A new method for estimating genetic correlations between complex traits and diseases using genome-wide association study (GWAS) summary statistics has been developed. This method, called cross-trait LD Score regression, allows for the estimation of genetic correlations without requiring individual genotype data or accounting for sample overlap, which can bias results. The method was applied to data from 24 GWAS, estimating genetic correlations among 276 pairs of traits. Results included genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity, and associations between educational attainment and several diseases. These findings highlight the power of genome-wide analyses, as there are currently few significantly associated SNPs for anorexia nervosa and only three for educational attainment. The method is computationally efficient and not biased by sample overlap or population stratification. It was validated through simulations and replication of previous findings, demonstrating its accuracy and reliability. The method also allows for the estimation of genetic correlations between case/control traits and quantitative traits without needing to specify a scale. The results show that genetic correlations can be close to zero, indicating little genetic overlap between certain traits, such as schizophrenia and rheumatoid arthritis. The study also highlights the importance of considering genetic architecture and potential biases in sampling when interpreting genetic correlations. The method is an advancement in genetic research as it allows for the estimation of genetic correlations for many more pairs of traits than previous methods. It is a useful addition to the epidemiological toolbox, enabling rapid screening for correlations among a diverse set of traits without the need for measuring multiple traits on the same individuals or genome-wide significant SNPs. The method is robust to various forms of biased sampling and can be applied to a wide range of traits and diseases.A new method for estimating genetic correlations between complex traits and diseases using genome-wide association study (GWAS) summary statistics has been developed. This method, called cross-trait LD Score regression, allows for the estimation of genetic correlations without requiring individual genotype data or accounting for sample overlap, which can bias results. The method was applied to data from 24 GWAS, estimating genetic correlations among 276 pairs of traits. Results included genetic correlations between anorexia nervosa and schizophrenia, anorexia and obesity, and associations between educational attainment and several diseases. These findings highlight the power of genome-wide analyses, as there are currently few significantly associated SNPs for anorexia nervosa and only three for educational attainment. The method is computationally efficient and not biased by sample overlap or population stratification. It was validated through simulations and replication of previous findings, demonstrating its accuracy and reliability. The method also allows for the estimation of genetic correlations between case/control traits and quantitative traits without needing to specify a scale. The results show that genetic correlations can be close to zero, indicating little genetic overlap between certain traits, such as schizophrenia and rheumatoid arthritis. The study also highlights the importance of considering genetic architecture and potential biases in sampling when interpreting genetic correlations. The method is an advancement in genetic research as it allows for the estimation of genetic correlations for many more pairs of traits than previous methods. It is a useful addition to the epidemiological toolbox, enabling rapid screening for correlations among a diverse set of traits without the need for measuring multiple traits on the same individuals or genome-wide significant SNPs. The method is robust to various forms of biased sampling and can be applied to a wide range of traits and diseases.