Dickkopf-1 (DKK1) is an inhibitor of canonical Wnt signaling pathway and regulates hair follicle morphogenesis and cycling. To investigate the potential involvement of DKK1 in wool production and quality traits, we characterized the genomic structure of ovine DKK1, performed polymorphism detection and association analysis of ovine DKK1 with wool production and quality traits in Chinese Merino. Our results showed that ovine DKK1 consists of four exons and three introns, which encodes a protein of 262 amino acids. The coding sequence of ovine DKK1 and its deduced amino acid sequence were highly conserved in mammals. Eleven single nucleotide polymorphisms (SNPs) were identified within the ovine DKK1 genomic region. Gene-wide association analysis showed that SNP5 was significantly associated with mean fiber diameter (MFD) in the B (selected for long wool fiber and high-quality wool), PW (selected for high reproductive capacity, high clean wool yield and high-quality wool) and U (selected for long wool fiber with good uniformity, high wool yield and lower fiber diameter) strains (p < 4.55 × 10⁻³ = 0.05/11). Single Nucleotide Polymorphisms wide association analysis showed that SNP8 was significantly associated with MFD in A strain and fleece weight in A (selected for large body size), PM (selected for large body size, high reproductive capacity and high meat yield) and SF (selected for mean fiber diameter less than 18 μm and wool fiber length between 5 and 9 cm) strains (p < 0.05), SNP9 was significantly associated with curvature in B and U strains (p < 0.05) and SNP10 was significantly associated with coefficient of variation of fiber diameter in A, PW and PM strains and standard deviation of fiber diameter in A and PM strains (p < 0.05). The haplotypes derived from these 11 identified SNPs were significantly associated with MFD (p < 0.05). In conclusion, our results suggest that DKK1 may be a major gene controlling wool production and quality traits, also the identified SNPs (SNPs5, 8, 9 and 10) might be used as potential molecular markers for improving sheep wool production and quality in sheep breeding.

As an excellent chicken breed found in a high-altitude zone of northern China, Lindian chickens are characterized by good egg and meat production, strong adaptability, cold tolerance, rough feeding resistance, excellent egg quality, and delicious meat quality. To facilitate the exploitation of the unique qualities of the Lindian chicken, the varying patterns and correlations of various body size and carcass traits of 3–22-week-old Lindian chickens were analyzed in this study. The optimal growth model of these traits was determined by growth curve fitting analysis. The results showed that most traits of Lindian chickens increased steadily with increasing age, and most of them increased rapidly before 10 weeks of age. In addition, the inflection point age of each trait was predicted to be between 4 and 10 weeks. Furthermore, this study revealed that body size traits were closely related to carcass traits in Lindian chickens. In summary, Lindian chickens are in a rapid growth stage before the age of 10 weeks, and better slaughter performance can be achieved through good feeding management during this stage. The reproductive traits and muscles are the main developmental focus after the age of 19 weeks, so it is important to adequately meet their energy requirements for subsequent good breeding performance.

To accelerate breeding progress for egg production traits in Lindian chickens, the genetic parameters for egg production and clutch-related traits in Lindian chickens were evaluated in the present study. Data regarding the age at first egg (AFE), egg number (EN), average clutch length (ACL), and average pause length (APL) were collected from two generations of Lindian chickens based on individual egg production records at 32 weeks of age (32–wk), 43–wk, and 52–wk. The results showed that the AFE of Lindian chickens was 179.3 d of age, with a heritability of 0.35. The heritability was 0.26 for EN32, 0.28 for EN43, and 0.34 for EN52. ACL showed moderate-to-high heritability (h² = 0.3–0.54), but APL traits showed low heritability (h² = 0.09–0.14). There were high positive genetic and phenotypic correlations for EN in the three periods from the start of laying up to 32–wk, 43–wk, and 52–wk. EN had high negative genetic correlations with AFE (r_G = −0.47–−0.80) and high positive genetic correlations with ACL (r_G = 0.45–0.81). The correlation between EN and APL was positive for 32–wk and 43–wk, but negative for 52–wk. These results indicated that the egg production of Lindian chickens could be improved by the selection of AFE, early EN, and ACL.

