biostudies-literature00530Conomos MPNIDDK NIH HHSNIA NIH HHSNHLBI NIH HHSNCI NIH HHSNIGMS NIH HHSWHI NIH HHS127-48https://www.ebi.ac.uk/biostudies/studies/S-EPMC4716688biostudies-literatureUnknown98(1)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. Here, we propose 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. PC-Relate 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, we demonstrate that PC-Relate provides accurate estimates of genetic relatedness and improved relationship classification over widely used approaches. We further demonstrate the utility of PC-Relate in applications to three ancestrally diverse samples that vary in both size and genealogical complexity.American journal of human geneticsModel-free Estimation of Recent Genetic Relatedness.PMC4716688P01 GM 099568HHSN268201100003CT32 GM081062HHSN268201100001IT32 GM 81062HHSN268201100001CHHSN268201300005CU01 DK085584R01 GM 075091P01 GM099568U01 DK085524U01 DK085501U01 DK085545R01 GM075091U01 DK085526HHSN268201100004CGM 075091N02HL64278K01 CA148958HHSN268201100002IHHSN271201100004CHHSN268201100004IR01 GM031575HHSN268201100002CHHSN268201100046CThornton TAReiner APConomos MPWeir BS53falseModel-free Estimation of Recent Genetic Relatedness.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. Here, we propose 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. PC-Relate 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, we demonstrate that PC-Relate provides accurate estimates of genetic relatedness and improved relationship classification over widely used approaches. We further demonstrate the utility of PC-Relate in applications to three ancestrally diverse samples that vary in both size and genealogical complexity.2016-01-01T00:00:00Z2016 Jan2021-03-06T08:26:11Z2019-03-27T02:07:04ZS-EPMC47166882674851610.1016/j.ajhg.2015.11.022