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 inPLoS genetics Vol. 15; no. 5; p. e1008124
Main Authors Shor, Tal, Kalka, Iris, Geiger, Dan, Erlich, Yaniv, Weissbrod, Omer
Format Journal Article
LanguageEnglish
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.
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
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– 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
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I have read the journal's policy and the authors of this manuscript have the following competing interests: TS and YE are employees of MyHeritage Ltd.
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Snippet The rapid digitization of genealogical and medical records enables the assembly of extremely large pedigree records spanning millions of individuals and...
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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
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https://doaj.org/article/bfcced51bd3e40c0bd126f054c350b2f
http://dx.doi.org/10.1371/journal.pgen.1008124
Volume 15
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