Livestock Research for Rural Development 18 (11) 2006 Guidelines to authors LRRD News

Citation of this paper

Characterization of Krishna Valley breed of cattle (Bos indicus) in south India using microsatellite markers

S M K Karthickeyan, R Saravanan and P Thangaraju

Department of Animal Genetics and Breeding, Madras Veterinary College, Chennai - 600 007, Tamil Nadu, India
kannikarthi@yahoo.co.in


Abstract

With the help of 25 microsatellite markers, a total of 50 Krishna Valley cattle were screened. The mean number of allele was found to be 4.72 ± 0.25 per microsatellite locus with the range of 3 to 7. The range in the allele size was from 94 (CSRM060) to 300 (ILSTS006) bp. These microsatellite alleles distributed at a minimum frequency of 0.0116 (ILSTS054) to a maximum of 0.8128 (ILSTS030). The mean polymorphism information content was found to be 0.6209 ± 0.03 ranging from 0.2583 (ILSTS030) to 0.7975 (INRA035). The overall means for observed and expected heterozygosities were 0.6683 ± 0.06 and 0.6569 ± 0.03 respectively.

The markers used in the study were highly informative and high heterozygosity value is indicative of the higher amount of genetic variability that can be exploited even in population of small sizes.

Keywords: Allele frequency, Krishna Valley, microsatellites, PIC


Introduction

KrishnaValley is the draught breed of cattle, braving extreme hot, humid climatic conditions and is able to work well in the black cotton soil in the valleys of Krishna river in Karnataka state in India. It was reported that the kings of Southern Mahratta country, which lied in the watershed areas of the rivers (Krishna, Ghataprabha and Malaprabha), tried to evolve a powerful bullock for agricultural purposes in the sticky black cotton soil during the last two decades of the nineteenth century (Joshi and Phillips 1953). It was claimed that Gir and possibly Kankrej cattle from Gujarat state, Ongole cattle from Andhra Pradesh state and local cattle having Mysore-type blood were used to evolve the Krishna Valley breed. The king of Sangli, a well-known breeder of Krishna Valley, contributed substantially in making judicious use of all these strains to produce the desired type of animal.

Earlier, the distribution of Krishna Valley cattle was wide, including the districts of Satara, Sangali and Solapur in Maharashtra; and Belgaum, Bijapur and Raichur districts of Karnataka states (Nivsarkar et al 2000). But a shift in the breeding tract of this breed from Maharashtra and Karnataka states to northern Karnataka alone was reported in a pilot survey conducted by Ramesha et al (2001). At present, only a few hundred animals of true to type are available in and around few villages of Jamkhandi, Mudhol and Athani taluks of northern parts of Karnataka. The reasons for the decline in number are selling of animals of Krishna Valley due to continuous drought in the tract and preference of the farmers for Khillari breed which is more attractive and massive in appearance resulted in lack of Krishna Valley Breeding bulls.

The first step towards conservation of livestock genetic resources is the genetic characterization with respect to phenotypic parameters, unique qualities and utility. Subsequently finding out the genetic architecture through molecular means and evolutionary relationship with other related breeds would provide valuable information about the breed for taking up conservation measures. The physical characterization had already been done by Ramesha et al(2001) in the native tract. Considering these facts, the present study has been carried out to characterize the Krishna Valley breed of cattle using the molecular marker, such as microsatellites.


Materials and methods

DNA samples

The microsatellite analysis was carried out in a sample of 50 unrelated Krishna Valley cattle reared in the villages of Jamkhandi Taluk (part of its breeding tract) in Karnataka State, India. The samples were collected at random form different villages of the tract. The DNA for the study was isolated from peripheral blood by a rapid, non-enzymatic method as described by Lahiri and Nurnberger (1991). The quantity and quality of the isolated DNA samples were tested by spectrophotometric measurements for subsequent analysis.

Microsatellite analysis

As per the secondary guidelines of Food and Agriculture Organisation of United Nations (FAO 2004), a total of 25 microsatellite markers were selected to screen the population of Krishna Valley cattle. The amplification of DNA was carried out using thermal cycler (MJ Research) with the PCR reaction mixture of 20ml. The mixture was prepared by adding 50-100ng of template DNA; 1.5mM MgCl2; 5 picomoles each of forward and reverse primers; 0.75 units of Taq DNA Polymerase and 100mM dNTPs. Amplification was carried out with annealing temperatures ranging from 51°C to 60°C for different primers for 30 cycles. Amplified PCR products were resolved through a 6% denaturing PAGE along with a single stranded 10 bp DNA ladder (Invitrogen, USA) as marker and the PCR products were sized after silver-staining procedure as recommended by Comincini et al (1995).

