ESTIMATION OF THE GENETIC PARAMETERS AND G x E INTERACTIONS FOR GROWTH TRAITS AND SHELL-CLOSING STRENGTH IN PACIFIC OYSTERS (CRASSOSTREA GIGAS).
The Pacific oyster (Crassostrea gigas) is one of the most important worldwide aquaculture species, ranking first in production compared with other aquatic species. Oyster production in 2016 was 4.84 million tons in China, which was ranked the top in the worldwide production of oyster (FAO 2016; Fisheries Administration of Ministry of Agriculture and Rural Affairs of People's Republic of China 2017). In China, Pacific oyster has become the predominant farmed species, which is cultured along the Chinese coast. The primary production areas are Fujian and Guangdong provinces in the south and Liaoning and Shandong provinces in the north. The brood stock used today remains largely unselected, and wild or cultured brood oysters are common in many oyster hatcheries (Wang et al 2012). Selective breeding programs are expected to make a major contribution to the improvement of C. gigas productivity.
Shell-closing strength (SCS) was reported to be a useful indicator for selecting oysters because SCS was a heritable character (Ishikawa et al. 2009, Hideo et al. 2010), and oysters with higher SCS had lower summer mortality (Poulet et al. 2003, Okamoto et al. 2006). Therefore, SCS measurement and estimation are urgently needed to provide basic information for reducing mortality during the summer season.
Reliable estimates of genetic parameters (i.e., heritability and genetic correlation) are necessary for designing rational plans to optimize a breeding program (Mgaya 2000). Heritability predicts the success of improving a trait via selective breeding (Falconer & Mackay 1996), and the genetic correlation reflects the relevance between different traits. An estimate of heritability has been reported in some studies (Lannan 1972, Hedgecock et al. 1991, Evans & Langdon 2006a, Appleyard et al. 2007, Li et al. 2011, Wang et al. 2012, Kong et al. 2013), and the genetic correlation (Evans & Langdon 2006a, Wang et al. 2012, Kong et al. 2013) of growth traits of Crassostrea gigas has also been reported.
Falconer (1952) first proposed the concept of regarding performance in different environments as different but genetically correlated traits. Because of variation in the farm environments of Crassostrea gigas, genetic and environmental correlations (G x E interactions) are necessary to set up optimized breeding programs. Moreover, G x E interactions are the key to determine whether a genetically improved strain in one environment would have superior performance in another environment.
Environmental conditions have a significant impact on the performance of Crassostrea gigas. Most estimates of genetic parameters in C. gigas were determined using the separate rearing method. Fortunately, an alternative is available--a mixed rearing of families in a communal environment with a posteriori reconstruction of pedigrees using highly variable markers such as microsatellites. This alternative was first proposed by Herbinger et al. (1995) and Estoup et al. (1998), and it has been used to estimate genetic parameters in C. gigas. The primary advantage of this method is the absence of a between-families environmental effect. Compared with a full factorial design, a partial factorial design is more feasible because it can save breeding and rearing space and allow the use of more breeders (Gheyas et al. 2009). In addition, the partial factorial design has a better correlation between full-sib and half-sib families than a nested design (Gjedrem 2005). Therefore, a mixed-family approach combined with a partial factorial mating design was applied to obtain the experimental materials in this study.
MATERIALS AND METHODS
Spawning and Nursery Protocol
Two sets of Pacific oyster families were established using a partly factorial mating design in Laizhou Bay, Shandong Province, China (Fig. 1, LZ, 37[degrees] N, 120[degrees] E) on May 27, 2012 (Set 0527), and June 12, 2012 (Set 0612). The maternal oysters were taken from three F4 selection lines that were collected from RS, Shandong, China (Fig. 2, C; 36.4[degrees] N, 121.3[degrees] E), Onagawa Bay, Miyagi Prefecture, Japan (Fig. 2, J; 38.3[degrees] N, 141.3[degrees] E), and Pusan, South Korea (Fig. 2, K; 35.1[degrees] N, 129.1[degrees] E). Set 0527 and Set 0612 were obtained from nine females and nine males, which consisted of three females and three males chosen randomly from the three selected lines (C, J, and K), respectively. A partial factorial mating design was used to produce 27 full-sib families for each set (Table 1). As a result, a total of 54 full-sib families were established for the two sets in the present study. In the experiment, the female was dissected and the egg was placed in a 10-L bucket. After ripening at 22[degrees]C for 30 min, male gonads were dissected and sperms were isolated to fertilize eggs.
