学术文献库

Genome-wide association studies(GWAS) have proven to be highly useful in revealing the genetic basis of complex diseases. At present, most GWAS are studies of a particular single disease diagnosis against controls. However, in practice, an individual is often affected by more than one condition/disorder. For example, patients with coronary artery disease(CAD) are often comorbid with diabetes mellitus(DM). Along a similar line, it is often clinically meaningful to study patients with one disease but without a comorbidity. For example, obese DM may have different pathophysiology from non-obese DM. Here we developed a statistical framework to uncover susceptibility variants for comorbid disorders (or a disorder without comorbidity), using GWAS summary statistics only. In essence, we mimicked a case-control GWAS in which the cases are affected with comorbidities or a disease without a relevant comorbid condition (in either case, we may consider the cases as those affected by a specific subtype of disease, as characterized by the presence or absence of comorbid conditions). We extended our methodology to deal with continuous traits with clinically meaningful categories (e.g. lipids). In addition, we illustrated how the analytic framework may be extended to more than two traits. We verified the feasibility and validity of our method by applying it to simulated scenarios and four cardiometabolic (CM) traits. We also analyzed the genes, pathways, cell-types/tissues involved in CM disease subtypes. LD-score regression analysis revealed some subtypes may indeed be biologically distinct with low genetic correlations. Further Mendelian randomization analysis found differential causal effects of different subtypes to relevant complications. We believe the findings are of both scientific and clinical value, and the proposed method may open a new avenue to analyzing GWAS data.