Document Type
Article
Publication Date
12-3-2019
Keywords
JGM
JAX Source
Genet Med 2019 Dec 3; 10(1):5508
Volume
10
Issue
1
First Page
5508
Last Page
5508
ISSN
2041-1723
PMID
31796735
DOI
https://doi.org/10.1038/s41467-019-13455-0
Abstract
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
Recommended Citation
Jia G,
Li Y,
Zhang H,
Chattopadhyay I,
Boeck Jensen A,
Blair D,
Davis L,
Robinson P,
Dahlén T,
Brunak S,
Benson M,
Edgren G,
Cox N,
Gao X,
Rzhetsky A.
Estimating heritability and genetic correlations from large health datasets in the absence of genetic data. Genet Med 2019 Dec 3; 10(1):5508
Comments
This open access article is licensed under a Creative Commons Attribution 4.0 International License.