Jul 2026· Gomal Journal of Agriculture and Biology· Vol 4, pp. 98-118· 0 citations
Abstract
The current experiment was carried out to assess the effectiveness of genomic selection in maximizing milk output and functional characteristics in milk-producing cows, particularly the dilemma of the size of the reference population, the heritability of a trait, and the cost of genotyping. A 4,500- Holstein and Jersey cows population was stratified into a reference and a validation population, high-density genotyping of the reference population and low-density genotyping of the validation population followed by high-density coverage imputation. GBLUP and Bayesian mixture models estimated the breeding values genomic and compared them with the traditional pedigree-based BLUP. Genomic selection enhanced prediction accuracy by about forty to sixty-five percent, with the biggest improvements noted in low-heritability traits like daughter pregnancy rate and longevity. The annual genetic gain increased more than twice on production traits and thrice on fertility traits with the highest rates obtained when functional annotations were included. Low-density chip imputation was found to have concordance rates of 0.96 with a reference population of 3,000 animals, approximately eighty percent of the genotyping costs. The average generation intervals decreased by nearly sixty-nine percent to 1.9 years. Significant quantitative trait loci such as DGAT1, which accounted more than eighteen percent of the fat yield variance, GHR, and CSN1S1 were confirmed as key contributors to production traits. Imputation concordance of 10,000 animals in multi-breed panels reached over 0.98 in common single nucleotide polymorphisms with 0.81 in rare ones. The economic analysis showed net growth of more than four hundred and fifty eight thousand dollars per 1000 cows/year and payback period of more than two thousand four hundred percent in five years. These results prove that genomic selection is a revolutionary technology that will greatly accelerate the genetic advancement, decrease expenses, and boost the economic viability of the dairy breeding programs.
Genomic selection (GS) has transformed modern animal breeding by enabling the prediction of genetic merit using dense genome-wide molecular markers rather than relying solely on pedigree and phenotypic information. Since its conceptual introduction in 2001, GS has become a cornerstone of genetic improvement programs in livestock species, particularly dairy cattle, and has subsequently expanded to beef cattle, sheep, goats, swine, poultry, and aquaculture. The integration of high-density single nucleotide polymorphism (SNP) genotyping with advanced statistical prediction models has substantially increased the accuracy of breeding value estimation, shortened generation intervals, and accelerated rates of genetic gain. Compared with conventional best linear unbiased prediction (BLUP) and marker-assisted selection (MAS), genomic selection captures the combined effects of thousands of loci distributed throughout the genome, making it highly effective for complex quantitative traits governed by many genes of small effect. Recent advances, including single-step genomic BLUP, Bayesian prediction methods, whole-genome sequence analysis, functional genomics, multi-omics integration, artificial intelligence, and precision livestock farming technologies, have further enhanced the scope and efficiency of genomic prediction. These innovations are facilitating simultaneous improvement in productivity, fertility, feed efficiency, disease resistance, animal welfare, and environmental sustainability. Moreover, genomic information is increasingly being integrated with genome editing technologies such as CRISPR to support precision breeding strategies. This review summarizes the historical evolution, fundamental principles, methodological developments, and practical applications of genomic selection in livestock breeding while highlighting emerging innovations and future research directions that are expected to shape next-generation animal improvement programs.
S. Pathak, Amit Kumar, Vaishali Sah· International Journal of Env...· 0 citations
Background: Herd life is a crucial economic trait, since it affects both total milk yield over a lifetime and the expenses associated with herd replacement. The genetic improvement of longevity traits is frequently challenging due to their typically poor heritability. Consequently, employing indirect selection based on production and reproductive attributes genetically related to longevity may yield a more efficacious breeding strategy. Assessing genetic parameters and interrelations among these traits is crucial for developing effective breeding strategies in jersey crossbred cattle. Methods: This research examined the performance records of 357 Jersey crossbred cows over a span of 39 years (1980-2018). Genetic parameters were assessed for various longevity-related traits, including herd life (HL), productive herd life (PHL), total milk production (TMP), number of days in lactation (NDL) and number of lactations completed (NLC). Their correlations with production traits, including first lactation 305-day milk yield (FL305MY) and first lactation total milk yield (FLTMY), alongside reproductive traits. [Age at first calving, service period and calving interval] were examined. Heritability estimations were derived via the paternal half-sib approach and a restricted maximum likelihood (REML)-based animal model. Covariance component analysis was employed to determine genetic and phenotypic associations among characteristics. Result: Estimates of heritability obtained from paternal half-sib analysis were 0.08±0.15 for HL, 0.06±0.14 for PHL, 0.23±0.17 for TMP, 0.26±0.17 for NDL and 0.38±0.18 for NLC. Estimates derived from the animal model varied between 0.05 and 0.18, signifying a modest to moderate additive genetic impact on longevity traits. The genetic correlations observed among longevity and productivity traits varied from 0.37 to 0.71, indicating positive correlations, although the phenotypic correlations were somewhat less. Most reproductive traits demonstrated low to moderate positive genetic associations with longevity traits, with the exception of the association between NLC and AFC. The identified correlation structure indicates that the selection for increased longevity may concurrently boost milk production without adversely impacting reproductive efficiency.
