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Article

Mitigating Genotype–Environment Interaction Effects in a Genetic Improvement Program for Liptopenaeus vannamei

by
Tran Thi Mai Huong
1,2,
Nguyen Huu Hung
3,
Vu Dinh Ty
3,
Dinh Cong Tri
3 and
Nguyen Hong Nguyen
1,*
1
School of Science, Technology and Engineering, University of the Sunshine Coast, Maroochydore, QLD 4558, Australia
2
Centre for Bioinnovation, University of the Sunshine Coast, Locked Bag 4, Maroochydore, QLD 4558, Australia
3
Research Institute for Aquaculture No. 3, Nha Trang 650000, Khanh Hoa, Vietnam
*
Author to whom correspondence should be addressed.
J. Mar. Sci. Eng. 2024, 12(10), 1855; https://doi.org/10.3390/jmse12101855
Submission received: 24 September 2024 / Revised: 13 October 2024 / Accepted: 16 October 2024 / Published: 17 October 2024
(This article belongs to the Section Marine Biology)
Versions Notes

Abstract

:
The genotype-by-environment interaction (G × E) might have crucial impacts on the performance and fitness of agricultural species, such as Pacific whiteleg shrimp (Litopenaeus vannamei). This study explores how enhancements in management practices can counteract G × E effects on growth traits. We analyzed a selectively bred population of whiteleg shrimp spanning the latest two generations, encompassing 259 full-sib and half-sib families with 40,862 individual shrimp, measured for body weight and total length. Our analysis revealed moderate genetic correlations (0.60–0.65) between trait expressions in pond and tank environments, a significant improvement compared to earlier generations. Employing the average information-restricted maximum likelihood (REML) approach in mixed model analysis showed significant differences in heritability (h2) estimates between the two environments; however, the extent of these differences varied by trait (h2 = 0.68 in pond vs. 0.37 in tank for weight, and 0.41 vs. 0.67 for length). Our results indicate that G × E effects on growth traits in this population of L. vannamei were moderate but biologically significant. Consistent with our previous estimates in this population, genetic correlations between body weight and total length remained high (close to one) in pond and tank environments. The present findings collectively demonstrate that management improvements targeting stocking density, aeration, water quality, feeds, and feeding regimes mitigated the G × E effects on two economically significant traits in this population of whiteleg shrimp.

1. Introduction

Genotype-by-environment interaction, G × E [1], denotes how genotypes respond differently to various environments, driven by intrinsic genetic and environmental factors. The differential response could stem from specific ambient conditions where animals or populations reside, or from intricate biological mechanisms encompassing gene interactions and epigenetic influences [2,3]. Consequently, the G × E effects profoundly impact the performance and fitness of agricultural species, as extensively reported in both animals and plants [4,5].
In aquaculture, extensive research has examined diverse G × E systems, covering various culture systems such as ponds, cages, tanks, lakes, or integrated rice–fish farming [6,7]. Other studies also consider scales of production environment, e.g., smallholder to industrial operations [8]. Many nutritional factors such as diets, feed types, feeding schedules, and environmental or climatic stresses showed substantial G × E effects on important aquaculture species [9,10]. Across species and systems, synthesized literature results [11,12] highlight the relevance of G × E effects, particularly when the selection environment in nucleus herds diverges from practical production settings [13,14]. Conversely, G × E effects diminish when environments align closely [15].
In whiteleg shrimp (Litopenaeus vannamei), past studies dissected the G × E effects based on farming locations [16] or interactions with stocking densities [17,18,19]. Recent genetic evaluations of L. vannamei lines cultured in tank and pond environments revealed significant G × E impacts, hindering growth improvements under artificial selection [20]. To address these challenges without running multiple breeding programs, immediate enhancements in management practices focused on aeration, water quality, stocking density, feeds, and feeding regimes. These improvements aimed to mitigate G × E effects in this shrimp population's breeding program.
Thus, the primary objectives of this study were to evaluate how improvements in management practices can mitigate G × E effects on growth traits and to update genetic parameters for growth traits (weight and length) in the current shrimp population. Specifically, the present study explored whether trait inheritance (heritability) differed between pond and tank environments, whether G × E interaction effects remained significant for growth traits, and whether the genetic relationships between weight and length changed between the two environments.

