This study examines the relationship between Transferrin genotypes and the growth rate of Arbor Acre broiler chickens. One-hundred one-day-old Arbor Acre broiler birds were sampled. Body measurements were recorded weekly. Blood samples were collected and analyzed by cellulose acetate electrophoresis to determine Transferrin genotypes. Body measurements recorded included: Body weight (BW), Body length (BL), Body girth (BG), Shank length (SL), Beak length (BeL), Drumstick length (DL), Wing length (WL), Keel length (KL), and Shank diameter (SD). Estimation of broiler growth potential within a four-week period was done using the growth rate formula of Maciejowski and Zieba (1982). Population parameters were estimated using PopGen 32.0 software, while the relationships between the Transferrin genotypes and weekly growth rate were determined using SPSS version 22.0(2017). The blood sample analysis revealed four distinct Transferrin genotypes: TF AA (69%), TF AB (17%), and TF AC (13%), which were previously reported in the Yoruba Ecotype of Nigeria Indigenous Chicken (Ige & Salako, 2014). Furthermore, we identified the TF AE (1%) genotype, which was associated with the highest body weight (421.67g), toe length (2 cm), and respiratory rate (50 breaths per min). The TF AA genotype was associated with the longest toe length (6.28 cm), neck circumference (1.01 cm), wing feather length (4.13 cm), and body length (5.43 cm). The TF AC genotype resulted in the greatest beak length (2.35 cm), body height (4.92 cm), shank length (1. 42 cm) and shank diameter (0.75 cm), whereas the AB genotype was linked to the longest keel length (2.42 cm), wing length ( 3.06 cm), drumstick length (2.01 cm), head length (1.55 cm) and neck length (3.37 cm). A significant positive correlation was observed between Transferrin genotype and neck length (r = 0.15, p < 0.05) and head length (r = 0.13, p < 0.05), indicating a potential genetic influence of Transferrin on growth traits in broiler chickens.

Introduction:

Cattle production underpins Nigeria's agricultural economy and rural livelihoods, with over 20 million indigenous animals adapted to harsh environments. However, genetic erosion from cross-breeding threatens these resources. Blood protein polymorphisms like carbonic anhydrase (CA) offer a cost-effective marker for diversity assessment, revealing adaptation and selection patterns. This study characterised CA polymorphism in four key Nigerian breeds—White Fulani, Red Bororo, Sokoto Gudali, and Kuri—to inform conservation and breeding strategies.

Methods:

Blood samples (5 mL) were collected from 351 healthy cattle (121 White Fulani, 97 Red Bororo, 100 Sokoto Gudali, 33 Kuri) across southwestern Nigeria. Erythrocytes were lysed and analysed via cellulose acetate electrophoresis (pH 5.6, 200 V, 45 min). Allele/genotype frequencies were calculated by direct counting, and the Hardy–Weinberg equilibrium (HWE) was tested via χ² and G². Diversity metrics (Shannon’s I and Nei’s heterozygosity), F-statistics, genetic distances (Nei’s test), and neutrality (Ewens–Watterson test) were computed using POPGENE 1.32.

Results:

Two co-dominant alleles (A, B) and three genotypes (AA, AB, BB) were detected. Allele B predominated in White Fulani (0.926) and overall (0.667), while A was highest in Kuri (0.697). Only White Fulani met HWE (P = 0.057); others showed severe heterozygote deficits (e.g., Sokoto Gudali observed AB = 1 vs. expected = 50; P < 0.001). Diversity was highest in Sokoto Gudali (ne = 1.990, I = 0.691, Nei = 0.498) and lowest in White Fulani (ne = 1.220, I = 0.325, Nei = 0.180). FST = 0.191 indicated moderate differentiation; Red Bororo and Sokoto Gudali clustered closely (I = 0.982). The Ewens–Watterson test suggested selection occurred in all three breeds considered in this study.

Conclusions:

CA polymorphism highlights breed-specific diversity and selection pressures in Nigerian cattle, underscoring the need for targeted conservation of unique lineages like Kuri. These findings support sustainable genomics-informed breeding for climate-resilient livestock.

Purpose:

This study aimed at determining genetic variants of prolactin gene in the Muscovy and Mallard ducks in Nigeria, and to evaluate the genetic relatedness or diversity of the duck species at the exon 5 of Prolactin (PRL) gene loci in the two selected Duck breeds, by determining the genetic polymorphisms, genetic parameters and genetic variation in the duck breeds. 80 adult ducks sourced from Lagos, Ogun and Oyo states of Nigeria, were used for this study.

