Abstract
Motivation: Pairwise relatedness estimation is important in many contexts such as disease mapping and population genetics. However, all existing estimation methods are based on called genotypes, which is not ideal for next-generation sequencing (NGS) data of low depth from which genotypes cannot be called with high certainty.Results: We present a software tool, NgsRelate, for estimating pairwise relatedness from NGS data. It provides maximum likelihood estimates that are based on genotype likelihoods instead of genotypes and thereby takes the inherent uncertainty of the genotypes into account. Using both simulated and real data, we show that NgsRelate provides markedly better estimates for low-depth NGS data than two state-of-the-art genotype-based methods.Availability: NgsRelate is implemented in C++ and is available under the GNU license at www.popgen.dk/software.Contact: ida@binf.ku.dkSupplementary information: Supplementary data are available at Bioinformatics online.
Highlights
Motivation: Pairwise relatedness estimation is important in many contexts such as disease mapping and population genetics
All existing estimation methods are based on called genotypes, which is not ideal for next-generation sequencing (NGS) data of low depth from which genotypes cannot be called with high certainty
Estimation of how related two individuals are from genetic data plays a key role in several research areas, including medical genetics and population genetics
Summary
Estimation of how related two individuals are from genetic data plays a key role in several research areas, including medical genetics and population genetics. All existing estimation methods are based on called genotypes, which is not ideal for next-generation sequencing (NGS) data of low depth from which genotypes cannot be called with high certainty. Results: We present a software tool, NgsRelate, for estimating pairwise relatedness from NGS data.
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