Genetic parameter estimates indicate the possibility of genetic gain by selecting for reproductive traits of females from a commercial tilapia population (Oreochromis sp.)

doi:10.21203/rs.3.rs-1746489/v1

Download PDF

Research Article

Genetic parameter estimates indicate the possibility of genetic gain by selecting for reproductive traits of females from a commercial tilapia population (Oreochromis sp.)

https://doi.org/10.21203/rs.3.rs-1746489/v1

This work is licensed under a CC BY 4.0 License

Journal Publication

published 29 Feb, 2024

Read the published version in Aquaculture →

Version 1

posted

You are reading this older preprint version

Read the latest preprint version →

This study aimed to verify the possibility of obtaining genetic gain by selecting breeders based on genetic parameter estimates for reproductive performance traits of female tilapia (Oreochromis sp.) from a commercial population. The study was conducted using a database containing information on reproductive traits collected from 2009 to 2021. During the breeding period, the animals were stocked in tanks in a 1:3 male: female ratio. Eggs were collected from the mouth of the females every five days, and the collected eggs were measured in 100 mL beakers to estimate the egg volume. To estimate the variance components and genetic parameters (heritability, correlation, and repeatability), a mixed animal model was used with the fixed effects of the production cycle and breeding tanks, in addition to the additive direct genetic and permanent environmental random effects. The heritability values were 0.26, 0.18, 0.15, and 0.13 for total egg volume, average egg volume, spawning frequency, and average spawning interval, respectively, with repeatability above 0.43 for all analyzed variables, demonstrating the ability of the evaluated breeds to maintain the same production level during the different cycles. The genetic correlations among the variables were high, with a correlation of -0.99 between the total egg volume and the average spawning interval. Thus, selecting for total egg volume, which had the highest heritability, would result in animals that produce a large volume of eggs and have short spawning intervals.

genetic correlation

heritability

repeatability

spawn interval

Globally, tilapia (Oreochromis sp.) is one of the most important species in continental aquaculture, and its production has grown considerably in recent years. In 2010, global tilapia production reached 2.66 million tons, making tilapia the fourth most-produced fish species. As of 2020, the production has almost doubled (4.52 million tons) and represents 8.3% of the global fish production, making tilapia the third most-produced fish species after herbivorous carp (Ctenopharyngodon idellus) and silver carp (Hypophthalmichthys molitrix) (FAO, 2020).

Tilapia is the most produced fish species in Brazil and is gradually becoming a commodity. In recent years, tilapia fillets have been exported to several countries in the Asian and American continents, including the United States (Peixe BR, 2021). Despite the obstacles caused by the pandemic, the production of tilapia in Brazil has sustained good levels of growth, reaching 12.5% from 2019 to 2020 and 486,155 tons, making Brazil the fourth-largest tilapia producer in the world (Peixe BR, 2021).

Strengthening the links in the production chain is essential for maintaining the supply of inputs required to ensure the growth of tilapia production. Therefore, research that promotes technological advances in nutrition, management, production systems, genetic improvement, and reproduction is fundamental.

Under appropriate temperature and photoperiod conditions, tilapia reproduce naturally throughout the year and can be prolific, producing up to 3,000 eggs per spawning (Duponchelle et al., 1997). However, according to Hughes & Behrends (1983) and Little et al. (1993), there are still some factors that affect the offering of fingerlings to the production sector, one of which is the characteristic of split spawning of the species that can impair the amount and frequency of eggs available for fertilization (Couvard & Bromage, 2000).

A reduction in the period between successive spawnings will make the supply of fingerlings more constant, and consequently, females will spawn more often per reproductive cycle, resulting in a higher final volume of eggs produced. This would optimize space utilization and reduce the production costs, benefitting the fish farmer. In addition to the productivity issue, animals that spawn most frequently are interesting for breeding programs, which find in the production phase of families one of the biggest operational bottlenecks.

