Search

The identification of candidate genes related to pigmentation and under selective sweep provides insights into the genetic basis of pigmentation and the evolutionary forces that have shaped this variation. The selective sweep events in the genes responsible for normal coat color in Indian cattle groups are still unknown. To find coat color genes displaying signs of selective sweeps in the indigenous cattle, we compiled a list of candidate genes previously investigated for their association with coat color and pigmentation. After that, we performed a genome-wide scan of positive selection signatures using the BovineSNP50K Bead Chip in 187 individuals of seven indigenous breeds. We applied a wide range of methods to find evidence of selection, such as Tajima's D, CLR, iHS, varLD, ROH, and FST. We found a total of sixteen genes under selective sweep, that were involved in coat color and pigmentation physiology. These genes are CRIM1 in Gir, MC1R in Sahiwal, MYO5A, PMEL and POMC in Tharparkar, TYRP1, ERBB2, and ASIP in Red Sindhi, MITF, LOC789175, PAX3 and TYR in Ongole, and IRF2, SDR165 and, KIT in Nelore, ADAMTS19 in Hariana. These genes are related to melanin synthesis, the biology of melanocytes and melanosomes, and the migration and survival of melanocytes during development. [ABSTRACT FROM AUTHOR]

Major Histocompatibility Complex (MHC) genes are among the immune genes that have been extensively studied in vertebrates and are necessary for adaptive immunity. In the immunological response to infectious diseases, they play several significant roles. This research paper provides the selection signatures in the MHC region of the bovine genome as well as how certain genes related to innate immunity are undergoing a positive selective sweep. Here, we investigated signatures of historical selection on MHC genes in 15 different cattle populations and a total of 427 individuals. To identify the selection signatures, we have used three separate summary statistics. The findings show potential selection signatures in cattle from whom we isolated genes involved in the MHC. The most significant regions related to the bovine MHC are BOLA, non-classical MHC class I antigen (BOLA-NC1), Microneme protein 1 (MIC1) , Cluster of Differentiation 244 (CD244), Gap Junction Alpha-5 Protein (GJA5). It will be possible to gain new insight into immune system evolution by understanding the distinctive characteristics of MHC in cattle. [ABSTRACT FROM AUTHOR]

The present study was conducted on the MHC class I (BoLA-A/BuLA-A) gene in Sahiwal, Jersey, Hariana, and Tharparkar breeds of cattle and Murrah, Mehsana, and Bhadawari breeds of buffalo to study the polymorphism. Exons 7–8 of the MHC class I gene was first characterized for polymorphism study in buffalo and the results reveal that this gene has a higher level of nucleotide changes than the cattle. Genes were investigated for polymorphisms in 285 animals of cattle and buffalo breeds. Molecular characterization of the MHC class I (BoLa-A/Bula-A) gene reveals a higher degree of polymorphism at the nucleotide level in cattle and buffalo. Results revealed this region has a higher level of polymorphisms in buffalo as campared to the cattle. Alul restriction patterns were monomorphic except for three different patterns but it was able to illustrate the differences in buffalo and cattle. SSCP analysis of exons 7–8 showed remarkable differences in cattle and buffalo. Sequence analysis revealed more closeness of Murrah breed with crossbred and indigenous cattle than Holstein Friesian. Exon 8 had more deletion and stop codon as compared to exon 7. The investigation confirmed that MHC class I BoLa-A/Bula-A exons 7–8 is highly polymorphic in buffalo as compared to cattle. [ABSTRACT FROM AUTHOR]

Knowledge about genetic diversity is very essential for the management and sustainable utilization of livestock genetic resources. In this study, we presented a comprehensive genome-wide analysis of genetic diversity, ROH, inbreeding, linkage disequilibrium, effective population size and haplotype block structure in Tharparkar cattle of India. A total of 24 Tharparkar animals used in this study were genotyped with Illumina BovineSNP50 array. After quality control, 22,825 biallelic SNPs were retained, which were in HWE, MAF > 0.05 and genotyping rate >90%. The overall mean observed (HO) and expected heterozygosity (HE) were 0.339 ± 0.156 and 0.325 ± 0.129, respectively. The average minor allele frequency was 0.234 with a standard deviation of ± 0.131. We identified a total of 1832 ROH segments and the highest autosomal coverage of 13.87% was observed on chromosome 23. The genomic inbreeding coefficients estimates by FROH, FHOM, FGRM and FUNI were 0.0589, 0.0215, 0.0532 and 0.0160 respectively. The overall mean linkage disequilibrium (LD) for a total of 133,532 pairwise SNPs measured by D' and r2 was 0.6452 and 0.1339, respectively. In addition, we observed a gradual decline in effective population size over the past generations. [ABSTRACT FROM AUTHOR]

Single nucleotide polymorphisms (SNPs) have now replaced microsatellite markers in several species for various genetic investigations like parentage assignment, genetic breed composition, assessment for individuality and, most popularly, as a useful tool in genomic selection. However, such a resource, which can offer to assist breed identification in a cost-effective manner is still not explored in cattle breeding programs. In our study, we have tried to describe methods for reducing the number of SNPs to develop a breed-specific panel. We have used SNP data from Dryad open public access repository. Starting from a global dataset of 178 animals belonging to 10 different breeds, we selected five panels each comprising of similar number of SNPs using different methods i.e., Delta, Pairwise Wright's FST, informativeness for assignment, frequent item feature selection (FIFS) and minor allele frequency–linkage disequilibrium (MAF-LD) based method. MAF-LD based method has been recently developed by us for construction of breed-specific SNP panels. The STRUCTURE software analysis of MAF-LD based method showed appropriate clustering in comparison to other panels. Later, the panel of 591 breed-specific SNPs was called to their respective breeds using Venny 2.1.0 and UGent web tools software. Breed-specific SNPs were later annotated by using various Bioinformatics softwares. [ABSTRACT FROM AUTHOR]