Moment estimators of relatedness from low-depth whole-genome sequencing data

Abstract

Background: Estimating relatedness is an important step for many genetic study designs. A variety of methods for estimating coefficients of pairwise relatedness from genotype data have been proposed. Both the kinship coefficient [Formula: see text] and the fraternity coefficient [Formula: see text] for all pairs of individuals are of interest. However, when dealing with low-depth sequencing or imputation data, individual level genotypes cannot be confidently called. To ignore such uncertainty is known to result in biased estimates. Accordingly, methods have recently been developed to estimate kinship from uncertain genotypes.

Results: We present new method-of-moment estimators of both the coefficients [Formula: see text] and [Formula: see text] calculated directly from genotype likelihoods. We have simulated low-depth genetic data for a sample of individuals with extensive relatedness by using the complex pedigree of the known genetic isolates of Cilento in South Italy. Through this simulation, we explore the behaviour of our estimators, demonstrate their properties, and show advantages over alternative methods. A demonstration of our method is given for a sample of 150 French individuals with down-sampled sequencing data.

Conclusions: We find that our method can provide accurate relatedness estimates whilst holding advantages over existing methods in terms of robustness, independence from external software, and required computation time. The method presented in this paper is referred to as LowKi (Low-depth Kinship) and has been made available in an R package ( https://github.com/genostats/LowKi ).

Keywords: Fraternity coefficient; Genotype likelihoods; Kinship; Low-depth; Moment estimators; Sequencing data.

Fig. 1

a LowKi estimates for kinship and fraternity for CilentoSim. Off-diagonal elements of the estimated kinship and fraternity matrices against the true simulated IBD sharing coefficient in CilentoSim at three different simulated mean read depths (2.5$\times$, 5$\times$, and 10$\times$). The number of variants (M) and the time (T) required for the calculation of both kinship and fraternity matrices are overlaid on the figure (for SEEKIN the time refers only to the calculation of kinship). The mean-squared error (MSE) between the estimators and their respective benchmarks are also given. b–c Corresponding estimates from SEEKIN (kinship only) and NGSRelateV2