Several factors can affect the interval between spawnings, including ambient conditions such as temperature and photoperiod (Campos-Mendoza et al., 2004; Yoshida et al., 2017), nutrition of the breeders (Bombardelli et al., 2017; Sarmento et al., 2018), reproductive management strategies such as egg collection in the mouth of females (Silva et al. 2020), and the density and male to female ratio of breeders (Hughes & Behrends, 1983; Lovshin, 1982.) However, in addition to environmental and management factors, individual genetic traits also influence the reproductive performance of tilapia.

Although tilapia breeding programs have made rapid progress in selecting superior genotypes for growth and disease resistance, reproductive traits are rarely included in these programs. This could be a problem in the near future, as the availability of superior genetic animals must be sufficient in quantity and constancy. Therefore, research that generates information necessary for breeding programs to work on maternal lines with adequate reproductive performance is essential to ensure high production.

Thus, the present study sought to verify the possibility of a genetic gain by selecting breeders based on the reproductive traits of tilapia females from a commercial population using genetic parameter estimates to demonstrate the technical feasibility of such selection for breeding programs and to place a strain with better reproductive performance in the market.

A database with more than 6,000 records of reproductive variables from 2,675 females, recorded from 2009 to 2021, was used to estimate variance components and genetic parameters. Data were obtained from a commercial population belonging to a commercial fish farm (S3) in the city of Registro, SP, Brazil (24° 25′ 3″ S, 47° 47′ 34″ W). This commercial fish farm has been producing fingerlings and juveniles for the tilapia production chain since 2009, always keeping records of the reproductive variables of female breeders. It is important to highlight that the information collected is used by the fish farm primarily for the performance control of females; thus, some data considered important for the analyses were not collected because they are not considered essential for the objective of fish farming, as is the case with the weight of the animals. Thus, we attempted to contemplate this information that was not collected in alternative ways in the evaluated models.

Tilapia Breeders

Females breeders used in the fish farm came from the genetically improved farmed tilapia (GIFT) project, which began in Brazil in 2005 through a partnership between the State University of Maringá (UEM) and the WorldFish Center, Malaysia. The first animals used in this study were part of three different families of 4th generation GIFT in Brazil and were acquired in 2008. The fish were tagged with microchips and a mating protocol predetermined by the breeding program was followed to prevent reproduction among related individuals and avoid inbreeding problems. After obtaining the first generation of breeders, the fish farm acquired animals from the GIFT program in two more opportunities, in 2010 and 2011, with three and two distinct families of the 5th and 6th generations of the GIFT program in Brazil, respectively.

Currently, the fish farm uses descending female breeders of the original GIFT fish, which were phenotypically selected using reproductive performance as a selection criterion. Thus, the database contained information on females from seven families in the GIFT program and spare females, of which only the maternal parent was known (Table 1).

Table 1

Origin of animals evaluated in each reproductive cycle
2009/2010	Families I, II, and III from the 4th generation of GIFT
2010/2011	Families I, II, and III from the 4th generation of GIFT; families IV, V, and VI from the 5th generation of GIFT
2011/2012	Families I, II, and III from the 4th generation of GIFT; families IV, V, and VI from the 5th generation of GIFT
2012/2013	Families I, II, and III from the 4th generation of GIFT; families IV, V, and VI from the 5th generation of GIFT
2013/2014	Families I, II, and III from the 4th generation of GIFT; families IV, V, and VI from the 5th generation of GIFT; families VII and VIII from the 6th generation of GIFT
2014/2015	Families I, II, and III from the 4th generation of GIFT; families IV, V, and VI from the 5th generation of GIFT; families VII and VIII from the 6th generation of GIFT
2015/2016	Families IV, V, and VI from the 5th generation of GIFT; families VII and VIII from the 6th generation of GIFT
2016/2017	Families IV, V, and VI from the 5th generation of GIFT; families VII and VIII from the 6th generation of GIFT
2017/2018	Families IV, V, and VI from the 5th generation of GIFT; families VII and VIII from the 6th generation of GIFT; 1st generation of spare females
2018/2019	Families VII and VII from the 6th generation of GIFT; 1st and 2nd generation of spare females
2019/2020	Families VII and VII from the 6th generation of GIFT; 1st and 2nd generation of spare females
2020/2021	Families VII and VII from the 6th generation of GIFT; 1st and 2nd generation of spare females

