Multiple trait and random regression models using linear splines for genetic evaluation of multiple breed populations

被引:4
|
作者
Ribeiro, V. M. P. [1 ]
Raidan, F. S. S. [2 ]
Barbosa, A. R. [1 ]
Silva, M. V. G. B. [3 ]
Cardoso, F. F. [4 ]
Toral, F. L. B. [1 ]
机构
[1] Univ Fed Minas Gerais, Dept Zootecnia, Escola Vet, BR-31270901 Belo Horizonte, MG, Brazil
[2] CSIRO Agr & Food, Brisbane, Qld 4067, Australia
[3] Embrapa Gado Leite, BR-36038330 Juiz De Fora, MG, Brazil
[4] Embrapa Pecuaria Sul, BR-96401970 Bage, RS, Brazil
关键词
crossing; dairy cattle; heritability; heterosis; selection; CATTLE; PARAMETERS; HOLSTEINS; INFERENCE; WEIGHT;
D O I
10.3168/jds.2017-14321
中图分类号
S8 [畜牧、 动物医学、狩猎、蚕、蜂];
学科分类号
0905 ;
摘要
This study aimed to verify if random regression models using linear splines (RRMLS) are suitable for identifying genetic parameters in multiple-breed populations and also to investigate whether an interaction exists between the breeding value (BV) of sires and their progeny breed group. Ten populations were simulated by crossing 2 breeds with distinct genetic variance and nonzero segregation variance. To obtain the genetic parameters, 2 models were used: a multiple-trait model (MULT), in which the trait was considered distinct when evaluated in each group (1/2P(1) + 1/2P(2), 5/8P(1) + 3/8P(2), and 3/4P(1) + 1/4P(2)), and a RRMLS with the spline polynomial knots adjusted to these same groups. The genetic parameters estimated through MULT and RRMLS did not differ from the simulated values. The correlations between BV (simulated and estimated) of animals were high and varied from 0.74 to 0.76, which indicates the efficiency of using MULT and RRMLS for predicting BV. Using field data, the traits age at first calving (AFC), first lactation length (LL), and 305-d milk yield (MY-305) from a multiple-breed population of Holstein-Gyr cattle were analyzed. The BV of animals were modeled through RRMLS with 3, 5, and 7 knots, distributed in accordance with the fraction of Holstein breed in each progeny breed group. It was verified that RRMLS with 7 knots for adjusting mean trajectories arid genetic effects, with homogeneous residual variance, best fit AFC and LL. For MY-305, the best fit for mean trajectory and genetic effects was the RRMLS with 5 knots and with homogeneous residual variance. The posterior means of heritability varied from 0.21 to 0.48, 0.21 to 0.38, and 0.10 to 0.33 for AFC, LL, and MY-305, respectively. Estimates from genetic parameters obtained by using RRMLS with field data showed that this model is a useful tool for genetic evaluations of populations formed by a great number of breed groups. An interaction occurred between the BV of sires and their progeny breed group, and the genetic parameters for AFC, LL, and MY-305 traits from a multiple-breed population depend on breed composition of the progeny from which the evaluations are based.
引用
收藏
页码:464 / 475
页数:12
相关论文
共 50 条
  • [1] Studies on multiple trait and random regression models for genetic evaluation of beef cattle for growth
    Bohmanova, J
    Misztal, I
    Bertrand, JK
    [J]. JOURNAL OF ANIMAL SCIENCE, 2005, 83 (01) : 62 - 67
  • [2] Genetic evaluation of growth in Nellore cattle by multiple-trait and random regression models
    Nobre, PRC
    Misztal, I
    Tsuruta, S
    Bertrand, JK
    Silva, LOC
    Lopes, PS
    [J]. JOURNAL OF ANIMAL SCIENCE, 2003, 81 (04) : 927 - 932
  • [3] Properties of random regression models using linear splines
    Misztal, I.
    [J]. JOURNAL OF ANIMAL SCIENCE, 2005, 83 : 73 - 73
  • [5] Evaluating genetic parameters and trends of weight traits for the Hays Converter beef breed by multiple-trait and random regression models
    Khorshidi, R.
    MacNeil, M.
    Hays, D.
    Abo-Ismail, M.
    Crowley, J.
    Akanno, E.
    Wang, Z.
    Plastow, G.
    [J]. JOURNAL OF ANIMAL SCIENCE, 2018, 96 : 60 - 61
  • [6] Properties of random regression models using linear splines.
    Misztal, I.
    [J]. JOURNAL OF DAIRY SCIENCE, 2005, 88 : 73 - 73
  • [7] Genetic evaluation of lactation persistency in the Gyr breed by using a two-trait random regression model
    Gonzalez-Herrera, L. G.
    Pereira, R. J.
    El Faro, L.
    Albuquerque, L. G.
    [J]. ANIMAL PRODUCTION SCIENCE, 2022, 62 (03) : 216 - 224
  • [8] Comparison between multiple-trait and random regression models for genetic evaluation of weight traits in Australian meat sheep
    Paneru, Uddhav
    Moghaddar, Nasir
    van der Werf, Julius
    [J]. JOURNAL OF ANIMAL SCIENCE, 2024, 102
  • [9] Modeling lactation curves and estimation of genetic parameters in Holstein cows using multiple-trait random regression models
    Kheirabadi, Khabat
    Rashidi, Amir
    Alijani, Sadegh
    Imumorin, Ikhide
    [J]. ANIMAL SCIENCE JOURNAL, 2014, 85 (11) : 925 - 934
  • [10] Genetic evaluation of partial growth trajectory of Santa Ines breed using random regression models
    Pinto de Oliveira, Kassiana Adriano
    Braga Lobo, Raimundo Nonato
    Faco, Olivardo
    [J]. REVISTA BRASILEIRA DE ZOOTECNIA-BRAZILIAN JOURNAL OF ANIMAL SCIENCE, 2010, 39 (05): : 1029 - 1036