Estimating variance components in population scale family trees
The rapid digitization of genealogical and medical records enables the assembly of extremely large pedigree records spanning millions of individuals and trillions of pairs of relatives. Such pedigrees provide the opportunity to investigate the sociological and epidemiological history of human popula...
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Published in | PLoS genetics Vol. 15; no. 5; p. e1008124 |
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Main Authors | , , , , |
Format | Journal Article |
Language | English |
Published |
United States
Public Library of Science
09.05.2019
Public Library of Science (PLoS) |
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Abstract | The rapid digitization of genealogical and medical records enables the assembly of extremely large pedigree records spanning millions of individuals and trillions of pairs of relatives. Such pedigrees provide the opportunity to investigate the sociological and epidemiological history of human populations in scales much larger than previously possible. Linear mixed models (LMMs) are routinely used to analyze extremely large animal and plant pedigrees for the purposes of selective breeding. However, LMMs have not been previously applied to analyze population-scale human family trees. Here, we present Sparse Cholesky factorIzation LMM (Sci-LMM), a modeling framework for studying population-scale family trees that combines techniques from the animal and plant breeding literature and from human genetics literature. The proposed framework can construct a matrix of relationships between trillions of pairs of individuals and fit the corresponding LMM in several hours. We demonstrate the capabilities of Sci-LMM via simulation studies and by estimating the heritability of longevity and of reproductive fitness (quantified via number of children) in a large pedigree spanning millions of individuals and over five centuries of human history. Sci-LMM provides a unified framework for investigating the epidemiological history of human populations via genealogical records. |
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AbstractList | The rapid digitization of genealogical and medical records enables the assembly of extremely large pedigree records spanning millions of individuals and trillions of pairs of relatives. Such pedigrees provide the opportunity to investigate the sociological and epidemiological history of human populations in scales much larger than previously possible. Linear mixed models (LMMs) are routinely used to analyze extremely large animal and plant pedigrees for the purposes of selective breeding. However, LMMs have not been previously applied to analyze population-scale human family trees. Here, we present Sparse Cholesky factorIzation LMM (Sci-LMM), a modeling framework for studying population-scale family trees that combines techniques from the animal and plant breeding literature and from human genetics literature. The proposed framework can construct a matrix of relationships between trillions of pairs of individuals and fit the corresponding LMM in several hours. We demonstrate the capabilities of Sci-LMM via simulation studies and by estimating the heritability of longevity and of reproductive fitness (quantified via number of children) in a large pedigree spanning millions of individuals and over five centuries of human history. Sci-LMM provides a unified framework for investigating the epidemiological history of human populations via genealogical records. The rapid digitization of genealogical and medical records enables the assembly of extremely large pedigree records spanning millions of individuals and trillions of pairs of relatives. Such pedigrees provide the opportunity to investigate the sociological and epidemiological history of human populations in scales much larger than previously possible. Linear mixed models (LMMs) are routinely used to analyze extremely large animal and plant pedigrees for the purposes of selective breeding. However, LMMs have not been previously applied to analyze population-scale human family trees. Here, we present S parse C holesky factor I zation LMM (Sci-LMM), a modeling framework for studying population-scale family trees that combines techniques from the animal and plant breeding literature and from human genetics literature. The proposed framework can construct a matrix of relationships between trillions of pairs of individuals and fit the corresponding LMM in several hours. We demonstrate the capabilities of Sci-LMM via simulation studies and by estimating the heritability of longevity and of reproductive fitness (quantified via number of children) in a large pedigree spanning millions of individuals and over five centuries of human history. Sci-LMM provides a unified framework for investigating the epidemiological history of human populations via genealogical records. The advent of online genealogy services allows the assembly of population-scale family trees, spanning millions of individuals and centuries of human history. Such datasets enable answering genetic epidemiology questions on unprecedented scales. Here we present Sci-LMM, a pedigree analysis framework that combines techniques from animal and plant breeding research and from human genetics research for large-scale pedigree analysis. We apply Sci-LMM to analyze population-scale human genealogical records, spanning trillions of relationships. We have made both Sci-LMM and an anonymized dataset of millions of individuals freely available to download, making the analysis of population-scale human family trees widely accessible to the research community. Together, these resources allow researchers to investigate genetic and epidemiological questions on an unprecedented scale. |
Audience | Academic |
Author | Shor, Tal Weissbrod, Omer Erlich, Yaniv Kalka, Iris Geiger, Dan |
AuthorAffiliation | 4 Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel 3 Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel 5 The New York Genome Center, New York, NY, United States of America 7 Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America 2 MyHeritage Ltd., Or Yehuda, Israel The University of Queensland, AUSTRALIA 1 Computer Science Department, Technion—Israel Institute of Technology, Haifa, Israel 6 Department of Computer Science, Fu School of Engineering, Columbia University, NY, United States of America |
AuthorAffiliation_xml | – name: 5 The New York Genome Center, New York, NY, United States of America – name: 7 Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America – name: 6 Department of Computer Science, Fu School of Engineering, Columbia University, NY, United States of America – name: The University of Queensland, AUSTRALIA – name: 1 Computer Science Department, Technion—Israel Institute of Technology, Haifa, Israel – name: 3 Department of Computer Science and Applied Mathematics, Weizmann Institute of Science, Rehovot, Israel – name: 2 MyHeritage Ltd., Or Yehuda, Israel – name: 4 Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel |
Author_xml | – sequence: 1 givenname: Tal orcidid: 0000-0003-1432-7461 surname: Shor fullname: Shor, Tal organization: MyHeritage Ltd., Or Yehuda, Israel – sequence: 2 givenname: Iris surname: Kalka fullname: Kalka, Iris organization: Department of Molecular Cell Biology, Weizmann Institute of Science, Rehovot, Israel – sequence: 3 givenname: Dan surname: Geiger fullname: Geiger, Dan organization: Computer Science Department, Technion-Israel Institute of Technology, Haifa, Israel – sequence: 4 givenname: Yaniv surname: Erlich fullname: Erlich, Yaniv organization: Department of Computer Science, Fu School of Engineering, Columbia University, NY, United States of America – sequence: 5 givenname: Omer orcidid: 0000-0001-9860-0626 surname: Weissbrod fullname: Weissbrod, Omer organization: Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA, United States of America |
BackLink | https://www.ncbi.nlm.nih.gov/pubmed/31071088$$D View this record in MEDLINE/PubMed |
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SubjectTerms | Animals Biology and Life Sciences Breeding of animals Computer science Computer Simulation Decomposition Digitization Epidemiology Family Female Genealogy Genealogy and Heraldry Genetic engineering Genetic Fitness Genetics, Population Heritability Humans Linear Models Longevity - genetics Male Medical records Medical research Medicine and Health Sciences Methods Models, Genetic Pedigree Physical Sciences Plant breeding Plants - genetics Population Quantitative genetics Reproductive fitness Research and Analysis Methods Social Sciences Software Sparsity Trees |
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Title | Estimating variance components in population scale family trees |
URI | https://www.ncbi.nlm.nih.gov/pubmed/31071088 https://www.proquest.com/docview/2251078571 https://search.proquest.com/docview/2231937477 https://pubmed.ncbi.nlm.nih.gov/PMC6529016 https://doaj.org/article/bfcced51bd3e40c0bd126f054c350b2f http://dx.doi.org/10.1371/journal.pgen.1008124 |
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