Reproductive Management

The collection of eggs from the mouth of females is a breeding strategy used in fish farming. The reproductive cycle of tilapia is from September to May and may be shorter or longer depending on the climatic variations that influence water temperature. Outside the reproductive period, the male and female breeders remained separated in maintenance tanks. During the reproductive period, the breeders were redistributed to 10 m x 3 m breeding tanks of 1.2 m depth. Each tank had a constant water flow, with an average daily renewal of 20%. The male:female ratio used for reproduction was 1:3 (Hughes & Behrends, 1983; Siddiqui & Al-Harbi, 1997); 10 males and 30 females were stored in each breeding tank, where they remained throughout the reproductive period. Breeders were fed daily using a commercial diet with 32% crude protein. Feeding occurred according to water temperature, ranging from 1 to 1.5% of the biomass of each tank during the reproductive period.

Before storage in the breeding tanks, the identification of the animals was verified to determine the direction of mating according to the guidelines of the breeding program.

The collection of eggs from the mouth of the females was repeated every five days in each tank. Management occurs quickly to minimize animal stress. Fish were contained in a defishing net and caught individually to verify the presence or absence of eggs in the mouth. Based on the presence of eggs, the females were identified, and eggs were collected and measured in 100 mL beaders with an accuracy of 1mL to estimate the volume of eggs obtained. The eggs were then transferred to incubators installed in an incubation laboratory and larviculture system equipped with a water recirculation system with controlled temperature and oxygen. The incubators received eggs according to their families and embryonic stages, and eggs from different females were incubated simultaneously, which did not allow us to estimate the hatching rate individually.

Water quality parameters [dissolved oxygen (mg/L) and temperature ( °C)] in each tank were measured every morning (6:00 h) with the aid of a multiparameter equipment (MO-900; Insthruterm, Brazil).

Evaluated Variables

The mean spawning interval for each female was calculated using the relationship between the breeding period in days (a maximum of 240 days) and the number of times the female spawned. On the other hand, the spawning frequency of females was calculated as the sum of all spawning throughout the cycle.

The total volume of eggs obtained per female was calculated as the sum of the volumes of all collections in a given cycle, and the average egg volume per female was calculated as the ratio of the total volume to the number of spawns in a given reproductive cycle.

Statistical Analyses

Initially, records with inconsistent information, such as fish with the same identification, females from the GIFT program without parental information, fish without age records, and means of analyzed variables having unlikely values, were discarded from the database. After deletion, the number of records fell from over 6,000 to approximately 4,000.

With the “clean” data, an analysis including all environmental variables (age, reproductive cycle, and reproduction tank) and their respective interactions was performed to verify which factors significantly influence the mean of the variables analyzed. From the results of this analysis, contemporary groups (CG) were constituted, which were eventually included in the genetic evaluation models as fixed effects.

In the analyses performed, five models were tested, as described in Table 2. It was possible to include the fixed deeds of the tank, reproductive cycle, contemporary group, and age as covariates, considering the linear and quadratic behavior according to the model. In addition to fixed effects, additive genetic and permanent environmental random effects were included in the models.

Table 2

Variables included in the five models tested
Model	Tank	Cycle	Age Linear	Permanent Environment	Animal	Contemp Group.	Quadratic Age
1	√	√	√	√	√
2			√	√	√	√
3	√	√	√	√	√		√
4			√	√	√	√	√
5	√	√		√	√

As the weight of females was not recorded during reproductive management, this effect was not included in the models; however, it is understood that the influence of weight on the analyzed traits is already considered when age or reproductive cycle is included in the models. Within the reproductive cycle effect, we considered the influence of environmental effects, such as water temperature and photoperiod.

Using the selected model, bi-characteristic analyses were performed for the evaluated traits.

