TY - JOUR
T1 - Human-Disease Phenotype Map Derived from PheWAS across 38,682 Individuals
AU - the DiscovEHR Collaboration
AU - Verma, Anurag
AU - Bang, Lisa
AU - Miller, Jason E.
AU - Zhang, Yanfei
AU - Lee, Ming Ta Michael
AU - Zhang, Yu
AU - Byrska-Bishop, Marta
AU - Carey, David J.
AU - Ritchie, Marylyn D.
AU - Pendergrass, Sarah A.
AU - Kim, Dokyoon
N1 - Funding Information:
This work was supported by the National Library of Medicine ( NLM ) R01 NL012535 . This project is also funded, in part, by a grant provided by the Pennsylvania Department of Health (# SAP 4100070267 ). The Department of Health specifically disclaims responsibility for any analyses, interpretations, or conclusions.
Funding Information:
This work was supported by the National Library of Medicine (NLM) R01 NL012535. This project is also funded, in part, by a grant provided by the Pennsylvania Department of Health (#SAP 4100070267). The Department of Health specifically disclaims responsibility for any analyses, interpretations, or conclusions.
Publisher Copyright:
© 2018 The Author(s)
PY - 2019/1/3
Y1 - 2019/1/3
N2 - 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.
AB - 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.
UR - http://www.scopus.com/inward/record.url?scp=85058227390&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=85058227390&partnerID=8YFLogxK
U2 - 10.1016/j.ajhg.2018.11.006
DO - 10.1016/j.ajhg.2018.11.006
M3 - Article
C2 - 30598166
AN - SCOPUS:85058227390
VL - 104
SP - 55
EP - 64
JO - American Journal of Human Genetics
JF - American Journal of Human Genetics
SN - 0002-9297
IS - 1
ER -