Allele frequency-free inference of close familial relationships from genotypes or low-depth sequencing data

TY - JOUR

T1 - Allele frequency-free inference of close familial relationships from genotypes or low-depth sequencing data

AU - Waples, Ryan K.

AU - Albrechtsen, Anders

AU - Moltke, Ida

PY - 2019

Y1 - 2019

N2 - Knowledge of how individuals are related is important in many areas of research, and numerous methods for inferring pairwise relatedness from genetic data have been developed. However, the majority of these methods were not developed for situations where data are limited. Specifically, most methods rely on the availability of population allele frequencies, the relative genomic position of variants and accurate genotype data. But in studies of non-model organisms or ancient samples, such data are not always available. Motivated by this, we present a new method for pairwise relatedness inference, which requires neither allele frequency information nor information on genomic position. Furthermore, it can be applied not only to accurate genotype data but also to low-depth sequencing data from which genotypes cannot be accurately called. We evaluate it using data from a range of human populations and show that it can be used to infer close familial relationships with a similar accuracy as a widely used method that relies on population allele frequencies. Additionally, we show that our method is robust to SNP ascertainment and applicable to low-depth sequencing data generated using different strategies, including resequencing and RADseq, which is important for application to a diverse range of populations and species.

AB - Knowledge of how individuals are related is important in many areas of research, and numerous methods for inferring pairwise relatedness from genetic data have been developed. However, the majority of these methods were not developed for situations where data are limited. Specifically, most methods rely on the availability of population allele frequencies, the relative genomic position of variants and accurate genotype data. But in studies of non-model organisms or ancient samples, such data are not always available. Motivated by this, we present a new method for pairwise relatedness inference, which requires neither allele frequency information nor information on genomic position. Furthermore, it can be applied not only to accurate genotype data but also to low-depth sequencing data from which genotypes cannot be accurately called. We evaluate it using data from a range of human populations and show that it can be used to infer close familial relationships with a similar accuracy as a widely used method that relies on population allele frequencies. Additionally, we show that our method is robust to SNP ascertainment and applicable to low-depth sequencing data generated using different strategies, including resequencing and RADseq, which is important for application to a diverse range of populations and species.

KW - ascertainment bias

KW - IBD

KW - identity by descent

KW - low-depth

KW - NGS

KW - non-model

KW - relatedness

U2 - 10.1111/mec.14954

DO - 10.1111/mec.14954

M3 - Journal article

C2 - 30462358

AN - SCOPUS:85060388563

SN - 0962-1083

VL - 28

SP - 35

EP - 48

JO - Molecular Ecology

JF - Molecular Ecology

IS - 1

ER -