Method:

Blood samples (2ml) collected by jugular venipuncture from the ducks, using sterile needles and syringes, were stored in ethylene diamine tetra acetic acid (EDTA) bottles, to prevent coagulation and preserve the integrity of the samples, were transported in ice packs, to the laboratory for analysis. Genomic DNA was extracted from whole blood samples, amplified using PCR were digested using HinfI Restriction Enzyme. The data were analyzed using POPGENE 1.32 software.

Results:

Two alleles with frequencies A (0.1034, 0.2188), B (0.8966, 0.7812), and three genotypes with frequencies AA (0.0000, 1.0000), AB (6.0000, 5.0000) and BB (23.000, 10.0000) were identified at the Exon 5 of of PRL gene in Mallard and Muscovy ducks respectively. Indicating that prolactin gene was polymorphic in both populations. The Chi-square test (0.367, 0.2381) suggests that the populations conforms to Hardy-Weinberg equilibrium. Also, noted were observed heterozygosity (0.2069, 0.3125) and expected heterozygosity (0.1887, 0.3528).

Conclusion:

The sampled populations were polymorphic for PRL gene at exon 5, and are also in Hardy–Weinberg equilibrium, based on the observed parameters and statistical analysis. The values of observed heterozygosity and expected heterozygosity, indicates higher genetic variation within the Mallard breed population, compared to the Muscovy population, which can be attributed to inbreeding. This indicates the need for Genetic intervention to prevent this indigenous breed of ducks from extinction.

Genomic analysis in dairy cattle is considered a practical technology in the genetic management of livestock. The objective of this study was to analyse the results derived from genomic testing of a commercial Ecuadorian Jersey herd between 2021 and 2025. A total of 409 genomic analyses using the Illumina Bovine SNP50 BeadChip (Clarifide–Zoetis, San Diego, CA, USA) were performed from hair root samples to evaluate the traits of interest related to milk production (quantity and quality), health, and economic indices, all of which are considered in the Council on Dairy Cattle Breeding (CDCB) database. In order to determine the genetic progress of the population, the year of birth of each animal was considered as a fixed factor. The data were processed and analysed using RStudio v. 1.4.1564 software. It was observed that between 2021 and 2025, production parameters such as milk (37.75 lb to 496.86 lbs), fat (-13.58 to 8.3 lbs), and protein (-6.8 to 14.52 lbs) increased dramatically in value, while the average percentage of fat (-0.078-0.081%) and protein (-0.042-0.020%), as well as somatic cells (2.94-2.97 x 100,000 cells/mL), improved slightly, albeit with high variability. On the other hand, no individuals were found to be carriers of the recessive BLAD and DUMPS genes (0%), and only one animal was a carrier of the CMV gene (0.24%). In addition, the Beta Casein A1/A2 (p=0.001), Alpha S-1 Casein (p<0.0001), and Kappa Casein I (p<0.0001) genes were found to be in Hardy–Weinberg disequilibrium, while β-lactoglobulin was the only gene found to be in equilibrium (p=0.0872). Economic indices such as net merit ($ -211.89–38), cheese merit ($ -221.93–37.95), and fluid merit ($ -186.89–38.69) increased over time. In conclusion, genomic evaluation improves the selection process for the best Jersey cows by increasing the efficiency of traditional genetic analyses, which only took into account the genetic characteristics of the sire (imported bulls), thus efficiently improving genetic progress in this breed.

