Development of a model webserver for breed identification using microsatellite DNA marker
© Iquebal et al.; licensee BioMed Central Ltd. 2013
Received: 24 September 2013
Accepted: 4 December 2013
Published: 9 December 2013
Identification of true to breed type animal for conservation purpose is imperative. Breed dilution is one of the major problems in sustainability except cases of commercial crossbreeding under controlled condition. Breed descriptor has been developed to identify breed but such descriptors cover only “pure breed” or true to the breed type animals excluding undefined or admixture population. Moreover, in case of semen, ova, embryo and breed product, the breed cannot be identified due to lack of visible phenotypic descriptors. Advent of molecular markers like microsatellite and SNP have revolutionized breed identification from even small biological tissue or germplasm. Microsatellite DNA marker based breed assignments has been reported in various domestic animals. Such methods have limitations viz. non availability of allele data in public domain, thus each time all reference breed has to be genotyped which is neither logical nor economical. Even if such data is available but computational methods needs expertise of data analysis and interpretation.
We found Bayesian Networks as best classifier with highest accuracy of 98.7% using 51850 reference allele data generated by 25 microsatellite loci on 22 goat breed population of India. The FST values in the study were seen to be low ranging from 0.051 to 0.297 and overall genetic differentiation of 13.8%, suggesting more number of loci needed for higher accuracy. We report here world’s first model webserver for breed identification using microsatellite DNA markers freely accessible at http://cabin.iasri.res.in/gomi/.
Higher number of loci is required due to less differentiable population and large number of breeds taken in this study. This server will reduce the cost with computational ease. This methodology can be a model for various other domestic animal species as a valuable tool for conservation and breed improvement programmes.
Breed of a given species are known to emerge over years during evolution within a specific ecological niche. Each breed is a unique combination of gene in a given gene pool and over the period of time with selection for survival as well as also for productivity due to human intervention. Except cases of commercial crossbreeding under controlled condition, the breed dilution is one of the major problems in sustainability of the breed. The identification of true to breed type animal for conservation purpose is imperative. If we conserve crossbred or admixtured breed, its long term sustenance is compromised as breed is not well adapted over period of time to its native ecological niche. Cross breeding of native goats with exotic breeds of goats (Alpine, Saanen and Boer) has shown poor reproductive performance and high mortality rate in higher grade crosses thus selective breeding of true to the breed type animals is desirable with maintained diversity level for successful conservation and long term sustainability of breed . Such identification tool is also needed to establish breed product’s origin in today’s global market .
Though breed descriptor has been developed in India to identify breed but such descriptors cover only “pure breed” type animals which excludes more than 2/3rd of population which are either undefined or admixture [3–5]. In case of close resemblance of phenotype it becomes subjective to identify the breed. Moreover, when degree of admixture is not so conspicuously visible then it is hard to differentiate between true to breed type and “admixtured breed”. Advent of molecular tools like microsatellite and SNP have revolutionized the breed identification even from small samples of biological tissue or germplasm without having ova and semen. In case of semen, ova or embryo the breed cannot be identified as there are no visible breed descriptors.
Microsatellite DNA marker based breed identification has been reported in various domestic animals like cattle [6, 7], sheep [8, 9], goat [10, 11], pig , horse , dog  poultry and rabbit . Such methods have limitations namely, non-availability of allele data in public domain, thus each time all reference breed has to be genotyped which is neither logical nor economical. Even if such data is available but computational methods needs expertise of data analysis and interpretation.
The present work aims at development of a model web server for breed identification where one need not to do genotyping of all referral breeds each time increasing the cost of molecular level identification. In order to achieve this, we have used 51850 allelic data of microsatellite marker obtained from DNA fingerprinting of 22 goat breeds on 25 loci across India. This methodology demonstrates that it can be used as model for other domestic animal species and breed for identification and conservation for long term sustainability endeavor.
Genomic DNA isolation and creation of data set
Blood samples were collected from a total of 1037 unrelated animals belonging to twenty two different Indian goat breeds. The breeds selected were from diverse geographical regions and climatic conditions with varying utilities and body sizes. Genomic DNA was isolated from the blood samples by using SDS-Proteinase-K method [16, 17].
The quality and quantity of the DNA extracted was assessed by Nanodrop 1000 (Thermo Scientific, USA) before further use. A total of 51850 allelic data generated by 25 microsatellite (details can be seen at http://cabin.iasri.res.in/gomi/algorithm.html) loci based DNA fingerprinting on 22 goat breeds i.e. Blackbengal, Ganjam, Gohilwari, Jharkhand black, Attapaddy, Changthangi, Kutchi, Mehsana, Sirohi, Malabari, Jamunapari, Jhakarana, Surti, Gaddi, Marwari, Barbari, Beetal, Kanniadu, Sangamnari, Osmanabadi, Zalawari and Cheghu across India were collected. In India, there are 23 registered breeds though FAO reports 32 which are due to vernacular name, geographical name and synonymous name with language diversity.
