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Abstract:
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Genome-wide association study(GWAS) have identified thousands of genetic variants that are related with complex diseases and traits. Among these discoveries, many complex traits and diseases are found to have shared genetic etiology. The quantity genetic covariance has been defined to measure this genetic overlapping architectures. Under generalized linear model, we propose an estimator for genetic covariance which can accommodate both discrete and continuous outcomes. Besides, estimating genetic covariance usually involve two outcome models. We show the proposed procedure is still valid when one outcome model is correctly specified but the other one may be misspecified. Corresponding inference property is established and a general empirical method for estimating the variance is proposed.
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