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Evaluation of factors affecting individual assignment precision using microsatellite data from horse breeds and simulated breed crosses.
Abstract: Assignment tests have been utilized to investigate population classification, measure genetic diversity and to solve forensic questions. Using microsatellite data from 26 loci genotyped in eight horse breeds we examined how population differentiation, number of scored loci, number of scored animals per breed and loci variability affected individual assignment precision applying log likelihood methods. We found that both genetic differentiation and number of scored loci were highly important for recognizing the breed of origin. When comparing two and two breeds, a proportion of 95% of the most differentiated breeds (0.200 < or = FST < or = 0.259) could be identified scoring only three loci, while the corresponding number was six for the least differentiated breeds (0.080 < or = FST or = 20 animals per population) and fairly variable loci were used.
Publication Date: 2002-07-26 PubMed ID: 12139505DOI: 10.1046/j.1365-2052.2002.00868.xGoogle Scholar: Lookup The Equine Research Bank provides access to a large database of publicly available scientific literature. Inclusion in the Research Bank does not imply endorsement of study methods or findings by Mad Barn.
- Evaluation Study
- Journal Article
- Research Support
- Non-U.S. Gov't
Summary
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This research paper investigates what factors influence the accuracy of identifying the breed of a horse by studying microsatellite data from eight different horse breeds. The study found that both genetic differentiation and the number of analysed segments of DNA were significant in identifying an animal’s breed origin.
Investigating Population Classification, Genetic Diversity, and Forensic Questions
- This research paper uses assignment tests, which are tools used to classify different populations or groups, to measure genetic diversity and to answer forensic questions.
- The researchers examined microsatellite data, or short, repeated sequences of DNA, from 26 loci (locations in the DNA) in eight horse breeds. This data was analyzed to understand how various factors affect the ability to accurately assign an individual horse to its correct breed.
Importance of Genetic Differentiation and Number of Scored Loci
- The study found that two factors were highly important in correctly identifying the breed of a horse: genetic differentiation and the number of scored loci.
- Genetic differentiation refers to the genetic differences between different populations or groups. In this case, it would refer to the genetic differences between the eight horse breeds.
- Scored loci are the specific regions of DNA that were analysed. In this study, the researchers found that the more loci that were analysed, the more accurate the assignment of a horse to its correct breed.
Differentiation and Identification in Breeds
- The study also found that when comparing two breeds, 95% of the most genetically different breeds could be correctly assigned with just three scored loci. However, for the least genetically different breeds, six scored loci were necessary for the same level of correct assignment.
- Similar results were found for simulated breed crosses, which are bred from two distinct breed lines, where 12 scored loci were needed for correct assignment.
Population Sample Size and Locus Variability
- The researchers also examined how population sample size and locus variability affected assignment precision.
- Population sample size refers to the number of horses from each breed that were included in the study. Locus variability refers to the variations in the sequences of DNA at each location.
- The study concluded that as long as a moderately large sample size (at least 20 animals per population) and a fair bit of locus variability were used, these two factors were not critical for the assignment precision.
Cite This Article
APA
Bjørnstad G, Røed KH.
(2002).
Evaluation of factors affecting individual assignment precision using microsatellite data from horse breeds and simulated breed crosses.
Anim Genet, 33(4), 264-270.
https://doi.org/10.1046/j.1365-2052.2002.00868.x Publication
Researcher Affiliations
- Department of Morphology, Genetics and Aquatic Biology, The Norwegian School of Veteranary Science, Oslo, Norway. g.bornstad@cgiar.org
MeSH Terms
- Animals
- Chromosome Mapping / veterinary
- Crosses, Genetic
- Genotype
- Horses / classification
- Horses / genetics
- Microsatellite Repeats / genetics
- Species Specificity
Citations
This article has been cited 11 times.- Ryan CA, Berry DP, O'Brien A, Pabiou T, Purfield DC. Evaluating the use of statistical and machine learning methods for estimating breed composition of purebred and crossbred animals in thirteen cattle breeds using genomic information. Front Genet 2023;14:1120312.
- Nikbakhsh M, Varkoohi S, Seyedabadi HR. Mitochondrial DNA D-loop hyper-variable region 1 variability in Kurdish horse breed. Vet Med Sci 2023 Mar;9(2):721-728.
- Mazzatenta A, Vignoli M, Caputo M, Vignola G, Tamburro R, De Sanctis F, Roig JM, Bucci R, Robbe D, Carluccio A. Maternal Phylogenetic Relationships and Genetic Variation among Rare, Phenotypically Similar Donkey Breeds. Genes (Basel) 2021 Jul 22;12(8).
- Breidenbach N, Gailing O, Krutovsky KV. Genetic structure of coast redwood (Sequoia sempervirens [D. Don] Endl.) populations in and outside of the natural distribution range based on nuclear and chloroplast microsatellite markers. PLoS One 2020;15(12):e0243556.
- Syahida Kasim N, Mat Jaafar TNA, Mat Piah R, Mohd Arshaad W, Mohd Nor SA, Habib A, Abd Ghaffar M, Sung YY, Danish-Daniel M, Tan MP. Recent population expansion of longtail tuna Thunnus tonggol (Bleeker, 1851) inferred from the mitochondrial DNA markers. PeerJ 2020;8:e9679.
- Putnová L, Štohl R. Comparing assignment-based approaches to breed identification within a large set of horses. J Appl Genet 2019 May;60(2):187-198.
- Li YH, Chu HP, Jiang YN, Lin CY, Li SH, Li KT, Weng GJ, Cheng CC, Lu DJ, Ju YT. Empirical Selection of Informative Microsatellite Markers within Co-ancestry Pig Populations Is Required for Improving the Individual Assignment Efficiency. Asian-Australas J Anim Sci 2014 May;27(5):616-27.
- Banks MA, Jacobson DP, Meusnier I, Greig CA, Rashbrook VK, Ardren WR, Smith CT, Bernier-Latmani J, Van Sickle J, O'Malley KG. Testing advances in molecular discrimination among Chinook salmon life histories: evidence from a blind test. Anim Genet 2014 Jun;45(3):412-20.
- Iquebal MA, Sarika, Dhanda SK, Arora V, Dixit SP, Raghava GP, Rai A, Kumar D. Development of a model webserver for breed identification using microsatellite DNA marker. BMC Genet 2013 Dec 9;14:118.
- Hogan JD, Thiessen RJ, Sale PF, Heath DD. Local retention, dispersal and fluctuating connectivity among populations of a coral reef fish. Oecologia 2012 Jan;168(1):61-71.
- Burocziova M, Riha J. Horse breed discrimination using machine learning methods. J Appl Genet 2009;50(4):375-7.
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