The microsatellite alleles were sized using Diversity Database software (BioRad, USA) and followed by manual verification. Effective number of alleles, allele frequencies and heterozygosity were estimated using the software POPGENE 32. The polymorphism information content (PIC) was analysed using Nei's formula (1978).


Results and discussion

The percentage of polymorphic loci obtained was 96 since all microsatellite loci, except ILSTS030 (only 2 alleles), screened in Krishna Valley cattle exhibited polymorphism. The microsatellite allele number, size and frequency, polymorphism information content and heterozygosity values are presented in Table 1.


Table 1.  Allele frequency, polymorphic information content (PIC) and heterozygosity of microsatellite loci in Krishna Valley cattle

Sl. No.

Locus

Allele No.

Allele size (bp) and frequency

PIC

He*

1.

ILSTS005

4

182

186

190

194

 

 

 

0.6954

0.7433

0.2396

0.1875

0.2917

0.2812

2.

ILSTS006

5

286

290

292

296

300

 

0.7203

0.7578

0.1304

0.3587

0.2500

0.1304

0.1304

3.

ILSTS011

3

262

264

268

 

 

 

 

0.4140

0.4606

0.7021

0.1170

0.1809

4.

ILSTS030

2

152

154

 

 

 

 

 

0.2583

0.3047

0.8128

0.1875

5.

ILSTS033

5

138

142

146

148

150

 

 

0.7036

0.7450

0.0435

0.1848

0.1739

0.2283

0.3696

6.

ILSTS034

7

124

126

128

130

132

136

144

0.7725

0.7962

0.0897

0.0897

0.1026

0.3590

0.1281

0.1667

0.0641

7.

ILSTS054

6

132

138

140

144

146

148

 

0.6795

0.7182

0.1977

0.1163

0.0116

0.1744

0.4419

0.0581

8.

ETH003

5

104

114

116

118

124

 

 

0.6137

0.6633

0.0761

0.2826

0.4891

0.0652

0.0870

9.

ETH010

4

210

212

216

220

 

 

 

0.6205

0.6773

0.0778

0.4444

0.1889

0.2889

10.

ETH152

4

194

200

204

208

 

 

 

0.3722

0.3945

0.7660

0.0745

0.0957

0.0638

11.

ETH225

4

146

152

154

160

 

 

 

0.6668

0.7193

0.2326

0.1163

0.3023

0.3488

12.

INRA005

5

134

136

138

140

142

 

 

0.7203

0.7612

0.1889

0.3111

0.2444

0.2111

0.0444

13.

INRA032

6

166

174

176

180

188

196

 

0.6571

0.6998

0.2083

0.0833

0.0417

0.4583

0.0139

0.1944

14.

INRA035

7

102

104

106

108

112

122

124

0.7975

0.8220

 

 

 

0.0238

0.2024

0.1310

0.2262

0.2143

0.0714

0.1310

 

 

15.

INRA063

4

174

180

186

188

 

 

 

0.6234

0.3978

 

 

 

0.1413

0.4348

0.1087

0.3152

 

 

 

 

 

16.

HEL001

4

100

106

108

110

 

 

 

0.6265

0.6827

 

 

 

0.1778

0.3000

0.4333

0.0889

 

 

 

 

 

17.

HEL005

4

150

154

158

166

 

 

 

0.5322

0.5988

 

 

 

0.5610

0.0854

0.0854

0.2683

 

 

 

 

 

18.

HEL009

5

148

152

156

160

164

 

 

0.6101

0.6707

 

 

 

0.1364

0.0455

0.3977

0.3864

0.0341

 

 

 

 

19.

BM1818

6

248

252

260

264

276

278

 

0.6741

0.7036

 

 

 

0.1042

0.0938

0.4896

0.1458

0.1146

0.0521

 

 

 

20.

BM2113

4

136

138

144

148

 

 

 

0.6224

0.6836

 

 

 

0.2396

0.4062

0.3021

0.0521

 

 

 

 

 

21.

MM8

4

132

138

144

146

 

 

 

0.4647

0.5617

 

 

 

0.0444

0.4778

0.4556

0.0222

 

 

 

 

 

22.

HAUT024

5

122

124

128

130

136

 

 

0.6725

0.7190

 

 

 

0.3977

0.1250

0.1591

0.2841

0.0341

 

 

 

 

23.

HAUT027

3

148

152

154

 

 

 

 

0.5681

0.6440

 

 

 

0.2128

0.4149

0.3723

 

 

 

 

 

 

24.

CSSM066

6

168

170

178

182

186

196

 

0.6585

0.6906

 

 

 

0.1333

0.0444

0.5000

0.0556

0.1556

0.1111

 

 

 

25.