2. Materials and Methods

2.1. Animal Population

The animals used in this study originated from a genetic improvement program for high growth at the Research Institute for Aquaculture No. 3 (RIA3) in Vietnam. Briefly, the population was established in 2014, and the first generation was produced in 2015 (G1), from which animals (2–3 males and 4–6 females per family × 60 families) with the highest Estimated Breeding Value (EBV) for body weight were selected [20]. The selection program continued until 2017. Between 2018 and 2021, the pedigreed population was maintained without any selection due to a lack of funding and the COVID-19 pandemic during this period. However, the genetic program resumed in 2022 and was ongoing in 2023, during which data were collected for this study. The same breeding protocol and selection procedures used in our previous study were implemented for these generations (2022 and 2023). Briefly, the shrimp were tested in ponds and tanks that differed in two main environmental parameters (salinity level by 2‰ and water temperature by 1 °C) [20]. During a grow-out period of 184 days, the animals were fed a 32% protein content diet at the rate of 3–5% of the body mass. After data collection, genetic evaluation was performed using a linear mixed model that included fixed effects of generation, line, sex, environment, and age, as well as the random additive genetic effect of the individual animal, to estimate EBVs for body weight. Based on the EBV rankings, approximately two males and four females per family were selected to produce subsequent generations. Overall, one male was mated to two females of different families to avoid full-sib and half-sib matings and manage inbreeding. When mortality or breeding failure occurred, only full-sib families were available. Details of our breeding and rearing protocol are detailed below.

2.2. Breeding and Rearing

During the breeding program, artificial methods were used to breed shrimp. This involved taking sperm packets from healthy adult males and inserting them into the thelycums of mature females with large red-green ovaries. These females, now carrying eggs, were moved to 300 L tanks with specific water conditions (salinity 30‰, pH 8–8.5, oxygen >5 mg/L, and water temperature 28–29 °C), one female per tank. Typically, spawning occurred at night within 1 to 4 h, and the next morning, newly hatched larvae (45,000–90,000 per family) were collected and treated with 34–36% formalin for about thirty minutes to disinfect them.
The larvae were then placed in separate tanks (1 m3) for rearing. After a day or two, approximately 30,000 larvae sampled from each family was transferred to larger tanks with a density of 60 larvae per liter. These tanks maintained specific conditions (salinity 30‰, temperature 29–30 °C, pH 8.2–8.6) and were fed a diet of Chaetoceros algae and synthetic food. As the larvae grew, they were gradually transitioned to a commercial diet supplemented with vitamin C.
After reaching the post-larvae stage (about 25 days post-hatch), a random sample of 10,000 individuals from each family was transferred to separate tanks (2.5 m3) and fed a commercial prawn pellet diet (45% crude protein and 12% fat content, Cargill Feed Ltd, Vietnam). After 1.5–2 months, when shrimp juveniles reached an average weight of 2 g, they were tagged with visible implant elastomer tags for identification purposes. These tags were applied to the first left and sixth right segments of the prawns' abdomens, using six available colors to track their family lineage.
Once tagged, the juvenile prawns were acclimatized in a 1000 L tank with aeration for three days before being transferred to communal grow-out tanks and ponds.

2.3. Test Environments and Trait Measurements

Pond: Marked shrimp from each family were divided into two groups: one group was raised in a pond with an area of 1000 square meters, while the other was placed in tanks, as described below. Similar stocking density and feed were used in both tanks and ponds to minimize potential impacts of this factor on shrimp’s growth. Specifically, unmarked shrimp were added to the grow-out ponds to maintain a commercial stocking density of 100 juveniles per m2 of water surface. Additionally, differences in tank and pond environments, as well as management practices such as aeration, water exchange, and water sources, were kept minimal. Water quality parameters and environmental factors were monitored weekly in both environments.
Tank: In every generation, tagged shrimp from all full- and half-sib families were raised in three tanks, each with a capacity of 25,000 L. An equal number of siblings from each family were randomly distributed among these tanks. To maintain an initial stocking density of 100 juveniles per m2 of water surface, surplus untagged juveniles were also raised alongside the experimental shrimp. These shrimp individuals were provided with a commercial dry pellet feed containing 32% protein content four times a day (at 6 am, 11 am, 4 pm, and 10 pm), with the amount ranging from 3% to 5% of their body weight.
The key improvement in management practices between generations in the two environments lies in the controlled outdoor field conditions for pond culture to match with the indoor system for tank culture. Specific improvements included the minimal differences in stocking density, the same duration of aeration, the same commercial diet, similar time of feeding, minimal variations in water parameters (salinity level, temperature, and water exchange). Further detail is given in Supplementary Table S1. Despite these efforts, differences were still observed in the pond environment, which exhibited greater variability in salinity levels (31.3 ± 1.7 vs. 33.5 ± 1.2‰) and slightly higher water temperatures (29 ± 1.1 vs. 28 ± 0.8 °C, with a range of 25–33 °C vs. 26–30 °C) compared to the tank environment across the two generations.
Trait measurements: After approximately 184 days of communal grow-out, the shrimp were harvested following pond drainage for body trait measurements. Individual shrimp were weighed using a digital scale with a precision of 0.1 g, and their standard length (distance from eye orbit to telson) was measured using a ruler. At harvest, shrimp sex and pedigree information were recorded. This dataset was used for statistical and genetic analyses, as follows.

