The paper introduces a novel method called cross-trait LD Score regression for estimating genetic correlations between complex traits and diseases using only genome-wide association study (GWAS) summary statistics. This method overcomes the limitations of existing approaches by not requiring individual genotype data and being unbiased by sample overlap. The authors apply this method to data from 24 GWAS, estimating genetic correlations for 276 pairs of phenotypes. Key findings include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity, and associations between educational attainment and several diseases. The results highlight the power of genome-wide analyses and provide new insights into the genetic architecture of complex traits and diseases. The method is computationally efficient and scalable, making it a valuable tool for epidemiological research.The paper introduces a novel method called cross-trait LD Score regression for estimating genetic correlations between complex traits and diseases using only genome-wide association study (GWAS) summary statistics. This method overcomes the limitations of existing approaches by not requiring individual genotype data and being unbiased by sample overlap. The authors apply this method to data from 24 GWAS, estimating genetic correlations for 276 pairs of phenotypes. Key findings include genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity, and associations between educational attainment and several diseases. The results highlight the power of genome-wide analyses and provide new insights into the genetic architecture of complex traits and diseases. The method is computationally efficient and scalable, making it a valuable tool for epidemiological research.