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Quantitative analysis of population-scale family trees using millions of relatives

Joanna Kaplanis, Assaf Gordon, Mary Wahl, Michael Gershovits, Barak Markus, Mona Sheikh, Melissa Gymrek, Gaurav Bhatia, Daniel G. MacArthur, Alkes L. Price, View ORCID ProfileYaniv Erlich
doi: https://doi.org/10.1101/106427
Joanna Kaplanis
1New York Genome Center, New York, NY 10013, USA
2Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
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Assaf Gordon
1New York Genome Center, New York, NY 10013, USA
2Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
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Mary Wahl
1New York Genome Center, New York, NY 10013, USA
3Harvard Medical School, Boston, MA 02115, USA
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Michael Gershovits
2Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
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Barak Markus
2Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
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Mona Sheikh
2Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
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Melissa Gymrek
1New York Genome Center, New York, NY 10013, USA
2Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
4Harvard-MIT HST program, Cambridge, MA 02142, USA
5Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
6Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
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Gaurav Bhatia
7Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA
8Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA
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Daniel G. MacArthur
3Harvard Medical School, Boston, MA 02115, USA
5Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts 02114, USA
6Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
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Alkes L. Price
6Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA 02142, USA
7Department of Biostatistics, Harvard School of Public Health, Boston, MA 02115, USA
8Department of Epidemiology, Harvard School of Public Health, Boston, MA 02115, USA
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Yaniv Erlich
1New York Genome Center, New York, NY 10013, USA
2Whitehead Institute for Biomedical Research, Cambridge, MA 02142, USA
9Department of Computer Science, Fu Foundation School of Engineering, Columbia University, New York, NY, USA.
10Center for Computational Biology and Bioinformatics (C2B2), Department of Systems Biology, Columbia University, New York, NY, USA.
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  • ORCID record for Yaniv Erlich
  • For correspondence: yaniv@cs.columbia.edu
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Abstract

Family trees have vast applications in multiple fields from genetics to anthropology and economics. However, the collection of extended family trees is tedious and usually relies on resources with limited geographical scope and complex data usage restrictions. Here, we collected 86 million profiles from publicly-available online data from genealogy enthusiasts. After extensive cleaning and validation, we obtained population-scale family trees, including a single pedigree of 13 million individuals. We leveraged the data to partition the genetic architecture of longevity by inspecting millions of relative pairs and to provide insights to population genetics theories on the dispersion of families. We also report a simple digital procedure to overlay other datasets with our resource in order to empower studies with population-scale genealogical data.

One Sentence Summary Using massive crowd-sourced genealogy data, we created a population-scale family tree resource for scientific studies.

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The copyright holder for this preprint is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under a CC-BY-NC 4.0 International license.
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Posted February 07, 2017.
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Quantitative analysis of population-scale family trees using millions of relatives
Joanna Kaplanis, Assaf Gordon, Mary Wahl, Michael Gershovits, Barak Markus, Mona Sheikh, Melissa Gymrek, Gaurav Bhatia, Daniel G. MacArthur, Alkes L. Price, Yaniv Erlich
bioRxiv 106427; doi: https://doi.org/10.1101/106427
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Quantitative analysis of population-scale family trees using millions of relatives
Joanna Kaplanis, Assaf Gordon, Mary Wahl, Michael Gershovits, Barak Markus, Mona Sheikh, Melissa Gymrek, Gaurav Bhatia, Daniel G. MacArthur, Alkes L. Price, Yaniv Erlich
bioRxiv 106427; doi: https://doi.org/10.1101/106427

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