- Research article
- Open Access
Microsatellite markers of water buffalo, Bubalus bubalis - development, characterisation and linkage disequilibrium studies
© Nagarajan et al; licensee BioMed Central Ltd. 2009
- Received: 5 May 2009
- Accepted: 21 October 2009
- Published: 21 October 2009
Microsatellite markers are highly polymorphic and widely used in genome mapping and population genetic studies in livestock species. River buffalo, Bubalus bubalis is an economically important livestock species, though only a limited number of microsatellite markers have been reported thus far in this species.
In the present study, using two different approaches 571 microsatellite markers have been characterized for water buffalo. Of the 571 microsatellite markers, 498 were polymorphic with average heterozygosity of 0.51 on a panel of 24 unrelated buffalo. Fisher exact test was used to detect LD between the marker pairs. Among the 137550 pairs of marker combination, 14.58% pairs showed significant LD (P < 0.05). Further to check the suitability of these microsatellite markers to map these on a radiation hybrid map of buffalo genome, the markers were tested on Chinese hamster genomic DNA for amplification. Only seven of these markers showed amplification in Chinese hamster, and thus 564, of these can be added to the radiation hybrid map of this species.
The high conservation of cattle microsatellite loci in water buffalo promises the usefulness of the cattle microsatellites markers on buffalo. The polymorphic markers characterised in this study will contribute to genetic linkage and radiation hybrid mapping of water buffalo and population genetic studies.
- Microsatellite Marker
- Water Buffalo
- Average Heterozygosity
- Marker Pair
- Cumulative Frequency Distribution
Genetic maps provide new insights into genome structure and chromosomal architecture of the genome, and also serve as framework for identification and location of genes linked with economically important traits. Except for water buffalo, the genetic maps have been reported for most of the important livestock species. Water buffalo (Bubalus bubalis) is one among the important livestock species and has a wide geographical distribution in the Indian sub-continent, Middle East, Eastern Europe and several other Asian countries. To develop genetic maps of water buffalo, identification and characterisation of polymorphic microsatellite markers is a prerequisite.
Microsatellite markers are tandemly repeated short DNA sequences, often highly polymorphic. These have proved useful in marker assisted selection of desirable traits to which they are linked; hence are the markers of choice for genome mapping studies . The repeat-flanking sequences of microsatellite loci are often conserved between closely related species [2, 3], thus allowing cross species amplification on related species for which microsatellite markers have not been developed. Such an approach has been proved effective by several previous studies including by us . A large number of microsatellite loci have been characterized for domesticated cattle [5–7], although in the recent past a very few studies have used cattle microsatellite markers to amplify on water buffalo genome [2, 4]. The number of microsatellite markers developed for buffalo has been very small. In our earlier study, we have used 108 cattle markers to amplify buffalo microsatellite loci, of the 108 markers 81 were amplified and 61 were polymorphic in buffalo genome. Most importantly, no de-novo microsatellite markers have been reported for this species. In the present study, we have characterised 571 microsatellite markers for water buffalo using two different approaches and tested their suitability in construction of genome map for water buffalo.
To further generate new microsatellite markers for buffalo genome, we constructed a small insert library. Four hundred sixty clones hybridizing to CA/GA repeats were picked up and the plasmid DNA was isolated and inserts were sequenced from both directions. Out of these, 303 sequences contained microsatellite repeat motif, from which 177 sequences were selected to design primers based on the GC content and length of the flanking region. Of which, 114 primer pairs amplified discrete products [see Additional file 3 &4] and 107 of the corresponding loci revealed extensive polymorphism. The number of alleles per locus ranged from 2 to 19 with an average of 8.04 (Figure 1D & Additional file 3). The observed heterozygosity of the polymorphic markers ranged from 0 to 1 (62.29%) against the expected heterozygosity values from 0.05 to 0.93 (Figure 1B & Additional file 3). Of the 107 polymorphic loci, 32 loci significantly (P < 0.05) deviated from the Hardy-Weinberg equilibrium after the bonferroni correction [see Additional file 3].
Thus, we have characterised 498 polymorphic microsatellite markers in this study for an economically important livestock species, Bubalus bubalis. The average number of alleles observed was 5.37 for all the 498 markers, whereas the average heterozygosity was 0.51. The markers with a high number of alleles tended to be more heterozygous whereas the markers with a small number of alleles exhibited diverse level of heterozygosity (Figure 1E &1F).
