Genetic Estimates of Relatedness: Established Practices and New Opportunities Through Low Coverage Whole-Genome Sequencing.

Identifying close relatives in wild animal populations is fundamental across many research fields. Genetic estimates of relatedness have expanded rapidly in recent decades, drawing on a range of genetic data types. Here, we review their use and outline opportunities for future studies by combining two complementary approaches. First, we conducted a systematic literature review, assessing 2861 articles in depth to identify how genetic relatedness has been estimated over time. Second, we compare widely used genetic data types for inferring relatedness, conducting computational experiments using data from a rhesus macaque (Macaca mulatta) population in Puerto Rico. We compared other methods against precise identity-by-descent segment-based estimates of relatedness. Our results show that most studies of relatedness (87.8%) continue to rely on short tandem repeat (STR) markers, despite their limited precision. Single-nucleotide polymorphism (SNP)-marker-based relatedness estimates remain underused (8% of studies), even though they yield more reliable estimates when sampled in sufficient numbers. Finally, we find that the simple pairwise-mismatch rate (PMR) method for estimating relatedness in whole-genome sequencing (WGS) data (commonly used in human ancient DNA studies) performs robustly on low-coverage data, for example, DNA retrieved from faecal samples or from cost-effective low-coverage WGS (lcWGS). Together, our findings highlight that lcWGS, combined with PMR-based relatedness estimation, is a promising, cost-effective alternative when DNA quality is limited, genomic resources are scarce, or economic efficiency is essential.