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Journal of Integrative Agriculture  2014, Vol. 13 Issue (1): 148-157    DOI: 10.1016/S2095-3119(13)60383-4
Animal Science · Veterinary Science Advanced Online Publication | Current Issue | Archive | Adv Search |
Determination of Residual Feed Intake and Its Associations with Single Nucleotide Polymorphism in Chickens
 XU Zhen-qiang, CHEN Jie, ZHANG Yan, JI Cong-liang, ZHANG De-xiang , ZHANG Xi-quan
1.Guangdong Provincial Key Lab of Agro-Animal Genomics and Molecular Breeding/Key Lab of Chicken Genetics, Breeding and Reproduction,
Ministry of Agriculture/South China Agricultural University, Guangzhou 510642, P.R.China
2.Wens Nanfang Poultry Breeding Co. Ltd., Yunfu 527400, P.R.China
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摘要  Marker assisted selection (MAS) for residual feed intake (RFI) is considered to be one of the powerful means to improve feed conversion efficiency, and therefore reduce production costs. To test the inner relationship among body compositions, growth traits and RFI, four models were proposed to assess the extensively explanatory variables accounting for partial variables in feed intake besides metabolic body weight and growth rate. As a result, the original model (Koch’s model) had the lowest R2 (80.78%) and the highest Bayesian information criterion (1 323.3) value among the four models. Moreover, the effects on RFI caused by single nucleotide polymorphisms (SNPs) were assessed in this study. Twelve SNPs from 7 candidate genes were genotyped in 2 Chinese native strains. rs14743490 of RPLP2 gene showed suggestively significant association with initial body weight in both strains (P<0.10). rs15047274 of TAF15 was significantly associated with growth weight, final weight, and feed intake (P<0.05) in N301 strain, in contrast, it was only suggestively significant associated with feed intake (P<0.10) in N414 strain. rs15869967 was significantly associated with RFI in N414 strain but not in N301 strain. This study has identified potential genetic markers suitable for MAS in improving the above mentioned traits, but these associations need to be rectified in other larger populations in future.

Abstract  Marker assisted selection (MAS) for residual feed intake (RFI) is considered to be one of the powerful means to improve feed conversion efficiency, and therefore reduce production costs. To test the inner relationship among body compositions, growth traits and RFI, four models were proposed to assess the extensively explanatory variables accounting for partial variables in feed intake besides metabolic body weight and growth rate. As a result, the original model (Koch’s model) had the lowest R2 (80.78%) and the highest Bayesian information criterion (1 323.3) value among the four models. Moreover, the effects on RFI caused by single nucleotide polymorphisms (SNPs) were assessed in this study. Twelve SNPs from 7 candidate genes were genotyped in 2 Chinese native strains. rs14743490 of RPLP2 gene showed suggestively significant association with initial body weight in both strains (P<0.10). rs15047274 of TAF15 was significantly associated with growth weight, final weight, and feed intake (P<0.05) in N301 strain, in contrast, it was only suggestively significant associated with feed intake (P<0.10) in N414 strain. rs15869967 was significantly associated with RFI in N414 strain but not in N301 strain. This study has identified potential genetic markers suitable for MAS in improving the above mentioned traits, but these associations need to be rectified in other larger populations in future.
Keywords:  RFI       model       SNPs       growth traits       association  
Received: 10 December 2012   Accepted:
Fund: 

This work was supported by the China Agriculture Research System (CARS-42-G05, CARS-42-Z17), the High Technology Research and Development Program of China (2013AA102501).

Corresponding Authors:  ZHANG De-xiang, Tel: +86-20-85285759, Fax: +86-20-85280740, E-mail: zhangdexiang0001@sina.com     E-mail:  zhangdexiang0001@sina.com
About author:  XU Zhen-qiang, E-mail: zhenqiangxu@163.com

Cite this article: 

XU Zhen-qiang, CHEN Jie, ZHANG Yan, JI Cong-liang, ZHANG De-xiang , ZHANG Xi-quan. 2014. Determination of Residual Feed Intake and Its Associations with Single Nucleotide Polymorphism in Chickens. Journal of Integrative Agriculture, 13(1): 148-157.

