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Inference of Breed Structure in Farm Animals: Empirical Comparison between SNP and Microsatellite Performance.

Abbas LaounSahraoui HarkatMohamed LafriSemir Bachir Souheil GaouarIbrahim BelabdiElena CianiMaarten De GrootVéronique BlanquetGregoire LeroyXavier RognonAnne Da Silva
Published in: Genes (2020)
Knowledge of population structure is essential to improve the management and conservation of farm animal genetic resources. Microsatellites, which have long been popular for this type of analysis, are more and more neglected in favor of whole-genome single nucleotide polymorphism (SNP) chips that are now available for the main farmed animal species. In this study, we compared genetic patterns derived from microsatellites to that inferred by SNPs, considering three pairs of datasets of sheep and cattle. Population genetic differentiation analyses (Fixation index, FST), as well as STRUCTURE analyses showed a very strong consistency between the two types of markers. Microsatellites gave pictures that were largely concordant with SNPs, although less accurate. The best concordance was found in the most complex dataset, which included 17 French sheep breeds (with a Pearson correlation coefficient of 0.95 considering the 136 values of pairwise FST, obtained with both types of markers). The use of microsatellites reduces the cost and the related analyses do not require specific computer equipment (i.e., information technology (IT) infrastructure able to provide adequate computing and storage capacity). Therefore, this tool may still be a very appropriate solution to evaluate, in a first stage, the general state of livestock at national scales. At a time when local breeds are disappearing at an alarming rate, it is urgent to improve our knowledge of them, in particular by promoting tools accessible to the greatest number.
Keyphrases
  • genome wide
  • genetic diversity
  • dna methylation
  • copy number
  • healthcare
  • gene expression
  • minimally invasive
  • single cell
  • computed tomography
  • magnetic resonance
  • deep learning
  • rna seq