Rearing of fish
The study was carried out on the GIFT (Genetically Improved Farmed Tilapia) strain of Nile tilapia [33] selected for growth using a rotational breeding scheme. The families were produced by natural spawning from December 2014 to December 2015 at the WorldFish Jitra Research station, Malaysia. The pedigree of each fish was registered for the genetic parameter estimations. The total pedigree file included 3383 fish from the 15 generations of GIFT (fish measured in the present study and their ascendants). The experiment was undertaken using four batches representing 40 families (8 families starting in June 2015; 8 families starting in November 2015; 12 families starting in February 2016; 12 families starting in April 2016).
After donation and transfer to the Penang WorldFish station, fish were reared until the fry reached approximately 10 g of body weight. After a week of quarantine in tanks, the beginning of the experiment consisted to place 30 fish per family in 2 distinct 100 L indoor tanks (120 cm length, 35 cm width and 24 cm depth) in a recirculating water system.
In total, 1200 fish were studied during this experiment. The average temperature was 28 °C ± 1 °C and the photoperiod 12 L: 12D. After anesthesia with clove oil, each fish was tagged in the dorsal muscle with two colored T-bar tags (Avery Dennison tags, 25 mm) using an Avery Dennison Mark III pistol Grip tool. Each fish in a tank was tagged with a unique colored T-bar tag to be able to identify each fish individually. Commercial feed with 34% of crude proteins, 5% of crude fat, 5% of crude fiber and 12% of moisture was used to feed the fish. A specific daily feed ration was used, calculated following the equation of Mélard, et al. [34]:
$$ \mathrm{DFR}=14.23\ast \mathrm{Mean}\ {\mathrm{body}\ \mathrm{weight}}^{-0.322} $$
where DFR is the daily food ration (% of body weight per day) and mean body weight was the average body weight of the 15 fish within each aquarium. Using this feed ration, fish were not underfed and no competition for feed was observed during the experiment. The use of this calculation was done because feeding the fish until apparent satiation can vary lot according to the observer, thus reducing repeatability of the measurements and increasing the tank effects. If a group of fish stopped to eat before the end of the daily feed ration, the uneaten pellets were removed from the aquarium.
Mortality was recorded daily and the feed ration changed accordingly. At the beginning of the refeeding period, a relative high mortality was observed (around 100 fish in total), probably because these fish were unable to get up after the stress of fasting.
Experimental design and trait measurements
The experimental protocol was previously described in details by de Verdal et al. [32] and is summarized in Fig. 1. Body weight was measured at the beginning and end of the four time periods shown in Fig. 1: adaptation period (15 days), fasting period (10 days), feeding period (17 days) and FI period (7 days). After being tagged, fish were kept in groups in their aquarium to be acclimated to their new rearing system for 15 days before the beginning of the experiment as an adaptation period. Then, during the fasting period, fish did not have any feed and the aim was to measure the loss of weight during fasting. Following a fasting period, fish tends to compensate the loss of growth lived during the fasting period and by increasing their growth more than normally. It is known as the compensatory growth period, here noted as feeding period. Finally, after the growth compensation, feed intake was measured accurately using video records during the FI period.
The difference of weight between the beginning and end of each period of measurement was calculated as the thermal growth coefficient (TGC), which uses the cubic relation between BW and length to make growth rate linear over time and corrected for the water temperature (T) of the rearing environment during the measurement period:
$$ TGC=\left({BW}_2^{\left(1/3\right)}-{BW}_1^{\left(1/3\right)}\right)/\left(T\times \Delta t\right)\times 100. $$
With BW2 the body weight at the end of the period; BW1 the body weight at the beginning of the period; T the rearing temperature and ∆t the number of days of the measured period. The TGC is widely used in fish to be able to compare the growth of different fish species with different optimal rearing temperature. The loss of weight during the fasting period was noted as LOSSTGC and the gain of weight during the compensatory refeeding period was noted as COMPTGC.
After the compensatory growth period, FI was recorded for each fish over a period of 7 days (13 meals) to estimate feed efficiency traits as detailed in de Verdal et al. [32]. During this individual FI period, feed was delivered to each aquarium pellet by pellet by hand through two pipes by an observer screened from view in order reduce the closeness between the person who give the pellets and the aquarium. This was done twice daily at 7.00 am and 1.00 pm. The first day, fish were weighed the morning and consequently, they received only one meal at 1.00 pm. Fish were fed until the calculated feed ration was finished. Video records of each meal was performed and video analyses were done to account for the number of pellets eaten by each individual fish during each meal. The day after the end of the FI measurement period, fish were anaesthetised with a high dose of clove oil and killed by decapitation. Fish were autopsied to measure different portions of the gastro-intestinal tract and sexed by visual observations of the gonads. The fish were too young to be reliably sexed using external morphology. Fish carcass were put in special bags, frozen and put in rendering wastes.
