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dc.contributor.authorJia, Gengjie
dc.contributor.authorLi, Yu
dc.contributor.authorZhang, Hanxin
dc.contributor.authorChattopadhyay, Ishanu
dc.contributor.authorBoeck Jensen, Anders
dc.contributor.authorBlair, David R.
dc.contributor.authorDavis, Lea
dc.contributor.authorRobinson, Peter N.
dc.contributor.authorDahlén, Torsten
dc.contributor.authorBrunak, Søren
dc.contributor.authorBenson, Mikael
dc.contributor.authorEdgren, Gustaf
dc.contributor.authorCox, Nancy J.
dc.contributor.authorGao, Xin
dc.contributor.authorRzhetsky, Andrey
dc.date.accessioned2019-12-11T08:34:46Z
dc.date.available2019-12-11T08:34:46Z
dc.date.issued2019-12-03
dc.identifier.citationJia, 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/s41467-019-13455-0
dc.identifier.doi10.1038/s41467-019-13455-0
dc.identifier.urihttp://hdl.handle.net/10754/660515
dc.description.abstractTypically, 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).
dc.description.sponsorshipWe 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
dc.publisherSpringer Science and Business Media LLC
dc.relation.urlhttp://www.nature.com/articles/s41467-019-13455-0
dc.relation.urlhttps://www.nature.com/articles/s41467-019-13455-0.pdf
dc.rightsThis article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/ licenses/by/4.0/.
dc.rights.urihttps://creativecommons.org/licenses/by/4.0
dc.titleEstimating heritability and genetic correlations from large health datasets in the absence of genetic data
dc.typeArticle
dc.contributor.departmentComputer Science Program
dc.contributor.departmentComputational Bioscience Research Center (CBRC)
dc.contributor.departmentComputer, Electrical and Mathematical Sciences and Engineering (CEMSE) Division
dc.identifier.journalNature Communications
dc.eprint.versionPublisher's Version/PDF
dc.contributor.institutionDepartment of Medicine, Institute of Genomics and Systems Biology, University of Chicago, Chicago, IL, 60637, USA.
dc.contributor.institutionInstitute for Next Generation Healthcare, Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
dc.contributor.institutionDepartment of Pediatrics, University of California San Francisco, San Francisco, CA, 94158, USA.
dc.contributor.institutionDivision of Genetic Medicine, Vanderbilt University, Nashville, TN, 37232, USA.
dc.contributor.institutionJackson Laboratory for Genomic Medicine, Farmington, CT, 06032, USA.
dc.contributor.institutionDepartment of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, 171 77, Sweden.
dc.contributor.institutionNovo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, 1017, Denmark.
dc.contributor.institutionCentre for Individualized Medicine, Department of Pediatrics, Linkoping University, Linkoping, 58183, Sweden.
kaust.personLi, Yu
kaust.personGao, Xin
kaust.grant.numberFCC/1/1976-18-01
kaust.grant.numberFCC/1/1976-23-01
kaust.grant.numberFCC/1/1976-25-01
kaust.grant.numberFCC/1/1976-26-01
refterms.dateFOA2019-12-11T08:35:23Z
kaust.acknowledged.supportUnitSupercomputing Laboratory at KAUST
dc.date.published-online2019-12-03
dc.date.published-print2019-12


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This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/ licenses/by/4.0/.
Except where otherwise noted, this item's license is described as This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The images or other third party material in this article are included in the article’s Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the article’s Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this license, visit http://creativecommons.org/ licenses/by/4.0/.