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Model-free Estimation of Recent Genetic Relatedness

NCJ Number
American Journal of Human Genetics Volume: 98 Issue: 1 Dated: 2016 Pages: 127-148
Date Published
22 pages

This article proposes PC-Relate, a model-free approach for estimating commonly used measures of recent genetic relatedness, such as kinship coefficients and IBD sharing probabilities, in the presence of unspecified structure.


Genealogical inference from genetic data is essential for a variety of applications in human genetics. In genome-wide and sequencing association studies, for example, accurate inference on both recent genetic relatedness, such as family structure, and more distant genetic relatedness, such as population structure, is necessary for protection against spurious associations. Distinguishing familial relatedness from population structure with genotype data, however, is difficult, because both manifest as genetic similarity through the sharing of alleles. Existing approaches for inference on recent genetic relatedness have limitations in the presence of population structure, where they either (1) make strong and simplifying assumptions about population structure, which are often untenable, or (2) require correct specification of and appropriate reference population panels for the ancestries in the sample, which might be unknown or not well defined. PC-Relate, which is featured in the current article, uses principal components calculated from genome-screen data to partition genetic correlations among sampled individuals due to the sharing of recent ancestors and more distant common ancestry into two separate components, without requiring specification of the ancestral populations or reference population panels. In simulation studies with population structure, including admixture, this project demonstrated that PC-Relate provides accurate estimates of genetic relatedness and improved relationship classification over widely used approaches. This study further demonstrates the utility of PC-Relate in applications to three ancestrally diverse samples that vary in both size and genealogical complexity. (publisher abstract modified)

Date Published: January 1, 2016