Detection of genetic diversity in cattle by microsatellite and SNP markers - a review

Bilginer Ü., Ergin M., Demir E., Yolcu H. İ., Argun Karslı B.

ANIMAL SCIENCE PAPERS AND REPORTS, vol.40, no.4, pp.375-392, 2022 (SCI-Expanded)

  • Publication Type: Article / Review
  • Volume: 40 Issue: 4
  • Publication Date: 2022
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, Animal Behavior Abstracts, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, Veterinary Science Database
  • Page Numbers: pp.375-392
  • Akdeniz University Affiliated: Yes


Environmental challenges and preference of high-yielding breeds have resulted in extinction of various local cattle populations as well as the loss of genetic diversity in modern cattle breeds. Genetic diversity, however, plays a vital role both in cattle industry to meet current and future demand for milk and beef and in adaptation to different environmental challenges for animals. Thanks to developing molecular genetics and bioinformatics tools, genetic data including microsatellites and Single Nucleotide Polymorphisms (SNPs) can be detected across the genome and can be analysed to reveal genetic diversity within and between cattle populations. Until recently, microsatellite markers were commonly used to estimate genetic diversity in both local and exotic cattle breeds. Today, however, SNP arrays are the most preferred technology for genetic diversity analysis, since they are time-efficient and easy to access and apply. Moreover, developments in sequencing technology with affordable costs have made it possible to obtain SNP data across the genome via whole genome resequencing. It is foreseen that whole genome resequencing will be routinely used to estimate genetic diversity periodically not only in cattle but also in the other livestock species as well in the future. In this study, the most commonly preferred molecular methods to reveal genetic diversity in cattle were discussed and some bioinformatics tools to analyse genetic data were summarised.