Heritability and repeatability for each trait were calculated using the (co)variance components estimated for each model, and the genetic correlations between traits were calculated using the (co)variance components generated by the bi-characteristic models according to the following equations:

h² = σ²a /σ²p

r = (σ^2a + σ ^2pe)/ σ ^2p

_rg1.2 = Pit_1.2/√ ( σ 2a1. σ 2a2)

where:

h ² is heritability,

r is repeatability,

_rg1.2 is a genetic correlation between traits 1 and 2,

σ ^2a is the additive genetic variance,

σ ^2p permanent room variance,

σ ^2p phenotypic variance,

Pit _1.2 is the genetic covariance between traits 1 and 2.

The restricted maximum likelihood method was used to estimate (co)variance components using the AIREMLF90 software (Mistzal et al. 2014). Phenotypic correlations between the same traits were also determined.

Models 2 and 4, which included contemporary groups and age in linear and quadratic ways, were discarded because they generated inconsistent estimates of genetic parameters with heritability values close to zero and high errors for some variables analyzed (Table 3).

Table 3

Estimates of heritability, repeatability, and permanent environment for the five mixed models tested
Trait	Model	Heritability^#	Repeatability^#	Permanent environment^#
Total egg volume	1*	0.19 ± 0.11	0.43 ± 0.039	0.24 ± 0.078
	2**	0.06 ± 0.38	0.34 ± 0.021	0.29 ± 0.030
	3***	0.18 ± 0.11	0.42 ± 0.038	0.24 ± 0.076
	4****	0.03 ± 0.02	0.35 ± 0.018	0.32 ± 0.022
	5*****	0.26 ± 0.14	0.45 ± 0.046	0.19 ± 0.094
Average egg volume	1	0.08 ± 0.06	0.4 ± 0.025	0.32 ± 0.042
	2	0.15 ± 0.09	0.4 ± 0.033	0.25 ± 0.063
	3	0.07 ± 0.05	0.39 ± 0.023	0.33 ± 0.038
	4	0.12 ± 0.07	0.4 ± 0.028	0.29 ± 0.052
	5	0.18 ± 0.10	0.43 ± 0.037	0.24 ± 0.072
Spawning frequency	1	0.17 ± 0.10	0.46 ± 0.035	0.29 ± 0.074
	2	0 ± 0.05	0.35 ± 0.017	0.35 ± 0.018
	3	0.16 ± 0.10	0.45 ± 0.034	0.29 ± 0.073
	4	0 ± 0.004	0.36 ± 0.018	0.36 ± 0.018
	5	0.15 ± 0.09	0.45 ± 0.031	0.3 ± 0.065
Average spawning interval	1	0.06 ± 0.04	0.55 ± 0.019	0.49 ± 0.038
	2	0.02 ± 0.02	0.51 ± 0.017	0.49 ± 0.022
	3	0.05 ± 0.04	0.55 ± 0.018	0.5 ± 0.035
	4	0 ± 0.07	0.51 ± 0.017	0.51 ± 0.018
	5	0.13 ± 0.08	0.58 ± 0.024	0.44 ± 0.06
^# Data are presented as mean ± standard deviation
*Model 1: fixed tank, fixed cycle, covariable linear age, permanent environment, animals
**Model 2: linear age covariate, permanent environment, animal, contemporary group
***Model 3: fixed tank, fixed cycle, covariate linear and quadratic age, permanent environment, animal
****Model 4: covariate linear and quadratic age, permanent environment, animal, and contemporary group
*****Model 5: fixed tank, fixed cycle, permanent environment, animal

Models 1 and 3 resulted in slightly more accurate estimates; however, in both models, the age of the animals was included as a covariate, and the estimates of the age effect, both linear and quadratic, on the variables analyzed were of very low magnitude and still presented a large dispersion compared to the estimates of the other fixed effects (Table 4). The estimates generated from model 5 were consistent and accurate, and this model was the simplest among the tested models, which favored their selection for the bi-characteristic analyses and the selection of their estimates for the discussions.

