Sabyasachi Mukherjee

The Karan Fries (KF), a newly developed dairy cattle (Holstein Friesian × Tharparkar), after nine generations of inter se mating (mating among crossbreds) by maintaining a stable, composite breed structure, is expected to preserve some heterosis or hybrid vigour through the retention of heterozygosity and selective pressure for performance traits. Although a decline in productivity is generally seen from F1 to later inter se generations, the rate of loss of heterosis slows down, allowing the population to reach a new equilibrium. This seems to be an interesting proposition. Therefore, to assess the retained heterosis and to evaluate the current status of the KF population, the present study was conducted, which included a sizeable phenotyped and genotyped animals (n = 355). The analysis identified a stabilized genomic architecture with a mean exotic inheritance of 64.34 ± 0.74% (predominantly Holstein Friesian) and an indigenous contribution of 35.66 ± 0.95% (primarily Tharparkar). Genomic indicators of heterosis revealed a mean Genomic Retained Heterosis (RH_g) of 59% which accounts for 15.82% of the total observed KF average yield (4153.14 ± 167.32 kg). Genomic Retained Heterozygosity (RHET_g) of 0.44%. Notably, a near-perfect correlation (r = 0.98) was observed between these two metrics, indicating that RHET_g can be used interchangeably with RH_g to assess heterosis and its effects in instances where parental genotypic records were absent. Linear regression analysis demonstrated that RH_g was a primary driver of productivity, with Total Milk Yield (TMY) increasing by 1113.81 ± 301.91 kg per unit increase in RH_g (p < 0.001). The impact on TMY was significantly larger than on 305-day milk yield (339.08 ± 282.92 kg), suggesting that genomic heterosis specifically enhances lactation persistence and environmental robustness. A joint-model GWAS identified 182 SNPs with significant additive effects and 117 SNPs with significant dominance effects for TMY. Functional annotation revels candidate genes for milk synthesis (SLC25A1, PIP4K2A, LATS2), heat stress resilience (DNAJB5, DNAJC1), and immunological defence (ARHGAP15, LPAR3). Protein network analysis identified the Mitochondrial Ribosomal Protein (MRP) family, specifically hub gene MRPL22, as the central driving gene for both additive and dominance components. These findings confirm that KF cattle effectively leverage retained heterosis to integrate high production potential with robust tropical climatic adaptability.

Background: This study evaluates machine learning (ML) algorithms as a robust alternative to traditional linear models, which often overlook complex biological patterns for predicting lactation performance in Karan Fries (KF) cattle. Using phenotypic records from 473 lactating animals, the analysis focuses on predicting 305-day milk yield (305 DMY) and total milk yield (TMY). The population exhibited an average peak yield of 13.28±0.25 kg and a persistent average fat concentration of 4.14%. Methods: This study was done by integrating Wood’s incomplete gamma function to model lactation curves, compressing longitudinal test-day data into mathematically significant parameters. These parameters, alongside fixed environmental factors, served as predictors for three models: Multiple linear regression (MLR), random forest (RF) and extreme gradient boosting (XGBoost). Result: Results revealed that the RF model significantly outperformed both MLR and XGBoost. For 305DMY, RF achieved a high coefficient of determination (R2 = 0.88) compared to the poor performance of the MLR baseline (R2 = 0.21). A similar hierarchy persisted for TMY, where RF attained an R2 of 0.87, effectively reducing the root mean square error (RMSE) by over 50% compared to MLR. The study concludes that integrating Wood’s parameters with ensemble-based ML models offers a transformative tool for dairy breeding by facilitating early selection and optimized herd management.

