Human-Disease Phenotype Map Derived from PheWAS across 38,682 Individuals

Abstract

Phenome-wide association studies (PheWASs) have been a useful tool for testing associations between genetic variations and multiple complex traits or diagnoses. Linking PheWAS-based associations between phenotypes and a variant or a genomic region into a network provides a new way to investigate cross-phenotype associations, and it might broaden the understanding of genetic architecture that exists between diagnoses, genes, and pleiotropy. We created a network of associations from one of the largest PheWASs on electronic health record (EHR)-derived phenotypes across 38,682 unrelated samples from the Geisinger’s biobank; the samples were genotyped through the DiscovEHR project. We computed associations between 632,574 common variants and 541 diagnosis codes. Using these associations, we constructed a “disease-disease” network (DDN) wherein pairs of diseases were connected on the basis of shared associations with a given genetic variant. The DDN provides a landscape of intra-connections within the same disease classes, as well as inter-connections across disease classes. We identified clusters of diseases with known biological connections, such as autoimmune disorders (type 1 diabetes, rheumatoid arthritis, and multiple sclerosis) and cardiovascular disorders. Previously unreported relationships between multiple diseases were identified on the basis of genetic associations as well. The network approach applied in this study can be used to uncover interactions between diseases as a result of their shared, potentially pleiotropic SNPs. Additionally, this approach might advance clinical research and even clinical practice by accelerating our understanding of disease mechanisms on the basis of similar underlying genetic associations.

Human disease cost network analysis

“…Such studies have a limited scope and cannot reveal the “big picture” of disease interconnections. A major breakthrough is the HDN (human disease network) study, under which a large number of diseases are systematically analyzed. The HDN studies have the potential to reveal previously unknown disease interconnections and “globally” describe diseases.…”

Pleiotropy in the Genetic Predisposition to Rheumatoid Arthritis: A Phenome‐Wide Association Study and Inverse Variance–Weighted Meta‐Analysis

“…For example, several non‐MHC RA risk alleles have shown pleiotropic effects in several disorders, including type 1 DM, MS, and lupus , and a cross‐phenotype meta‐analysis of 7 common autoimmune diseases (RA, type 1 DM, MS, lupus, psoriasis, Crohn's disease, and celiac disease) showed that ~44% of disease‐associated SNPs were shared by some of these disorders . Similarly, a study that investigated interconnections across multiple diseases in a large EHR system found that the strongest disease network (indicated by the highest number of shared SNPs) was composed of RA, type 1 DM, MS, and psoriasis . The SNPs linking these conditions mapped to 18 loci in the MHC region and 1 in PTPN22 .…”

“…Although inflammation is an important component of these cardiometabolic traits, and many inflammatory genes have been associated with susceptibility to some of these traits , we did not find significant genetic pleiotropy between RA and these phenotypes. An alternative explanation is that it is not a genetic predisposition toward RA, but development of the inflammatory milieu that results from disease which causes the associations with these phenotypes .…”

Future directions for high‐throughput splicing assays in precision medicine

“…Lastly, large data sets containing genetic variants for screening by MPRAs often lack corresponding phenotypic or other relevant MyCode participants are also consented for recontact for additional research which allows for additional clinical evaluation with more targeted phenotyping to supplement the rich data set that already exists in the EHR. DiscovEHR has already been proven to be a tremendous resource for genomic discovery (Abul-Husn et al, 2018; Gusarova et al, 2018;Verma et al, 2019).…”