Table 4

Estimates of fixed effects on reproductive variables for the five different mixed models
Variable	Model	Tank^#	Reproductive cycle^#	Linear age^#	Contemporary group ^#	Quadratic age^#
Total egg volume	1*	43.15 ± 31.37	-37.20 ± 22.90	13.47 ± 6.91	-	-
	2**	-	-	-4.18 ± 3.90	100.64 ± 22.31	-
	3***	43.11 ± 30.94	-35.66 ± 22.57	15.45 ± 7.35	-	-0.33 ± 0.42
	4****	-	-	24.01 ± 4.22	66.51 ± 17.75	-3.62 ± 0.38
	5*****	92.20 ± 19.84	-17.58 ± 8.14	-	-	-
Average volume eggs	1	16.07 ± 2.85	-2.52 ± 1.95	1.68 ± 0.57	-	-
	2	-	-	0.94 ± 0.74	18.41 (4.19)	-
	3	15.52 ± 2.73	-2.065 ± 1.86	2.27 ± 0.63	-	-0.10 ± 0.06
	4	-	-	3.13 ± 0.74	15.83 (3.74)	-0.28 ± 0.05
	5	22.25 ± 2.22	-0.13 ± 0.98	-	-	-
Spawning frequency	1	3.43 ± 0.95	-1.24 ± 0.69	0.17 ± 0.21	-	-
	2	-	-	-0.40 ± 0.05	5.12 ± 0.32	-
	3	3.41 ± 0.95	-1.22 ± 0.69	0.19 ± 0.23	-	0.19 ± 0.23
	4	-	-	0.40 ± 0.11	4.04 ± 0.34	-0.10 ± 0.01
	5	4.02 ± 0.56	-0.99 ± 0.25	-	-	-
Average spawning interval	1	148.15 ± 21.91	37.42 ± 15.06	-11.62 ± 4.37	-	-
	2	-	-	-1.92 ± 2.70	125.42 ± 15.52	-
	3	151.32 ± 21.10	34.79 ± 14.36	-14.98 ± 4.91	-	0.56 ± 0.45
	4	-	-	-16.66 ± 3.35	141.43 ± 11.46	1.99 ± 0.35
	5	105.76 ± 16.64	20.82 ± 7.48	-	-	-
^# Data are presented as mean ± standard deviation
*Model 1: fixed tank, fixed cycle, covariable linear age, permanent environment, animals
**Model 2- linear age covariate, permanent environment, animal, contemporary group
***Model 3: fixed tank, fixed cycle, covariate linear and quadratic age, permanent environment, animal
****Model 4: covariate linear and quadratic age, permanent environment, animal, and contemporary group
*****Model 5 – fixed tank, fixed cycle, permanent environment, animal

In Model 5, heritability values ranging from low (0.13, mean spawning interval; 0.15, sot frequency; 0.18, average egg volume) to moderate magnitude (0.26, total egg volume) were observed, indicating that there is a good potential for genetic gain from selecting for the total egg volume; however, the earning potential is low considering the other traits. On the other hand, the estimated values for repeatability were always high (0.45, total egg volume; 0.43, average egg volume; 0.45, spooling frequency; 0.58, mean interval), demonstrating that females have the genetic potential to maintain the same performance for several cycles for the reproductive traits evaluated (Table 3).

The bi-characteristic models generated high positive and negative values for genetic correlations (Table 5). For most estimates, the deviations were quite high, indicating a low precision. Among the genetic correlations estimated with greater precision, the negative association (-0.99) between the mean spawning interval and the total egg volume stands out.

Table 5

Genetic (above diagonal) and phenotypic (below diagonal) correlations between reproductive traits of tilapia
	Spawning frequency	Average spawning interval	Total egg volume	Average egg volume
Spawning frequency	1	-0.83 ± 1.3	0.89 ± 0.6	0.69 ± 0.9
Average spawning interval	-0.76 ± 0.08*	1	-0.99 ± 0.008	-0.96 ± 0.6
Total egg volume	0.89 ± 0.11 *	-0.67 ± 0.08*	1	0.97 ± 0.03
Average egg volume	0.23 ± 0.1*	-0.17 ± 0.09*	0.62 ± 0.012*	1
* Significant based on the t-test (0.05)

In general, the results suggest selecting the animals based on the characteristic of total egg volume, which presented a moderate heritability and thus has good possibilities for obtaining considerable genetic gains. In addition, the total egg volume is inversely correlated with the mean spawning interval, and it is also expected to reduce these intervals when selecting the volume.

