2025/12/03 by Rebecca S. Taylor, Micheline Manseau, Paul J. Wilson · 1 voice · 2 citations
Biochemistry, Genetics and Molecular Biology · #Effective population size #Estimation #Genetic and phenotypic traits in livestock #Genetic diversity and population structure #Genome #Genomics and Phylogenetic Studies #Inbreeding #Inference #Loss of heterozygosity #Population #Runs of Homozygosity #Whole genome sequencing
paper · doi:10.1111/1755-0998.70083
published in Molecular Ecology Resources 26(1), e70083 (Wiley)
openalex created_date 2025/12/03 · openalex publication_date 2025/12/03 · openalex updated_date 2026/07/28
Runs of homozygosity (ROH) are increasingly being analysed using whole genome sequences in non-model species as a measure of inbreeding and to assess demographic history, thus providing useful information for conservation. However, most studies have used Plink for ROH inference which performs poorly when sequencing depth is below 10×, often underestimating ROH. This can lead to erroneous status assessment and poor management decisions. We assessed the performance of ROHan, a program developed for ROH and heterozygosity estimation using lower coverage sequences that have so far only been optimised for human data. Using high coverage whole genomes from 22 caribou, a non-model species at risk presenting varying levels of inbreeding, we assessed the effects of sequencing depth (1-15×), the input parameter 'rohmu' that determines the heterozygosity rate that is tolerated within ROH regions, and demographic history on the ROH inference and heterozygosity. Accurate estimation of the percentage of the genome and lengths of ROH could be achieved at depths as low as 3-5×. However, the rohmu parameter and individual demographic history had a significant effect on the results. Heterozygosity was also overestimated at low depth. Using our optimised rohmu parameter, we re-analysed low coverage sequences from a small and isolated caribou population and demonstrated high inbreeding levels that had previously been missed. We provide recommendations for optimisation of the rohmu parameter and demonstrate the need for careful interpretation of outputs to enable robust ROH inference using low coverage whole genome sequences in wildlife species.