Background: The identification of markers and genes for growth traits may not only benefit for marker assist selection /genomic selection but also provide important information for understanding the genetic foundation of growth traits in broilers. Results: In the current study, we estimated the genetic parameters of eight growth traits in broilers and carried out the genome-wide association studies for these growth traits. A total of 113 QTNs discovered by multiple methods together, and some genes, including ACTA1, IGF2BP1, TAPT1, LDB2, PRKCA, TGFBR2, GLI3, SLC16A7, INHBA, BAMBI, APCDD1, GPR39, and GATA4, were identified as important candidate genes for rapid growth in broilers. Conclusions: The results of this study will provide important information for understanding the genetic foundation of growth traits in broilers.

Chickens are the most abundant agricultural animals globally, with controlling abdominal fat deposition being a key objective in poultry breeding. While GWAS can identify genetic variants associated with abdominal fat deposition, the precise roles and mechanisms of these variants remain largely unclear. Here, we use male chickens from two lines divergently selected for abdominal fat deposition as experimental models. Through the integration of genomic, epigenomic, 3D genomic, and transcriptomic data, we build a comprehensive chromatin 3D regulatory network map to identify the genetic regulatory mechanisms that influence abdominal fat deposition in chickens. Notably, we find that the rs734209466 variant functions as an allele-specific enhancer, remotely enhancing the transcription of IGFBP2 and IGFBP5 by the binding transcription factor IRF4. This interaction influences the differentiation and proliferation of preadipocytes, which ultimately affects phenotype. This work presents a detailed genetic regulatory map for chicken abdominal fat deposition, offering molecular targets for selective breeding.

Rapid growth is one of the most important economic traits in broiler breeding programs. Identifying markers and genes for growth traits may not only benefit marker-assisted selection (MAS)/genomic selection (GS) but also provide important information for understanding the genetic architecture of growth traits in broilers. In the present study, an F₂ resource population derived from a cross between the broiler and Baier yellow chicken (a Chinese local breed) was used and body weights from 1 to 12 weeks of age [body weight (BW) 1–BW12)] were measured. A total of 519 F₂ birds were genome re-sequenced, and a combination of genome-wide association study (GWAS) and selective sweep analysis was carried out to characterize the genetic architecture affecting chicken body weight comprehensively. As a result, 1,539 SNPs with significant effects on body weights at different weeks of age were identified using a genome-wide efficient mixed-model association (GEMMA) package. These SNPs were distributed on chromosomes 1 and 4. Besides, windows under selection identified for BW1–BW12 varied from 1,581 to 2,265. A total of 42 genes were also identified with significant effects on BW1–BW12 based on both GWAS and selective sweep analysis. Among these genes, diacylglycerol kinase eta (DGKH), deleted in lymphocytic leukemia (DLEU7), forkhead box O17 (FOXO1), karyopherin subunit alpha 3 (KPNA3), calcium binding protein 39 like (CAB39L), potassium voltage-gated channel interacting protein 4 (KCNIP4), and slit guidance ligand 2 (SLIT2) were considered as important genes for broiler growth based on their basic functions. The results of this study may supply important information for understanding the genetic architecture of growth traits in broilers.