The modeling of factors is essential for estimating the components of (co)variance. Among the models tested in the present study, Model 5, which included only the fixed effects of the tank and reproductive cycle, produced genetic parameters with reliable values. Two other models, Models 1 and 3, also produced reliable estimates; however, the inclusion of age in these models was not justified because the contribution of this factor, whether linear or quadratic, was minimal for the adjustment of the models. Thus, Model 5 was selected because, in addition to generating very consistent and accurate estimates, it was the simplest model among the ones tested, meeting the criterion of parsimony.

The low importance of the age factor for the analyses was not expected because the reproductive physiology of this species is described by the increasing egg production with increasing age of females up to two or three years old, after which egg production gradually decreases with increasing age (Valentin et al. 2015). One hypothesis to explain the small influence of age on the measured variables is that the age effect is confused with the fixed effect of the reproductive cycle.

In a study by Yoshida et al. (2017) on the genetic evaluation of tilapia reproductive traits, the model used included the covariate age and location as fixed effects, obtaining estimates of genetic parameters similar to those of this study. However, Yoshida et al. (2017) did not include the reproductive cycle as a fixed effect, so the inclusion of age in the model was justified.

Traits of spawning frequency, mean spawning interval, and average egg volume showed heritability estimates of 0.15, 0.13, and 0.18, respectively. Similar heritability values were reported by Yoshida et al. (2017), with 0.14 for spawning success (0.14) and multiple spawning (0.16) in Unicaracter analysis. These low heritability estimates highlight the considerable influence of the environment on reproductive traits. However, according to the model considered for the discussions, the total egg volume presented a heritability of 0.26, a value of moderate magnitude, which indicates good possibilities of obtaining genetic gain by selecting this trait. In a study by Yoshida et al. (2017), the heritability estimate for the same trait in tilapia was much lower (0.02), whereas, in rainbow trout, Gall and Huang (1988) estimated a heritability value (0.30) similar to that of the present study.

Repeatability estimates were high for all traits, which means that it is not necessary to evaluate several cycles to accurately estimate the genetic value of the breeding stock because these animals tend to maintain the same reproductive performance for several cycles. Moreover, from the point of view of fingerling producers, high repeatability is quite interesting since animals of superior genetics can remain in the breeding stock for a long time, maintaining good production. Only a few studies have conducted the repeatability analysis for reproductive traits of aquatic species. Yoshida et al. (2017) found a repeatability of 0.28 for produced egg volume, which is below that found in the present study (0.45). Furthermore, Trong et al. (2013) observed low repeatability (between 0.04 and 0.17) for spawn success in Nile tilapia, which is different from the results obtained for the same trait in the present study (0.58). This difference between repeatability estimates was probably due to the difference in the genetic structure of the animals evaluated in different studies because the components of (co)variance were inherent to the populations for which they were estimated (Falconer, 1984).

In bi-characteristic analyses, a high negative correlation was observed between the mean spawning interval and the total egg volume (-0.99), which suggests that both traits are controlled by the same group of genes. As described by Campos-Mendoza et al. (2004), spawning in tilapia can occur at intervals of less than 21 days, which, despite resulting in a reduction in the number of eggs produced during each spawning, generates an increase in the final volume of eggs produced (Tsadik, 2008). Thus, considering each spawn separately, genetic selection to produce females that spawn at shorter intervals does not result in a practical advantage. However, considering the long reproductive period, the high frequency at which these breeders would produce eggs in a complete cycle would compensate for the drop in spawning production.

All other correlations presented high values, but they had high mean deviations. Only the correlation between total egg volume and average egg volume presented a positive and high value with good precision; however, the high correlation between these two traits was expected because they are similar and interconnected.

The results obtained in the bi-characteristic analyses converged to a clear improvement strategy for the evaluated population. Genetic selection for total egg volume can, after a few generations, result in breeders with a higher production of eggs in the reproductive cycle and smaller intervals between spawning. This will maximize the utilization of the available space and facilitate stable production, enabling the producer to achieve greater productivity with a lower number of females.