The Kinovea 0.8.15 software (Copyright © 2006–2011 – Joan Charmant & Contrib.) was used to analyse the videos of the meals. The main advantage of this software was to be able to play with the speed of reading and the zoom of the video for more accuracy. After weighing 500 pellets (mean = 16.4 ± SD = 1.76 mg, CV = 10.7%), the choice was done to consider that the pellet weight variability was low enough to assume that all the pellets had the same weight, which give the opportunity to calculate FI in grams. The total FI for an individual was calculated as the sum of the FI of all meals consumed. The thermal growth coefficient during the period when the feed intake was individually measured was noted as TGCFI. The body weight gain (BWG), during the feed intake measurement period was calculated as the difference in two body weight measurements taken at the beginning and end of the FI period.
The feed conversion ratio (FCR = FI/ BWG) was used as an indicator of feed efficiency.
The residual feed intake (RFI) was calculated as the difference between the feed consumed by a fish and the prediction of the feed consumption of this fish using a regression model estimation, taking into account the feed required for maintenance and growth [35]. The equation used to estimate RFI was as follows:
$$ \mathrm{RFI}=\mathrm{FI}-{\upbeta}_0-{\upbeta}_1\times {BW_f}^{0.8}-{\upbeta}_2\times \mathrm{BWG}\ \left(\mathrm{r}2\ \mathrm{of}\ \mathrm{the}\ \mathrm{model}=0.58\right) $$
with, β0, β1 and β2are the intercept of the regression, the partial regression coefficient of animal’s FI on metabolic body weight, and the partial regression coefficient of animal’s FI on BWG (measured as BWG = BWf − BWi), respectively, BW0.8 is the metabolic body weight using 0.8 as the metabolic body coefficient, calculated by Lupatsch, et al. [36]. The more efficient fish are those with negative RFI, since these fish consume less than the average of fish with the same body weight and body weight gain whereas the less efficient fish are those with positive RFI, consuming more. The REG procedure of SAS (version 9.3, SAS Institute, Cary NC) was used to estimate the parameters of the regression equation.
Estimation of genetic parameters
Genetic parameters were estimated by the REML (Restricted Maximum Likelihood) method using the VCE6 software [37, 38]. The following model was used for all the traits:
$$ {y}_{ijkl}=\mu +{Sex}_i+{Batch}_j+{Aquarium}_k+{Animal}_l+{e}_{ijkl} $$
(2)
Where Sexi and Batchj are fixed effects, Aquariumk is a random environmental effect, and Animall is the random additive genetic effect of the animal l (N = 3383). The pedigree file included animals from the 15 generations of the selection process. Significance of fixed effects was tested using SAS (GLM procedure). As the aquarium effect was a random effect, it represented the common environmental effect, taking into account the non-genetic effect of the family as the fixed effect of the aquarium. Some of the studied traits showed very strong genetic correlations with each other, it was not possible to run a multiple trait analysis that include all traits, meaning that distinct bivariate analyses were performed. A total of 36 analyses were performed with two traits each. When the genetic parameter of a trait was estimated several times, the average of the estimation obtained was calculated.
To estimate the impact of the selection criterion on the other traits, the following equation from Falconer and Mackay [39] was used to compare the expected direct and indirect correlated response to selection (CRX, Y with Y the selection objective and X the selection criterion) on the different criteria:
$$ {CR}_{X,Y}={i}_X\times \sqrt{\left({h}_X^2\times {h}_Y^2\right)}\times {rg}_{XY}\times {\sigma}_{P_Y} $$
where CRX, Y is the expected correlated response of trait Y when selection is on X; iXis the intensity of selection on X, considered equal for all the traits and estimated equal to 2.34 for the Nile tilapia selection breeding program in the present study (corresponding to 2.85% of fish kept as breeders for the next generation, as was previously done in the GIFT Nile tilapia breeding program); \( {h}_X^2 \) and \( {h}_Y^2 \) are the heritability estimated for X and Y, respectively; rgXY is the genetic correlation between X and Y; and \( {\sigma}_{P_Y} \) is the standard deviation of Y phenotype. For the direct expected response, \( {h}_X^2 \) and \( {h}_Y^2 \) were similar (since X and Y were confounded) and rgXY was equal to 1. Expected responses to selection were expressed in units of trait Y to improve.