Archer J A, Arthur P F, Herd R M, Parnell P F, Pitchford W S. 1997. Optimum postweaning test for measurement of growth rate, feed intake, and feed efficiency in British breed cattle. Journal of Animal Science, 75, 2024-2032

 Barendse W, Reverter A, Bunch R J, Harrison B E, Barris W, Thomas M B. 2007. A validated whole-genome association study of efficient food conversion in cattle. Genetics, 176, 1893-1905

 Bottje W G, Carstens G E. 2009. Association of mitoch-ondrial function and feed efficiency in poultry and livestock species. Journal of Animal Science, 87, E48-E63.

Chen Y, Gondro C, Quinn K, Herd R M, Parnell P F, Vanselow B. 2011. Global gene expression profiling reveals genes expressed differentially in cattle with high and low residual feed intake. Animal Genetics, 42, 475-490

 Chen W, Guo Y M, Huang Y Q, Shi Y H, Zhang C X, Wang J W. 2012. Effect of energy restriction on growth, slaughter performance, serum biochemical parameters and Lpin2/WDTC1 mRNA expression of broilers in the later phase. Journal of Poultry Science, 49, 12-19

 Fan B, Lkhagvadorj S, Cai W, Young J, Smith R M, Dekkers J C, Huff-Lonergan E, Lonergan S M, Rothschild M F. 2010. Identification of genetic markers associated with residual feed intake and meat quality traits in the pig. Meat Science, 84, 645-650

 Filleur S, Hirsch J, Wille A, Schon M, Sell C, Shearer M H, Nelius T, Wieland I. 2009. INTS6/DICE1 inhibits growth of human androgen-independent prostate cancer cells by altering the cell cycle profile and Wnt signaling. Cancer Cell International, 9, 28.

Fontana E A, Weaver W J, Watkins B A, Denbow D M. 1992. Effect of early feed restriction on growth, feed conversion, and mortality in broiler chickens. Poultry Science, 71, 1296-1305

 Frank D, Kuhn C, van Eickels M, Gehring D, Hanselmann C, Lippl S, Will R, Katus H A, Frey N. 2007. Calsarcin-1 protects against angiotensin-II induced cardiac hypertrophy Circulation, 116, 2587-2596.

Giachetto P F, Guerreiro E N, Ferro J A, Ferro M I T, Furlan R L, Macari M. 2003. Performance and hormonal profile in broiler chickens fed with different energy levels during post restriction period. Pesquisa Agropecuaria Brasileira, 38, 697-702

 Han S M, Lee T H, Mun J Y, Kim M J, Kritikou E A, Lee S J, Han S S, Hengartner M O, Koo H S. 2006. Deleted in cancer 1 (DICE1) is an essential protein controlling the topology of the inner mitochondrial membrane in C. elegans. Development, 133, 3597-3606

 Herd R M, Arthur P F. 2009. Physiological basis for residualfeed intake. Journal of Animal Science, 87, E64-E71.

Hess C W, Byerly T C, Jull M A. 1941. The efficiencyof feed utilization by barred plymouth rock crossbredbroilers. Poultry Science, 20, 210-216

Hess C W, Morley. 1948. A study of the inheritance of feedutilization efficiency in the growing domestic fowl.Poultry Science, 27, 24-39

Jones W K, Grupp I L, Doetschman T, Grupp G, Osinska H, Hewett T E, Boivin G, Gulick J, Ng W A, Robbins J.1996. Ablation of the murine alpha myosin heavy chaingene leads to dosage effects and functional deficits in theheart. Journal of Clinical Investigation, 98, 1906-1917

Kelly A K, McGee M, Crews D J, Fahey A G, Wylie A R,Kenny D A. 2010. Effect of divergence in residual feedintake on feeding behavior, blood metabolic variables,and body composition traits in growing beef heifers.Journal of Animal Science, 88, 109-123

Kiss A M, Jady B E, Bertrand E, Kiss T. 2004. Humanbox H/ACA pseudouridylation guide RNA machinery.Molecular and Cellular Biology, 24, 5797-5807

Koch R M, Swiger L A, Chambers D, Gregory K E. 1963.Efficiency of feed use in beef cattle. Journal of AnimalScience, 22, 486-494

Kong B W, Song J J, Lee J Y, Hargis B M, Wing T, LassiterK, Bottje W. 2011. Gene expression in breast muscle associated with feed efficiency in a single male broiler line using a chicken 44K oligo microarray. I. Topdifferentially expressed genes. Poultry Science, 90,2535-2547

de Koning D J, Haley C S, Windsor D, Hocking P M, GriffinH, Morris A, Vincent J, Burt D W. 2004. Segregationof QTL for production traits in commercial meat-type chickens. Genetical Research, 83, 211-220

de Koning D J, Windsor D, Hocking P M, Burt D W, LawA, Haley C S, Morris A, Vincent J, Griffin H. 2003.Quantitative trait locus detection in commercial broiler lines using candidate regions. Journal of Animal Science,81, 1158-1165

