Disease risk factors identified through shared genetic architecture and electronic medical records.
Genome-wide association studies have identified genetic variants for thousands of diseases and traits. We evaluated the relationships between specific risk factors (for example, blood cholesterol level) and diseases on the basis of their shared genetic architecture in a comprehensive human disease-single-nucleotide polymorphism association database (VARIMED), analyzing the findings from 8962 published association studies. Similarity between traits and diseases was statistically evaluated on the basis of their association with shared gene variants. We identified 120 disease-trait pairs that were statistically similar, and of these, we tested and validated five previously unknown disease-trait associations by searching electronic medical records (EMRs) from three independent medical centers for evidence of the trait appearing in patients within 1 year of first diagnosis of the disease. We validated that the mean corpuscular volume is elevated before diagnosis of acute lymphoblastic leukemia; both have associated variants in the gene IKZF1. Platelet count is decreased before diagnosis of alcohol dependence; both are associated with variants in the gene C12orf51. Alkaline phosphatase level is elevated in patients with venous thromboembolism; both share variants in ABO. Similarly, we found that prostate-specific antigen and serum magnesium levels were altered before the diagnosis of lung cancer and gastric cancer, respectively. Disease-trait associations identify traits that could serve as future prognostics, if validated through EMR and subsequent prospective trials.
Division of Systems Medicine, Department of Pediatrics, Stanford University School of Medicine, 1265 Welch Road, Stanford, CA 94305, USA.
Li
L
Li
David J
DJ
Ruau
Chirag J
CJ
Patel
Susan C
SC
Weber
Rong
R
Chen
Nicholas P
NP
Tatonetti
Joel T
JT
Dudley
Atul J
AJ
Butte
D057286:Electronic Health Records; D055106:Genome-Wide Association Study / Q000379:methods*; D006801:Humans; D008954:Models, Biological; D012307:Risk Factors