The landscape of genetic susceptibility correlations among diseases and traits. (21st March 2017)

• Record Type: Journal Article
• Authors: Ohn, Jung Hun
• Is Part Of: Journal of the American Medical Informatics Association. Volume 24:Number 5(2017:Sep.)
• Page Start: 921
• Page End: 926
• Publication Date: 2017-03-21
• Subjects: genetic risk score -- disease network -- the 1000 Genomes Project; Medical informatics -- Periodicals; Information Services -- Periodicals; Medical Informatics -- Periodicals; Médecine -- Informatique -- Périodiques; Informatica; Geneeskunde; Informatique médicale; Computer network resources; Electronic journals; 610.285
• DOI: 10.1093/jamia/ocx026
• Languages: English
• ISSNs: 1067-5027

Abstract

Abstract: Objective : The aim of the study was to comprehensively explore the genetic susceptibility correlations among diseases and traits from large-scale individual genotype data. Materials and Methods : Based on a knowledge base of genetic variants significantly (P < 5 × 10 −8 ) linked with human phenotypes, genetic risk scores (GRSs) of diseases or traits were calculated for 2504 individuals with whole-genome sequencing data from the 1000 Genomes Project. Associations between diseases/traits were statistically evaluated by pairwise correlation analysis of GRSs. Overlaps between the genetic susceptibility correlations and disease comorbidity associations from hospital claims data in more than 30 million patients in United States were assessed. Results : Correlation analysis of GRSs revealed 823 significant correlations among 78 diseases and 89 traits (false discovery rate adjusted P -value or Q -value < 0.01). It is noticeable that GRSs were correlated in 464 associations (56.4%) even if they were combinations of distinct sets of risk variants without chromosomal linkage, suggesting the presence of genetic interactions beyond chromosome position. When 312 significant genetic susceptibility correlations between diseases were compared to nationwide disease comorbidity correlations obtained from data from 32 million Medicare claims in the United States, 108 overlaps (34.6%) were found that had both genetic susceptibility and epidemiologic comorbid correlations. Conclusion : The study suggests that common genetic background exists between diseases and traits with epidemiologic associations. The GRS correlation approach provides a rich source of candidate associations among diseases and traits from the genetic perspective, warranting further epidemiologic studies.