Are shed hair genomes the most effective noninvasive resource for estimating relationships in the wild?

Abstract

Knowledge of relationships in wild populations is critical for better understanding mating systems and inbreeding scenarios to inform conservation strategies for endangered species. To delineate pedigrees in wild populations, study genetic connectivity, study genotype‐phenotype associations, trace individuals, or track wildlife trade, many identified individuals need to be genotyped at thousands of loci, mostly from noninvasive samples. This requires us to (a) identify the most common noninvasive sample available from identified individuals, (b) assess the ability to acquire genome‐wide data from such samples, and (c) evaluate the quality of such genome‐wide data, and its ability to reconstruct relationships between animals within a population. We followed identified individuals from a wild endangered tiger population and found that shed hair samples were the most common compared to scat samples, opportunistically found carcasses, and opportunistic invasive samples. We extracted DNA from these samples, prepared whole genome sequencing libraries, and sequenced genomes from these. Whole genome sequencing methods resulted in between 25%–98% of the genome sequenced for five such samples. Exploratory population genetic analyses revealed that these data were free of holistic biases and could recover expected population structure and relatedness. Mitochondrial genomes recovered matrilineages in accordance with long‐term monitoring data. Even with just five samples, we were able to uncover the matrilineage for three individuals with unknown ancestry. In summary, we demonstrated that noninvasive shed hair samples yield adequate quality and quantity of DNA in conjunction with sensitive library preparation methods, and provide reliable data from hundreds of thousands of SNPs across the genome. This makes shed hair an ideal noninvasive resource for studying individual‐based genetics of elusive endangered species in the wild.

Recent Evolutionary History of Tigers Highlights Contrasting Roles of Genetic Drift and Selection

“…It will be critical for future conservation efforts to prioritize population recovery and gene flow through connectivity and to promote population size increases. Critical to such action is a better understanding of within population genetic variation using spatially explicit, non-invasive sampling (e.g., Khan et al. 2020 ).…”

Genomic evidence for inbreeding depression and purging of deleterious genetic variation in Indian tigers

“…Such dense sampling of genomes can benefit from sequencing of non-invasive samples since acquiring blood samples from endangered species can be difficult. Methods involving sequencing genomes from shed hair (Khan et al 2020) or fecal samples (Chiou & Bergey 2018) will be useful in creating such translocation networks and monitoring the populations for gene flow.…”

“…We used the reads aligned to the mitochondrial scaffold of BenTig1.0. We called consensus fasta files for each individual from this as described in Khan et al (2020). These multiple consensus fasta files were aligned using clustal omega (https://www.ebi.ac.uk/Tools/msa/clustalo/) and written to nexus output files.…”

“…We used the reads aligned to the mitochondrial scaffold of BenTig1.0. We called consensus fasta files for each individual from this as described in Khan et al (2020).…”

Sample identification and pedigree reconstruction in Wolverine (Gulo gulo) using SNP genotyping of non-invasive samples

“…The main limitations for non-invasively collected genetic samples are that they often contain low-quality and quantity of DNA, limiting the use of high-throughput genomic analysis methods (Carroll et al 2018; but see Khan et al 2020), and high cost of sequencing large number of samples (Aylward et al 2018;Förster et al 2018;Snyder-Mackler et al 2016). Consequently, many genetic studies have been restricted to more traditional low-throughput genetic markers such as microsatellites (SSRs), hampering the uptake of genomics applications in practical conservation projects, and creating the so called "conservation genomics gap" (Shafer et al 2015).…”