Genotype-by-environment interaction in white shrimp associated with white spot syndrome.

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White shrimp production units are commonly affected by diseases entailing high morbidity and mortality rates, such as BMS. In this framework, this study aimed to estimate the genotype-by-environment interaction for and survival to harvest (SH) in the presence and absence of white spot syndrome (WSS) in two genetic lines of Litopenaeus vannamei.

Although the linear model suggests a genotype-by-environment interaction, the estimates propose independence of the same feature between environments. Correlations between traits for the resistance line suggest selecting features independently in the presence of WSS.

World production of Pacific white shrimp (L. vannamei) it’s based on the production of genetic lines selected for growth and overall survival. However, the control of White Spot Syndrome (WSS) it’s been a challenging goal to achieve. A selection criterion related to the resistance of this disease has been added to the selection objective of Genetic Improvement Programs (GIP) in penaeids.

In this order, and adequate heritability (h2) and genetic correlation (rG) estimators should be considered to formulate selection strategies.

These genetic parameters, estimated under natural outbreak conditions, can provide vital information to be considered in GIPs.

Some studies report that, in the presence of WSS, it was unattainable to estimate the rG for weight and survival in shrimp due to the loss of information structure derived from the high mortality in the population.

“Moreover, there is no information on how rG gets altered for weight and survival across different environments in shrimp production. Therefore, it is critical to estimate these genetic parameters (h2 and rG) in the presence or absence of the WSS to achieve an optimal GIP design.”

This study aimed to estimate the effects of IGE for BW and SH in two commercial environments -presence or absence of natural SMB outbreak-, in two genetic lines of Pacific white shrimp (L. vannamei); one selected for growth and the other with a history of resistance to SMB.

Material and Methods

Data were obtained from a shrimp larval production company located in northwestern Mexico. Shrimp were grown under commercial conditions in three ponds. A line of individuals from 1998 was selected for growth and SH (GRW). Another shrimp line with resistance to WSS (RES) record, was also used. Families included in this study had a maximum of 25% of genes from the other line and were analyzed independently.

Origin and development of the genetic lines

The GRW line was produced using shrimp from Mexico, Venezuela, Colombia, United States, and Ecuador. The RES line is composed of shrimp with a history of resistance to SMB from 2014 from Ecuador, Panama, and United States.

Management of families

Families were produced by artificial insemination, using a ratio of one male per every two females. Inseminated female families spawned in individual tanks to ease nauplii counting per family (full siblings). Full-sub families were kept in the same tank, tagging them at around 60 days old.

Management of growth ponds

Ten days after tagging, an average of 36 shrimp per family were transferred to each pond. The daily water exchange rate varied from 5% to 20%. The feed provided contained between 34 and 40% of protein at a rate of 3% of the total biomass in the pond. The quantity of daily feed required (35% – 40% protein); was calculated as 6% of their biomass.

Data collection for bodyweight happened at 130 days old, and the survival rate was considered from 70 to 130 days old. Estimation of SH considered individuals recovered at the end of the period as alive (1), while the animals not recovered deemed the difference between the live organisms of each family and those inseminated as dead (0).

Data Analysis

BW also included gender and pond data were. Genetic parameters for BW and SH were estimated for each line, using an animal model and restricted maximum likelihood, with ASReml software. Normality was assumed in the SH analysis, considering the criteria for approximating a binomial distribution to a normal distribution.

“Genetic correlations between both traits in the line-stock combination were estimated with ASReml, using bivariate models, and with the same model, but considering the vector and information vector of BW and SH. In the covariance structure and the environmental effects of a regular family, no restrictions were used both were considered independent.”

The fixed effects included in the estimate of the genetic parameters model for BW were: sex, age at harvest linear, and quadratic. In addition, the pond effect (Kino and High tide) in WSS-presence was included.

As for SH for affected ponds, the only fixed effect considered was the pond effect in WSS-presence. Meanwhile, in the WSS-absence environment, no fixed effect was considered.

The phenotypic variance for each feature was estimated as the sum of the variance components of the random effects (animal genetic and family common). The h2 was estimated as the proportion of the phenotypic variance due to the additive genetic variance. Moreover, the rG was estimated as the covariance divided by the product of the corresponding standard deviations.

