Scientia Agricultura Sinica ›› 2024, Vol. 57 ›› Issue (22): 4568-4577.doi: 10.3864/j.issn.0578-1752.2024.22.014

• ANIMAL SCIENCE·VETERINARY SCIENCE • Previous Articles     Next Articles

Random Regression Analysis on Residual Feed Intake in Laying Hens with and Identification of the Genetic Markers

GUO Jun(), SHAO Dan, DOU TaoCun, MA Meng, LU Jian, HU YuPing, WANG XingGuo, WANG Qiang, LI YongFeng, GUO Wei, TONG HaiBing(), QU Liang()   

  1. Jiangsu Institute of Poultry Science, Yangzhou 225125, Jiangsu
  • Received:2024-07-11 Accepted:2024-09-10 Online:2024-11-16 Published:2024-11-22
  • Contact: TONG HaiBing, QU Liang

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Abstract:

【Objective】In this study, the polynomial coefficients of the random regression model were analyzed with genome wide association study (GWAS) to dissect the genetic architecture of the residual feed intake (RFI) in the lay, so as to lay the foundation for the selection of the RFI in laying hens. 【Method】 Data were collected from an F2 segregation population of Dongxiang blue-shelled chickens and White Leghorn. The phenotypic data set included body weight, egg size, egg production, and feed intake. The residual feed intake at 40 and 60 weeks of age were calculated with Koch’s method, respectively. Blood samples were collected in F2 generation. Genomic DNA was extracted with the phenol chloroform method. 1 534 hens were genotyped using 600 K gene microarray. The SNPs data were quality controlled. Missing data was imputed by Beagle software. The principal components were determined by using Plink software. The random regression model was used to analyze the variance components and genetic parameters of the segregation population. Gemma software was used to analyze the polynomial coefficients with a mixed linear model to obtain the significant P-value associated with the RFI in the lay. Gene annotation was carried out on the significant SNP. Chromosome heritability and haplotype block were calculated for RFI polynomial coefficients. 【Result】The second-order Legendre polynomials should be embedded in fixed, additive genetic, and permanent environmental effects. The homogeneous residual variance was harbored in the random regression model. The RFI in the lay showed moderate levels of heritability. The heritability on the RFI ranged from 0.23 to 0.32, and increased with hen age. The repeatabilities were 0.63-0.68, so this indicated that the RFI phenotype value could be accurately estimated by 3-4 repeated measurements. The genetic correlation coefficients in the RFI gradually decreased as the week intervals increased, as did the phenotypic correlation coefficients. The first principal component of the additive genetic matrix accounts for 96.12%. The inflation factor of the GWAS analysis was 1.026, indicating that the principal components of the linear mixed model had eliminated the potential stratified structure. There was a locus close to NAV2 on chromosome 5 that was significantly associated with the RFI, which was supported by the additional 11 SNPs. This QTL explained 1.73% of the phenotypic variance, and the haplotype block size reached 286 kb. Furthermore, potential SNPs were also detected on chromosomes 12 and 27. The potential SNP markers on chromosome 27 were located the upstream of NGFR. The chromosome heritability was directly proportional to the chromosome size, indicating that the small-effect polygenes controlled the RFI. 【Conclusion】The random regression model was used to estimate the genetic parameters on the RFI in the lay. Using polynomial coefficients as GWAS pseudo-phenotypic data, it was found that the NAV2 gene was significantly associated with the RFI.

Key words: RFI, random regression, heritability, repeatability, GWAS

Table 1

Variance component and genetic parameters on RFI during hen laying phase"

周龄
Week		 加性遗传方差
Va		 永久环境方差
Vpe		 残差
VR		 表型方差
Vp		 遗传力
h2		 重复力
r
40 9.68±2.57 17.12±2.18 15.89 42.69±1.70 0.23±0.05 0.63±0.01
44 9.96±2.45 16.83±1.98 15.89 42.68±1.53 0.23±0.05 0.63±0.01
48 10.76±2.54 16.78±1.96 15.89 43.44±1.50 0.25±0.05 0.63±0.01
52 12.09±2.84 16.99±2.12 15.89 44.97±1.61 0.27±0.05 0.65±0.01
56 13.95±3.32 17.44±2.45 15.89 47.27±1.85 0.30±0.05 0.66±0.01
60 16.33±3.98 18.13±2.94 15.89 50.34±2.21 0.32±0.05 0.68±0.01

Fig. 1

The eigenfunction on RFI during laying phase"

Table 2

The correlation coefficients on RFI among different weeks"

周龄
Week		 周龄 Week
40 44 48 52 56 60
40 0.995 0.982 0.96 0.932 0.898
44 0.987 0.996 0.983 0.962 0.936
48 0.948 0.987 0.996 0.984 0.965
52 0.892 0.954 0.989 0.996 0.985
56 0.827 0.908 0.963 0.992 0.996
60 0.759 0.855 0.926 0.971 0.994

Table 3

Candidate gene associated with RFI in the lay"

染色体
Chromosome		 物理位置*
Position		 候选基因
Candidate		 SNP位置
SNP position		 碱基替换
Allele		 单倍型片段
Length of block		 P值
P value
5 2123697 NAV2 第7内含子 Intron Ⅶ T/C 236kb 5.81×10-7

Fig. 2

Genome-wide associated analysis on RFI during laying phase: Manhattan plot"

Fig. 3

Genome-wide associated analysis on RFI during laying phase: QQ plot The abscissa is the expected log-P value and the ordinate represents the observed log-P value"

Fig. 4

Regression on heritability and chromosome sizes The horizontal axis represents the chromosome sizes. Red circles stand for the chromosome number. The blue line represents the linear fitting result. Grey area around the blue line is the 95% confidence level interval for prediction from the linear model"

Fig. 5

Association of polymorphism of NAV2 with RFI in the lay"

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