The high heritability of total egg volume demonstrates the potential for genetic gain, indicating that it is a trait that can be used as a selection criterion in a breeding program.

The mean spawning interval and total egg volume are highly negatively correlated, which is favorable to a breeding program, since the increase in egg volume results in a smaller mean spawning interval. Thus, the selection of fish with a high total egg volume is a relevant selection strategy.

Finally, the repeatability found in the analyses indicates the constancy of the reproductive performance for a long time, which is desirable for fingerling fish farmers and genetic breeding programs.

FUNDING

This work was supported by São Paulo Research Support Foundation FAPESP (Grant Number - 2020/06349-9), Coordenação de Aperfeiçoamento de Pessoal de Nível Superior – Brasil (CAPES - Finance Code 001) and S3 aquaculture company.

COMPETING INTERESTS

The authors have no relevant financial or non-financial interests to disclose.

AUTHOR CONTRIBUTIONS

R.V.R.N and R.C. conceptualized and supervised this project; T.G.G. and F.A.P.C.N. worked on data collection and organization; K.S.A performed data analysis with R.C.; R.V.R.N. and T.G.G. wrote the manuscript.

ETHICS APPROVAL

This study was not submitted to an ethics committee because the database used was collected in a commercial fish farm.

Bombardelli RA, Reis Goes ES, Negreiros Sousa SM, Syperreck MA, Goes MD, Oliveira Pedreira AC, Meurer F (2017) Growth and reproduction of female Nile tilapia fed diets containing different levels of protein and energy. Aquaculture, 479, 817-823. https://doi.org/10.1016/j.aquaculture.2017.07.031
Campos-Mendoza A, Mcandrew BJ, Coward K, Bromage N (2004). Reproductive response of Nile tilapia (Oreochromis niloticus) to photoperiodic manipulation; effects on spawning periodicity, fecundity and egg size. Aquaculture 231(1), 299-314.
Coward K, Bromage NR (2000). Reproductive physiology of female tilapia broodstock. Reviews in Fish Biology and Fisheries 10, 1 –25.
Duponchelle F, Pouyaud L, Legendre M (1997). Variation in reproductive characteristics of Oreochromis niloticus populations: genetic or environmental effects. In: Fitzsimmons, K. Ed., Tilapia Aquaculture: Proceedings from the 4th International Symposium on Tilapia in Aquaculture, NRAES-106, Northeast Regional Agricultural Engineering Service, Ithaca, NY, 808 pp.
FAO (2020). The State of World Fisheries and Aquaculture: Sustainability in Action. Rome: Food and Agriculture Organization. doi: https://doi.org/10.4060/ca9229en
Falconer DS (1984). Einführung in die Quantitative Genetik. Verlag Eugen Ulmer, Stuttgart.
Gall GAE, HUANG N (1988). Heritability and selection schemes for rainbow trout: female reproductive performance. Aquaculture, v. 73, n. 1-4, p. 57-66.
Hughes DG, Behrends LL (1983). Mass production of Tilapia nilotica seed in suspended net enclosures. In: Fishelson, L., Yaron, Z. (Eds.), Tilapia Aquaculture. Proceedings of the International Symposium on Tilapia Aquaculture, Israel Tel Aviv University, Nazareth, pp. 394 – 401.
Little DC, Macintosh DJ, Edwards P (1993). Improving spawning synchrony in the Nile tilapia, Oreochromis niloticus. Aquaculture and Fisheries Management 24, 399–405.
Lovshin LL (1992). Tilapia hybridization. p. 279–308. In: Pullin, R.S.V., Lowe-McConnell, R.H. Eds., Thebiology and culture of tilapias, ICLARM Conference Proceedings 7, International Center for Living Aquatic Resources Management, Manila, Philippines, 432 pp.
Misztal I, Legarra A, Aguilar I (2014). Using recursion to compute the inverse of the
genomic relationship matrix J. Dairy Sci. 97: 3943-3952
PEIXE BR. (2020). PEIXEBR Yearbook of Fish Farming. Avaliable in: < https://www.peixebr.com.br/anuario-2020/> (27/02/2022).
Sarmento NL, Martins E., Costa DC, Mattioli CC, Costa Julio G S, Figueiredo G, Luz R K (2018). Reproductive efficiency and egg and larvae quality of Nile tilapia fed different levels of vitamin C. Aquaculture, 482, 96-102.
Siddiqui AQ, Al-Harbi AH (1997). Effects of sex ratio, stocking density and age of hybrid tilapia on seed production in concrete tanks in Saudi Arabia. Aquaculture International 5(3), 207-216.
Silva ACF, Corrêa Filho RAC, Ventura AS, Nunes AL, Laice LM, Ribeiro RP, Povh, JA (2020). Reproductive traits in different Nile tilapia genetic groups. Arquivo Brasileiro de Medicina Veterinária e Zootecnia, 72, 1797-1804.
Trọng TQ, Arendonk JAM, Komen K (2013). Genetic parameters for reproductive traits in female Nile tilapia (Oreochromis niloticus): I. Spawning success and time to spawn. Aquaculture 416-417, 57-64.
Tsadik GG, Bart AN (2007). Effects of feeding, stocking density and water-flow rate on fecundity, spawning frequency and egg quality of Nile tilapia, Oreochromis niloticus (L.).Aquaculture 272(1), 380-388.
Valentin FN, Nascimento NFD, Silva RCD, Tsuji EA, Paes MDCF, Koberstein TCRD, Nakaghi LSO (2015). Maternal age influences on reproductive rates in Nile tilapia (Oreochromis niloticus). Revista Brasileira de Zootecnia, 44, 161-163.
Yoshida GM, Oliveira CAD, Kunita NM, Rizzato GS, Ribeiro RP (2017). Genetic parameters in female reproductive traits of Nile tilapia (Oreochromis niloticus). Anais da Academia Brasileira de Ciências, 89, 2515-2523.

