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"

[1]
孙从佼, 于爱芝, 汪洋, 杨宁. 2023年蛋鸡产业发展情况、未来发展趋势及建议. 中国畜牧杂志, 2024, 60(3): 307-311.
SUN C J, YU A Z, WANG Y, YANG N. Development situation, future development trend and suggestions of laying hens industry in 2023. Chinese Journal of Animal Science, 2024, 60(3): 307-311. (in Chinese)
[2]
KOCH R M, SWIGER L A, CHAMBERS D, GREGORY K E. Efficiency of feed use in beef cattle. Journal of Animal Science, 1963, 22(2): 486-494.
[3]
BYERLY T C. Feed and other costs of producing market eggs. Maryland Agricultural Experiment Station Bulletin A, 1941, 1(1): 1-29.
[4]
SCHAEFFER L R. Application of random regression models in animal breeding. Livestock Production Science, 2004, 86(1/2/3): 35-45.
[5]
郭军, 曲亮, 窦套存, 王星果, 沈曼曼, 胡玉萍, 王克华. 应用随机回归模型估计蛋鸡体重遗传参数. 中国农业科学, 2020, 53(11): 2297-2304.

doi: 10.3864/j.issn.0578-1752.2020.11.015
GUO J, QU L, DOU T C, WANG X G, SHEN M M, HU Y P, WANG K H. Using random regression models to estimate genetic parameters on body weights in layers. Scientia Agricultura Sinica, 2020, 53(11): 2297-2304. (in Chinese)

doi: 10.3864/j.issn.0578-1752.2020.11.015
[6]
HOULAHAN K, SCHENKEL F S, MIGLIOR F, JAMROZIK J, STEPHANSEN R B, GONZÁLEZ-RECIO O, CHARFEDDINE N, SEGELKE D, BUTTY A M, STRATZ P, et al. Estimation of genetic parameters for feed efficiency traits using random regression models in dairy cattle. Journal of Dairy Science, 2024, 107(3): 1523-1534.
[7]
LIDAUER M H, NEGUSSIE E, MÄNTYSAARI E A, MÄNTYSAARI P, KAJAVA S, KOKKONEN T, CHEGINI A, MEHTIÖ T. Estimating breeding values for feed efficiency in dairy cattle by regression on expected feed intake. Animal, 2023, 17(9): 100917.
[8]
RAMOS P V B, DE OLIVEIRA MENEZES G R, DA SILVA D A, LOURENCO D, SANTIAGO G G, TORRES JÚNIOR R A A, SILVA F F E, LOPES P S, VERONEZE R. Genomic analysis of feed efficiency traits in beef cattle using random regression models. Journal of Animal Breeding and Genetics, 2024, 141(3): 291-303.
[9]
COYNE J M, BERRY D P, MATILAINEN K, SEVON-AIMONEN M L, MANTYSAARI E A, JUGA J, SERENIUS T, MCHUGH N. Genetic co-variance functions for live weight, feed intake, and efficiency measures in growing pigs. Journal of Animal Science, 2017, 95(9): 3822-3832.

doi: 10.2527/jas2017.1408 pmid: 28992029
[10]
HERRERA-CÁCERES W, RAMAYO-CALDAS Y, SÁNCHEZ J P. Longitudinal modelling of performance and feed efficiency traits in growing Duroc pigs. Livestock Science, 2022, 256: 104824.
[11]
SHIRALI M, NIELSEN V H, MØLLER S H, JENSEN J. Longitudinal analysis of residual feed intake and BW in mink using random regression with heterogeneous residual variance. Animal, 2015, 9(10): 1597-1604.

doi: 10.1017/S1751731115000956 pmid: 26051560
[12]
GILLEY A. The Evaluation of Genetic and Phenotypic Differences Associated with Short Term Selection of Four Different Feed Efficiency Strategies in Japanese Quail[D]. USA: University of Arkansas, 2018.
[13]
BEGLI H E, TORSHIZI R V, MASOUDI A A, EHSANI A, JENSEN J. Longitudinal analysis of body weight, feed intake and residual feed intake in F2 chickens. Livestock Science, 2016, 184: 28-34.
[14]
MIYUMO S, WASIKE C B, KAHI A K. Genetic and phenotypic parameters for feed efficiency in indigenous chicken in Kenya. Livestock Science, 2018, 207: 91-97.
[15]
OLIVEIRA H R, BRITO L F, LOURENCO D A L, SILVA F F, JAMROZIK J, SCHAEFFER L R, SCHENKEL F S. Invited review: advances and applications of random regression models: from quantitative genetics to genomics. Journal of Dairy Science, 2019, 102(9): 7664-7683.