The usefulness of cattle microsatellite markers in molecular genetic studies have been reported for several bovidae species and showed extensive genomic conservation between cattle and other bovidae species. However, the extent of conservation is varied between species. The percentage of conservation of cattle microsatellite loci in water buffalo obtained (76.9%) in the present study was comparable with other bovidae species [10–13] and supports the previous finding on the relative usefulness of cattle primers across closely related bovidae species. Among the amplified cattle markers, 85% of the markers were polymorphic, this number was slightly high when compared with previous studies on water buffalo [2, 4]. In the present study, the average heterozygosity of the cattle markers in buffalo was 0.52, but in general the average heterozygosity of these markers is significantly higher on cattle populations [9, 14]. The average heterozygosity of the newly isolated buffalo markers was significantly higher (0.62) than those of cattle markers in buffalo. It could be due to selection of microsatellite markers with high number of repeats from buffalo genomic library which in turn is likely to be associated with high level of polymorphism for these markers in buffalo. There is no option for such kind of selection when the markers are used in related species . Out of 498 polymorphic loci, 56 loci showed significant (P < 0.05) departure from Hardy-Weinberg equilibrium after applying bonferroni correction, reflecting an excess of homozygous individuals in the population. Several hypotheses have been mentioned to explain homozygote excess, including inbreeding, population admixture and null alleles. However, null allele is a usually referred one for homozygote excess in many cases. Therefore, null allele presence was checked at each locus, notably 79% of the loci that deviated from HWE showed null allele presence in the cattle markers whereas this value was 62% for buffalo markers.
Fisher's exact test P-values for linkage disequilibrium for 27 microsatellite markers genotyped on eight different buffalo breeds.
No. of marker pairs significant at P < 0.05
Till date no comprehensive genome mapping efforts have been devoted to water buffalo. Hence, to develop a microsatellite based linkage map of water buffalo, we have been evaluating genetic markers for water buffalo and here we have reported the characteristics of 571 microsatellite markers. Further to check their applicability in radiation hybrid map of water buffalo, these markers were tested on Chinese hamster genomic DNA for amplification, only seven markers showed amplification in Chinese hamster [see Additional file 3] suggesting that the rest of the 564 markers would be immediately useful for defining their position on a radiation hybrid map of buffalo genome. These 498 polymorphic markers will be very useful in population genetic studies and for genetic dissection of complex traits in buffalo. At the same time, the newly developed buffalo markers can be tested on other bovidae species to amplify corresponding loci.
Microsatellite markers development
To develop microsatellite markers for buffalo genome, we used comparative genomics approach. 594 cattle microsatellite markers distributed across 23 chromosomes were chosen to test on water buffalo. All the markers and primers details were obtained from http://www.marc.usda.gov, BOVMAP and Bishop et al . Additional buffalo microsatellite markers were isolated through a small genomic library construction using the standard protocol described previously . Genomic DNA was extracted from a Murrah buffalo blood sample by phenol-chloroform method . PRIMER 3.0 software  was used to design the specific primer sets.
Validation of microsatellite markers
By these two approaches, we got a total of 803 microsatellite markers and tested them for amplification on buffalo genome. The PCR reaction was carried out in a total volume of 10 μl using 50 ng template DNA, 1 pM of each primer and AmpliTaq Gold PCR master mix (Applied Biosystems, Roche Molecular Systems, Inc.). Polymerase chain reactions were performed using Mastercycler (Eppendorf) and GeneAmp® PCR System 9700 (Applied Biosystems) under the following conditions: an initial denaturation at 95°C for 5 min followed by 30 cycles at 94°C for 1 min, respective primer annealing temperature for 45 sec and 72°C for 1 min. A final elongation step of 7 min was carried out at 72°C. The PCR products were visualized on 2% agarose gel. Primers presenting discrete bands with expected size were further tested on a panel of 24 unrelated Murrah buffaloes. Murrah animals with typical phenotypic features have collected from different places from Haryana state (India). The amplified PCR products were multiplexed, for multiplex development the forward primers were labelled with four different fluorescent dyes (FAM, VIC, PET, NED) supplied by Applied Biosystems (Roche Molecular Systems, Inc. USA). Genotyping was done using ABI 3730 automated DNA sequencer. To avoid having false negative results due to PCR artefacts, instability at a locus was scored only when microsatellite alterations could be reproducible in repeat PCR reactions.