Selective pressures, both natural and artificial, have significantly influenced the genomic architecture of domesticated sheep. Understanding their underlying molecular mechanisms is critical for developing efficient breeding programs to conserve and improve economically important traits in native breeds. In this study, we analysed high-density 50K SNP data from three Indigenous sheep breeds: Chanthangi (CHA, n = 29), Garole (GAR, n = 24), and Deccani (IDC, n = 26), each native to diverse climatic regions of India. We implemented a novel SNP-based de-correlated composite of multiple signals (DCMS) statistic, which integrates p- values from five selection metrics viz., FST, H1, H12, Tajima’s D, and nucleotide diversity (π) into a unified measure. The SNP-based DCMS approach offers finer resolution and complements window-based methods by enabling more precise localisation of selection signals and candidate genes. Multiple testing correction was applied at a False Discovery Rate (FDR) threshold of . Comprehensive gene and quantitative trait loci (QTL) annotation and enrichment analysis of these regions were also performed for each breed. The DCMS analysis identified 21, 10, and 14 novel and breed-specific putative genes in the Chanthangi, Garole, and Deccani breeds, respectively, as well as 10, 28, and 13breed-specific QTL regions. The identified genes and QTLs are associated with diverse phenotypic traits, including growth and muscle development ( CNTNAP5, DOCK3 ), reproduction ( TCERG1L, BUB1, UNC5C, C2CD5, BBX ), wool trait ( TPPP3, P2RY6 , FGF10, POU2F1 , FAM168A ), disease resistance ( MTSS1, B4GALNT3 ), environment adaptation ( TRMT12, MAPKAPK3 ), domestication ( LRRC36 ). The QTLs identified are associated with body conformation (body measurements and bone area), production (milk fat yield), reproduction (total lambs born), disease resistance (hemonchus resistance, foot rot, and pneumonia susceptibility), and health (platelet count and entropion). Our SNP-based DCMS method enabled high-resolution detection of breed-specific selection signatures. It facilitated the discovery of both known and novel genomic regions, candidate genes, and QTLs unique to Indian sheep breeds. This comprehensive approach provides valuable insights into the molecular mechanisms underlying economically important traits and offers a robust foundation for targeted genetic improvement and conservation of indigenous sheep breeds.

Background: Genetic parameter and covariance component estimation is an important aspect of animal breeding, as it helps to quantify additive and non-additive genetic variances. The precise estimation is also essential for reliable prediction of breeding values. The milk production for each animal in lactation has shown increasing trend over the last seven decades in the herd under study. The important fact is that more than 50% progress is associated with genetic progress and rest is credited to advances in management practices. However, on the other hand the picture is different while tracking for fertility over this period that showed a decline trend. The continuous selection for milk production traits leads to decline in reproduction traits. Thus, present study is planned to study the effect of continuous selection for milk yield on fertility traits and the probable measures to overcome these effects. The study was conducted on Sahiwal cattle herd maintained at ICAR-NDRI, Karnal. Methods: The data was collected from records belonging to 1955-2024 on first lactation production and reproduction like FLTMY, FL305dMY, FSP, FCI, DPR and AFC traits. The effect of non-genetic factors was estimated using least-squares analysis by General Linear Model of SPSS. The variance and (co)variance components were estimated by using the BLUPF90 family of programs. Result: The genetic variability measured by genetic parameters indicated that genetic and phenotypic correlation between production and reproduction traits was negative and between productions traits were high in the study. Overall, the genetic trend was positive in the present study for production traits whereas the reproduction traits exhibited unfavourable or inconsistent trends. The information generated in the present study will be useful in planning future breeding activities for further improvement of the breed.

The present study conducted on an organised herd of Sahiwal cattle to elucidate the population structure parameter based on seven decades of genealogical information. The genealogical data (N = 4164) spanning 1949 to 2023 maintained at ICAR-National Dairy Research Institute, Karnal, India was considered with the aim to evaluate depth and accuracy of pedigree, population parameter and genetic diversity in different breeding schemes for whole population and pertinent reference cohort. The animal born from 1979 to 2009 grouped as reference 1 and from 2010 to 2023 as reference 2 cohort. The comprehensiveness of the whole population, reference 1 and reference 2 was over 50% up to the fourth, third and sixth generations respectively. The average relatedness and inbreeding coefficient were 5.92% and 2.69% for whole population, 5.44% and 1.96% for reference 1 and 6.98% and 3.85% for reference 2 respectively. The effective population size varied from 32.31 to 145.51 in the study. The average generation interval varies from 9.2 years in reference 1 to 8.2 years in whole pedigree and 6.6 years in reference 2 under study. The ratio (f_e/f_a) of effective number of founders (f_e) and ancestors (f_a) were 1.67, 1.59 and 1.90 for the whole, reference 1 and reference 2 respectively. The estimate for f_g/f_a ratio indicated that 54% of ancestral genetic diversity is retained in the current generation. Thus, present study provides critical insights to maintain effective population size, rate and level of inbreeding with contribution of founders in current population for optimum genetic diversity and improving breeding efficiency in planning future breeding programs.