2.4. Statistical Analysis

Two main analyses were conducted: (i) analyzing each environment separately and (ii) combing data from both pond and tank environments.

2.4.1. Separate Analysis of Tank and Pond Environments

In this analysis, genetic parameters (heritability and correlations) for traits studied were estimated using a mixed model that included both fixed and random effects, as described in Equation (1).
yijklm = µ + Gi + Sj + Lk + Il + am + eijklm
Here, yijklm represents observations of mth individual (i.e., body weight and total length). The fixed effects of the model included generation (Gi, i = 2 corresponding to 2022 and 2023), sex (Sj, female and male) line (Lk including the selection line and control group), and age (Il) fitted as a linear covariate. The experimental conditions of rearing tanks were similar and hence, the effect of tanks was not statistically significant (p > 0.05). The random term was the additive genetic effect of individual shrimp, am~(0, A σ a 2 ), where A is the numerator relationship matrix calculated from the pedigree traced back a previous generation. The residual error component of the model is represented by eijklm~(0, I σ e 2 ) with I as the identity matrix. Under this model (Equation (1)), var(a) = G = A σ a 2 . The remaining effects are assumed to be distributed as var(e) = R = I σ e 2 . The expectations of the random effects are zero, cov (a,e) = 0.

2.4.2. Combined Analysis of Both Tank and Pond Data

The combined analysis of both tank and pond data used a similar model to Equation (1). In addition to the fixed factors described above, the model also included the effect of the environment. The full model is written as Equation (2).
yijklnmv = µ + Gi + Sj + Lk + Il + Ev + am + eijklmv
where yijklmv indicates observations of mth individual (i.e., body weight and total length). The fixed effects of the model include generation (Gi), sex (Sj), line (Lk), environment (Ev), and age (Il), which was fitted as a linear covariate. The random term was the additive genetic effect of individual shrimp, am. The residual error component of the model is denoted as eijklmv. The assumptions of this model were the same as described above for Equation (1).
In both analyses using models 1 and 2, the heritability for body traits (weight and length) was estimated as h 2 = σ ^ a 2 σ ^ a 2 + σ ^ e 2 , where σ ^ a 2 is the additive genetic variance and the residual variance ( σ ^ e 2 ).
The genetic and phenotypic correlations between weight (W) and length (L) were estimated as r = σ W L σ W 2 σ L 2 , where the numerator represents the covariance between the two traits, and the denominator specifies the genetic or phenotypic variance of individual traits.
To assess the genotype-by-environment interaction, a preliminary analysis using a fixed-effect model that includes the G × E term as a factor showed a significant G × E interaction effect (p < 0.05). Next, this study employed a multivariate model and treated phenotype expressions in the tank and the pond as a separate character; hence, there is no phenotypic correlation between homologous traits between the two studied environments. The genetic correlation between homologous trait expressions in the tank and the pond was estimated as r = σ T P σ T 2 σ P 2 , where σ T P is the estimated additive genetic covariance of weight or length between the tank (T) and the pond (P), and σ T 2 and σ P 2 are the additive genetic variances of weight (or length) in the tank and the pond, respectively.
Using ASReml software version 4.1 [21], the body weight and total length data were analyzed through univariate and multivariate models, all of which successfully converged.

3. Results

3.1. Characteristics of the Population and Data Structure

In each generation, the project produced 120 full-sib families, from which progeny were sampled for performance testing in tanks and ponds. Although fewer progeny were tested in generation 2023 compared to 2022, the sample size remained substantial in each testing environment. Totally, performance tests were conducted on 40,862 individual shrimp when combining the data from both the tank and pond environments (Table 1).

3.2. Descriptive Statistics

The average body weight of the population in combined tank and pond environments was 19.8 g. The shrimp raised in ponds exhibited a small growth difference compared to those in tanks (Table 2). Although the standard deviation was similar between the two environments, the coefficient of variation (CV, %) for body weight was higher in tanks than in ponds (60.6% vs. 53.7%). Similar patterns of results were also observed for total length (Table 2).

3.3. Heritability

The genetic, environmental, and phenotypic variance components and heritability estimates for two key growth traits, body weight, and total length, are presented in Table 3. For body weight, genetic variances were higher in ponds compared to tanks, while environmental variances were similar between the two environments. Consequently, the heritability of body weight was greater in ponds than in tanks (0.68 vs. 0.37). In contrast, a different pattern was observed for total length. Despite in ponds total length exhibiting greater genetic variance than tanks, the environmental variance was significantly higher in ponds. When considering both environments together, the heritabilities for both weight and length were high (0.76 and 0.74, respectively).

