High-throughput Genotype based Population Structure Analysis of Selected Buffalo Breeds

The water buffalo ( Bubalus bubalis ) has shown enormous milk production potential in many Asian countries. India is considered as the home tract of some of the best buffalo breeds. However, genetic structure of the Indian river buffalo is poorly understood. Hence, for selection and breeding strategies, there is a need to characterize the populations and understand the genetic structure of various buffalo breeds. In this study, we have analysed genetic variability and population structure of seven buffalo breeds from their respective geographical regions using Axiom ® Buffalo Genotyping Array having 124,030 Single Nucleotide Polymorphisms (SNPs). Blood samples were obtained from 302 buffaloes comprising Murrah, Nili-Ravi, Mehsana, Jaffarabadi, Banni, Pandharpuri and Surti breeds. Diversity, as measured by expected heterozygosity (H e ) ranged from 0.364 in the Surti to 0.384 in the Murrah breed. All the breeds showed negligible inbreeding coefficient. Pair-wise F ST values revealed the lowest genetic distance between Mehsana and Nili-Ravi (0.0022) while highest between Surti and Pandharpuri (0.030). Principal component analysis and structure analysis unveiled the differentiation of Surti, Pandharpuri and Jaffarabadi in first two PCs, while remaining breeds were grouped together as a separate single cluster. Murrah and Mehsana showed early linkage disequilibrium decay while Surti breed showed late decay, similarly LD based Ne was drastically declined for Murrah and Mehsana since last 100 generations. In LD blocks to QTLs concordance analysis, 14.19 per cent of concordance was observed with 873 (out of 1144) LD blocks overlapped with 8912 (out of 67804) QTLs. Overall, total 4090 markers were identified from all LD blocks for six types of traits. Results of this study indicated that these SNP markers could differentiate phenotypically distinct breeds like Surti, Pandharpuri and Jaffarabadi but not others. So, there is a need to develop SNP chip based on SNP markers identified by sequence information of local breeds.