Perilipin 1 (PLIN1) is one of the most abundant lipid droplet-related proteins on the surface of adipocytes. Our previous results showed that PLIN1 plays an important role in chicken lipid metabolism. To further reveal the role of PLIN1 in the growth and development of adipocytes, a chicken preadipocyte line with a PLIN1 gene knockout was established by the CRISPR/Cas9 gene editing technique, and the effects of the PLIN1 gene on the proliferation, apoptosis, differentiation and lipolysis of chicken preadipocytes were detected. The results showed that the CRISPR/Cas9 system effectively mediated knockout of the PLIN1 gene. After the deletion of PLIN1, the differentiation ability and early apoptotic activity of chicken preadipocytes decreased, and their proliferation ability increased. Moreover, knockout of PLIN1 promoted chicken preadipocyte lipolysis under basal conditions and inhibited chicken preadipocyte lipolysis under hormone stimulation. Taken together, our results inferred that PLIN1 plays a regulatory role in the process of proliferation, apoptosis, differentiation and lipolysis of chicken preadipocytes.

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.

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.

The objective of this study was to characterize the genetic basis of metatarsal length (MeL) and circumference (MeC), identify candidate loci and genes associated with these traits, and evaluate the predictive utility of integrated candidate-marker sets for broiler leg traits. A total of 330 male broilers phenotyped at 49 days of age and subjected to whole-genome resequencing were used for the analyses. Genomic heritability estimates were 0.266 for MeL and 0.516 for MeC. Single-trait GWAS identified 102 and 165 suggestive SNPs for MeL and MeC, respectively, while bivariate analysis identified 263 SNPs, including 159 not detected by either single-trait analysis. Integration with genetic differentiation and nucleotide diversity prioritized 55 candidate SNPs for MeL and 62 for MeC, with three loci shared between the two traits. Linkage disequilibrium refinement and functional annotation identified 120 candidate variants and 64 candidate genes, including APP, LDB3, ADH6, MTTP, and a missense variant in ANKRD50. Across six models, mean heritability-adjusted prediction estimates ranged from 0.197 to 0.230 for MeL and 0.273 to 0.322 for MeC. Exploratory within-population analyses indicated that the candidate-marker sets were enriched for trait-relevant predictive information. These results provide insights into the genetic architecture of broiler leg traits and provide candidate markers for further validation and evaluation in genomic prediction.

Broilers are one of the most important sources of animal protein worldwide due to their rapid growth rate and high feed efficiency. However, excessive abdominal fat deposition in broilers reduces feed efficiency and carcass quality, resulting in substantial economic losses to the poultry industry. In previous studies, we have demonstrated that connective tissue growth factor (CTGF) is a key gene affecting chicken fat deposition, but its specific molecular mechanism is still unclear. This study aimed to identify functional single-nucleotide polymorphisms (SNPs) within the CTGF gene associated with abdominal fat deposition and to elucidate their potential regulatory mechanisms in broilers. Whole-genome resequencing data from the 19th generation of broiler lines divergently selected for abdominal fat content were used to identify SNPs within CTGF and its flanking regions (±3 kb). Association analyses between SNPs and abdominal fat traits, including abdominal fat weight (AFW) and abdominal fat percentage (AFP), were subsequently performed. Seven SNPs were significantly associated with AFW and AFP, among which four were further validated as functional SNPs by Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR) and luciferase reporter assay, including g + 516T > C, g + 954T > C, g − 982A > G, and g *2702A > T. The results of RT-qPCR indicated that the exonic SNPs g + 516T > C and g + 954T > C may regulate CTGF expression by altering mRNA secondary structure stability. The SNP g − 982A > G, located in the 5′ flanking region, may affect CTGF transcriptional activity through allele-dependent responses to transcription factors CCAAT/enhancer-binding protein alpha (C/EBPα), upstream stimulatory factor 1 (USF1), and MYC-associated factor X (MAX). In addition, the SNP g *2702A > T, located in the 3′ flanking region, may influence CTGF regulation by affecting a potential gga-miR-1812-5p-responsive element. These findings improve our understanding of the genetic mechanisms underlying abdominal fat deposition and provide potential molecular markers for marker-assisted selection for abdominal fat content in broiler breeding programs.