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Accurate and Robust Prediction of Genetic Relationship from Whole-Genome Sequences

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  • Hong Li
  • Gustavo Glusman
  • Chad Huff
  • Juan Caballero
  • Jared C Roach

Abstract

Computing the genetic relationship between two humans is important to studies in genetics, genomics, genealogy, and forensics. Relationship algorithms may be sensitive to noise, such as that arising from sequencing errors or imperfect reference genomes. We developed an algorithm for estimation of genetic relationship by averaged blocks (GRAB) that is designed for whole-genome sequencing (WGS) data. GRAB segments the genome into blocks, calculates the fraction of blocks sharing identity, and then uses a classification tree to infer 1st- to 5th- degree relationships and unrelated individuals. We evaluated GRAB on simulated and real sequenced families, and compared it with other software. GRAB achieves similar performance, and does not require knowledge of population background or phasing. GRAB can be used in workflows for identifying unreported relationships, validating reported relationships in family-based studies, and detection of sample-tracking errors or duplicate inclusion. The software is available at familygenomics.systemsbiology.net/grab.

Suggested Citation

  • Hong Li & Gustavo Glusman & Chad Huff & Juan Caballero & Jared C Roach, 2014. "Accurate and Robust Prediction of Genetic Relationship from Whole-Genome Sequences," PLOS ONE, Public Library of Science, vol. 9(2), pages 1-6, February.
    • Handle: RePEc:plo:pone00:0085437
    DOI: 10.1371/journal.pone.0085437
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    Cited by:

    1. Yumi Jin & Alejandro A Schäffer & Stephen T Sherry & Michael Feolo, 2017. "Quickly identifying identical and closely related subjects in large databases using genotype data," PLOS ONE, Public Library of Science, vol. 12(6), pages 1-28, June.

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