GENEMAPPER version 3.5 (Applied Biosystem) was used to resolve the microsatellite allele size. The number of alleles, observed heterozygosity (Ho) and expected heterozygosity (He) per locus were estimated using the software MICROSATELLITE ANALYSER (MSA) version 3.15 . Hardy-Weinberg equilibrium of the each loci were tested using an exact test implemented in the GENEPOP software . The exact linkage disequilibrium P values for the observed allelic association under the null hypothesis of random allelic assortment were estimated by Markov chain-Monte Carlo algorithm using ARLEQUIN software . Also, In order to determine the effect of sample size on LD, we used different datasets, and only 27 highly polymorphic cattle derived markers were selected to use (Table 1). Because it has been observed by in this study that, heterozygosity positively correlated with LD on the other side the heterozygosity level of the cattle markers slightly lower than the buffalo markers, so that the outcome of the study can easily compare with previous reports. The probability of null alleles at each locus was calculated using MICRO CHECKER . The plots were drawn using R software http://www.r-project.org.
We thank Mr. Ashok Sugandhi, AI Dairy Farm, Hyderabad for permitting us to use their animals and the National Dairy Development Board, Anand, Gujarat, and the Department of Biotechnology, Government of India, New Delhi for the financial assistance.
- Kappes SM, Keele JW, Stone RT, McGraw RA, Sonstegard TS, Smith TP, Lopez-Corrales NL, Beattie CW: A second-generation linkage map of the bovine genome. Genome Res. 1997, 7: 235-249. 10.1101/gr.7.3.235.View ArticlePubMedGoogle Scholar
- Moore SS, Evans D, Byrne K, Barker JS, Tan SG, Vankan D, Hetzel DJ: A set of polymorphic DNA microsatellites useful in swamp and river buffalo (Bubalus bubalis). Anim Genet. 1995, 26: 355-359.View ArticlePubMedGoogle Scholar
- Pépin L, Amigues Y, Lépingle A, Berthier JL, Bensaid A, Vaiman D: Sequence conservation of microsatellites between Bos taurus (cattle), Capra hircus (goat) and related species. Examples of use in parentage testing and phylogeny analysis. Heredity. 1995, 74: 53-61. 10.1038/hdy.1995.7.View ArticlePubMedGoogle Scholar
- Navani N, Jain PK, Gupta S, Sisodia BS, Kumar S: A set of cattle microsatellite DNA markers for genome analysis of riverine buffalo (Bubalus bubalis). Anim Genet. 2002, 33: 149-154. 10.1046/j.1365-2052.2002.00823.x.View ArticlePubMedGoogle Scholar
- Bishop MD, Kappes SM, Keele JW, Stone RT, Sunden SL, Hawkins GA, Toldo SS, Fries R, Grosz MD, Yoo J, et al: A genetic linkage map for cattle. Genetics. 1994, 136: 619-639.PubMed CentralPubMedGoogle Scholar
- Barendse W, Armitage SM, Kossarek LM, Shalom A, Kirkpatrick BW, Ryan AM, Clayton D, Li L, Neibergs HL, Zhang N, et al: A genetic linkage map of the bovine genome. Nat Genet. 1994, 6: 227-235. 10.1038/ng0394-227.View ArticlePubMedGoogle Scholar
- Ihara N, Takasuga A, Mizoshita K, Takeda H, Sugimoto M, Mizoguchi Y, Hirano T, Itoh T, Watanabe T, Reed KM, Snelling WM, Kappes SM, Beattie CW, Bennett GL, Sugimoto Y: A comprehensive genetic map of the cattle genome based on 3802 microsatellites. Genome Res. 2004, 14: 1987-1998. 10.1101/gr.2741704.PubMed CentralView ArticlePubMedGoogle Scholar
- McRae AF, McEwan JC, Dodds KG, Wilson T, Crawford AM, Slate J: Linkage disequilibrium in domestic sheep. Genetics. 2002, 160: 1113-1122.PubMed CentralPubMedGoogle Scholar
