Statistical Inference for Genetic Relatedness Based on High-Dimensional Logistic Regression

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This paper studies the problem of statistical inference for genetic relatedness between binary traits based on individual-level genome-wide association data.

• Specifically, under the high-dimensional logistic regression models, we define parameters characterizing the cross-trait genetic correlation, the genetic covariance and the trait-specific genetic variance.
• A novel weighted debiasing method is developed for the logistic Lasso estimator and computationally efficient debiased estimators are proposed.
• The rates of convergence for these estimators are studied and their asymptotic normality is established under mild conditions.