Identifying diets with desirable ratios of omega-6 (n-6) and omega-3 (n-3) fatty acids (FAs) plays a key role in regulating inflammation, maintaining membrane fluidity, facilitating β-oxidation, and promoting overall metabolic health. Previously, we showed that pigs fed 1.5% soybean oil (SOY1.5) had greater hepatic linoleic acid (an n-6 FA) deposition than pigs fed 3.0% soybean oil (SOY3.0). A balanced dietary ratio of n-6:n-3 FA is crucial to support optimal health and reduce the risk of chronic diseases. Thus, this study aimed to identify gene co-expression modules correlated with n-6 deposited in hepatic tissue of pigs fed diets containing either SOY 1.5 or SOY3.0. mRNA-seq data from 35 immunocastrated Large White pigs (CEUA2018-28) were analyzed using standard bioinformatics pipelines. Co-expression modules were identified with WGCNA, followed by functional enrichment using DAVID. Strong co-expression patterns related to n-6 were observed in both diets, but they involved distinct modules and pathways. In pigs fed SOY1.5, one gene cluster was negatively correlated with linoleic acid and total n-6. In pigs fed SOY3.0, however, another module was negatively correlated with the n-6:n-3 ratio. Functional enrichment analysis (FDR < 0.05) highlighted the fatty acid degradation pathway (ssc00071) in SOY1.5-fed pigs and Bile secretion pathway (ssc04976) in the SOY3.0 group. These findings suggest that dietary oil inclusion modulates distinct co-expression networks and metabolic pathways in porcine liver. These molecular insights can optimize nutritional strategies, thereby enhancing animal health and aligning with the FAO’s One Health initiative.

Long non-coding RNAs (lncRNAs) are increasingly being recognized as key regulators of gene expression in several organisms. In this study, we aimed to identify lncRNAs that may regulate protein-coding genes that had been previously associated with fertility in Retinta cattle via the formation of DNA:RNA triplexes. Sixteen lncRNAs located within ±50 kb genomic windows, mainly found on BTA5, around the transcription start sites of target genes linked with female fertility or male scrotal circumference were further selected. We observed a range of distances, from 0.61 to 49.8 kb, which is compatible with cis-regulation. A total of 341 triplex-forming oligonucleotides (TFOs) of high quality were identified by LongTarget, with TFO lengths falling between 20 and more than 70 nucleotides. AMHR2 was the target gene with the higher proportion of lncRNA-binding sites, as well as the one with the most stable interactions. Furthermore, the accessibility of the lncRNAs in the context of their secondary structure was predicted using RNAplfold, revealing the presence of predicted TFOs in exposed regions for two lncRNAs, ENSBTAT00000117549 and ENSBTAT00000088580, located in the proximity of genes AMHR2 and KRT8, respectively. Our results also showed that the presence of lncRNA-binding sites in DNA are frequent in the upstream regions of the target genes, where promoters and other regulatory regions lie. This observation is consistent with the putative regulatory role of lncRNAs at the gene expression level. Our findings provide a first step towards improving our understanding of the molecular basis behind the lncRNA-mediated regulation of the reproductive performance in cattle.

Inbreeding increases homozygosity, favoring the expression of deleterious recessive alleles and resulting in the phenomenon of inbreeding depression, which can compromise the productive and reproductive performance of animals. The objective of this study was to estimate the inbreeding coefficients of Holstein cows from genealogical records and evaluate their association with the 305 d milk yield (MY305), 305 d fat yield (FY305), and 305 d protein yield (PY305) of the first lactation. The data analyzed belong to an agricultural company in the southeastern region of Brazil. The pedigree file contained information on 7,561 animals born between 1911 and 2023, of which 763 founders were identified, in addition to 790 bulls and 4,953 cows with progeny, covering 16 generations. Within this relationship matrix, 5,307 animals are inbred, with an average inbreeding coefficient (F) of 2.26% and a maximum of 28%. The F value of each animal was used as a linear covariate in the statistical model, including the contemporary group effect (year and month of birth), age class at calving, and additive genetic and residual random effects. In this analysis, the first lactations of 2,228 cows were used, with averages of 10,383 ± 1,291 kg, 369 ± 87 kg, and 312 ± 63 kg for MY305, FY305, and PY305, respectively. The estimated heritabilities for MY305, FY305, and PY305 were 0.17 ± 0.05, 0.19 ± 0.04, and 0.12 ± 0.01, respectively. The F effect prediction solution described a decrease in MY305, FY305, and PY305 of 33.96, 95.81, and 21.6 kg for each 1% increase in F. These results show the importance of monitoring inbreeding and directing animal mating, given that inbreeding depression leads to economic losses.