3.4. Correlations

The genetic correlations between body weight and length were consistently high and approached unity (0.95–0.99) across the pond, tank, and combined environments. Correspondingly, at the phenotypic level, these traits exhibited strong correlations, ranging from 0.85 to 0.92 (refer to Table 4). Overall, both phenotypic and genetic correlations between weight and length did not differ between the two environments.

3.5. Genotype-by-Environment Interactions

Table 5 presents genetic correlations for homologous body traits between pond and tank environments as a measure of genotype–environment interaction (G × E). The results are shown for each of the two generations separately and when combined. In the generation produced in 2022, the genetic correlations between homologous traits were negative, indicating potential G × E effects despite large standard errors associated with the estimates. This was likely due to differences in management practices between the ponds and the tanks in this generation (2022). However, in the generation produced in 2023, where improved management practices (stocking density, aeration, feeds and feeding regimes, and water parameters) were implemented, the genetic correlations between trait expressions in tanks and ponds were remarkedly higher and significant (p < 0.001). Similar results were observed when data from both generations were combined, suggesting that the implementation of improved management practices may have contributed to mitigating the G × E interaction effects in this population of Pacific whiteleg shrimp.

4. Discussion

The presence of G × E interaction in our study indicates a need to refine breeding strategies that consider both genetic potential and environmental influences on phenotypic traits in this population of whiteleg shrimp. Genetic effects, measured by genetic correlations between trait expressions in tank and pond environments, were moderate (0.60–0.65) but significantly different from 1 in the latest two generations, suggesting that the G × E interaction effects on body traits were still biologically important in the population. This study aimed to reduce environmental variability in the latest generation within each rearing system by controlling five main factors: similar stocking density, synchronous aeration, same feeds and feeding regimes, and less variable water parameters. Standardizing the environmental conditions in tank and pond environments reduced confounding effects and improved the accuracy of genotype–environment evaluations in the latest generation compared to earlier stages of the breeding program. These improvements may have contributed to the reduced G × E effects on growth traits in this population of whiteleg shrimp, although other factors may have been involved. While the improvements were observed, the G × E interaction remained biologically significant, as the genetic correlations of homologous traits differed from 0.8 or unity [22]. This reflects re-ranking G × E effects, i.e., breeding candidates are ranked differently in tank and pond environments. Our previous evaluations of the between-environment genetic correlations of growth traits were low (−0.39 to 0.03) when two different statistical models (sire-dam or standard animal model) were employed [20]. These results, along with our current findings, align with published estimates across aquaculture species from fish [23,24] to crustaceans [25,26] and mollusks [27,28] when tanks and ponds were used for on growing. Studies in other species using diverse testing systems, such as sea cage vs. tank in Asian seabass [13] or monoculture vs. polyculture in catla and rohu carps [29], also showed the existence of G × E effects on growth traits. However, when the testing environments were similar, these effects were less significant, as demonstrated in Neotropical fish pacu, Piaractus mesopotamicus [30].
In addition to the re-ranking effects mentioned earlier, the G × E interaction in this shrimp population was attributed to scaling effects, which refer to differences or heterogeneities in variance components for growth traits between tank and pond environments. This was supported by the observed differential heritability of both weight and length in tank versus pond conditions. The scaling effect was eliminated after data transformation. For instance, when square root transformation was used, the phenotypic variances for body weight were 4.45 g2 in tank and 4.42 g2 in pond. Similar results were also observed when the logarithmic method was tested. Our previous study revealed that the scaling G × E effects due to variance heterogeneity on genetic gain were not significant, being less than 6% for body weight. They were also less economically important compared to the re-ranking effects. A thorough the assessment of the genetic enhancement program for common carp estimated the economic loss resulting from re-ranking G × E interactions to be around USD 11–20 million relative to only USD 3–5 million due to scaling effects [31].
The current study focused on improving five environmental parameters (aeration, water quality, stocking density, feeds, and feeding regimes). While applying the same feeds, feeding regimes, and stocking density across environments and generations is simple and straightforward, managing environmental and water parameters, including reducing water temperature in the pond by increasing the frequency of water exchange and volume, remains challenging. There are also many other management practices, such as housing conditions and environmental factors (e.g., ambient temperature or climate index) that should be considered in future studies. Furthermore, the G × E effects should be re-estimated to confirm the current findings when more data from multiple generations are accumulated. Due to breeding failure and mortality of broodstock, the numbers of half-sib families were limited to allow fitting additional random effects to obtain accurate genetic parameters for weight and length in this population. Advanced statistical procedures can also mitigate the G × E impacts on production traits of this shrimp population. One option is to test animals in multiple environments representative of the target production conditions to identify genotypes that consistently perform well or genotype–environment combinations that maximize trait expression [32]. This method, combined with genomic information, also enables breeders to accurately predict individual genetic merit, considering G × E effects and selecting genotypes with improved stability across environments [33]. Apart from the multivariate approach used in our study, there are advanced statistical methods and modelling techniques (such as factor analysis and reaction norm models) to identify stable genotypes or genomic regions less influenced by environmental variation [34,35]. Tailoring breeding objectives and selection criteria based on specific target environments or production systems prioritizes genotypes that perform well under relevant environmental conditions, promoting adaptation and resilience [36]. Regardless of the approach to be employed, the evaluation of genotype performance across a range of environments before widespread deployment in commercial production would help identify genotypes with consistent performance, reducing the risk of economic loss in genetic improvement programs for whiteleg shrimp and other aquaculture species.

