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Nat Commun. 2019 Dec 3;10(1):5508. doi: 10.1038/s41467-019-13455-0.

Estimating heritability and genetic correlations from large health datasets in the absence of genetic data.

Author information

1
Department of Medicine, Institute of Genomics and Systems Biology, University of Chicago, Chicago, IL, 60637, USA.
2
Computational Bioscience Research Center, Computer, Electrical and Mathematical Sciences and Engineering Division, King Abdullah University of Science and Technology (KAUST), Thuwal, 23955, Saudi Arabia.
3
Committee on Genomics, Genetics, and Systems Biology, University of Chicago, Chicago, IL, 60637, USA.
4
Institute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
5
Department of Pediatrics, University of California San Francisco, San Francisco, CA, 94158, USA.
6
Division of Genetic Medicine, Vanderbilt University, Nashville, TN, 37232, USA.
7
Jackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA.
8
Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, 171 77, Sweden.
9
Novo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 1017, Denmark.
10
Centre for Individualized Medicine, Department of Pediatrics, Linkoping University, Linkoping, 58183, Sweden.
11
Department of Medicine, Institute of Genomics and Systems Biology, University of Chicago, Chicago, IL, 60637, USA. andrey.rzhetsky@uchicago.edu.
12
Committee on Genomics, Genetics, and Systems Biology, University of Chicago, Chicago, IL, 60637, USA. andrey.rzhetsky@uchicago.edu.
13
Department of Human Genetics, University of Chicago, Chicago, IL, 60637, USA. andrey.rzhetsky@uchicago.edu.

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).

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