Indigenous chicken products are increasingly favored by consumers due to their unique meat and egg quality.  However, the relatively poor egg-laying performance largely impacts the economic benefits and hinders sustainable development of the local chicken industry.  Thus, excavating key genes and effective molecular markers associated with egg-laying performance is necessary to improve egg production via genetic selection in indigenous breeds.  In the present study, comparative hypothalamic transcriptome between pre-laying (15 weeks old) and peak-laying (30 weeks old) Lushi blue-shelled-egg (LBS) chicken was performed.  A total of 518 differentially expressed genes (DEGs) were identified.  Among the DEGs, 64 genes were enriched in 10 Gene Ontology (GO) terms associated with reproductive regulation via GO analysis and considered as potential candidate genes regulating egg-laying performance.  Of the 64 genes, 16 showed high connectivity (degree≥12) by protein–protein interaction (PPI) network analysis and were considered as potential core candidate genes (PCCGs).  To further look for key candidate genes from the PCCGs, firstly, the expression patterns of the 16 genes were examined in the hypothalamus of two indigenous breeds (LBS and Gushi (GS) chickens) between the pre-laying and peak-laying stages using quantitative real-time PCR (qRT-PCR).  Eleven out of the 16 genes showed significantly differential expression (P<0.05) with the same changing trends in the two breeds.  Then, correlations between the expression levels of the above 11 genes and egg numbers and reproductive hormone concentrations in serum were investigated in high-yielding and low-yielding GS chickens.  Of the 11 genes, eight showed significant correlations (P<0.05) between their expression levels and egg numbers, and between expression levels and reproductive hormone concentration in serum.  Furthermore, an association study on single nucleotide polymorphisms (SNPs) identified in these eight genes and egg production traits was carried out in 640 GS hens, and a significant association (P<0.05) between the SNPs and egg numbers was confirmed.  In conclusion, the eight genes, including CNR1, AP2M1, NRXN1, ANXA5, PENK, SLC1A2, SNAP25 and TRH, were demonstrated as key genes regulating egg production in indigenous chickens, and the SNPs sites within the genes might be served as markers to provide a guide for indigenous chicken breeding.  These findings provide a novel insight for further understanding the regulatory mechanisms of egg-laying performance and developing molecular markers to improve egg production of indigenous breeds.

Paired-like homeodomain transcription factor 1 (PITX1) plays an important role in pituitary development by indirectly regulating the expression of the GH and PRL genes, and therefore PITX1 gene is regarded as a potential candidate gene for building the relationship between the gene polymorphism and milk traits. The aim of this study was to explore the novel genetic variant in PITX1 gene and its effect on milk performance in dairy goats. Herein, a novel genetic variation (NW_00314033: g.201G>A or IVS1+41G>A) located at nt41 position of the first intron of the goat PITX1 gene was reported at the P1 locus, which can be genotyped by the Msp I PCR-RFLP. In the Msp I PCR-RFLP analyis, the GG variant was a major genotype, and the A variant was a minor allele in Guanzhong dairy goats which was at Hardy-Weinberg disequilibrium (chi-square c2=140, P<0.01). The establishment of associations between different genotypes and milk performance was performed in the analyzed population. A total of three significant associations of the polymorphism with average milk fat content (%) (P=0.045), morning milk fat content (%) (P=0.049), and afternoon milk fat content (%) (P=0.050), were found, respectively. A significant relationship between the polymorphism and average total solid content (P=0.029) was also detected. This novel single nucleotide polymorphism (SNP) extended the spectrum of genetic variation of the goat PITX1 gene, and its significant association with milk performance would benefit from the application of DNA markers related to improving milk performance through marker-assisted selection (MAS) in dairy goats.