Neelanjan Rakshit, A. Mandal· Agricultural Science Digest...· 0 citations
Goats (Capra hircus) are among the world’s most important livestock, providing milk, meat, and fiber across diverse agro-ecological zones. Traditional breeding relying on pedigree-based estimated breeding values (EBVs) has driven steady genetic progress but is constrained by long generation intervals and limited accuracy for sex-limited or difficult-to-measure traits. High-throughput single nucleotide polymorphism (SNP) chips and genomic selection (GS) have transformed goat breeding by enabling early, accurate selection independent of phenotypic records. This review synthesizes the development of goat SNP chip platforms from the foundational 52 K GoatSNP50 BeadChip through high-density solid-phase arrays and low-cost liquid-phase capture panels, with emphasis on their relative performance, cost-effectiveness, imputation potential, and suitability for different breeding systems. In addition to genomic selection (GS), genome-wide association studies (GWAS), and genetic diversity assessment, we also discuss candidate-gene selection and marker-assisted selection (MAS) as practical intermediate approaches that remain relevant in many goat breeding programs. GS has achieved genomic estimated breeding value (GEBV) prediction accuracies of 0.35–0.79 for key production traits across multiple countries and breeds. GWAS has identified candidate genes for milk composition (DGAT1, CSN1S1), growth (PLAG1, HMGA2), reproduction (BMPR1B, GDF9), and fiber quality (KRT, KRTAP families). We compare GS with traditional BLUP-based approaches, assess economic benefits, and discuss key challenges including reference population construction, genotype imputation, inbreeding management via Optimum Contribution Selection (OCS), and multi-omics integration. Future directions include customized chip design, AI-assisted genomic prediction, climate adaptation breeding, and CRISPR/Cas9 gene editing for precision improvement.
Ting-Chieh Kang, Hisn-Hung Lin, Kai-Fei Tseng et al.· Frontiers in Veterinary Scie...· 0 citations
In dairy farming, reproductive efficiency is vital to both profitability and sustainability. However, years of selective breeding for increased milk yield have adversely affected reproductive potential. This study aimed to pinpoint genomic regions and identify potential candidate genes associated with reproductive traits in Chinese Holstein cattle. In this study, a single-step genome-wide association study (ssGWAS) was conducted using 33,202 phenotypic records from 16,379 animals, 55,244 pedigree records, and genomic data from 1,698 cows. These data were integrated into the ssGWAS analysis, resulting in a total pedigree structure of 21,635 animals. A total of 12 significant markers were identified for calving interval (IC), days open (DO), number of services per conception (NS), and conception rate (CR). Among these significant SNPs, 3 SNPs were for IC, 2 SNPs were for DO, 3 SNPs were for NS, and 4 SNPs were for CR. Several promising candidate genes located near these SNPs have been identified, including SFXN4, B3GAT2, GRK5, PRDX3, and MTHFD1L, highlighting their potential involvement in fertility-related biological processes. Furthermore, functional enrichment analysis identified significant enrichment of pathways associated with cell adhesion and embryonic development, suggesting a potential mechanistic role for DSG family members (DSG1, DSG2, DSG3, and DSG4) in fertility regulation. Collectively, our findings enhance understanding of the complex genetic basis of reproductive traits in dairy cattle and may offer a valuable set of genomic targets for precision breeding of Chinese Holsteins. Integrating these markers into genomic selection programs may contribute to genetic improvements in reproductive efficiency and support the long-term sustainability of dairy production.
