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Determination of genetic relatedness from low‐coverage human genome sequences using pedigree simulations
Author(s) -
Martin Michael D.,
Jay Flora,
Castellano Sergi,
Slatkin Montgomery
Publication year - 2017
Publication title -
molecular ecology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 2.619
H-Index - 225
eISSN - 1365-294X
pISSN - 0962-1083
DOI - 10.1111/mec.14188
Subject(s) - biology , genome , pairwise comparison , dna sequencing , python (programming language) , 1000 genomes project , evolutionary biology , genetics , ancient dna , computational biology , genotype , computer science , population , dna , artificial intelligence , gene , single nucleotide polymorphism , demography , sociology , operating system
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.