  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.