Download PDF

Journal Publication

published 29 Feb, 2024

Read the published version in Aquaculture →

Version 1

posted

You are reading this older preprint version

Read the latest preprint version →

Genetic parameter estimates indicate the possibility of genetic gain by selecting for reproductive traits of females from a commercial tilapia population (Oreochromis sp.)

Status:

Journal Publication

Version 1

Abstract

Introduction

Material And Methods

Tilapia Breeders

Reproductive Management

Evaluated Variables

Statistical Analyses

h² = σ²a /σ²p

r = (σ^2a + σ ^2pe)/ σ ^2p

Results

Discussion

Conclusions

Declarations

References

Status:

Journal Publication

Version 1

Model	Tank	Cycle	Age Linear	Permanent Environment	Animal	Contemp Group.	Quadratic Age
1	√	√	√	√	√
2			√	√	√	√
3	√	√	√	√	√		√
4			√	√	√	√	√
5	√	√		√	√

Model	Tank	Cycle	Age Linear	Permanent Environment	Animal	Contemp Group.	Quadratic Age
1	√	√	√	√	√
2			√	√	√	√
3	√	√	√	√	√		√
4			√	√	√	√	√
5	√	√		√	√

Genetic parameter estimates indicate the possibility of genetic gain by selecting for reproductive traits of females from a commercial tilapia population (Oreochromis sp.)

Status:

Journal Publication

Version 1

Abstract

Introduction

Material And Methods

Tilapia Breeders

Reproductive Management

Evaluated Variables

Statistical Analyses

h2 = σ2a /σ2p

r = (σ2a + σ 2pe)/ σ 2p

Results

Discussion

Conclusions

Declarations

References

Status:

Journal Publication

Version 1

h² = σ²a /σ²p

r = (σ^2a + σ ^2pe)/ σ ^2p

Model	Tank	Cycle	Age Linear	Permanent Environment	Animal	Contemp Group.	Quadratic Age
1	√	√	√	√	√
2			√	√	√	√
3	√	√	√	√	√		√
4			√	√	√	√	√
5	√	√		√	√