数字农科院2.0

Identification of regulatory loci and candidate genes related to body weight traits in broilers based on different models

文献类型： 外文期刊

作者： Na Luo;Keqi Cai;Limin Wei;Huanxian Cui;Jie Wen;Bingxing An;Guiping Zhao

作者机构：

关键词： Body weight;Chicken 55 K SNP array;Genome-wide association studies;Longitudinal;Transcriptome

期刊名称： BMC Genomics

ISSN： 1471-2164

年卷期： 2025 年 26 卷 1 期

页码：

收录情况： SCIE（2025版）

摘要： Background: Growth traits are crucial for the economic viability in broiler production, as they significantly contribute to the cost of rearing. Maximizing body weight (BW) while minimizing feed intake is key to enhancing the efficiency of broiler breeding. Identifying the genetic architecture associated with BW trait is therefore a critical step in enhancing breeding strategies. Results: We conducted a genome-wide association study (GWAS) using two statistical approaches: single-trait GWAS and longitudinal GWAS. The study was performed on the BW trait at five developmental stages (72, 81, 89, 113, and 120 days) and mid-test metabolic weight (MWT) across four growth cycles. Transcriptome sequencing analysis was also included to investigate the differential expression of candidate genes identified through the GWAS models, particularly linked to BW and MWT traits. Utilizing the chicken 55K single nucleotide polymorphism (SNP) array, we identified 52,060 SNPs in the genomic data of 4,493 Wenchang chickens. The single-trait GWAS model revealed 42 BW-associated SNPs, corresponding to 18 potential genes. For MWT, 47 SNPs were associated, mapping to 31 candidate genes. The longitudinal GWAS model identified 34 BW-linked SNPs, annotated with 22 candidate genes, and 21 MWT-linked SNPs, annotated with 10 candidate genes. Notably, 16 SNPs on chromosome 4 were associated with both BW and MWT, located within the 73.08Mb-76.82Mb region. Nine genes were annotated from this region, including STIM2, SEL1L3, SEPSECS,LGI2, SOD3, KCNIP4, NCAPG, FAM184B, LDB2. Notably, there are 32 overlapping SNPs identified in both the single-trait and longitudinal GWAS models, suggesting consistent associations for both BW and MWT. These overlapping SNPs represent robust loci that may influence both traits across different statistical approaches. Transcriptome sequencing indicated differential expression of LDB2 and SEL1L3 between high and low BW groups. Conclusion: Our study has uncovered novel candidate genes that are potentially involved in growth traits, providing valuable insights for broiler breeding. The identified SNPs and genes could serve as genetic markers for selecting broilers with improved growth efficiency, which may lead to more cost-effective and productive broiler farming.

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