Fertilized eggs were allowed to develop into veliger larvae (D-larvae) for 24 h at a concentration of 30-50 larvae [mL.sup.-1], and the water temperature was maintained at 22[degrees]C-23[degrees]C. The same amount of veliger larvae was collected from each family and mixed in a 200-L tank at a final concentration of five larvae [mL.sup.-1]. The exchange of water volume was 1/3-1/2 once a day during the early rearing and twice per day during the later cultivation of the larvae using a sand filter. The concentration was adjusted to two larvae [mL.sup.-1] when the shell height (SH) was approximately 200 gm. The larvae were fed daily with a mixture mainly of Isochrysis galbana and Cholorella pyrenoidosa, and supplementary with Platymonas helgolandica during the later rearing of the larvae.
When eyed larvae reached 20%, a substrate (scallop shells) was placed in the tank to ensure the attachment of 5-10 juveniles per substrate. Ten days later, the substrates were transferred to an outdoor nursery tank. After 2 mo in the nursery tank, the families were divided into two groups and cultured at RS (Fig. 1; 36[degrees] 46' 4" N, 121[degrees] 38' 31" E) and KTD (Fig. 1; 37[degrees] 33' 33" N, 121[degrees] 29' 38" E) separately using the cage culture method. After 120 days of attachment, the oyster was stripped off, separated into the individual oyster, and cultured in single.
Sampling and Trait Measurement
After 560 days, 1,077 offspring were sampled at random from the 54 families for an analysis of the genetic parameters and G x E interactions. The SH, shell length (SL) and shell width (SW) were measured for each offspring using an electronic Vernier caliper (0.01 mm), and the wet weight (WW), meat weight (MW), and adductor muscle weight (AMW) were measured with an electronic balance (0.1 g). The SCS was determined by an invention patent (ZL 201310216337.1.). The adductor muscles of parents and offspring were stripped and stored in 70% ethanol for DNA analysis.
All of the oysters were genetically characterized using the two sets of high-level 5-multiplex PCRs (Ji et al. 2015). The genomic DNA of all parents and sampled offspring was extracted via a phenol-chloroform-isopentanol method (Li et al. 2002). Fragment analysis was performed using a Biosystems ABI 3730x1 96-capillary DNA analyzer (Applied Biosystems) equipped with the Gene mapper v.3.7 software (Applied Biosystems). The parentage assignment was carried out with the CERVUS 3.0 software (Kalinowski et al. 2007).
Phenotypic Value of Crassostrea gigas in RS and KTD Area
Software of SPSS 23.0 was used to analyze the data of each trait. Meanwhile, the variance analysis of the differences of each trait was conducted between RS and KTD area, and T test was conducted.
Both the genetic parameters and the G x E interactions were analyzed with a mixed linear model and restricted maximum likelihood (REML) using the AS restricted maximum likelihood R software (Butler et al. 2009).
Five models were used for heritability and genetic correlation, and the best model was chosen. The models were as follows:
Sire model: y = [mu] + Site + S + e (model 1 a),
Sire-dam model: y = [mu] + Site + S + D + e (model 2a),
Family model: y = [mu] + Site + F+ e (model 3a),
Sire-dam-family model: y = [mu] + Site + S + D + F+ e (model 4a),
Animal model: y = [mu] + Site + A + e (model 5a).
Here, y is the observations, g is the average phenotypic value, and Site is the site effect. Both [mu] and Site are the fixed effects in the models. S is the sire effect, D is the dam effect, Fis the family effect, and A is the animal effect. S, D, F, and A are the random effects in the models, and e is the residual error. To eliminate the influence of hybridization effect from different populations on genetic parameter estimation, the parents were set from different populations as genetic groups in the animal model (i.e., model 5a and model 513) (Gilmour et al. 2014). The heritability of each trait and genetic correlation between each pair of traits were estimated using these five models. The best model was chosen according to the Akaike information criterion (AIC), which provides a metric that quantifies how well the model fits the data, and a lower AIC value indicates a better fit.
