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Dating genomic variants and shared ancestry in population-scale sequencing data

View ORCID ProfilePatrick K. Albers, View ORCID ProfileGil McVean
doi: https://doi.org/10.1101/416610
Patrick K. Albers
1Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Old Road Campus, Oxford OX3 7LF, United Kingdom
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  • For correspondence: patrick.albers@bdi.ox.ac.uk
Gil McVean
1Big Data Institute, Li Ka Shing Centre for Health Information and Discovery, University of Oxford, Old Road Campus, Oxford OX3 7LF, United Kingdom
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Abstract

The origin and fate of new mutations within species is the fundamental process underlying evolution. However, while previous efforts have been focused on characterizing the presence, frequency, and phenotypic impact of genetic variation, the evolutionary histories of most variants are largely unexplored. We have developed a non-parametric approach for estimating the date of origin of genetic variants that can be applied to large-scale genomic variation data sets. We demonstrate the accuracy and robustness of the approach through simulation and apply it to over 16 million single nucleotide poly-morphisms (SNPs) from two publicly available human genomic diversity resources. We characterize the differential relationship between variant frequency and age in different geographical regions and demonstrate the value of allele age in interpreting variants of known functional and selective importance. Finally, we use allele age estimates to power a rapid approach for inferring the genealogical history of a single genome or a group of individuals.

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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-ND 4.0 International license.
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Posted September 13, 2018.
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Dating genomic variants and shared ancestry in population-scale sequencing data
Patrick K. Albers, Gil McVean
bioRxiv 416610; doi: https://doi.org/10.1101/416610
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Dating genomic variants and shared ancestry in population-scale sequencing data
Patrick K. Albers, Gil McVean
bioRxiv 416610; doi: https://doi.org/10.1101/416610

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