Determination of genetic relatedness from low‐coverage human genome sequences using pedigree simulations

Abstract

We develop and evaluate methods for inferring relatedness among individuals from low-coverage DNA sequences of their genomes, with particular emphasis on sequences obtained from fossil remains. We suggest the major factors complicating the determination of relatedness among ancient individuals are sequencing depth, the number of overlapping sites, the sequencing error rate and the presence of contamination from present-day genetic sources. We develop a theoretical model that facilitates the exploration of these factors and their relative effects, via measurement of pairwise genetic distances, without calling genotypes, and determine the power to infer relatedness under various scenarios of varying sequencing depth, present-day contamination and sequencing error. The model is validated by a simulation study as well as the analysis of aligned sequences from present-day human genomes. We then apply the method to the recently published genome sequences of ancient Europeans, developing a statistical treatment to determine confidence in assigned relatedness that is, in some cases, more precise than previously reported. As the majority of ancient specimens are from animals, this method would be applicable to investigate kinship in nonhuman remains. The developed software grups (Genetic Relatedness Using Pedigree Simulations) is implemented in Python and freely available.

Estimating genetic kin relationships in prehistoric populations

“…Before classifying the degree of relationship of a pair of individuals, we need to normalize P 0 using the expected value for a randomly chosen pair of unrelated individuals from the same population in order to make the classification independent of within population diversity, SNP ascertainment and marker density. In most applications, that expected value is difficult to infer which is why several proxies can be used: a pair of unrelated individuals from the same population (similar to [ 46 , 47 ]), a pair of individuals from a different population with similar expected diversity, or the median of all average pairwise P 0 across all individuals which should correspond to a pair of unrelated individuals if the sample size is sufficient. The latter setting is the default option for READ and we are using it in all major simulations as well as the empirical data analysis of this study.…”

Estimation of kinship coefficient in structured and admixed populations using sparse sequencing data

“…The shallow whole-genome sequencing design is widely used in large population-based studies, in which individual genotypes might be inaccurate but the statistical power for association tests is optimized as the sample size increases [ 26 – 28 ]. Additionally, due to sample quality, shallow sequencing data are typical from studies of wild animals, forensics, and ancient human DNA [ 29 – 31 ]. Target sequencing is another widely used design in human genetic studies by focusing on candidate loci of interests or the whole exome [ 32 – 37 ].…”

Linkage disequilibrium connects genetic records of relatives typed with disjoint genomic marker sets

“…This study contributes to a growing body of work on inference of genetic relationships in scenarios more challenging than when relatives are typed for the same markers [22][23][24][25][26]. In the setting of ancient DNA, Vohr et al [22] focused on the scenario in which DNA sequence is generated for different DNA samples possibly representing the same or related individuals-from a burial site, for example-but sequence is sufficiently sparse that reads do not necessarily overlap between samples.…”