Microsatellite DNA markers selection
List of 25 loci along with the primer pairs
No. of observed allele
Data Generation by allele detection and genotyping
PCR products were mixed in ratio of 1:1.5:2:2 of FAM (blue), VIC (green), NED (yellow) and PET (red) labelled respectively after determining the optimal pooling ratio and dilution ratio for a set of primers. In order to ensure size calibration of alleles 0.5 μL of this mixture was combined with 0.3 μL of Liz 500 as internal lane standard (Applied Biosystems) and 9.20 μL of Hi-Di Formamide per sample. The resulting mixture was denatured by incubation for 5 min at 95°C to run on automated DNA sequencer of Applied Biosystems (ABI 3100 Avant). The electropherograms were drawn through Gene Scan and used to extract DNA fragment sizing details using Gene Mapper software (version 3.0) (Applied Biosystems). Generated data is numeric in terms of base pair which is size of each allele along with genotype (combination of allele at every diploid locus). The protocol has been described at http://cabin.iasri.res.in/gomi/tutorial.html. The obtained allelic data were further analysed using FSTAT software (http://www2.unil.ch/popgen/softwares/fstat.htm) to compute relative locus differentiation of each breed in the entire dataset.
Bayesian networks as classifiers
Classification is a technique to identify class labels for instances based on a set of features (attributes). Building accurate classifiers from pre-classified data is a very active research topic of machine learning and data mining. In last two decades, many classification algorithms have been proposed including Naïve-Bayes, Neural Network (Multilayer Perceptron), Random Forest and Bayesian Network based classifiers.
The first term in above equation measures efficiency of network B to estimate the probability of a class given set of attribute values. The second term measures how well network B estimates the joint distribution of the attributes. Since the classification is determined based on P B (C|A1, A2, …, A n ), only the first term is related to the score of the network as a classifier i.e., its predictive accuracy. This term is dominated by the second term, when there are many observations. As n grows larger, the probability of each particular assignment to A1, A2, …, A n becomes smaller, since the number of possible assignments grows exponentially in n. In our study, number of feature (n) are the number of alleles (two alleles per locus) i.e. 50 and the total number of samples is 1037 which includes 22 breeds (classes). Prediction performance of a Bayesian network has also been compared with Multilayer Perceptron  and Random forest algorithm .
In this study, WEKA machine learning workbench with extensive collection of machine learning algorithms and data pre-processing methods was used for classification and prediction .
Assessment of the prediction accuracy
where TP = True Positive, TN = True Negative, FP = False Positive, FN = False Negative.
The server is developed using CGI-Perl script, Hyper Text Markup Language (HTML) and Java Scripts to make it more user-friendly and launched using open source web server software program, Apache. Other models like Random Forest, Multiple Layer Perceptron were logically excluded in web implementation ensuring objectivity of identification accuracy. The user needs to submit the microsatellite allelic data having numeric values in base pairs at http://cabin.iasri.res.in/gomi/gomi.html. The data can also be uploaded either using .csv or .txt format or direct entry in the submission form. The server has tutorial for the users for easy understanding with a sample data at http://cabin.iasri.res.in/gomi/tutorial.html.
Results and discussion
Performance of different classifiers
Similar case of microsatellite data based breed identification using Bayesian method has been found with much higher accuracy for example 99.63% accuracy in five Spanish sheep breed viz. Churra, Latxa, Castellana, Rasa-Aragonesa and Merino using 18 microsatellite markers . Similar works have been reported in cattle , camel  and dog .
The novel approach and methodology developed in this study gives higher accuracy which is in similar range of earlier studies in cattle . In some reported cases number of loci needed for breed identification ranged much lower like 3-10 [26, 28]. For our study, all the 25 loci were needed which is due to poor differentiation of loci in the breeds. Populations having higher FST values always needed minimum loci. Contrary to this, population having low FST needs more number of loci and still the accuracy is compromised. For example, Murciana and Granadina populations with 25 microsatellites of low FST value (0.0432) have been reported with just 80% accuracy . Contrary to this, in case of horse, where FST was having a range of 0.2 to 0.259, the accuracy has been high up to 95%, even with minimum of 3 loci .
Prediction accuracies obtained on twenty two breeds of goat
Through the present study, we are reporting first web server for breed prediction with accuracy of more than 98% using 22 goat breeds of India. The number of loci needed is relatively high due to less differentiable population and large number of breeds taken in this study. The web server can be used for other domestic species thus relevant for global use. Further studies are warranted to look for new algorithm to reduce the number of loci in prevailing conditions of large number of breeds and with lower differentiation especially prevailing in “breed melting pot” regions like India and other major diversity regions of the world. This server will reduce the cost with computational ease. This methodology would become a model for all flora and fauna for variety and breed identification required in improvement, conservation, sovereignty issues in trans-border germplasm movement and management.