5. Concluding Remarks

Improvements in management practices have helped mitigate the G × E interaction effects in this population of Pacific whiteleg shrimp, as shown by the moderate genetic correlations for two key growth traits between ponds and tanks. The heritabilities for body weight and total length were generally moderate in both testing environments, although the differences in the estimates between tanks and ponds depending on the trait. However, the heritability estimates may have been somewhat biased upwards, due to the existing pedigree structure that does not allow the inclusion of additional random effects such as common full-sib families. Future studies should consider obtaining genome sequence information to combine with phenotype data for better assessment of the G × E effects in this shrimp species. Overall, our results suggest that the current shrimp population will continue to respond to selection. However, future programs should further enhance other management and husbandry practices to improve the G × E impacts and maximize production and revenue for the shrimp sector.

Supplementary Materials

The following supporting information can be downloaded at: https://www.mdpi.com/article/10.3390/jmse12101855/s1, Table S1: Differences in management practices between pond and tank in generations or years of selection (2022 and 2023).

Author Contributions

Conceptualization, N.H.H. and N.H.N.; Methodology, T.T.M.H., D.C.T. and N.H.N.; Validation, N.H.H. and D.C.T.; Formal analysis, T.T.M.H. and N.H.N.; Investigation, T.T.M.H., N.H.H., V.D.T., D.C.T. and N.H.N.; Resources, N.H.H., V.D.T. and D.C.T.; Data curation, T.T.M.H., N.H.H., V.D.T., D.C.T. and N.H.N.; Writing—original draft, T.T.M.H. and N.H.N.; Writing—review & editing, N.H.N.; Visualization, V.D.T. and D.C.T.; Supervision, N.H.H. and N.H.N.; Project administration, N.H.H., V.D.T. and D.C.T.; Funding acquisition, N.H.H. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by Ministry of Agriculture and Rural Development of Vietnam, grant number KHCN20-23.

Institutional Review Board Statement

The animal study protocol was approved by the Institutional Review Board (or Ethics Committee) of the Research Institute for Aquaculture No.3 (protocol code ANE20157, 15 January 2021).

Informed Consent Statement

Not applicable.

Data Availability Statement

The data presented in this study are available on request from the corresponding author. The data are not publicly available due to commercial in confidence.

Acknowledgments

I would like to acknowledge the financial support from Ministry of Agriculture and Rural Development, Vietnam as well as the various staff involved in this project at the Research Institute for Aquaculture No. 3 (RIA3), Vietnam.

Conflicts of Interest

There are no conflicts of interest to declare.

