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Inferring Identical-by-Descent Sharing of Sample Ancestors Promotes High-Resolution Relative Detection.

Monica D Ramstetter1, Sushila A Shenoy2, Thomas D Dyer3

  • 1Department of Biological Statistics and Computational Biology, Cornell University, Ithaca, NY 14853, USA.

American Journal of Human Genetics
|June 26, 2018
PubMed
Summary
This summary is machine-generated.

DRUID infers deep genetic relatedness by analyzing identity by descent (IBD) sharing profiles of ungenotyped ancestors. This method accurately identifies relatives, including siblings and aunts/uncles, improving upon existing tools for large genetic datasets.

Keywords:
cryptic relatednessidentical by descentpedigree reconstructionrelationship inference

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Area of Science:

  • Genetics
  • Bioinformatics
  • Computational Biology

Background:

  • Increasing genetic dataset sizes lead to more samples with close relatives.
  • Identifying deep genetic relationships is challenging with current methods.

Purpose of the Study:

  • Introduce DRUID (Deep Relatedness Utilizing Identity by Descent) for inferring ungenotyped ancestor IBD profiles.
  • Improve the accuracy and depth of relatedness inference in large genetic datasets.

Main Methods:

  • DRUID infers the identity by descent (IBD) sharing profile of an ungenotyped ancestor.
  • It combines information from multiple related samples to bridge generational gaps.
  • The method identifies full siblings and aunts/uncles with high accuracy.

Main Results:

  • DRUID recovers 92.2% of real aunts/uncles with zero false positives.
  • It infers 10.5%-31.3% more relatives than PADRE in various sibling and aunt/uncle datasets.
  • DRUID correctly infers relationships or within one degree for 79.6%-96.7% of tenth-degree relatives.

Conclusions:

  • DRUID significantly enhances the inference of deep genetic relatedness.
  • The method is effective in large genetic datasets with complex family structures.
  • DRUID offers a more accurate and comprehensive approach to reconstructing family trees.