Estimating heritability and genetic correlations from large health datasets in the absence of genetic data
Year: 2019
Type: article
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 < 10–16); and (3) the disease onset age and heritability are negatively correlated (ρ = −0.46, p < 10–16).
Source: Nature Communications
Institutions: University of Chicago, King Abdullah University of Science and Technology, Icahn School of Medicine at Mount Sinai, University of California, San Francisco, Vanderbilt University, The Jackson Laboratory for Genomic Medicine, Karolinska Institutet, University of Copenhagen, Novo Nordisk Foundation, Linköping University
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: gold
Estimated APC paid: $5,200
Awards: G1347451286, G1620712797, G1830504713, G2185879301, G2702908835, G2725772924, G2765503861, Mendelian Disease - Asthma Comorbidity to Find Subgroup-Specific Asthma Genes, G3847064998, BIG MACHINE SCIENCE, G4433044775, G4836667524, G4921001326, Conte Center for Computational Systems Genomics of Neuropsychiatric Phenotypes, G5629790366, G6011993089, G6055510217, G6542029475, MAPGen Knowledge Base (MAPGenKB) and Coordination Center, G7150028087, G7240333416, G7249641080, G7434019081, G8722125687, G9000117623, G925292540
OpenAlex ID: W2993012267