Estimating heritability and genetic correlations from large health datasets in the absence of genetic data
License
https://creativecommons.org/licenses/by/4.0Type
ArticleAuthors
Jia, GengjieLi, Yu
Zhang, Hanxin
Chattopadhyay, Ishanu
Boeck Jensen, Anders
Blair, David R.
Davis, Lea
Robinson, Peter N.
Dahlén, Torsten
Brunak, Søren
Benson, Mikael
Edgren, Gustaf
Cox, Nancy J.
Gao, Xin
Rzhetsky, Andrey
KAUST Department
Computer Science ProgramComputational Bioscience Research Center (CBRC)
Computer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
KAUST Grant Number
FCC/1/1976-18-01FCC/1/1976-23-01
FCC/1/1976-25-01
FCC/1/1976-26-01
Online Publication Date
2019-12-03Print Publication Date
2019-12Date
2019-12-03Abstract
Typically, estimating genetic parameters, such as disease heritability and between-disease genetic correlations, demands large datasets containing all relevant phenotypic measures and detailed knowledge of family relationships or, alternatively, genotypic and phenotypic data for numerous unrelated individuals. Here, we suggest an alternative, efficient estimation approach through the construction of two disease metrics from large health datasets: temporal disease prevalence curves and low-dimensional disease embeddings. We present eleven thousand heritability estimates corresponding to five study types: twins, traditional family studies, health records-based family studies, single nucleotide polymorphisms, and polygenic risk scores. We also compute over six hundred thousand estimates of genetic, environmental and phenotypic correlations. Furthermore, we find that: (1) disease curve shapes cluster into five general patterns; (2) early-onset diseases tend to have lower prevalence than late-onset diseases (Spearman's ρ = 0.32, p < 10-16); and (3) the disease onset age and heritability are negatively correlated (ρ = -0.46, p < 10-16).Citation
Jia, G., Li, Y., Zhang, H., Chattopadhyay, I., Boeck Jensen, A., Blair, D. R., … Rzhetsky, A. (2019). Estimating heritability and genetic correlations from large health datasets in the absence of genetic data. Nature Communications, 10(1). doi:10.1038/s41467-019-13455-0Acknowledgements
We are grateful to E. Gannon, R. Melamed, R. Mork, M. Rzhetsky, and E. Wachspress for comments on earlier versions of this manuscript, and to H. Sanayle for advising us on Autodesk Maya 2019 Python programming. This work was funded by the DARPA Big Mechanism program under ARO contract W911NF1410333, by National Institutes of Health grants R01HL122712, 1P50MH094267, and U01HL108634-01, by a gift from Liz and Kent Dauten, and by funding from King Abdullah University of Science and Technology (KAUST), under award number FCC/1/1976-18-01, FCC/1/1976-23-01, FCC/1/1976-25-01, FCC/1/1976-26-01, and FCS/1/4102-02-01. This research made use of the resources of the Supercomputing Laboratory at KAUSTPublisher
Springer NatureJournal
Nature CommunicationsDOI
10.1038/s41467-019-13455-0Additional Links
http://www.nature.com/articles/s41467-019-13455-0https://www.nature.com/articles/s41467-019-13455-0.pdf
Relations
Is Supplemented By:- [Software]
Title: jiagengjie/Estimating-Genetic-Parameters:. Publication Date: 2019-07-04. github: jiagengjie/Estimating-Genetic-Parameters Handle: 10754/667902