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Multivariate random regression analysis for body weight and main morphological traits in genetically improved farmed tilapia (Oreochromis niloticus)

Overview of attention for article published in Genetics Selection Evolution, November 2017
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Title
Multivariate random regression analysis for body weight and main morphological traits in genetically improved farmed tilapia (Oreochromis niloticus)
Published in
Genetics Selection Evolution, November 2017
DOI 10.1186/s12711-017-0357-7
Pubmed ID
Authors

Jie He, Yunfeng Zhao, Jingli Zhao, Jin Gao, Dandan Han, Pao Xu, Runqing Yang

Abstract

Because of their high economic importance, growth traits in fish are under continuous improvement. For growth traits that are recorded at multiple time-points in life, the use of univariate and multivariate animal models is limited because of the variable and irregular timing of these measures. Thus, the univariate random regression model (RRM) was introduced for the genetic analysis of dynamic growth traits in fish breeding. We used a multivariate random regression model (MRRM) to analyze genetic changes in growth traits recorded at multiple time-point of genetically-improved farmed tilapia. Legendre polynomials of different orders were applied to characterize the influences of fixed and random effects on growth trajectories. The final MRRM was determined by optimizing the univariate RRM for the analyzed traits separately via penalizing adaptively the likelihood statistical criterion, which is superior to both the Akaike information criterion and the Bayesian information criterion. In the selected MRRM, the additive genetic effects were modeled by Legendre polynomials of three orders for body weight (BWE) and body length (BL) and of two orders for body depth (BD). By using the covariance functions of the MRRM, estimated heritabilities were between 0.086 and 0.628 for BWE, 0.155 and 0.556 for BL, and 0.056 and 0.607 for BD. Only heritabilities for BD measured from 60 to 140 days of age were consistently higher than those estimated by the univariate RRM. All genetic correlations between growth time-points exceeded 0.5 for either single or pairwise time-points. Moreover, correlations between early and late growth time-points were lower. Thus, for phenotypes that are measured repeatedly in aquaculture, an MRRM can enhance the efficiency of the comprehensive selection for BWE and the main morphological traits.

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Geographical breakdown

Country Count As %
Unknown 27 100%

Demographic breakdown

Readers by professional status Count As %
Researcher 6 22%
Student > Ph. D. Student 3 11%
Student > Master 3 11%
Student > Bachelor 2 7%
Professor 2 7%
Other 4 15%
Unknown 7 26%
Readers by discipline Count As %
Agricultural and Biological Sciences 16 59%
Environmental Science 1 4%
Veterinary Science and Veterinary Medicine 1 4%
Unknown 9 33%
Attention Score in Context

Attention Score in Context

This research output has an Altmetric Attention Score of 1. This is our high-level measure of the quality and quantity of online attention that it has received. This Attention Score, as well as the ranking and number of research outputs shown below, was calculated when the research output was last mentioned on 27 March 2018.
All research outputs
#19,951,180
of 25,382,440 outputs
Outputs from Genetics Selection Evolution
#640
of 821 outputs
Outputs of similar age
#248,802
of 340,903 outputs
Outputs of similar age from Genetics Selection Evolution
#14
of 20 outputs
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