As genome-wide SNP data became widely available, novel approaches were developed to estimate pairwise genetic correlations using GWAS results75; e.g. the development of computational tools using genome-wide association statistics (estimated effect sizes and standard errors for each variant analyzed in a GWAS) instead of individual-level data (genotypes and trait information for each participant tested in a GWAS)76. Methods based on genome-wide association summary statistics no longer contain data from individual subjects and greatly reduce privacy concerns and other logistic issues related to individual-level genetic data, permitting wide data sharing that has allowed a growing number of investigators to explore the genetic correlation between SUDs and other complex traits.
