Novel insights into genome-wide associations in Bos indicus reveal genetic linkages between fertility and growth.

Bos indicus breed Sahiwal, famous for its optimum performance, has so far been genetically improved for performance traits based on phenotypic records and the genomic knowhow regarding genes, regions and biological processes underlying the complex quantitative traits is lacking. In this context, a Genome-wide Association Study was performed for fertility and growth traits in Sahiwal cattle to shed light on its genomic profile. A total of 46 SNPs were found associated with the traits at genome-wide suggestive threshold of P ≤ 10-4. USP32, LRPPRC, PLA2G10, RRN3 and ASAP1 were identified as putative candidate genes for body weight at different ages. However, several genes mapped for growth traits like GREB1, PLA2G10, RAD51C, BIRC6, TEX14 and PEBP4 had significant physiological underpinnings in determining fertility of the animals. Moreover, Quantitative trait loci (QTL) identification revealed potential overlaps with the already reported QTLs for both fertility and growth for most of the traits. Further, candidate SNP enrichment analysis revealed an enriched biological process for birth weight with a significant reproductive role. Based on the findings, genetic linkages underlying fertility and growth could be discerned in Sahiwal population and may be utilized for improving fertility traits in future.

Understanding the genomic architecture of clinical mastitis in Bos indicus.

This study elucidated potential genetic variants and QTLs associated with clinical mastitis incidence traits in Bos indicus breed, Sahiwal. Estimated breeding values for the traits (calculated using Bayesian inference) were used as pseudo-phenotypes for association with genome-wide SNPs and further QTL regions underlying the traits were identified. In all, 25 SNPs were found to be associated with the traits at the genome-wide suggestive threshold (p ≤ 5 × 10-4) and these SNPs were used to define QTL boundaries based on the linkage disequilibrium structure. A total of 16 QTLs were associated with the trait EBVs including seven each for clinical mastitis incidence (CMI) in first and second lactations and two for CMI in third lactation. Nine out of sixteen QTLs overlapped with the already reported QTLs for mastitis traits, whereas seven were adjudged as novel ones. Important candidates for clinical mastitis in the identified QTL regions included DNAJB9, ELMO1, ARHGAP26, NR3C1, CACNB2, RAB4A, GRB2, NUP85, SUMO2, RBPJ, and RAB33B genes. These findings shed light on the genetic architecture of the disease in Bos indicus, and present potential regions for fine mapping and downstream analysis in future.

Morphometric and microsatellite-based comparative genetic diversity analysis in Bubalus bubalis from North India.

To understand the similarities and dissimilarities of a breed structure among different buffalo breeds of North India, it is essential to capture their morphometric variation, genetic diversity, and effective population size. In the present study, diversity among three important breeds, namely, Murrah, Nili-Ravi and Gojri were studied using a parallel approach of morphometric characterization and molecular diversity. Morphology was characterized using 13 biometric traits, and molecular diversity through a panel of 22 microsatellite DNA markers recommended by FAO, Advisory Group on Animal Genetic Diversity, for diversity studies in buffaloes. Canonical discriminate analysis of biometric traits revealed different clusters suggesting distinct genetic entities among the three studied populations. Analysis of molecular variance revealed 81.8% of genetic variance was found within breeds, while 18.2% of the genetic variation was found between breeds. Effective population sizes estimated based on linkage disequilibrium were 142, 75 and 556 in Gojri, Nili-Ravi and Murrah populations, respectively, indicated the presence of sufficient genetic variation and absence of intense selection among three breeds. The Bayesian approach of STRUCTURE analysis (at K = 3) assigned all populations into three clusters with a degree of genetic admixture in the Murrah and Nili-Ravi buffalo populations. Admixture analysis reveals introgression among Murrah and Nili-Ravi breeds while identified the Gojri as unique buffalo germplasm, indicating that there might be a common origin of Murrah and Nili-Ravi buffaloes. The study provides important insights on buffalo breeds of North India that could be utilized in designing an effective breeding strategy, with an appropriate choice of breeds for upgrading local non-descript buffaloes along with conservation of unique germplasm.

Marker-assisted selection vis-à-vis bull fertility: coming full circle-a review.

Bull fertility is considered an indispensable trait, as far as farm economics is concerned since it is the successful conception in a cow that provides calf crop, along with the ensuing lactation. This ensures sustainability of a dairy farm. Traditionally, bull fertility did not receive much attention by the farm managers and breeding animals were solely evaluated based on phenotypic predictors, namely, sire conception rate and seminal parameters in bull. With the advent of the molecular era in animal breeding, attempts were made to unravel the genetic complexity of bull fertility by the identification of genetic markers related to the trait. Marker-Assisted Selection (MAS) is a methodology that aims at utilizing the genetic information at markers and selecting improved populations for important traits. Traditionally, MAS was pursued using a candidate gene approach for identifying markers related to genes that are already known to have a physiological function related to the trait but this approach had certain shortcomings like stringent criteria for significance testing. Now, with the availability of genome-wide data, the number of markers identified and variance explained in relation to bull fertility has gone up. So, this presents a unique opportunity to revisit MAS by selection based on the information of a large number of genome-wide markers and thus, improving the accuracy of selection.