ABSTRACT Carcass and meat quality traits are economically important in poultry breeding. To investigate their genetic architecture and identify potential candidate regulators, we performed whole‐genome resequencing (WGS) and an integrative genome‐wide association study (GWAS) on 315 breast muscle samples from yellow‐feathered broilers, jointly analyzing SNP and Indels across 14 carcass and meat quality traits. We identified about 13.5 million SNPs and 1.24 million Indels. Across 14 carcass and meat quality traits, 364 significant SNPs ( p < 1.0 × 10 6 ) and 52 significant Indels ( p < 1.0 × 10 −6 ) were detected, clustering into nine genomic regions. Key candidate genes included CNTN5 for leg muscle percentage, SORCS1 for crude fat (CF), CNN3 for shear force, and APOB and SQLE for total cholesterol. We further used tissue‐specific transcriptome‐derived lncRNA annotations to identify candidate lncRNAs located within ±100 kb of significant GWAS loci. These lncRNAs were prioritized as genomic‐proximity‐based candidates for future functional validation. Functional enrichment analysis indicated involvement of PI3K‐Akt and Wnt signaling pathways, lipid metabolism, and cytoskeletal regulation. These findings provide new insights into the genetic basis of carcass yield and meat quality traits in yellow‐feathered chickens and offer molecular markers and regulatory candidates for future molecular breeding strategies.
ABSTRACT Structural variants (SVs) contribute more nucleotide divergence between mammalian genomes than single nucleotide polymorphisms, yet no population‐scale, long‐read‐validated SV catalog has been available for Bos indicus cattle. Nelore, the most numerically important indicine breed worldwide, diverges substantially from the taurine‐derived reference genome used for most bovine SV discovery, leaving its structural variation largely uncharacterized. Long‐read sequencing of 20 Nelore bulls with two complementary callers (Sniffles2, SVIM) yielded 82 581 dual‐caller concordant SVs, of which 74 222 insertions and deletions passed independent read‐based validation. Ensembl VEP annotation identified 561 HIGH‐impact variants (0.75% of the catalog) across 7040 protein‐coding genes. Only 22 894 SVs (30.7%), including 147 of 561 HIGH‐impact variants (26.2%), could be tagged by an existing BovineHD SNP ( r 2 ≥ 0.8), showing that routine array genotyping captures a minority of this variation. Intersection with an indicine‐ancestry SNP map identified 6114 SVs (8.2%) enriched in Bos indicus , including 61 HIGH‐impact variants overlapping genes with plausible roles in immune/inflammatory defense and oxidative‐stress response, providing a resource and candidate variant set for genomic selection and tropical‐adaptation studies in indicine cattle.
ABSTRACT Bovine mastitis is a heterogeneous inflammatory disease that compromises milk production, animal welfare and dairy sustainability. Despite extensive transcriptomic and epigenomic research, a PubMed search completed in September 2026 identified only 12 primary studies addressing epitranscriptomic regulation in bovine mastitis, representing approximately 0.1% of indexed publications. This review critically evaluates N 6 ‐methyladenosine (m 6 A), the most extensively studied RNA modification in bovine mastitis, its regulatory machinery and effects on mammary immunity, while identifying m 5 C and A‐to‐I editing as priorities for mechanistic investigation. Current evidence identifies transcript‐specific RNA stability as the best‐supported mechanism and provides a foundation for investigating m 6 A‐dependent translation. Functional studies of ALKBH5– LOC4191 , YTHDF2– IER3 , YTHDF2‐associated CCL20 and CCL5 regulation, m 6 A‐regulated LncRNA7825 and FTO– FN1 demonstrate that m 6 A reshapes inflammatory signalling, mitochondrial stress, epithelial‐barrier integrity, cell survival and bacterial control. Synthesizing these findings, we propose a context‐dependent RNA‐fate checkpoint model in which biological effects depend on the modified transcript, effector protein, pathogen, cellular context and infection phase. The FTO– FN1 axis shows that bacterial control and tissue protection can diverge, establishing them as complementary outcomes. Future studies should test whether m 6 A provides an early post‐transcriptional response layer and whether pathogens manipulate it, using time‐resolved multi‐omics, nucleotide‐specific perturbation, primary cells, mammary organoids and longitudinal cattle samples. Parallel profiling of non‐m 6 A modifications should define interacting regulatory layers. These approaches should establish causal modification sites, resolve pathogen‐specific regulation, connect molecular mechanisms with disease trajectories, and support validated milk‐based biomarkers, mammary‐targeted interventions and prioritization of stable genetic variants for breeding mastitis‐resistant cattle.
ABSTRACT Heterozygosity‐rich regions (HRR) are genomic segments characterized by high heterozygosity that may arise from admixture, introgression, or balancing selection and may harbor loci associated with adaptation and fitness‐related traits. This study aimed to characterize HRR patterns in five Kazakh sheep breeds using a publicly available OvineSNP50 genotyping dataset. After quality control, ~25 000 SNPs were retained for 1634 Akzhaiyk, 1089 Edilbay, 532 Kazakh Fine‐Wool, 932 Kazakh Semi‐Coarse, and 282 Saryarka sheep. HRR were detected separately in each breed using the sliding‐window approach in detectRUNS R package, with a minimum segment length of 250 kb, at least 10 SNPs per window and segment, and a maximum of two opposing homozygous and one missing SNP per window. The average number of HRR per individual ranged from 40.75 ± 6.38 in Saryarka to 54.75 ± 7.34 in Kazakh Fine‐Wool sheep. HRR lengths ranged from 259.07 kb to 3.26 Mb, with an average of 0.7 Mb across breeds. Seventeen HRR islands, defined as regions shared by HRR in at least 25% of individuals within a breed, were identified, including regions on chromosomes 8 and 17 shared across all breeds. Genes overlapping these islands were associated with cellular homeostasis, immunity, metabolism, growth, and stress response, while the HRR islands overlapped with QTL associated with health, wool characteristics, and reproduction. These findings highlight the potential of HRR analysis to identify candidate genomic regions relevant to adaptation and economically important traits, providing insights for the conservation and genetic improvement of Kazakh sheep populations.
ABSTRACT Feature reduction of genetic markers and its impact on genomic selection has been studied extensively. Prior information borrowed from genome‐wide association studies (GWAS) can improve genomic selection accuracy. However, the effect of integration of posterior inclusion probability (PIP) information from fine mapping algorithm and linkage disequilibrium (LD) on genomic selection remains less studied. Here, three marker prioritization strategies—genome‐wide fine‐mapping (GWFM), LD pruning (LDFM), and GWAS–informed LD pruning ((GWAS + LD)FM)—were evaluated, using Bayesian fine‐mapping integrated with linear and nonlinear genomic selection frameworks, across 12 traits for three species. A consistent ranking of prediction performance (GWFM < LDFM < (GWAS + LD)FM) was observed across species and statistical models. Moreover, (GWAS + LD)FM achieved prediction accuracy (96% of maximal accuracy) with markedly minimal number of marker subsets. Gene ontology enrichment analysis showed that prioritized loci could converge on functional modules of biological importance. Independent validation using another fine mapping algorithm (FINEMAP) replicated the identical performance ordering. Genetic variants prioritized by LD and fine mapping reduced dimensional complexity and only were minimal number of marker subsets required for improved genomic selection accuracy. Fine mapping‐informed dimension reduction provides novel insights and avenue for the enhancement of genomic selection performance.
