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PADRE: Pedigree-Aware Distant-Relationship Estimation.

PADRE: Pedigree-Aware Distant-Relationship Estimation.

Authors :
Staples J
Witherspoon DJ
Jorde LB
Nickerson DA
Below JE
Huff CD
Source :
American journal of human genetics [Am J Hum Genet] 2016 Jul 07; Vol. 99 (1), pp. 154-62. Date of Electronic Publication: 2016 Jun 30.
Publication Year :
2016

Abstract

Accurate estimation of shared ancestry is an important component of many genetic studies; current prediction tools accurately estimate pairwise genetic relationships up to the ninth degree. Pedigree-aware distant-relationship estimation (PADRE) combines relationship likelihoods generated by estimation of recent shared ancestry (ERSA) with likelihoods from family networks reconstructed by pedigree reconstruction and identification of a maximum unrelated set (PRIMUS), improving the power to detect distant relationships between pedigrees. Using PADRE, we estimated relationships from simulated pedigrees and three extended pedigrees, correctly predicting 20% more fourth- through ninth-degree simulated relationships than when using ERSA alone. By leveraging pedigree information, PADRE can even identify genealogical relationships between individuals who are genetically unrelated. For example, although 95% of 13(th)-degree relatives are genetically unrelated, in simulations, PADRE correctly predicted 50% of 13(th)-degree relationships to within one degree of relatedness. The improvement in prediction accuracy was consistent between simulated and actual pedigrees. We also applied PADRE to the HapMap3 CEU samples and report new cryptic relationships and validation of previously described relationships between families. PADRE greatly expands the range of relationships that can be estimated by using genetic data in pedigrees.<br /> (Copyright © 2016 American Society of Human Genetics. Published by Elsevier Inc. All rights reserved.)

Details

Language :
English
ISSN :
1537-6605
Volume :
99
Issue :
1
Database :
MEDLINE
Journal :
American journal of human genetics
Publication Type :
Academic Journal
Accession number :
27374771
Full Text :
https://doi.org/10.1016/j.ajhg.2016.05.020