Genetic evaluations of milk production traits in dairy goats typically do not explicitly account for variability in major genes such as the polymorphisms at CSN1S1 (αS1-casein), despite its substantial impact on milk protein synthesis. This study assessed the effect of incorporating CSN1S1 genotypes on the reliability of genetic evaluations in the Palmera goat breed. The dataset comprised 37,875 test-day records from 3,168 goats, 4,342 pedigree animals, and the CSN1S1 locus genotypes for 156 bucks, being sires of 1,069 phenotyped animals. Traits analyzed included milk yield (MY), and protein (PC) and fat contents (FC). A gene-content multi-trait REML model (GCMT-REML), treating gene content at the CSN1S1 locus as a correlated additional trait, was implemented and compared against a baseline univariate animal model. Heritability estimates were similar between models, ranging from 0.19 (FC) to 0.31 (PC). The incorporation of gene content information increased reliability across traits, with overall gains from 0.07% (PC) to 1.82% (MY). Improvements were markedly higher in males than females, and in genotyped (5.52% to 9.54%) compared to non-genotyped animals (≈0%). Individuals with low baseline reliability benefited the most, showing gains from 7.2% to 14.27%. These results demonstrate that including CSN1S1 genotypes in routine genetic evaluations can enhance reliability in the Palmera breed. Future work should focus on expanding the genotyped population, including females, so that dominance effects in the locus could also be accounted for.

Genetic variation among domestic dog breeds is a determining factor in physiological traits, behavioral characteristics, and susceptibility to hereditary diseases. While conventional genomic approaches provide biological insights, mathematical modeling offers an innovative perspective to systematically analyze DNA variability and predict cross-breed genetic outcomes. This study proposes an interdisciplinary framework combining genetics, matrix theory, and mathematical optimization to investigate DNA connections among multiple dog breeds.

DNA sequence datasets were numerically encoded into binary matrix structures to represent nucleotide patterns. Linear algebraic techniques, including eigenvalue decomposition and vector space mapping, were applied to identify dominant genetic markers and quantify inter-breed genetic similarity. A predictive mathematical model was formulated to simulate genetic inheritance patterns, enabling estimation of potential outcomes in cross-breeding scenarios. Model validation was performed using comparative genomic references and statistical error minimization methods.

Results revealed that matrix-based classification accurately differentiated dog breeds with characteristic phenotypic traits. Eigenvalue distribution patterns indicated significant clustering around genes associated with immunity and neurological behavior. The predictive model demonstrated high reliability, with simulation accuracy exceeding 93% in identifying probable trait inheritance. Experimental simulations suggested a potential enhancement of 15–18% in genetically inherited health-related markers under controlled breeding strategies.

This research confirms that mathematical modeling, specifically through the application of linear algebra to genomic data, provides a powerful and novel analytical pathway in animal genetics. The proposed model supports sustainable and health-oriented selective breeding in dogs and demonstrates potential application to broader animal genetic studies. Future work will explore integration with nonlinear modeling and AI-based machine learning techniques for advanced predictive analytics.

Although genomic prediction has improved markedly in cattle, buffalo, sheep, and poultry, a substantial proportion of additive genetic variance remains unexplained, prompting continued debate on where “hidden heritability” resides. Since many livestock traits appear to be shaped by myriad regulatory variants with context-dependent effects, one might reasonably consider whether bulk-tissue datasets are masking cell-type-specific regulatory architecture. As recent breakthroughs in single-cell chromatin profiling illustrate, regulatory activity varies sharply across epithelial, immune, and stromal compartments, yet livestock breeding programs have scarcely incorporated this information.

This study employed single-cell ATAC-seq datasets newly available for bovine and ovine tissues to map cell-resolved open-chromatin peaks. Variants overlapping these peaks were then interpreted using a hybrid convolutional transformer deep learning model inspired by Enformer, enabling prediction of enhancer disruption and directionality of regulatory effects. Because deep models can integrate long-range chromatin interactions, they provide an opportunity to observe regulatory dependencies that conventional annotations often overlook. In comparison to baseline SNP models, cell-type-specific regulatory scores increased prediction accuracy across growth, mastitis resistance, and fertility traits by 10–18%. Immune-cell-specific regulatory polymorphisms expressed disproportionately significant effects on parasite-resilience traits and mastitis, supporting previous multi-tissue eQTL studies. Despite the limited tissue diversity and sample number, these advancements may represent the early promise of single-cell functional genomics.

The combination of deep learning and single-cell chromatin landscapes does, however, seem a rational and physiologically sound way to deal with latent heredity in cattle. This approach prioritizes variants with actual regulatory relevance, strengthening genomic selection processes and mechanistic interpretation.