Availability and requirements
Webserver can be accessed freely at http://cabin.iasri.res.in/gomi/
Server Name: http://cabin.iasri.res.in/
Project home page: http://cabin.iasri.res.in/gomi/
Operating system(s): e.g. Platform independent
Programming language: PERL, Java, PHP
Other requirements: Internet connectivity
License: No restrictions on non-commercial/Research use
Any restrictions to use by non-academics: Non-academicians may contact corresponding author
This work was supported by research project entitled “Establishment of National Agriculture Bioinformatics Grid in ICAR” funded by National Agricultural Innovation Project, Indian Council of Agricultural Research.
- Rai B, Singh MK, Singh SK: Goats for meat, milk and fibre: a review. Indian J Anim Sci. 2005, 75: 349-355.Google Scholar
- Negrini R, Nicoloso L, Crepaldi P, Milanesi E, Marino R, Perini D, Pariset L, Dunner S, Leveziel H, Williams JL, Ajmone-Marsan P: Traceability of four European Protected Geographic Indication (PGI) breed products using Single Nucleotide Polymorphisms (SNP) and Bayesian statistics. Meat Sci. 2008, 80: 1212-1217. 10.1016/j.meatsci.2008.05.021.View ArticlePubMedGoogle Scholar
- Sreenivas D: Breeding policy strategies for genetic improvement of cattle and buffaloes in India. Vet World. 2013, 6: 455-460. 10.5455/vetworld.2013.455-460.View ArticleGoogle Scholar
- Sharma R, Maitra A, Singh PK, Tatia MS: Genetic diversity and relationship of cattle populations of East India: distinguishing lesser known cattle populations and established breeds based on STR markers. Springer Plus. 2013, 2: 359-10.1186/2193-1801-2-359.PubMed CentralView ArticlePubMedGoogle Scholar
- ftp://ftp.fao.org/docrep/fao/010/a1250e/annexes/CountryReports/India.pdfGoogle Scholar
- Blott SC, Williams JL, Haley CS: Discriminating among cattle breeds using genetic markers. Heredity. 1999, 82: 613-619. 10.1046/j.1365-2540.1999.00521.x.View ArticlePubMedGoogle Scholar
- Maudet C, Luikart G, Taberlet P: Genetic diversity and assignment tests among seven French cattle breeds based on microsatellite DNA analysis. J Anim Sci. 2002, 80: 942-950.PubMedGoogle Scholar
- Arranz J, Bayon Y, Primitivo FS: Differentiation among Spanish sheep breeds using microsatellites. Genet Sel Evol. 2001, 33: 529-542. 10.1186/1297-9686-33-5-529.PubMed CentralView ArticlePubMedGoogle Scholar
- Niu LL, Li HB, Ma YH, Du LX: Genetic variability and individual assignment of Chinese indigenous sheep populations (Ovis aries) using microsatellites. Anim Genet. 2011, 43: 108-111.View ArticlePubMedGoogle Scholar
- Serrano M, Calvo JH, Martinez M, Marcos-Carcavilla A, Cuevas J, Gonzalez C, Jurado JJ, de Tejada PD: Microsatellite based genetic diversity and population structure of the endangered Spanish Guadarrama goat breed. BMC Genet. 2009, 10: 61-10.1186/1471-2156-10-61.PubMed CentralView ArticlePubMedGoogle Scholar
- Hoda A, Hyka GA, Dunner S, Obexer-Ruff G: Genetic diversity of Albanian goat breeds based on microsatellite markers. Arch Zootec. 2011, 60: 607-615. 10.4321/S0004-05922011000300049.View ArticleGoogle Scholar
- Fan B, Chen YZ, Moran C, Zhao SH, Liu B, Yu M, Zhu MJ, Xiong TA, Li K: Individual-breed assignment analyses in swine populations by using microsatellite marker. Asian-Aust J Anim Sci. 2005, 11: 1529-1534.View ArticleGoogle Scholar
- Bjornstad G, Roed KH: Breed demarcation and potential for breed allocation of horse assessed by microsatellite markers. Anim Genet. 2001, 32: 59-65. 10.1046/j.1365-2052.2001.00705.x.View ArticlePubMedGoogle Scholar
- Toskinen MT, Bredbadka P: A convenient and efficient microsatellite-based assay for resolving parentage in dogs. Anim Genet. 1999, 30: 148-149. 10.1046/j.1365-2052.1999.00438.x.View ArticleGoogle Scholar
