Pedigree Aware Distant Relationship Estimation (PADRE)

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Pedigree Aware Distant Relationship Estimation (PADRE)

Jeffrey Staples, David J. Witherspoon, Lynn B. Jorde, Deborah A. Nickerson, the University of Washington Center for Mendelian Genomics, Jennifer E. Below, Chad D. Huff: PADRE: Pedigree-Aware Distant-Relationship Estimation. In: The American Journal of Human Genetics, vol. 99, pp. 1-9, 2016.

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 13th-degree relatives are genetically unrelated, in simulations, PADRE correctly predicted 50% of 13th-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.

Licensing and Download

PADRE will be available for download with a free academic license soon.  In the meantime, if you are interested in the software, please email .

Software implementing the PADRE algorithm is now available. Register to download the most recent version (1.0) here.