%0 Journal Article
%A Rohrlach, Adam B.
%A Tuke, Jonathan
%A Popli, Divya Ratan
%A Haak, Wolfgang
%+ Department of Archaeogenetics, Max Planck Institute for Evolutionary Anthropology, Max Planck Society
Department of Archaeogenetics, Max Planck Institute for Evolutionary Anthropology, Max Planck Society
Department of Archaeogenetics, Max Planck Institute for Evolutionary Anthropology, Max Planck Society
%T BREADR: An R Package for the Bayesian Estimation of Genetic Relatedness from Low-coverage Genotype Data : 
%G eng
%U https://hdl.handle.net/21.11116/0000-0011-66CD-4
%R 10.21105/joss.07916
%7 2025
%D 2025
%* Review method: peer-reviewed
%X Robust and reliable estimates of how individuals are biologically related to each other are a key source of information when reconstructing pedigrees. In combination with contextual data, reconstructed pedigrees can be used to infer possible kinship practices in prehistoric populations. However, standard methods to estimate biological relatedness from genome sequence data cannot be applied to low coverage sequence data, such as are common in ancient DNA (aDNA) studies. Critically, a statistically robust method for assessing and quantifying the confidence of a classification of a specific degree of relatedness for a pair of individuals, using low coverage genome data, is lacking.<br>In this paper we present the R-package BREADR (Biological RElatedness from Ancient DNA in R), which leverages the so-called pairwise mismatch rate, calculated on optimally-filtered genome-wide pseudo-haploid sequence data, to estimate genetic relatedness up to the second degree, assuming an underlying binomial distribution. BREADR also returns a posterior probability for each degree of relatedness, from identical twins/same individual, first-degree, second-degree or “unrelated” pairs, allowing researchers to quantify and report the uncertainty, even for very low-coverage data. We show that this method accurately recovers degrees of relatedness for<br>sequence data with coverage as low as 0.04X using simulated data (produced as in Popli et<br>al.(Popli et al., 2023)).
%J Journal of Open Source Software 
%O JOSS
%V 10
%N 108
%] 7916
%@ 2475-9066