Construction of multiple linear regression models using blood biomarkers for selecting against abdominal fat traits in broilers

文献类型： 外文期刊

第一作者： Dong, J. Q.

作者： Dong, J. Q.;Zhang, X. Y.;Wang, S. Z.;Zhang, K.;Ma, G. W.;Wu, M. Q.;Li, H.;Zhang, H.;Dong, J. Q.;Zhang, X. Y.;Wang, S. Z.;Zhang, K.;Ma, G. W.;Wu, M. Q.;Li, H.;Zhang, H.;Dong, J. Q.;Zhang, X. Y.;Wang, S. Z.;Zhang, K.;Ma, G. W.;Wu, M. Q.;Li, H.;Zhang, H.;Dong, J. Q.;Jiang, X. F.

作者机构： Northeast Agr Univ, Minist Agr, Key Lab Chicken Genet & Breeding, Harbin 150030, Heilongjiang, Peoples R China;Northeast Agr Univ, Key Lab Anim Genet Breeding & Reprod, Educ Dept Heilongjiang Prov, Harbin 150030, Heilongjiang, Peoples R Ch

关键词： Abdominal fat;chicken;multiple linear regression;plasma biochemical parameter;very low-density lipoprotein

期刊名称：POULTRY SCIENCE （ 2020影响因子：3.352； 五年影响因子：3.679 ）

ISSN： 0032-5791

年卷期： 2018 年 97 卷 1 期

页码：

收录情况： SCI

摘要： Plasma very low-density lipoprotein (VLDL) can be used to select for low body fat or abdominal fat (AF) in broilers, but its correlation with AF is limited. We investigated whether any other biochemical indicator can be used in combination with VLDL for a better selective effect. Nineteen plasma biochemical indicators were measured in male chickens from the Northeast Agricultural University broiler lines divergently selected for AF content (NEAUHLF) in the fed state at 46 and 48 d of age. The average concentration of every parameter for the 2 d was used for statistical analysis. Levels of these 19 plasma biochemical parameters were compared between the lean and fat lines. The phenotypic correlations between these plasma biochemical indicators and AF traits were analyzed. Then, multiple linear regression models were constructed to select the best model used for selecting against AF content. and the heritabilities of plasma indicators contained in the best models were estimated. The results showed that 11 plasma biochemical indicators (triglycerides, total bile acid, total protein, globulin, albumin/globulin, aspartate transaminase, alanine transaminase, gamma-glutamyl transpeptidase, uric acid, creatinine, and VLDL) differed significantly between the lean and fat lines (P < 0.01), and correlated significantly with AF traits (P < 0.05). The best multiple linear regression models based on albumin/globulin, VLDL, triglycerides, globulin, total bile acid, and uric acid, had higher R-2 (0.73) than the model based only on VLDL (0.21). The plasma parameters included in the best models had moderate heritability estimates (0.21 <= h(2) <= 0.43). These results indicate that these multiple linear regression models can be used to select for lean broiler chickens.

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L.;Marin-Felix, Y.;Stadler, M.;Cedeno-Sanchez, M. L.;Marin-Felix, Y.;Stadler, M.;Marquez, R.;Masigol, H.;May, T. W.;Yuan, H. S.;Mendieta, S.;Meng, Q. F.;Menkis, A.;Menolli, N., Jr.;Nascimento, C. C.;Silva-Filho, A. G. S.;Meza Calvo, J. G.;Miller, S. L.;Moncada, B.;Moncada, B.;Moncalvo, J. M.;Moncalvo, J. M.;Monteiro, J. S.;Monteiro, J. S.;Monteiro, M.;Mora-Montes, H. M.;Moreau, P. A.;Mueller, G. M.;Nagy, L. G.;Najafiniya, M.;Nascimento, C. C.;Nei, Y.;Neves, M. A.;Niego, A. G. T.;Nilsson, R. H.;Niskanen, T.;Niveiro, N.;Noordeloos, M. E.;Noordeloos, M. E.;Nunez Otano, N. B.;O'Donnell, R. P.;Oehl, F.;Olariaga, I;Orlando, O. P.;Pang, K. L.;Pang, K. L.;Papp, V;Pawlowska, J.;Pereira, O. L.;Perera, R. H.;Perera, R. H.;Perez-Moreno, J.;Perez-Ortega, S.;Peter, G.;Phillips, A. J. L.;Jeronimo, G. H.;Jesus, A. L.;Pires-Zottarelli, C. L. A.;Poinar, G.;Prieto, M.;Quandt, C. A.;Radek, R.;Rahnama, K.;Raj, K. N. A.;Rajeshkumar, K. C.;Sruthi, O. P.;Rama, T.;Rama, T.;Rambold, G.;Rasconi, S.;Ren, G. C.;Ren, G. C.;Robledo, G. L.;Flakus, A.;Rodriguez-Flakus, P.;Ryberg, M.;Ryvarden, L. R.;Salvador-Montoya, C. A.;Samant, B.;Sanchez-Castro, I;Sanchez-Garcia, M.;Sarma, V. V.;Savchenko, A.;Savchenko, K.;Saxena, R. K.;Scholler, M.;Selbmann, L.;Selcuk, F.;Senanayake, I. C.;Shen, Y. M.;Simmons, D. R.;Singh, R.;Sir, E. B.;Yu, Q.;Stemler, J.;Stemler, J.;Stemler, J.;Stemler, J.;Stephenson, S. L.;Strassert, J. F. H.;Su, L.;Su, L.;Suetrong, S.;Norphanphoun, C.;Sun, Y. R.;Wang, Y.;Zeng, X. Y.;Svantesson, S.;Svantesson, S.;Takamatsu, S.;Tanaka, K.;Tang, A. M. C.;Tang, X.;Wei, D. P.;Tanney, J. B.;Taylor, J. E.;Taylor, P. W. J.;Tedersoo, L.;Zhang, F.;Zhang, J. F.;Thiyagaraja, V;Wanasinghe, D. N.;Tibell, L.;Tibell, S.;Tomsovsky, M.;Toome-Heller, M.;Yakovchenko, L.;Yang, H. D.;Yang, Z. L.;Yu, F. M.;Zeng, M.;Zhao, Q.;Tsurykau, A.;Tsurykau, A.;Untereiner, W. A.;Uzunov, B. A.;Valle, L. G.;Van den Wyngaert, S.;Van Vooren, N.;Velez, P.;Verma, R. K.;Vieira, W. A. S.;Vizzini, A.;Walker, A. K.;Yapa, N.;Joshi, Y.;Wang, S. X.;Wijesundara, D. S. A.;Xu, L. J.;Yu, Z. F.

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