Knott S A, Cummins L J, Dunshea F R, Leury B J. 2008. Theuse of different models for the estimation of residual feedintake (RFI) as a measure of feed efficiency in meat sheep.Animal Feed Science and Technology, 143, 242-255

Luiting P, Urff E M. 1991. Optimization of a model toestimate residual feed consumption in the laying hen.Livestock Production Science, 27, 321-338

Marygold S J, Roote J, Reuter G, Lambertsson A, AshburnerM, Millburn G H, Harrison P M, Yu Z, Kenmochi N,Kaufman T C, et al. 2007. The ribosomal protein genesand minute loci of Drosophila melanogaster. GenomeBiology, 8, R216.

McGrath M J, Cottle D L, Nguyen M A, Dyson J M, Coghill ID, Robinson P A, Holdsworth M, Cowling B S, HardemanE C, Mitchell C A, et al. 2006. Four and a half LIM protein1 binds myosin-binding protein C and regulates myosin filament formation and sarcomere assembly. Journal of Biological Chemistry, 281, 7666-7683

McNaughton L R, Pryce J E. 2007. Metabolic feed efficiency - opportunities for selection in dairy cows.Proceedings of the New Zealand Society of Animal Production, 67, 392-398

Mineva I, Gartner W, Hauser P, Kainz A, Loffler M, Wolf G,Oberbauer R, Weissel M, Wagner L. 2005. Differentialexpression of alphaB-crystallin and Hsp27-1 inanaplastic thyroid carcinomas because of tumor-specificalphaB-crystallin gene (CRYAB) silencing Cell Stress Chaperones, 10, 171-184

Montanholi Y R, Swanson K C, Schenkel F S, McBride B W,Caldwell T R, Miller S P. 2009. On the determinationof residual feed intake and associations of infraredthermography with efficiency and ultrasound traits inbeef bulls. Livestock Science, 125, 20-23

Ojano-Dirain C, Toyomizu M, Wing T, Cooper M, Bottje W G. 2007. Gene expression in breast muscle and duodenum from low and high feed efficient broilers.Poultry Science, 86, 372-381

Parsanejad R, Praslickova D, Zadworny D, Kuhnlein U.2004. Ornithine decarboxylase: haplotype structure andtrait associations in White Leghorn chickens. Poultry Science, 83, 1518-1523

Pym R A E, Nicholls P J. 1979. Selection for foodconversion in broilers: direct and correlated responses for body weight gain, food consumption and foodconversion ratio. British Poultry Science, 20, 73-86

Redden R R, Surber L M M, Roeder B L, Nichols B M, Paterson J A, Kott R W. 2011. Residual feed efficiency established in a post-weaning growth test maynot result in more efficient ewes on the range. Small Ruminant Research, 96, 155-159

Tun H W, Marlow L A, von Roemeling C A, Cooper S J,Kreinest P, Wu K, Luxon B A, Sinha M, Anastasiadis P Z, Copland J A. 2010. Pathway signature and cellulardifferentiation in clear cell renal cell carcinoma. PLoSOne, 5, e10696.

Uechi T, Nakajima Y, Nakao A, Torihara H, ChakrabortyA, Inoue K, Kenmochi N. 2006. Ribosomal protein geneknockdown causes developmental defects in zebrafish.PLoS One, 1, e37.

Williams Y J, Pryce J E, Grainger C, Wales W J, Linden N,Porker M, Hayes B J. 2011. Variation in residual feedintake in Holstein-Friesian dairy heifers in southernAustralia. Journal of Dairy Science, 94, 4715-4725

Xie L, Luo C, Zhang C, Zhang R, Tang J, Nie Q, Ma L, Hu X,Li N, Da Y, et al. 2012. Genome-wide association studyidentified a narrow chromosome 1 region associated withchicken growth traits. PLoS One, 7, e30910.

Zhang Y Z, Zhang L H, Gao Y, Li C H, Jia S Q, LiuN, Cheng F, Niu D Y, Cho W C, Ji J F, et al. 2011.Discovery and validation of prognostic markers ingastric cancer by genome-wide expression profiling.World Journal of Gastroenterology, 17, 1710-1717
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