Identifying the genetic basis and molecular mechanisms underlying phenotypic correlation between complex human traits using a gene-based approach

Phenotypic correlations between complex human traits have long been observed based on epidemiological studies. However, the genetic basis and underlying mechanisms are largely unknown. The recent accumulation of GWAS data has made it possible to analyze the genetic similarity between human traits through comparative analysis. Here we developed a gene-based approach to measure genetic similarity between a pair of traits and to delineate the shared genes/pathways, through three steps: 1) translating SNP-phenotype association profile to genephenotype association profile by integrating GWAS with eQTL data; 2) measuring the similarity between a pair of traits by a normalized distance between the two gene-phenotype association profiles; 3) delineating genes/pathways supporting the similarity. Application of this approach to a set of GWAS data covering 59 human traits detected significant similarity between many known and unexpected pairs of traits; a significant fraction of them are not detectable by SNP based similarity measures. Examples include Height and Schizophrenia, Cancer and Alzheimers Disease, and Rheumatoid Arthritis and Crohns disease. Functional analysis revealed specific genes/pathways shared by these pairs. For example, Height and Schizophrenia are co-associated with genes involved in neural development, skeletal muscle regeneration, protein synthesis, magnesium homeostasis, and immune response, suggesting growth and development as a common theme underlying both traits. Our approach can detect yet unknown relationships between complex traits and generate mechanistic hypotheses, and has the potential to improve diagnosis and treatment by transferring knowledge from one disease to another.