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In the present study, the population genomic data of different cattle breeds were explored to decipher the genomic regions affected due to selective events and reflected in the productive, reproductive, thermo-tolerance, and health-related traits. To find out these genomic deviations due to selective sweeps, we used eight different statistical tools (Tajima's D, Fu & Li's D*, CLR, ROH, iHS, F ST , FLK, and hapFLK) on seven indigenous and five exotic cattle breeds. We further performed composite analysis by comparing their covariance matrix. Several candidate genes were found to be related to milk production (ADARB, WDR70, and CA8), reproductive (PARN, FAM134B2, and ZBTB20), and health-related traits (SP110, CXCL2, CLXCL3, CXCL5, IRF8, and MYOM1). The outcome of this investigation provides a basis for detecting selective sweeps that explain the genetic variation of traits. They may possess functional importance for multiple cattle breeds in different subcontinents. However, further studies are required to improve the findings using high-density arrays or whole-genome sequencing with higher resolution and greater sample sizes., (Copyright © 2022 Elsevier B.V. All rights reserved.)

Tharparkar cattle breed is widely known for its superior milch quality and hardiness attributes. This study aimed to develop an ultra-low density breed-specific single nucleotide polymorphism (SNP) genotype panel to accurately quantify Tharparkar populations in biological samples. In this study, we selected and genotyped 72 Tharparkar animals randomly from Cattle & Buffalo Farm of IVRI, India. This Bovine SNP50 BeadChip genotypic datum was merged with the online data from six indigenous cattle breeds and five taurine breeds. Here, we used a combination of pre-selection statistics and the MAF-LD method developed in our laboratory to analyze the genotypic data obtained from 317 individuals of 12 distinct breeds to identify breed-informative SNPs for the selection of Tharparkar cattle. This methodology identified 63 unique Tharparkar-specific SNPs near intermediate gene frequencies. We report several informative SNPs in genes/QTL regions affecting phenotypes or production traits that might differentiate the Tharparkar breed., (Copyright © 2021 Elsevier B.V. All rights reserved.)

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 F ST , 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.

The cost of SNP genotyping to screen different breeds and to estimate the exact proportion of ancestry level is quite high, which can be compensated through deriving a small panel of ancestry informative markers (AIMs). Hence, we carried out the present study to provide an insight into ancestry level inferred from a panel of informative markers in the crossbred Vrindavani population developed at ICAR-IVRI, India. We have performed a new method i.e., discriminant analysis of principal components (DAPC) for the first time on the dataset of Vrindavani cattle. To confirm our method, we had performed DAPC on two other well-known crossbred cattle, i.e., Frieswal and Beefmaster. Three sets of panels (500, 1000 and 2000 markers) were tested for clustering of individuals. Among all the panels, we found the panel (1000 markers) with DAPC based contribution method was of the smallest size and comparatively of the highest accuracy., Competing Interests: Declaration of competing interest The authors declare that they have no conflict of interest., (Copyright © 2019 Elsevier Inc. All rights reserved.)

The aim of the present study was to assess the population structure and admixture levels in the Vrindavani composite population in India by using Bovine50KSNP BeadChip data. Genotypic data were generated for randomly selected animals (n = 72) of Vrindavani population and the data for parental breeds i.e., Hariana (n = 10), Holstein-Friesian (n = 63), Jersey (n = 28) and Brown Swiss (n = 22) were retrieved from a public repository. The indices of population structure were calculated using PLINK software and R-program. The merged dataset was analysed for assessing admixture levels and population stratification using three different approaches i.e., principal component analysis (PCA), multi-dimensional scaling (MDS) approach and the model-based approach in STRUCTURE software. The average minor allele frequency (MAF) value for Vrindavani population was estimated to be 0.235. Vrindavani population was found to possess an average ancestry of 39.5, 22.9, 26.9, and 10.7% inheritance levels from Holstein Friesian, Jersey, Hariana and Brown Swiss cattle breeds, respectively., Competing Interests: Declaration of Competing Interest The authors declare that they have no conflict of interest., (Copyright © 2019. Published by Elsevier Inc.)

Mature spermatozoa contain thousands of mRNA transcripts. These untranslated mRNA may perhaps serve as a "footprint" of spermatogenesis since many of them might directly or indirectly be involved in fertilization, early embryo cleavage, poor semen quality and fertility. In this study, we tried to isolate high-quality RNA from mature spermatozoa and to monitor the expression profile of protamine 1 (PRM1) and protamine 2 (PRM2) gene in ejaculated spermatozoa of normal (good, % initial progressive motility: 57.61±1.41, n=9) and motility impaired (poor, % initial progressive motility: 18.45±1.61, n=8) crossbred Frieswal (HF×Sahiwal) bulls semen using real time quantitative PCR. Semen samples were subjected to discontinuous (45:90) Percoll gradient centrifugation, specifically to eliminate damaged spermatozoa and contaminating somatic cells. Total RNA was extracted from sperm pellets and cDNA was synthesized. Furthermore, the absence of contamination of germ cells, epithelial cells and leucocytes in all the RNA extractions was tested by RT-PCR targeting specific molecular markers like KIT, CDH1 and CD4, respectively. The presence of transcripts like PRM1, PRM2, DAZL, and PPIA were demonstrated in ejaculated spermatozoa using appropriate PCR primers without RNA amplification. Expression of PRM1 and PRM2 genes were evaluated by real time quantitative PCR using TaqMan chemistry, where PPIA was used as internal control. The cDNA synthesized from normal buffalo testicular tissue was served as positive control. The good quality semen producing group showed significantly higher level of PRM1 mRNAs expression as compared to the poor quality semen producers (P<0.05) indicating putative role of the gene and semen quality parameters especially initial progressive motility. However, PRM2 transcript levels were not significantly different between the groups (P>0.05)., (Copyright © 2012 Elsevier Ltd. All rights reserved.)

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]

Leptin gene has its role in appetite, metabolism, growth and milk production in cattle. PCR-RFLP method was carried out to detect the polymorphism in leptin gene among Frieswal, Ongole and Sahiwal cattle. Frieswal population under study revealed that 11%, 51% and 38% are TT, CT, and CC respectively genotypes with respect to exon 2. But the same population had 22% TT, 51% CT and 27% CC genotype for first SNP in exon 3; 4% TT, 38% C Tand 58% CC genotype for second SNP in exon 3. Ongole population revealed only CT genotype with respect to exon 2, but it showed TT genotype for first SNP and CC genotype for second SNP in exon3. Sahiwal population had 4% TT, 88% CT and 8% CC genotype (exon 2), respectively. Whereas the same population revealed 4% TT, 55% CT and 41% CC genotype for first SNP in exon 3 and 4% CT and 96% CC genotype for second SNP in exon 3. Sahiwal population had no TT genotype for second SNP in exon 3. [ABSTRACT FROM AUTHOR]