doi: S0022-0302(19)30552-1 pmid: 31255270
[16]
NING C, KANG H M, ZHOU L, WANG D, WANG H F, WANG A G, FU J L, ZHANG S L, LIU J F. Performance gains in genome-wide association studies for longitudinal traits via modeling time-varied effects. Scientific Reports, 2017, 7(1): 590.
[17]
HOWARD J T, JIAO S H, TIEZZI F, HUANG Y J, GRAY K A, MALTECCA C. Genome-wide association study on Legendre random regression coefficients for the growth and feed intake trajectory on Duroc Boars. BMC Genetics, 2015, 16: 59.

doi: 10.1186/s12863-015-0218-8 pmid: 26024912
[18]
EMAMGHOLI BEGLI H, VAEZ TORSHIZI R, MASOUDI A A, EHSANI A, JENSEN J. Genomic dissection and prediction of feed intake and residual feed intake traits using a longitudinal model in F2 chickens. Animal, 2018, 12(9): 1792-1798.
[19]
OLIVEIRA H R, LOURENCO D A L, MASUDA Y, MISZTAL I, TSURUTA S, JAMROZIK J, BRITO L F, SILVA F F, CANT J P, SCHENKEL F S. Single-step genome-wide association for longitudinal traits of Canadian Ayrshire, Holstein, and Jersey dairy cattle. Journal of Dairy Science, 2019, 102(11): 9995-10011.

doi: S0022-0302(19)30756-8 pmid: 31477296
[20]
LUITING P, URFF E M. Optimization of a model to estimate residual feed consumption in the laying hen. Livestock Production Science, 1991, 27(4): 321-338.
[21]
YUAN J W, DOU T C, MA M, YI G Q, CHEN S R, QU L J, SHEN M M, QU L, WANG K H, YANG N. Genetic parameters of feed efficiency traits in laying period of chickens. Poultry Science, 2015, 94(7): 1470-1475.

doi: 10.3382/ps/pev122 pmid: 26009751
[22]
KRANIS A, GHEYAS A A, BOSCHIERO C, TURNER F, YU L, SMITH S, TALBOT R, PIRANI A, BREW F, KAISER P, et al. Development of a high density 600K SNP genotyping array for chicken. BMC Genomics, 2013, 14: 59.

doi: 10.1186/1471-2164-14-59 pmid: 23356797
[23]
YI G Q, SHEN M M, YUAN J W, SUN C J, DUAN Z Y, QU L, DOU T C, MA M, LU J, GUO J, CHEN S R, QU L J, WANG K H, YANG N. Genome-wide association study dissects genetic architecture underlying longitudinal egg weights in chickens. BMC Genomics, 2015, 16: 746.

doi: 10.1186/s12864-015-1945-y pmid: 26438435
[24]
MEYER K. WOMBAT: a tool for mixed model analyses in quantitative genetics by restricted maximum likelihood (REML). Journal of Zhejiang University Science B, 2007, 8(11): 815-821.

doi: 10.1631/jzus.2007.B0815 pmid: 17973343
[25]
KIRKPATRICK M, LOFSVOLD D, BULMER M. Analysis of the inheritance, selection and evolution of growth trajectories. Genetics, 1990, 124(4): 979-993.

doi: 10.1093/genetics/124.4.979 pmid: 2323560
[26]
BARRETT J C, FRY B, MALLER J, DALY M J. Haploview: analysis and visualization of LD and haplotype maps. Bioinformatics, 2005, 21(2): 263-265.

doi: 10.1093/bioinformatics/bth457 pmid: 15297300
[27]
GABRIEL S B, SCHAFFNER S F, NGUYEN H, MOORE J M, ROY J, BLUMENSTIEL B, HIGGINS J, DEFELICE M, LOCHNER A, FAGGART M, et al. The structure of haplotype blocks in the human genome. Science, 2002, 296(5576): 2225-2229.

doi: 10.1126/science.1069424 pmid: 12029063
[28]
KENNEDY B W, VAN DER WERF J H, MEUWISSEN T H. Genetic and statistical properties of residual feed intake. Journal of Animal Science, 1993, 71(12): 3239-3250.