- Farnir F, Coppieters W, Arranz JJ, Berzi P, Cambisano N, Grisart B, Karim L, Marcq F, Moreau L, Mni M, Nezer C, Simon P, Vanmanshoven P, Wagenaar D, Georges M: Extensive genome-wide linkage disequilibrium in cattle. Genome Res. 2000, 10: 220-227. 10.1101/gr.10.2.220.View ArticlePubMedGoogle Scholar
- Kim K, Min M, An J, Lee H: Cross-species amplification of Bovidae microsatellites and low diversity of the endangered Korean goral. J Hered. 2004, 95: 521-525. 10.1093/jhered/esh082.View ArticlePubMedGoogle Scholar
- van Hooft WF, Hanotte O, Wenink PW, Groen AF, Sugimoto Y, Prins HH, Teale A: Applicability of bovine microsatellite markers for population genetic studies on African buffalo (Syncerus caffer). Anim Genet. 1999, 30: 214-220. 10.1046/j.1365-2052.1999.00453.x.View ArticlePubMedGoogle Scholar
- de Gortari MJ, Freking BA, Kappes SM, Leymaster KA, Crawford AM, Stone RT, Beattie CW: Extensive genomic conservation of cattle microsatellite heterozygosity in sheep. Anim Genet. 1997, 28: 274-290. 10.1111/j.1365-2052.1997.00153.x.View ArticlePubMedGoogle Scholar
- Mommens G, Van Zeveren A, Peelman LJ: Effectiveness of bovine microsatellites in resolving paternity cases in American bison, Bison bison L. Anim Genet. 1998, 29: 12-18. 10.1046/j.1365-2052.1998.00252.x.View ArticlePubMedGoogle Scholar
- Vallejo RL, Li YL, Rogers GW, Ashwell MS: Genetic diversity and background linkage disequilibrium in the North American Holstein cattle population. J Dairy Sci. 2003, 86: 4137-4147.View ArticlePubMedGoogle Scholar
- Ellegren H, Primmer CR, Sheldon BC: Microsatellite 'evolution': directionality or bias?. Nat Genet. 1995, 11: 360-362. 10.1038/ng1295-360.View ArticlePubMedGoogle Scholar
- Ardlie KG, Kruglyak L, Seielstad M: Patterns of linkage disequilibrium in the human genome. Nat Rev Genet. 2002, 3: 299-309. 10.1038/nrg777.View ArticlePubMedGoogle Scholar
- Kumar S, Gupta J, Kumar N, Dikshit K, Navani N, Jain P, Nagarajan M: Genetic variation and relationships among eight Indian riverine buffalo breeds. Mol Ecol. 2006, 15: 593-600. 10.1111/j.1365-294X.2006.02837.x.View ArticlePubMedGoogle Scholar
- Slatkin M: Linkage disequilibrium in growing and stable populations. Genetics. 1994, 137: 331-336.PubMed CentralPubMedGoogle Scholar
- Edwards KJ, Barker JH, Daly A, Jones C, Karp A: Microsatellite libraries enriched for several microsatellite sequences in plants. Biotechniques. 1996, 20: 758-760.PubMedGoogle Scholar
- Sambrook J, Russell DW: Molecular Cloning: A Laboratory Manual. 2001, Cold Spring Harbor Laboratory Press, Cold Spring Harbor, NYGoogle Scholar
- Rozen S, Skaletsky HJ: Primer3 on the WWW for general users and for biologist programmers. Bioinformatics Methods and Protocols: Methods in Molecular Biology. Edited by: Krawetz S, Misener S. 2003, Humana Press, Totowa, NJ, 365-386.Google Scholar
- Dieringer D, Schlötterer C: Microsatellite analyzer (MSA): a platform independent analysis tool for large microsatellite data sets. Mol Ecol Notes. 2003, 3: 167-169. 10.1046/j.1471-8286.2003.00351.x.View ArticleGoogle Scholar
- Raymond M, Rousset F: GENEPOP (VERSION-1.2) - Population-genetics software for exact tests and ecumenicism. J Hered. 1995, 86: 248-249.Google Scholar
- Excoffier L, Laval LG, Schneider S: Arlequin ver. 3.0: An integrated software package for population genetics data analysis. Evolutionary Bioinformatics Online. 2005, 1: 47-50.PubMed CentralGoogle Scholar
- van Oosterhout C, Hutchinson WF, Wills DPM, Shipley P: Microchecker: software for identifying and correcting genotyping errors in microsatellite data. Molecular Ecology Notes. 2004, 4: 535-538. 10.1111/j.1471-8286.2004.00684.x.View ArticleGoogle Scholar
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