CSRM060

6

94

98

100

106

112

114

 

0.7789

0.8065

 

 

 

0.1702

0.2872

0.1277

0.1702

0.0638

0.1809

 

 

 

Overall mean / range

4.72 0.25

0.0116 - 0.8128

 

0.6209 0.03

0.6569 0.03

* – Expected heterozygosity


Microsatellite alleles

The mean number of allele was found to be 4.72 ± 0.25 per microsatellite locus. The number of alleles was in the range of 3 to 7. These polymorphic loci generated a total of 118 alleles in the breed. In general, the number and sizes of microsatellite alleles observed in this study fall within the range mentioned in the Secondary Guidelines for Development of National Farm Animal Genetic Resources Management Plans, published by FAO (2004). The mean number of allele observed in the study is lesser than that reported in other Indian breeds of cattle such as Sahiwal (5.2) and Deoni (5.9) (Mukesh et al 2004) using the same microsatellites; and more than the averages for Ongole (4.5) and Deoni (4.1) cattle for different set of markers (Metta et al 2004). The allele sizes observed for the loci ILSTS005, ILSTS006, ILSTS011 and ILSTS033 are in accordance with the sizes reported for Bos taurus cattle (Kemp et al 1995) and Spanish native cattle breeds (Martin-Burriel et al 1999).

The frequencies of microsatellite alleles in some representative loci are shown in figure 1.


Figure 1.  Graphs depicting distribution of allele frequencies at different microsatellite loci in Krishna Valley cattle


The allele sizes were ranging from 94 (CSRM060) to 300 (ILSTS006) bp. These microsatellite alleles occurred at a minimum frequency of 0.0116 (140 bp allele in ILSTS054) to a maximum of 0.8128 (152 bp allele in ILSTS030). Apart from 152 bp allele, 194 bp allele in ETH152 locus occurred at higher frequency of 77 per cent. This is followed by 262 bp allele in ILSTS011 (70 per cent), 178 bp allele in CSSM066 (50 per cent), 260 bp allele in BM 1818 and 116 bp allele in ETH003 (49 per cent).

Polymorphism information content (PIC)

The statistical assessment of informativeness of a marker, as denoted by PIC value, was ranging from 0.2583 (ILSTS030) to 0.7975 (INRA035) with a mean PIC value of 0.6209 ± 0.03. The high polymorphism information contents noticed in this study are in agreement with Belgian cattle breeds for loci ETH003, ETH010, ETH225 and BM1818 (Peelman et al 1998). The PIC values observed in most of the loci are comparable with those observed in other Indian cattle breeds such as Deoni, Hariana and Sahiwal. The PIC values for loci ILSTS005 (0.6954), ILSTS011 (0.4140) and ILSTS033 (0.7036) were slightly different from the PIC (0.59, 0.67 and 0.77 respectively) reported by Kemp et al (1995) in cattle while ILSTS006 locus had similar PIC value (0.72). Except ILSTS030 and ETH152, all other loci possessed high PIC values indicating that these markers are highly informative for characterization of Krishna Valley cattle.

Hardy-Weinberg equilibrium

The Chi-square (c2) test for Hardy-Weinberg equilibrium revealed that except in 7 loci (ILSTS005, ILSTS006, ILSTS030, ETH152, HEL001, INRA063 and HAUT027), the Krishna Valley population was not in equilibrium. The disequilibrium proportion observed in the population is attributed to the existence of null alleles, inherently high mutation rate of microsatellites and size homoplasy of loci, besides small population size as a result of shrinkage of breeding tract and availability of lesser number of bulls.

Within-population genetic variability

The overall means for observed and expected heterozygosities were 0.6683 ± 0.06 and 0.6569 ± 0.03 respectively with the ranges of 0.0930 (ETH225) to 1.000 (ETH003, INRA035 and BM2113) and 0.3047 (ILSTS030) to 0.8220 (INRA035). The mean expected heterozygosity value is comparable to that of Sahiwal (0.61), Hariana (0.66) and Deoni (0.70), the other Indian cattle breeds studied by Mukesh et al (2004). Though few loci exhibited lower heterozygosity values, majority of the loci had relatively higher expected heterozygosity, which reflects the existence of variation in spite of declining number of Krishna Valley in the breeding tract.

The high heterozygosity values observed indicate more number of polymorphic loci in Krishna Valley cattle. This implies the higher amount of genetic variability that can be exploited even in population of small sizes.


Conclusions


Acknowledgements

We express our sincere gratitude to the National Bureau of Animal Genetic Resources, Karnal for the financial support through the ICAR Network Projects - Core Laboratory.


References

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Received 9 September 2006; Accepted 26 September 2006; Published 1 November 2006

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