W. A. Lombebo, Mingxin Du, G. M. Tarekegn et al.· Journal of Animal Science· 0 citations
Dominance genetic effects, despite their recognized role in complex traits, are rarely included in livestock genetic evaluations. Using extensive phenotypic and pedigree data from 7537 dairy goat kids born between 2016 and 2023, we assessed the contribution of dominance variance to early growth traits through single-trait animal models fitted by the AI-REML algorithm in WOMBAT. The models combined additive, dominance, maternal genetic and maternal permanent environmental effects, and were evaluated using Akaike's Information Criterion (AIC), mean squared error (MSE) and Pearson's correlation between observed and predicted phenotypes (r( y $$ y $$ , y ̂ $$ \hat{y} $$ )). Incorporating dominance variance consistently improved model fit and predictive ability across traits, reducing residual variance by 13%-59% and increasing the accuracy of additive breeding values by up to 59%, without substantially altering the ranking of top animals. On average, dominance variance explained 30% of phenotypic variation, corresponding to 76% of the additive genetic variance. Dominance heritabilities for birth weight (BWT), weaning weight (WWT) and average daily gain (ADG) were 0.48 ± 0.06, 0.12 ± 0.03 and 0.32 ± 0.02, respectively, whereas additive heritabilities were lower (0.05-0.10). Genetic correlations differed notably between additive (0.59-0.66) and dominance (-0.33-0.41) components, highlighting the distinct contribution of non-additive effects. This study provides one of the first large-scale evaluations of dominance variance in dairy goats under commercial conditions. The results demonstrate that ignoring dominance can lead to underestimation of genetic variance and reduced predictive accuracy, underscoring the importance of modelling dominance effects in small ruminant breeding programs. These findings extend beyond a single breed, offering valuable implications for genetic evaluation strategies in dairy goats and other small ruminants.
J. Ehsaninia, A. Bagheripour· Journal of animal breeding a...· 0 citations
Multiple birth (MB) is associated with adverse effects on cow fertility and calf survival, thereby contributing to economic losses for dairy farmers; however, it is not currently included in routine genetic evaluations or selection indices in cattle worldwide. This study aimed to (i) estimate the h2 and repeatability of MB, (ii) assess its genetic and phenotypic associations with calving performance, postpartum health, fertility, and milk production traits, and (iii) quantify its impact on these traits in Austrian Holstein population. After data editing, the analyses used 678,877 records belonging to 262,192 cows across 2,350 dairy farms spanning the period from 2000 to 2024. The h2 and repeatability of MB were estimated using both single-trait linear and threshold repeatability animal models, whereas genetic and environmental correlations between MB and the other traits were estimated using bivariate models within a Bayesian framework implemented via Gibbs sampling. The effects of MB on functional and production traits were quantified using linear and logistic mixed regression models, according to the specific trait. The h2 of MB was low (linear model: 0.017, threshold model: 0.076) as well as repeatability (linear model: 0.044, threshold model: 0.217). Estimated genetic correlations of MB with 305-d milk, protein, and fat yields were weak (∼0.10). Multiple birth was weakly genetically correlated with maternal gestation length (-0.195), maternal calving ease (0.095), and maternal stillbirth (0.150). The interval from calving to first service, the interval from first to last service, the non-return rate at 56 d after first service, days open, and calving interval were all weakly genetically associated with MB. Fertility disorders showed the strongest genetic correlation with MB (0.308), whereas cystic ovaries showed a weak genetic correlation (0.068). Multiple birth was associated with shorter gestation length (-5 d), higher risk of stillbirth and fertility disorders, longer calving interval (16 d), and lower 305-d milk (-67 kg) and fat yield (-7 kg) compared with singleton birth. The phenotypic trend indicated that MB increased slightly from 3.01 to 3.34% between 2000 and 2024, whereas the genetic trend remained largely stable throughout the study period, suggesting that past selection decisions had little or no effect on the genetic merit for MB. Overall, the findings of the present study indicate that selection aimed at reducing or stabilizing MB would not substantially impair milk production traits and may provide indirect benefits for fertility and health traits in the long term.
J. Katende, A. Costa, M. Santinello et al.· Journal of Dairy Science· 0 citations
