Improved Estimation of Phenotypic Correlations Using Summary Association Statistics

Estimating the phenotypic correlations between complex traits and diseases based on their genome-wide association summary statistics has been a useful technique in genetic epidemiology statistical genetics inference. Two state-of-the-art strategies, Z-score correlation across null-effect single nucleotide polymorphisms (SNPs) LD score regression intercept, were widely applied to estimate correlations. Here, we propose an improved strategy SNPs with low minor allele frequencies (MAFs), show how this simple can correct bias generated by current methods. The MAF estimator improves estimation, thus it is beneficial for methods applications using inferred from statistics.