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The present study aims to understand the existing genetic diversity and structure of six native cattle breeds (Rathi, Tharparkar, Nagori, Mewati, Gir, and Kankrej) adapted to the north-western arid and semi-arid region of India based on microsatellite loci. Various diversity estimates, mean number of alleles (12.84); effective number of alleles (5.02); gene diversity (0.769), and observed heterozygosity (0.667) reflected the existence of substantial within-breed diversity in all the investigated cattle breeds. Mean estimates of F-statistics: FIT = 0.144 ± 0.023, FIS = 0.071 ± 0.021, and FST = 0.078 ± 0.014 were significantly different from zero (P < 0.05). The interbreed relationships indicated moderate level of breed differentiation between the six cattle breeds with least differentiation between Kankrej-Mewati pair. The phylogeny structuring further supported close grouping of Kankrej and Mewati breeds. Correspondence analysis plotted Rathi, Tharparkar, and Gir individuals into three separate areas of multivariate space; whereas, Kankrej, Mewati, and Nagori cattle showed low breed specific clustering. This reflected the existence of discrete genetic structure for Tharparkar, Rathi, and Gir, the prominent dairy breeds of the region; whereas, admixture was observed for Kankrej, Mewati, and Nagori individuals. [ABSTRACT FROM AUTHOR]

Abstract: In the present study, 272 individuals of Tharparkar (THC), Rathi (RAC), Nagori (NAC), Mewati (MEC) cattle breeds from Rajasthan and Gir (GIC), Kankrej (KAC) from Gujarat states of India were evaluated for assignment accuracy of individuals to their respective source population by employing multilocus genotypic data generated using 25 bovine specific microsatellite markers. The high diversity indices and moderate yet significant genetic differentiation values (F ST) across the set of markers indicated their utility for assigning individual genotype to its population of origin with high statistical precision. Different assignment tests based on likelihood and genetic distance approach were implemented to evaluate the assignment accuracy. In direct approach, an overall high percentage of correct assignment was observed with two likelihood-based methods (Bayesian approach; 93.7%, and frequency method; 92.6%), whereas, distance based criteria produced relatively lower assignment accuracy that ranged from 78.7% to 90.1%. Individuals of THC, RAC, GIC and NAC breeds were assigned to their respective populations with high accuracy rate. The animals of KAC and MEC breeds, however exhibited least assignment percentage reflecting the presence of admixture in these two populations. Similarly with 10,000 simulation and rejecting population if probability is <0.01, Bayesian method yielded highest assignment scores (95.6%) while frequency based approach resulted in 92.6% correct assignment. With distance based criteria the assignment accuracy ranged from 80.6% to 93.0%. The analysis indicated superiority of Bayesian method over frequency and distance distance based methods for assigning individuals to their source populations. RAC and GIC individuals displayed 100% accurate assignment while NAC and THC exhibited 97.3% and 95.7% of correct assignment with Bayesian criterion. Minimum assignment success rate was observed for KAC (92.9%) and MEC (93.7%) breeds. The overall high assignment accuracy observed for THC, RAC, GIC and NAC reflected their genetic structure as discrete cattle breeds. [Copyright &y& Elsevier]