References

  1. Falconer, D.; Mackay, T. Introduction to Quantitative Genetics; Longmans Green: Harlow, UK, 1996; Volume 4. [Google Scholar]
  2. Wu, H.; Eckhardt, C.M.; Baccarelli, A.A. Molecular mechanisms of environmental exposures and human disease. Nat. Rev. Genet. 2023, 24, 332–344. [Google Scholar] [CrossRef]
  3. Boye, C.; Nirmalan, S.; Ranjbaran, A.; Luca, F. Genotype × environment interactions in gene regulation and complex traits. Nat. Genet. 2024, 56, 1057–1068. [Google Scholar] [CrossRef]
  4. Fodor, I.; Spoelstra, M.; Calus, M.P.L.; Kamphuis, C. A systematic review of genotype-by-climate interaction studies in cattle, poultry and chicken. Front. Anim. Sci. 2023, 4, 1324830. [Google Scholar] [CrossRef]
  5. Napier, J.D.; Heckman, R.W.; Juenger, T.E. Gene-by-environment interactions in plants: Molecular mechanisms, environmental drivers, and adaptive plasticity. Plant Cell 2022, 35, 109–124. [Google Scholar] [CrossRef]
  6. Eknath, A.E.; Bentsen, H.B.; Ponzoni, R.W.; Rye, M.; Nguyen, N.H.; Thodesen, J.; Gjerde, B. Genetic improvement of farmed tilapias: Composition and genetic parameters of a synthetic base population of Oreochromis niloticus for selective breeding. Aquaculture 2007, 273, 1–14. [Google Scholar] [CrossRef]
  7. Fong, C.R.; Gonzales, C.M.; Rennick, M.; Gardner, L.D.; Halpern, B.S.; Froehlich, H.E. Global yield from aquaculture systems. Rev. Aquac. 2024, 16, 1021–1029. [Google Scholar] [CrossRef]
  8. Trọng, T.Q.; Mulder, H.A.; van Arendonk, J.A.M.; Komen, H. Heritability and genotype by environment interaction estimates for harvest weight, growth rate, and shape of Nile tilapia (Oreochromis niloticus) grown in river cage and VAC in Vietnam. Aquaculture 2013, 384–387, 119–127. [Google Scholar] [CrossRef]
  9. Torrecillas, S.; Rimoldi, S.; Montero, D.; Serradell, A.; Acosta, F.; Fontanillas, R.; Allal, F.; Haffray, P.; Bajek, A.; Terova, G. Genotype x nutrition interactions in European sea bass (Dicentrarchus labrax): Effects on gut health and intestinal microbiota. Aquaculture 2023, 574, 739639. [Google Scholar] [CrossRef]
  10. Oikonomou, S.; Kazlari, Z.; Loukovitis, D.; Dimitroglou, A.; Kottaras, L.; Tzokas, K.; Barkas, D.; Katribouzas, N.; Papaharisis, L.; Chatziplis, D. Genetic Parameters and Genotype × Diet Interaction for Body Weight Performance and Fat in Gilthead Seabream. Animals 2023, 13, 180. [Google Scholar] [CrossRef]
  11. Sae-Lim, P.; Gjerde, B.; Nielsen, H.M.; Mulder, H.; Kause, A. A review of genotype-by-environment interaction and micro-environmental sensitivity in aquaculture species. Rev. Aquac. 2016, 8, 369–393. [Google Scholar] [CrossRef]
  12. Nguyen, H.N. Genetic improvement for important farmed aquaculture species with a reference to carp, tilapia and prawns in Asia: Achievements, lessons and challenges. Fish Fish. 2016, 17, 483–506. [Google Scholar] [CrossRef]
  13. Khang, P.V.; Phuong, T.H.; Dat, N.K.; Knibb, W.; Nguyen, N.H. An 8-Year Breeding Program for Asian Seabass Lates calcarifer: Genetic Evaluation, Experiences, and Challenges. Front. Genet. 2018, 9, 191. [Google Scholar] [CrossRef]
  14. Gonzalez, C.; Gallardo-Hidalgo, J.; Yáñez, J.M. Genotype-by-environment interaction for growth in seawater and freshwater in Atlantic salmon (Salmo salar). Aquaculture 2022, 548, 737674. [Google Scholar] [CrossRef]
  15. Mengistu, S.B.; Mulder, H.A.; Benzie, J.A.H.; Khaw, H.L.; Megens, H.-J.; Trinh, T.Q.; Komen, H. Genotype by environment interaction between aerated and non-aerated ponds and the impact of aeration on genetic parameters in Nile tilapia (Oreochromis niloticus). Aquaculture 2020, 529, 735704. [Google Scholar] [CrossRef]
  16. Castillo-Juárez, H.; Casares, J.C.Q.; Campos-Montes, G.; Villela, C.C.; Ortega, A.M.; Montaldo, H.H. Heritability for body weight at harvest size in the Pacific white shrimp, Penaeus (Litopenaeus) vannamei, from a multi-environment experiment using univariate and multivariate animal models. Aquaculture 2007, 273, 42–49. [Google Scholar] [CrossRef]
  17. Ibarra, A.; Famula, T. Genotype by environment interaction for adult body weights of shrimp Penaeus vannamei when grown at low and high densitie. Genet. Sel. Evol. 2008, 40, 541–551. [Google Scholar] [CrossRef]
