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Evolution of genetic variability for growth-out survival rate in a selected population of Pacific white shrimp
Penaeus (Litopenaeus) vannamei
Nelson Cala-Moreno, Hugo Horacio Montaldo, Gabriel Ricardo Campos-Montes, Hector Castillo-Juárez
To cite this version:
Nelson Cala-Moreno, Hugo Horacio Montaldo, Gabriel Ricardo Campos-Montes, Hector Castillo-
Juárez. Evolution of genetic variability for growth-out survival rate in a selected population of Pacific
white shrimp Penaeus (Litopenaeus) vannamei. 10th World Congress of Genetics Applied to Livestock
Production, Aug 2014, Vancouver, Canada. �10.6084/m9.figshare.14363810.v2�. �hal-03189463�
Proceedings, 10th World Congress of Genetics Applied to Livestock Production Evolution of genetic variability for growth-out survival rate in a selected population of
Pacific white shrimp Penaeus (Litopenaeus) vannamei
N. Cala1, H.H. Montaldo1, G.R. Campos-Montes2 and H. Castillo-Juárez2
1Universidad Nacional Autónoma de México, Mexico, 2Universidad Autónoma Metropolitana, Mexico
ABSTRACT: Data from 164,023 offspring from 698 sires and 945 dams were used to estimate changes in the genetic variability for grow-out survival rate from 65 to 130 days of age (SR) in the 2004-2010 production cycles (7 generations), from a population of Pacific white shrimp selected for growth rate and SR. Estimation of variance components was performed using linear animal models with ASReml software. The fixed effect was year-pond combinations. Random effects were additive genetic, full- sib common environmental effects, and residual. Variances were considered as heterogeneous across years using full pedigree information. Also, within-year analyses were performed with similar models, but using only the pedigree information from that year. Estimates of trends for additive genetic variance, heritability and additive genetic coefficient of variation across years showed significant departures from linearity, suggesting the absence of a decline in the genetic variability.
Keywords:
genetic variability; survival rate; selection; shrimp;
heterogeneous variances model
Introduction
Knowledge about the magnitude of the genetic variability and the understanding of the forces that maintain such variability is of fundamental importance for the study of selection changes in breeding populations (Hill (2000)).
In the infinitesimal model introduced by Fisher (1918), it is assumed that many loci with very small effects affects quantitative traits, so no reductions in the genetic variability will occur by selection, however, selection and limited population sizes in breeding populations may lead to declines in genetic variability under more complex models for quantitative traits where the variance is a function of the allelic frequencies, major genes affect the trait, equilibrium between mutation and selection might occur and in presence of pleiotropic effects (Lynch and Walsh (1998)).
Studies about declines of genetic variability for quantitative traits in selected populations were performed in mice (Martínez et al. (2000); Hill et al. (2007)), hens (Wolc et al. (2010)) and P. vannamei (Andriantahina et al.
(2013)). Fewer studies have been oriented to look at the evolution of the genetic variability for survival in selected populations (Luan et al. (2013)). The aim of this study was to estimate the changes in the components of additive genetic variability for grow-out survival rate in a selected population of Pacific white shrimp (P. vannamei).
Materials and Methods
Data. Pedigreed data corresponding to seven discrete generations (2004-2010), obtained, from a Mexican genetic nucleus of Pacific white shrimp (Litopenaeus (Penaeus) vannamei) were used to estimate effect of grow- out survival rate (from 65-130 days of age) (SR). This population started in 1998, using wild shrimp from Sinaloa, Mexico, and domesticated shrimp from several other origins. Selection for BW was performed starting in 1998.
Since 2005, SR was incorporated to a selection index. A broodstock mating restriction, based on the average expected inbreeding of the progeny, has been used. Total number of measured shrimp for SR coded as 1 (alive) and 0 (dead), was 164,023 sibs from 698 sires and 945 dams.
Model. The linear mixed model used for analyzing SR with all data, included year-pond as fixed effect, plus animal and common full sib environment as random effects.
All genetic relationships from year 2002 were included in the numerator relationship matrix. In this model, variances were considered heterogeneous across years. Additionally, within-year analyses were performed with similar models, but using only the pedigree information from that year to evaluate the effect of excluding more remote genetic relationships. Analyses were done with ASReml software (Gilmour et al. (2009)).
Estimated parameters. Estimates of heritability (h2) were obtained as the ratio between additive genetic and phenotypic variances (σA2/σP2). Estimates of the coefficient of additive genetic variation (CVA) were obtained as the ratio between additive genetic standard deviation and the adjusted population mean ( σ!!/µμ). In order to measure trends in σA2, h2 and CVA, linear and 3rd degree polynomial regression was calculated in both log transformed and untransformed data.
Results and Discussion
Effective population size varied from 35 to 65 across generations. Linear genetic trend for SR was estimated as 0.29 ± 0.04%, using a linear animal model with homogeneous variances. Results for within-year analyses (not shown) were similar to those from the complete model analysis. Across years estimates for σA2 varied from 0.0005 to 0.0096, estimates of h2 varied from 0.003 to 0.072 and estimates for CVA from 2.8 to 12.1%.
Most h2 values were close to 0.03 (Table 1). Values of h2 <
0.10 for growth-out SR has also been estimated in this population (Campos-Montes et al. (2013)), in other P.
vannamei populations (Gitterle et al. (2005), and in other shrimp species populations (Luan et al. (2013)).
Linear trends across years were negative (P <
0.01) for all parameter estimates, however, third order polynomial regression models explained 114 to 146% more than linear ones. The increase in this higher order model is related to relatively large estimates of h2 and CVA for year 2005 and small ones for year 2009 (Table 1). These extreme values may be related to changes in the environmental conditions (Merilä and Sheldon (1999)).
These later results and the fact that changes in the parameter estimates from year 2004 to 2010 were slightly positive, are indications that there is no decline in the genetic variability for SR in this population.
Table 1. Across years heritability (h2), full-sib common environmental effects (c2) and additive genetic coefficient of variation (CVA) estimates in the Pacific white shrimp.
Full pedigree analysis estimates
Year LSM& σP2€ h2 CVA(%) c2
2004 0.78 0.14 0.027 7.7 0.017
2005 0.81 0.13 0.072 12.1 0.019
2006 0.77 0.14 0.031 8.6 0.016
2007 0.82 0.13 0.033 8.0 0.003
2008 0.84 0.12 0.030 7.3 0.011
2009 0.80 0.16 0.003 2.8 0.036
2010 0.83 0.15 0.029 8.0 0.030
R2(%)§ 28.0 29.4 26.0
R2(%)¥ 60.0 72.4 56.0
&Least-squares means
€Phenotypic variance
§Adjusted coefficient of determination for linear trend
¥Adjusted coefficient of determination for 3rd order polynomial model
In a previous similar study in this same population analyzing body weight at 130 days of age for years 2003- 2010, Cala et al. (2013), found no genetic variation decline across years, using either σA2 or h2 from log-transformed data, or CVA. Studies in other species, such as poultry (Dunnington et al. (2013)) showed that genetic variability for selected traits was maintained for 54 generations of selection for body weight traits. In our study, since SR is a fitness trait subject to natural selection, different results may be hypothesized. However, for the studied period, results showed that the general trend was the same.
Conclusion
Significant negative linear trends across years were found for additive genetic variance, heritability, and additive genetic coefficient of variation for SR, however, third order polynomial models explained approximately twice the variance of heritability than linear ones did, revealing the presence of influential points on years 2005
and 2009. Moreover, changes in the parameter estimates from year 2004 to 2010 were slightly positive. We conclude that there was no decline in the genetic variability for SR in this population.
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