Abstract
For numerically small breeds, obtaining a sufficiently large breed‐specific reference population for genomic prediction is challenging or simply not possible, but may be overcome by adding individuals from another breed. To prioritize among available breeds, the effective number of chromosome segments (M e) can be used as an indicator of relatedness between individuals from different breeds. The M e is also an important parameter in determining the accuracy of genomic prediction. The M e can be estimated both within a population and between two populations or breeds, as the reciprocal of the variance of genomic relationships. However, the threshold for number of individuals needed to accurately estimate within or between populations M e is currently unknown. It is also unknown if a discrepancy in number of genotyped individuals in two breeds affects the estimates of M e between populations. In this study, we conducted a simulation that mimics current domestic cattle populations in order to investigate how estimated M e is affected by number of genotyped individuals, single‐nucleotide polymorphism (SNP) density and pedigree availability. Our results show that a small sample of 10 genotyped individuals may result in substantial over or underestimation of M e. While estimates of within population M e were hardly affected by SNP density, between population M e values were highly dependent on the number of available SNPs, with higher SNP densities being able to detect more independent chromosome segments. When subtracting pedigree from genomic relationships before computing M e, estimates of within population M e were three to four times higher than estimates with genotypes only; however, between M e estimates remained the same. For accurate estimation of within and between population M e, at least 50 individuals should be genotyped per population. Estimates of within M e were highly affected by whether pedigree was used or not. For within M e, even the smallest SNP density (~11k) resulted in accurate representation of family relationships in the population; however, for between M e, many more markers are needed to capture all independent segments.
Highlights
Small breeds often have difficulties to compete with larger and highly performing mainstream breeds, which endangers their existence (Addo et al, 2017; Hiemstra et al, 2010)
Within Me was overestimated and showed high variation when number of individuals was 10, regardless whether Me was estimated at generation 10, 50 (Appendix S3) or 100 generations after splitting the populations (Table 2). These results indicate that at least 50 individuals are needed for accurate estimates of within Me and that decreasing single-nucleotide polymorphism (SNP) density had a very small effect on the estimated Me
While estimates of within population Me were hardly affected by SNP density, between population Me values were highly dependent on the number of available SNPs, with higher SNP densities being able to detect more independent
Summary
Small breeds often have difficulties to compete with larger and highly performing mainstream breeds, which endangers their existence (Addo et al, 2017; Hiemstra et al, 2010). These small breeds, are well worth preserving as they possess unique genetic diversity and show high adaptation to local environments. Can be used in small breeds to improve their competitiveness and economic perspectives for farmers to use these breeds on their farms. Methods such as genomic optimal contribution selection (Sonesson et al, 2012) can be applied to simultaneously assure genetic improvement of the breed and the maintenance of its diversity
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