  18. Campos-Montes, G.R.; Montaldo, H.H.; Martínez-Ortega, A.; Castillo-Juárez, H. Genotype by environment interaction effects for body weight at 130 days of age in the Pacific white shrimp [Penaeus (Litopenaeus) vannamei]. Vet. Mex. 2009, 40, 255–268. [Google Scholar]
  19. Tan, J.; Luan, S.; Luo, K.; Guan, J.; Li, W.; Sui, J.; Guo, Z.; Xu, S.; Kong, J. Heritability and genotype by environment interactions for growth and survival in Litopenaeus vannamei at low and high densities. Aquacult. Res. 2017, 48, 1430–1438. [Google Scholar] [CrossRef]
  20. Nguyen, N.H.; Ninh, N.H.; Hung, N.H. Evaluation of two genetic lines of Pacific White leg shrimp Liptopenaeus vannamei selected in tank and pond environments. Aquaculture 2020, 516, 734522. [Google Scholar] [CrossRef]
  21. Gilmour, A.; Gogel, B.; Cullis, B.; Welham, S.; Thompson, R. ASReml User Guide Release 4.1 Structural Specification; VSN International Ltd.: Hemel Hempstead, UK, 2015. [Google Scholar]
  22. Robertson, A. The Sampling Variance of the Genetic Correlation Coefficient. Biometrics 1959, 15, 469–485. [Google Scholar] [CrossRef]
  23. de Araújo, F.C.T.; de Oliveira, C.A.L.; Campos, E.C.; Yoshida, G.M.; Lewandowski, V.; Todesco, H.; Nguyen, N.H.; Ribeiro, R.P. Effects of genotype × environment interaction on the estimation of genetic parameters and gains in Nile tilapia. J. Appl. Genet. 2020, 61, 575–580. [Google Scholar] [CrossRef]
  24. Tollervey, M.J.; Bekaert, M.; González, A.B.; Agha, S.; Houston, R.D.; Doeschl-Wilson, A.; Norris, A.; Migaud, H.; Gutierrez, A.P. Assessing genotype–environment interactions in Atlantic salmon reared in freshwater loch and recirculating systems. Evol. Appl. 2024, 17, e13751. [Google Scholar] [CrossRef]
  25. Hasan, M.M.; Thomson, P.C.; Raadsma, H.W.; Khatkar, M.S. Genetic analysis of digital image derived morphometric traits of black tiger shrimp (Penaeus monodon) by incorporating G× E investigations. Front. Genet. 2022, 13, 1007123. [Google Scholar] [CrossRef]
  26. Hasan, M.M.; Thomson, P.C.; Raadsma, H.W.; Khatkar, M.S. A Review and Meta-Analysis of Genotype by Environment Interaction in Commercial Shrimp Breeding. Genes 2024, 15, 1222. [Google Scholar] [CrossRef]
  27. Barros, J.; Winkler, F.M.; Velasco, L.A. Heritability, genetic correlations and genotype-environment interactions for growth and survival of larvae and post-larvae of the Caribbean scallop, Argopecten nucleus (Mollusca: Bivalvia). Aquaculture 2018, 495, 948–954. [Google Scholar] [CrossRef]
  28. Enez, F.; Puyo, S.; Boudry, P.; Lapègue, S.; Dégremont, L.; Gonzalez-Araya, R.; Morvezen, R.; Chapuis, H.; Haffray, P. Strong genotype-by-environment interaction across contrasted sites for summer mortality syndrome in the Pacific oyster Crassostrea gigas. Aquaculture 2025, 595, 741501. [Google Scholar] [CrossRef]
  29. Hamilton, M.G.; Mekkawy, W.; Alam, M.B.; Barman, B.K.; Karim, M.; Benzie, J.A.H. Genotype-by-culture-system interaction in catla and silver carp: Monoculture and biculture. Aquaculture 2023, 562, 738846. [Google Scholar] [CrossRef]
  30. Freitas, M.V.; Lira, L.V.G.; Ariede, R.B.; Agudelo, J.F.G.; Oliveira Neto, R.R.d.; Borges, C.H.S.; Mastrochirico-Filho, V.A.; Garcia Neto, B.F.; Carvalheiro, R.; Hashimoto, D.T. Genotype by environment interaction and genetic parameters for growth traits in the Neotropical fish pacu (Piaractus mesopotamicus). Aquaculture 2021, 530, 735933. [Google Scholar] [CrossRef]
  31. Ponzoni, R.W.; Nguyen, N.H.; Khaw, H.L.; Ninh, N.H. Accounting for genotype by environment interaction in economic appraisal of genetic improvement programs in common carp Cyprinus carpio. Aquaculture 2008, 285, 47–55. [Google Scholar] [CrossRef]
  32. Mulder, H.; Bijma, P. Effects of genotype× environment interaction on genetic gain in breeding programs 1. J. Anim. Sci. 2005, 83, 49–61. [Google Scholar] [CrossRef]
  33. Crossa, J.; Montesinos-López, O.A.; Pérez-Rodríguez, P.; Costa-Neto, G.; Fritsche-Neto, R.; Ortiz, R.; Martini, J.W.R.; Lillemo, M.; Montesinos-López, A.; Jarquin, D.; et al. Genome and Environment Based Prediction ModelsPrediction models and Methods of Complex TraitsComplex traits Incorporating Genotype × Environment Interaction. In Genomic Prediction of Complex Traits: Methods and Protocols; Ahmadi, N., Bartholomé, J., Eds.; Springer: New York, NY, USA, 2022; pp. 245–283. [Google Scholar]