In addition to additive genetic effects, epistatic interactions can play key roles in the control of phenotypic variation of traits of interest. In the current study, 475 male birds from lean and fat chicken lines were utilized as a resource population to detect significant epistatic effects associated with growth and carcass traits. A total of 421 significant epistatic effects were associated with testis weight (TeW), from which 11 sub-networks (Sub-network1 to Sub-network11) were constructed. In Sub-network1, which was the biggest network, there was an interaction between GGA21 and GGAZ. Three genes on GGA21 (SDHB, PARK7 and VAMP3) and nine genes (AGTPBP1, CAMK4, CDC14B, FANCC, FBP1, GNAQ, PTCH1, ROR2 and STARD4) on GGAZ that might be potentially important candidate genes for testis growth and development were detected based on the annotated gene function. In Sub-network2, there was a SNP on GGA19 that interacted with 8 SNPs located on GGA10. The SNP (Gga_rs15834332) on GGA19 was located between C-C motif chemokine ligand 5 (CCL5) and MIR142. There were 32 Refgenes on GGA10, including TCF12 which is predicted to be a target gene of miR-142-5p. We hypothesize that miR-142-5p and TCF12 may interact with one another to regulate testis growth and development. Two genes (CDH12 and WNT8A) in the same cadherin signaling pathway were implicated as potentially important genes in the control of metatarsus circumference (MeC). There were no significant epistatic effects identified for the other carcass and growth traits, e.g. heart weight (HW), liver weight (LW), spleen weight (SW), muscular and glandular stomach weight (MGSW), carcass weight (CW), body weight (BW1, BW3, BW5, BW7), chest width (ChWi), metatarsus length (MeL). The results of the current study are helpful to better understand the genetic basis of carcass and growth traits, especially for testis growth and development in broilers.

1. Excessive deposition of body fat, especially abdominal fat, is detrimental in chickens and the prevention of excessive fat accumulation is an important problem. The aim of this study was to identify blood biochemical indicators that could be used as criteria to select lean Yellow-feathered chicken lines. 2. Levels of blood biochemical indicators in the fed and fasted states and the abdominal fat traits were measured in 332 Guangxi Yellow chickens. In the fed state, the genetic correlations (r_g) of triglycerides and very low density lipoprotein levels were positive for the abdominal fat traits (0.47 ≤ r_g ≤ 0.67), whereas total cholesterol, high-density lipoprotein cholesterol (HDL-C) and low-density lipoprotein cholesterol (LDL-C) showed higher negative correlations with abdominal fat traits (–0.59 ≤ r_g ≤ −0.33). Heritabilities of these blood biochemical parameters were high, varying from 0.26 to 0.60. 3. In the fasted state, HDL-C:LDL-C level was positively correlated with abdominal fat traits (0.35 ≤ r_g ≤ 0.38), but triglycerides, total cholesterol, HDL-C, LDL-C, total protein, albumin, aspartate transaminase, uric acid and creatinine levels were negatively correlated with abdominal fat traits (–0.79 ≤ r_g ≤ −0.35). The heritabilities of these 10 blood biochemical parameters were high (0.22 ≤ h² ≤ 0.59). 4. In the fed state, optimal multiple regression models were constructed to predict abdominal fat traits by using triglycerides and LDL-C. In the fasted state, triglycerides, total cholesterol, HDL-C, LDL-C, total protein, albumin and uric acid could be used to predict abdominal fat content. 5. It was concluded that these models in both nutritional states could be used to predict abdominal fat content in Guangxi Yellow broiler chickens.

Large amounts of fat deposition often lead to loss of reproductive efficiency in humans and animals. We used broiler chickens as a model species to conduct a two-directional selection for and against abdominal fat over 19 generations, which resulted in a lean and a fat line. Direct selection for abdominal fat content also indirectly resulted in significant differences (P < 0.05) in testis weight (TeW) and in TeW as a percentage of total body weight (TeP) between the lean and fat lines. A total of 475 individuals from the generation 11 (G11) were genotyped. Genome-wide association studies revealed two regions on chicken chromosomes 3 and 10 that were associated with TeW and TeP. Forty G16 individuals (20 from each line), were further profiled by focusing on these two chromosomal regions, to identify candidate genes with functions that may be potentially related to testis growth and development. Of the nine candidate genes identified with database mining, a significant association was confirmed for one gene, TCF21, based on mRNA expression analysis. Gene expression analysis of the TCF21 gene was conducted again across 30 G19 individuals (15 individuals from each line) and the results confirmed the findings on the G16 animals. This study revealed that the TCF21 gene is related to testis growth and development in male broilers. This finding will be useful to guide future studies to understand the genetic mechanisms that underlie reproductive efficiency.