Year: 2023
Type: dissertation
Abstract: Genomic data have been increasingly used to form recommendations for conservation practice. This thesis investigated two commonly used genomic metrics often used to measure extinction risk in small populations, runs of homozygosity (ROH) to detect signs of recent inbreeding and mutational load to detect the burden of our mutations, in modern and ancient whole genome sequences of Holarctic grey wolves (Canis lupus) and endemic Wallacea ungulates, anoa (Bubalus spp.) and babirusa (Babyrousa spp.). First, I tested a method to detect recent inbreeding and mutational load in published genomes of modern wolves with known inbreeding coefficient estimates. I further used the method to detect recent inbreeding in the ancient grey wolves and found that they were lacking signs of recent inbreeding nor accumulation of deleterious mutations compared to modern grey wolves. I then investigated how a measure of mutational load based on random alleles performs compared to the conventional way and found that the coverage, the exclusion or inclusion of transitions, and the type of conservation scores matters more than how the load was calculated. Afterwards, I assessed inbreeding and mutational load on anoa and babirusa in the Wallacea archipelago and discovered that most of the samples were recently inbred and have high abundance of deleterious mutations compared to closely related species. Interestingly, the most inbred populations experienced strong purifying selection. The ancient genomes of anoa and babirusa from the early 20th century showed that their genome-wide variation then did not differ from present, suggesting that their genome-wide variation may have already been low since they colonised the Wallacea archipelago. Using these results, I discussed how ancient DNA, ROH, and mutational load extended our view about the forces shaping the genome wide variation of small populations and the required steps for better assessment of these metrics in biodiversity conservation.
Author: Aninta, S
Language: English
Subfield: Genetics
Domain: Life Sciences
Open Access status: green
OpenAlex ID: W7000827282