disease prevalence. These results link existing collider-bias corrections to estimation of direct liability effects and clarify the interpretation of covariate-adjusted associations in population and case-control studies.