“The statistical significance of the estimated parameters was based on confidence intervals (95%), including default errors, assuming normality. The existence of IGE was determined when rG between environments was less than 0.80.”

Finally, Fisher’s Z transformation was employed to compare the estimated rG in each line analyzing features’ behavior and looking for similarities between genetic lines.

Results And Discussion

Comparison of productive behavior between lines The results showed differences in SH, where the GRW line has a low SH in WSS-presence, while the shrimp of the RES line has a lower SH in WSS-presence. The line-by environment interactions highlight the importance of considering the probability of SMB disease occurrence when choosing the line in the breeding program.

A number of individuals (n), and least-square means for body weight and survival rate at harvest in the growth line and the resistance line in the presence and absence of White Spot Syndrome.

Heritability for body weight at harvest

The difference between the heritability for SMB-presence (0.05 ± 0.16) and WSS-absence (0.35 ± 0.15) on the GRW line might be an indi- 32 » DECEMBER 2021-JANUARY 2022 ARTICLE cator of variance heterogeneity. Although, in this case, in addition to changes in the additive genetic variance, the source of this heteroscedasticity may be due to environmental variance changes related to the micro-environmental sensitivity of individuals.

Microenvironmental sensitivity of individuals could be the reason variations in heritability estimators for CP displayed. It’s worth noting these heritability changes could be altering the selection response prediction accuracy.

Genetic correlations for CP

There is no effect of IGE on RES for BW. Considering that both lines were under the same environmental management conditions and exposed to the same pathogen (WSS), the differences in the estimators of both lines may be the consequence of a low SH rate of the GRW line.

Heritability for survival to harvest in both genetic lines

The SH model results were consistent with the estimates, using univariate models, considering a binomial distribution.

Heritability for SRA were essentially zero in both environments, 0.01 ± 0.02 in SMB-presence and 0.02 ± 0.03 in SMB-absence, representing minimal possibilities of genetic advance by selection for this trait in both scenarios.

The minimal progress by selection could be related to difficulty in estimation because of the portality rate by the statistical models, a considerably low genetic proportion in survival expression, or possibly, damage to the structure of family genetic relationships when WSS was present.

“The heritability of SH for both environments were consistent in both lines, suggesting there is no compression of the additive variance, in the lines associated with the environment.”

The genetic correlation between the two features could not be estimated in WSS-presence in the GRW line, possibly due to the affectation in the information structure associated with the high mortality presented in that line. In the case of WSS absence for the GRW line, the rG was not different from zero, unlike that estimated in the RES line.

Differences between genetic correlation in RES may be indicating changes in variance components presumably associated with IGE, in turn, related to the corresponding covariances, which would have implications for the response to correlated selection.

The results obtained from this study suggest that selection indices for CP should account for the genetic line used in the breeding program.

On the other hand, the estimation of genetic parameters related to CP should consider the presence of endemic diseases, such as SMB in shrimp culture, and visualize CS in the presence and absence of SMB as independent traits in both genetic lines. Apart from the changes in heritability and genetic correlations in both lines, productivity was different in the studied environments.

The above could be read as an indicator of phenotypic plasticity, which is not rare in marine organisms and could represent the expression of different phenotypes in individuals with the same genotype, but under diverse environmental conditions.

Conclusions

The linear model results suggest differences between lines for both bodyweight and survival across environments. However, genetic correlations estimates do not conceive within-line IGE effects on both traits, which would indicate that they are independent. In addition, genetic correlations between resistance line traits propose to treat them as independent variables when WSS is present in the environment.

This is a summarized version developed by the editorial team of Aquaculture Magazine based on the review article titled “INTERACCIÓN GENOTIPO POR AMBIENTE EN CAMARÓN BLANCO ASOCIADA A SÍNDROME DE MANCHA BLANCA” developed by: CALA-MORENO NELSON, CAMPOS-MONTES GABRIEL, CABALLEROZAMORA ALEJANDRA, BERRUECOS-VILLALOBOS JOSÉ, CASTILLO-JUÁREZ HECTOR. The original article was published on APRIL 2021, through ABANICO VETERINARIO under the use of a GRWative commons open access license. The full version can be accessed freely online through this link:
http://www.scielo.org.mx/scielo.php?pid=S2448-61322021000100111&script=sci_arttext

VAN BEEST
GREENPIN
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