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      CONGA: Copy number variation genotyping in ancient genomes and low-coverage sequencing data

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      Author(s)
      Söylev, Arda
      Çokoglu, Sevim Seda
      Koptekin, Dilek
      Alkan, Can
      Somel, Mehmet
      Date
      2022-12-14
      Source Title
      PLoS Computational Biology
      Print ISSN
      1553734X
      Publisher
      Public Library of Science
      Volume
      18
      Issue
      12
      Pages
      1 - 32
      Language
      English
      Type
      Article
      Item Usage Stats
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      Abstract
      To date, ancient genome analyses have been largely confined to the study of single nucleotide polymorphisms (SNPs). Copy number variants (CNVs) are a major contributor of disease and of evolutionary adaptation, but identifying CNVs in ancient shotgun-sequenced genomes is hampered by typical low genome coverage (<1×) and short fragments ([removed]1 kbps with F-scores >0.75 at ≥1×, and distinguish between heterozygous and homozygous states. We used CONGA to genotype 10,002 outgroup-ascertained deletions across a heterogenous set of 71 ancient human genomes spanning the last 50,000 years, produced using variable experimental protocols. A fraction of these (21/71) display divergent deletion profiles unrelated to their population origin, but attributable to technical factors such as coverage and read length. The majority of the sample (50/71), despite originating from nine different laboratories and having coverages ranging from 0.44×-26× (median 4×) and average read lengths 52-121 bps (median 69), exhibit coherent deletion frequencies. Across these 50 genomes, inter-individual genetic diversity measured using SNPs and CONGA-genotyped deletions are highly correlated. CONGA-genotyped deletions also display purifying selection signatures, as expected. CONGA thus paves the way for systematic CNV analyses in ancient genomes, despite the technical challenges posed by low and variable genome coverage. © 2022 Söylev et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.
      Keywords
      DNA Copy Number Variations
      Genetics, Population
      Genome, Human
      Genomics
      Genotype
      Humans
      Polymorphism, Single Nucleotide
      Permalink
      http://hdl.handle.net/11693/111995
      Published Version (Please cite this version)
      https://dx.doi.org/10.1371/journal.pcbi.1010788
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