  34. Madsen, M.D.; van der Werf, J.H.J.; Clark, S. Macro- and micro-genetic environmental sensitivity of yearling weight in Angus beef cattle. Animal 2024, 18, 101068. [Google Scholar] [CrossRef]
  35. Raunsgard, A.; Persson, L.; Czorlich, Y.; Ugedal, O.; Thorstad, E.B.; Karlsson, S.; Fiske, P.; Bolstad, G.H. Variation in phenotypic plasticity across age-at-maturity genotypes in wild Atlantic salmon. Mol. Ecol. 2024, 33, e17229. [Google Scholar] [CrossRef]
  36. Olesen, I.; Groen, A.F.; Gjerde, B. Definition of animal breeding goals for sustainable production systems. J. Anim. Sci. 2000, 78, 570–582. [Google Scholar] [CrossRef]
Table 1. Data structure and pedigree of the whiteleg shrimp (Litopenaeus vannamei) population studied.
Table 1. Data structure and pedigree of the whiteleg shrimp (Litopenaeus vannamei) population studied.
GenerationEnvironmentDamSireFull-Sibs (Half-Sibs)No. of Progeny
G8 (2021–2022)Pond10286100 (32)12,425
Tank10286100 (32)12,425
Both10286100 (32)24,850
G9 (2022–2023)Pond12010496 (31)8009
Tank12010496 (31)8009
Both12010496 (31)16,018
Both G8 and G9Pond222190196 (63)20,431
Tank222190196 (63)20,431
Both222190196 (63)40,862
Table 2. Basic statistics (number of observations n, raw mean, standard deviation, SD, and coefficient of variation, CV) for body traits of the whiteleg shrimp (Litopenaeus vannamei) population.
Table 2. Basic statistics (number of observations n, raw mean, standard deviation, SD, and coefficient of variation, CV) for body traits of the whiteleg shrimp (Litopenaeus vannamei) population.
TraitEnvironmentnMeanSDCV (%)
Weight, gPond 16,259 19.747 4.153.7
Tank 8176 19.879 2.660.6
Both 24,435 19.791 3.753.8
Length, mmPond 16,260 136.73 11.158.2
Tank 8176 138.06 3.586.3
Both 24,436 137.17 9.358.4
Table 3. Heritability (±S.E.) for body traits of the whiteleg shrimp (Litopenaeus vannamei) population.
Table 3. Heritability (±S.E.) for body traits of the whiteleg shrimp (Litopenaeus vannamei) population.
TraitEnvironmentGenetic VarianceEnvironmental VariancePhenotypic VarianceHeritability
WeightPond12.045.7117.750.68 ± 0.04
Tank2.425.117.530.37 ± 0.03
Both12.313.9416.250.76 ± 0.04
LengthPond32.4647.0179.470.41 ± 0.04
Tank16.618.1624.770.67 ± 0.08
Both63.9222.7086.620.74 ± 0.04
Table 4. Phenotypic and genetic correlations (±S.E.) between body weight and length in pond, tank, and in both environments.
Table 4. Phenotypic and genetic correlations (±S.E.) between body weight and length in pond, tank, and in both environments.
GenerationEnvironmentPhenotypic CorrelationGenetic Correlation
Both G8 and G9Pond0.93 ± 0.0060.97 ± 0.005
Tank0.85 ± 0.0090.95 ± 0.001
Both0.92 ± 0.0040.99 ± 0.001
Table 5. Genetic correlations (±S.E.) between pond and tank environments in the latest two generations of the whiteleg shrimp (Litopenaeus vannamei) population.
Table 5. Genetic correlations (±S.E.) between pond and tank environments in the latest two generations of the whiteleg shrimp (Litopenaeus vannamei) population.
GenerationTraitGenetic Correlations between the Two Environments
G8 (2021–2022)Weight−0.149 ± 0.117
Length−0.072 ± 0.126
G9 (2022–2023)Weight0.61 ± 0.09
Length0.52 ± 0.13
Both G8 and G9Weight0.65 ± 0.04
Length0.60 ± 0.05
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MDPI and ACS Style

Huong, T.T.M.; Hung, N.H.; Ty, V.D.; Tri, D.C.; Nguyen, N.H. Mitigating Genotype–Environment Interaction Effects in a Genetic Improvement Program for Liptopenaeus vannamei. J. Mar. Sci. Eng. 2024, 12, 1855. https://doi.org/10.3390/jmse12101855

AMA Style

Huong TTM, Hung NH, Ty VD, Tri DC, Nguyen NH. Mitigating Genotype–Environment Interaction Effects in a Genetic Improvement Program for Liptopenaeus vannamei. Journal of Marine Science and Engineering. 2024; 12(10):1855. https://doi.org/10.3390/jmse12101855

Chicago/Turabian Style

Huong, Tran Thi Mai, Nguyen Huu Hung, Vu Dinh Ty, Dinh Cong Tri, and Nguyen Hong Nguyen. 2024. "Mitigating Genotype–Environment Interaction Effects in a Genetic Improvement Program for Liptopenaeus vannamei" Journal of Marine Science and Engineering 12, no. 10: 1855. https://doi.org/10.3390/jmse12101855

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