The present study aimed to identify the molecular markers for genes that influence intramuscular fat content (IFC), but not average backfat thickness (ABT). A total of 330 Suhuai pigs were slaughtered, and measurements of IFC and ABT were obtained. Phenotypic and genetic correlations between IFC and ABT were calculated. Thirteen single nucleotide polymorphisms (SNPs) among 12 candidate genes for IFC were analyzed, including FABP3, LIPE, IGF1, IGF2, LEP, LEPR, MC4R, PHKG1, RETN, RYR1, SCD, and UBE3C. Associations of the evaluated SNPs with IFCIFC and ABT were performed. Our results showed that the means of IFC and ABT were 1.99 ± 0.03 % and 26.68 ± 0.28 mm, respectively. The coefficients of variation (CVs) of IFC and ABT were 31.21% and 19.36%, respectively. The phenotypic and genetic correlations between IFC and ABT were moderate. Only the FABP3 (rs1110770079) was associated with IFC (p < 0.05) but not with ABT. Besides, there was a tendency for associations of RYR1 (rs344435545) and SCD (rs80912566) with IFC (p < 0.1). Our results indicated that the FABP3 (rs1110770079) SNP could be used as a marker to improve IFC without changing ABT in the Suhuai pig breeding system.

Shell color is a commercially valuable trait in eggs, and blue-green eggshells typically exhibit multiple color subtypes. To explore the relationship between the CIELab system and visual color classification and develop simplified discrimination indices, 2274 blue-green eggs across seven batches were selected. The L*, a*, and b* values of each egg were measured, and average visual classification (AveObs) was calculated from four numeric categories (Light = 1, Blue = 2, Green = 3, Olive = 4) separately assigned by four observers. After batch correction using ComBat, four algorithms—linear discriminant analysis (LDA), random forest (RF), support vector machine (SVM), and neural network (NNET)—were compared. Correction substantially reduced the coefficients of variation of the L*, a*, and b* values. Correlations emerged: L* and b* (−0.722), a* and b* (0.451), and L* and a* (−0.088), while correlations of the L*, a*, and b* values with AveObs were −0.713, 0.218, and 0.771, respectively. The LDA model achieved superior comprehensive performance across all data scenarios, with the highest accuracy and efficiency as compared to the SVM, NNET, and RF models. Among the LDA functions, LD1 explained 78.53% of the variance, with L*, a*, and b* coefficients of −0.134, 0.063, and 0.349, respectively (ratio ≈ 1:0.47:2.60). Simplified formulas based on the L*, a*, and b* values were constructed and compared to the existing indices C* (=$\sqrt{{a*}^2+{b*}^2}$) and SCI (=L* − a* − b*). The correlation between L* − 2b* and AveObs was −0.803, similar to those for C* (0.797) and SCI (−0.782), while the correlation between L* − 4C* and AveObs was −0.810, significantly higher than that for SCI (p < 0.05). In conclusion, the LDA model demonstrated optimal performance in predicting color classification, and L* − 4C* is an ideal index for grading of blue-green eggs.

Egg production traits are major breeding targets, but their genetic architecture remains poorly characterized in indigenous chickens. In this study, seven egg production traits were measured in the WG chicken line: age at first egg (AFE), egg number at 21–25 (EN21–25W), 26–30 (EN26–30W), 31–35 (EN31–35W), and 36–43 weeks of age (EN36–43W), total egg number from 21 to 43 weeks of age (TEN), and average clutch length (ACL). Heritability and genetic correlations among seven egg production traits were estimated. Genome-wide association studies (GWASs) were performed using mixed linear model (MLM) and FarmCPU, followed by multiple trait fine-mapping with mvSuSiE. Variants were prioritized based on posterior inclusion probability (PIP) and proportion of phenotypic variance explained (PVE), and genes were prioritized for further investigation based on their positions within linkage disequilibrium-defined QTL intervals. Heritability estimates ranged from 0.19 (EN26–30W) to 0.74 (EN36–43W). AFE was moderately to strongly negatively correlated with other traits ($r_g$ = −0.37 to −0.93), whereas genetic correlations between egg numbers across laying stages were strongly positively correlated. GWASs identified 26 significant SNPs within 24 independent regions across 14 chromosomes. Multi-trait fine-mapping prioritized 25 SNPs, among which rs732088229 showed the strongest evidence of pleiotropic effects. Seven genes (NHLH2, MAFB, SYNDIG1, BNC1, TFPI2, EDNRB, and GSS) were prioritized for further investigation based on QTL delimitation and functional annotation. These findings provide potential markers for improving egg production and support the application of multiple trait fine-mapping to reproductive traits in poultry.