- Gotz K, Thaller G: Assignment of individuals to populations using microsatellites. J Anim Breed Genet. 1998, 115: 53-61. 10.1111/j.1439-0388.1998.tb00327.x.View ArticleGoogle Scholar
- Dixit SP, Verma NK, Aggarwal RAK, Vyas MK, Rana J, Sharma A: Genetic diversity and relationship among Indian goat breeds based on microsatellite markers. Small Ruminant Res. 2012, 105: 38-45. 10.1016/j.smallrumres.2011.11.026.View ArticleGoogle Scholar
- Kumar S, Dixit SP, Verma NK, Singh DK, Pande A, Kumar S, Chander R, Singh LB: Genetic diversity analysis of the Gohilwari breed of Indian goat (Capra hircus) using microsatellite markers. Am J Anim Vet Sci. 2009, 4: 49-57.View ArticleGoogle Scholar
- Langley P, Iba W, Thompson K: An analysis of Bayesian classifiers. In Proceedings of. 1992, AAAI-92: 223-228.Google Scholar
- Friedman N, Geiger D, Goldszmidt M: Bayesian network classifiers. Mach Learn. 1997, 29: 131-161. 10.1023/A:1007465528199.View ArticleGoogle Scholar
- Hassoun MH: Fundamentals of artificial neural networks. 1995, Cambridge, MA: MIT PressGoogle Scholar
- Verikas A, Gelzinis A, Bacauskiene M: Mining data with random forests: a survey and results of new tests. Pattern Recognit. 2011, 44: 330-349. 10.1016/j.patcog.2010.08.011.View ArticleGoogle Scholar
- Hall M, Frank E, Holmes G, Pfahringer B, Reutemann P, Witten IH: The WEKA data mining software: an update. SIGKDD Explorations. 2009, 11: 10-18. 10.1145/1656274.1656278.View ArticleGoogle Scholar
- Efron B: Estimating the error rate of a prediction rule: improvement on cross-validation. J Am Stat Assoc. 1983, 78: 316-331. 10.1080/01621459.1983.10477973.View ArticleGoogle Scholar
- Canon J, Alexandrino P, Bessa I, Carleos C, Carretero Y, Dunner S, Ferran N, Garcia D, Jordana J, Laloe D, Pereira A, Sanchez A, Moazami-Goudarzi K: Genetic diversity measures of local European beef cattle breeds for conservation purposes. Genet Sel Evol. 2001, 33: 311-332. 10.1186/1297-9686-33-3-311.PubMed CentralView ArticlePubMedGoogle Scholar
- Mburu DN, Ochieng JW, Kuria SG, Jianlin H, Kaufmann B, Rege JE, Hanotte O: Genetic diversity and relationships of indigenous Kenyan camel (Camelus dromedarius) populations: implications for their classification. Anim Genet. 2003, 34: 26-32. 10.1046/j.1365-2052.2003.00937.x.View ArticlePubMedGoogle Scholar
- Koskinen MT: Individual assignment using microsatellite DNA reveals unambiguous breed identification in the domestic dog. Anim Genet. 2003, 34: 297-301. 10.1046/j.1365-2052.2003.01005.x.View ArticlePubMedGoogle Scholar
- MacHugh D, Loftus RT, Cunningham P, Bradley DG: Genetic structure of seven European cattle breeds assessed using 20 microsatellite markers. Anim Genet. 1998, 29: 333-340. 10.1046/j.1365-2052.1998.295330.x.View ArticlePubMedGoogle Scholar
- Bjornstad G, Roed KH: Evaluation of factors affecting individual assignment precision using microsatellite data from horse breeds and simulated breed crosses. Anim Genet. 2002, 33: 264-270. 10.1046/j.1365-2052.2002.00868.x.View ArticlePubMedGoogle Scholar
- Martinez AM, Vega-Pla JL, Leon JM, Camacho ME, Delgado JV, Ribeiro MN: Is the Murciano-Granadina a single goat breed? A molecular genetics approach. Arq Bras Med Vet Zootec. 2010, 62: 1191-1198. 10.1590/S0102-09352010000500023.View ArticleGoogle Scholar
- Gour DS, Malik G, Ahlawat SPS, Pandey AK, Sharma R, Gupta N, Gupta SC, Bisen PS, Kumar D: Analysis of genetic structure of Jamunapari goats by microsatellite markers. Small Ruminant Res. 2006, 66: 140-149. 10.1016/j.smallrumres.2005.07.053.View ArticleGoogle Scholar
- Kumar D, Dixit SP, Sharma R, Pandey AK, Sirohi G, Patel AK, Aggarwal NK, Gour DS, Ahlawat SPS: Population structure, genetic variation and management of Marwari goats. Small Ruminant Res. 2005, 59: 41-48. 10.1016/j.smallrumres.2004.11.013.View ArticleGoogle Scholar
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