Abdominal fat is a key indicator of chicken meat quality. Excessive deposition not only reduces meat quality but also decreases feed conversion efficiency, making the breeding of low-abdominal-fat strains economically important. Genomic selection (GS) uses information from genome-wide association studies (GWASs) and high-throughput sequencing data. It estimates genomic breeding values (GEBVs) from genotypes, which enables early and precise selection. Given that abdominal fat is a polygenic trait controlled by numerous small-effect loci, this study combined population genetic analyses with machine learning (ML)-based feature selection. Relevant single-nucleotide polymorphisms (SNPs) were first identified using a combined GWAS and linkage disequilibrium (LD) approach, followed by a two-stage feature selection process—Lasso for dimensionality reduction and recursive feature elimination (RFE) for refinement—to generate the model input set. We evaluated multiple machine learning models for predicting genomic estimated breeding values (GEBVs). The results showed that linear models and certain nonlinear models achieved higher accuracy and were well suited as base learners for ensemble methods. Building on these findings, we developed a Dynamic Adaptive Weighted Stacking Ensemble Learning Framework (DAWSELF), which applies dynamic weighting and voting to heterogeneous base learners and integrates them layer by layer, with Ridge serving as the meta-learner. In three independent validation populations, DAWSELF consistently outperformed individual models and conventional stacking frameworks in prediction accuracy. This work establishes an efficient GEBV prediction framework for complex traits such as chicken abdominal fat and provides a reusable SNP feature selection strategy, offering practical value for enhancing the precision of poultry breeding and improving product quality.

This study aims to identify molecular marker loci that could be applied in broiler breeding programs. In this study, we used public databases to locate the Transcription factor 21 (TCF21) gene that affected the economically important traits in broilers. Ten single nucleotide polymorphisms were detected in the TCF21 gene by monoclonal sequencing. The polymorphisms of these 10 SNPs in the TCF21 gene were significantly associated (p < 0.05) with multiple growth and body composition traits. Furthermore, the TT genotype of g.-911T>G was identified to significantly increase the heart weight trait without affecting the negative traits, such as abdominal fat and reproduction by multiple methods. Thus, it was speculated that the g.-911T>G identified in the TCF21 gene might be used in marker-assisted selection in the broiler breeding program.

Excessive abdominal fat deposition accompanying rapid growth in broiler chickens seriously affects production efficiency. Using divergently selected broiler lines from Northeast Agricultural University, we integrated transcriptome sequencing, whole-genome resequencing, and three-dimensional genomic data to identify key SNPs affecting abdominal fat deposition. From 3,850,758 initial SNPs, 22,721 high-quality SNPs were selected (|ΔAF| ≥ 0.9) and validated to obtain 7341 reliable SNPs. GWAS identified 16 SNPs significantly associated with abdominal fat weight, while LD analysis revealed 22 highly linked SNPs, finally determining 2302 candidate SNPs. Transcriptome analysis identified 825 differentially expressed genes (p ≤ 0.05, |FC| ≥ 1.5). Functional annotation revealed 201 SNPs located in differentially expressed gene regions, including 8 coding SNPs and 193 non-coding SNPs, with an additional 15 SNPs potentially regulating through long-range chromatin interactions. Mechanistic analysis showed that coding SNPs regulate gene expression by altering codon translation rates or mRNA stability, while non-coding SNPs regulate transcription by affecting transcription factor binding. Phenotypic association analysis demonstrated that all 213 SNPs can cause ≥2-fold differences in abdominal fat weight, with 182 SNPs causing ≥3-fold differences. This study successfully identified 213 functional SNPs affecting abdominal fat deposition in broilers and revealed their molecular basis for regulating fat metabolism through multiple mechanisms, providing important genetic markers for low-fat breeding in broilers.