TY - JOUR
T1 - Estimating heritability and genetic correlations from large health datasets in the absence of genetic data
AU - Jia, Gengjie
AU - Li, Yu
AU - Zhang, Hanxin
AU - Chattopadhyay, Ishanu
AU - Boeck Jensen, Anders
AU - Blair, David R.
AU - Davis, Lea
AU - Robinson, Peter N.
AU - Dahlén, Torsten
AU - Brunak, Søren
AU - Benson, Mikael
AU - Edgren, Gustaf
AU - Cox, Nancy J.
AU - Gao, Xin
AU - Rzhetsky, Andrey
N1 - KAUST Repository Item: Exported on 2020-10-01
Acknowledged KAUST grant number(s): FCC/1/1976-18-01, FCC/1/1976-23-01, FCC/1/1976-25-01, FCC/1/1976-26-01
Acknowledgements: 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 KAUST
PY - 2019/12/3
Y1 - 2019/12/3
N2 - 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).
AB - 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).
UR - http://hdl.handle.net/10754/660515
UR - http://www.nature.com/articles/s41467-019-13455-0
UR - http://www.scopus.com/inward/record.url?scp=85075913994&partnerID=8YFLogxK
U2 - 10.1038/s41467-019-13455-0
DO - 10.1038/s41467-019-13455-0
M3 - Article
C2 - 31796735
SN - 2041-1723
VL - 10
JO - Nature Communications
JF - Nature Communications
IS - 1
ER -