doi: 10.2527/1993.71123239x pmid: 8294275
[29]
TIXIER-BOICHARD M, BORDAS A, RENAND G, BIDANEL J P. Residual food consumption as a tool to unravel genetic components of food intake. Proceedings of the 7th World Congress on Genetics Applied to Livestock Production, Montpellier, France, 2002: 10-18.
[30]
TIXIER-BOICHARD M, BOICHARD D, GROENEVELD E, BORDAS A. Restricted maximum likelihood estimates of genetic parameters of adult male and female Rhode island red chickens divergently selected for residual feed consumption. Poultry Science, 1995, 74(8): 1245-1252.
[31]
WING T L, NORDSKOG A W. Use of individual feed records in a selection program for egg production efficiency I. heritability of the residual component of feed Efficiency1. Poultry Science, 1982, 61(2): 226-230.
[32]
KATLE J, KOLSTAD N. Selection for efficiency of food utilisation in laying hens: direct response in residual food consumption and correlated responses in weight gain, egg production and body weight. British Poultry Science, 1991, 32(5): 939-953.
[33]
SABRI H M, WILCOX C J, WILSON H R, HARMS R H. Measurements of genetic variation in residual metabolizable energy intake of laying hens 1. Poultry Science, 1991, 70(2): 222-228

pmid: 2027828
[34]
SCHULMAN N, TUISKULA-HAAVISTO M, SIITONEN L, MÄNTYSAARI E A. Genetic variation of residual feed consumption in a selected Finnish egg-layer population. Poultry Science, 1994, 73(10): 1479-1484.

pmid: 7816721
[35]
WOLC A, ARANGO J, JANKOWSKI T, SETTAR P, FULTON J E, O’SULLIVAN N P, FERNANDO R, GARRICK D J, DEKKERS J C M. Pedigree and genomic analyses of feed consumption and residual feed intake in laying hens. Poultry Science, 2013, 92(9): 2270-2275.

doi: 10.3382/ps.2013-03085 pmid: 23960108
[36]
KELLY A K, MCGEE M, CREWS D H Jr, SWEENEY T, BOLAND T M, KENNY D A. Repeatability of feed efficiency, carcass ultrasound, feeding behavior, and blood metabolic variables in finishing heifers divergently selected for residual feed intake. Journal of Animal Science, 2010, 88(10): 3214-3225.

doi: 10.2527/jas.2009-2700 pmid: 20525931
[37]
CONNOR E E, HUTCHISON J L, NORMAN H D, OLSON K M, VAN TASSELL C P, LEITH J M, BALDWIN R L 6th. Use of residual feed intake in Holsteins during early lactation shows potential to improve feed efficiency through genetic selection. Journal of Animal Science, 2013, 91(8): 3978-3988.

doi: 10.2527/jas.2012-5977 pmid: 23658326
[38]
CAVANI L, BROWN W E, PARKER GADDIS K L, TEMPELMAN R J, VANDEHAAR M J, WHITE H M, PEÑAGARICANO F, WEIGEL K A. Estimates of genetic parameters for feeding behavior traits and their associations with feed efficiency in Holstein cows. Journal of Dairy Science, 2022, 105(9): 7564-7574.

doi: 10.3168/jds.2022-22066 pmid: 35863925
[39]
MUKIIBI R. Identification of functional genes for feed efficiency traits via transcriptome analyses to enhance the genomic prediction accuracy in beef cattle[D]. Cannada: University of Alberta, 2019.
[40]
BRUNES L C, BALDI F, LOPES F B, LÔBO R B, ESPIGOLAN R, COSTA M F O, STAFUZZA N B, MAGNABOSCO C U. Weighted single-step genome-wide association study and pathway analyses for feed efficiency traits in Nellore cattle. Journal of Animal Breeding and Genetics, 2021, 138(1): 23-44.
[41]
YUAN J W, WANG K H, YI G Q, MA M, DOU T C, SUN C J, QU L J, SHEN M M, QU L, YANG N. Genome-wide association studies for feed intake and efficiency in two laying periods of chickens. Genetics, Selection, Evolution, 2015, 47: 82.

doi: 10.1186/s12711-015-0161-1 pmid: 26475174
[42]
SHAH T M, PATEL N V, PATEL A B, UPADHYAY M R, MOHAPATRA A, SINGH K M, DESHPANDE S D, JOSHI C G. A genome-wide approach to screen for genetic variants in broilers (Gallus gallus) with divergent feed conversion ratio. Molecular Genetics and Genomics, 2016, 291(4): 1715-1725.

doi: 10.1007/s00438-016-1213-0 pmid: 27174137
[43]
DE KONING D J, HALEY C S, WINDSOR D, HOCKING P M, GRIFFIN H, MORRIS A, VINCENT J, BURT D W. Segregation of QTL for production traits in commercial meat-type chickens. Genetical Research, 2004, 83(3): 211-220.
[44]
REYER H, HAWKEN R, MURANI E, PONSUKSILI S, WIMMERS K. The genetics of feed conversion efficiency traits in a commercial broiler line. Scientific Reports, 2015, 5: 16387.

doi: 10.1038/srep16387 pmid: 26552583
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