The G x E Interactions
The genotype by environment (G x E) interactions were estimated to fit the performance via genetic correlations for the same trait in different environments, which were considered as separate traits. The best model was selected from the following five models for the G x E interactions.
Sire model: y = [mu] + S + e (model lb),
Sire-dam model: y = [mu] + S + D + e (model 2b),
Family model: y = [mu] + F + e (model 3b),
Sire-dam-family model: y = [mu] + S + D + F + e (model 4b),
Animal model: y = [mu] + A + e (model 5b).
Here, y is the observations and it is the average phenotypic value, which is the fixed effect in the models. S is the sire effect, D is the dam effect, Fis the family effect, and A is the animal effect. S, D, F, and A are the random effects in the models, and e is the residual error.
Excluding 42 polluted offspring, the remaining 1,035 offspring were all unambiguously allocated to a unique pair of parents using the two sets of 5-multiplex PCRs. The number of offspring per family varied greatly, from 1 to 72 (Table 1), and the mean number of offspring for each family was 19.
Phenotypic Value of Crassostrea gigas in RS and KTD Area
Phenotypic value of each trait was analyzed using twosample T-test with SPSS 23.0. Results show that SH, SL, SW, AMW, and MW had no significant difference between RS and KTD. Two of the seven traits for WW and SCS were significantly different between RS and KTD, and the average value was much higher in the RS area (Table 2).
To estimate the heritability of a specific trait, the AIC of each model was examined, and the model with the lowest AIC of the five models was selected as the best model. The best models for the heritability estimates for the seven traits of RS were model la for SH, SW, WW, MW, and SCS; model 2a for SL; and model 3a for AMW. The heritability for the seven traits of RS ranged from 0.19 to 0.43 (Table 3), SH (0.38 [+ or -] 0.18, mean [+ or -] SD), SW (0.19 [+ or -] 0.12), SL (0.25 [+ or -] 0.08), WW (0.43 [+ or -] 0.19), MW (0.43 [+ or -] 0.20), AMW (0.26 [+ or -] 0.14), and SCS (0.29 [+ or -] 0.15). For the heritability estimates of the seven traits of KTD, the best models were model la for SL, MW, and SCS; model 2a for AMW; model 3a for SW and WW; and model 5a for SH. The heritability for the seven traits is shown in Table 4, ranging from 0.11 to 0.44. Compared with SW (0.19 + 0.08), SH (0.12 [+ or -] 0.09), and MW (0.11 [+ or -] 0.09), the heritability of SCS (0.44 [+ or -] 0.19), WW (0.37 + 0.17), SL (0.30 + 0.15), and AMW (0.30 [+ or -] 0.12) were relatively large.
Genetic and Phenotypic Correlation
The genetic and phenotypic correlations were calculated using the best model selected according to the AIC value, similarly to the heritability estimates. In the RS area, model 2a was used for SL and SH, model 3a was used for SL and SW, model 5a was used for SL and AMW, and model 1a was used for the rest of the correlations. The genetic and phenotypic correlations between traits for RS are shown in Table 3. All of the genetic and phenotypic correlations were significant and positive. The genetic correlations ranged from 0.48 f 0.31 (SH and SCS) to 0.99 + 0.13 (SW and WW), 0.99 + 0.02 (WW and MW), and 0.99 +0.22 (AMW and SCS). The range of variation of the phenotypic correlations between the seven traits was larger than that of the genetic correlations. The lowest was 0.14 [+ or -] 0.05 (SW and SCS) and the greatest was 0.84 [+ or -] 0.02 (WW and MW).
In KTD area, model la was used for SH and SL, SH and AMW, SL and SW, SL and AMW, SL and MW, SCS and SL, SCS and AMW, SCS and MW, and SCS and WW; model 5a was used for SH and AMW, SH and WW, and SH and SCS; and model 3a was used for the rest of the correlations. Table 4 shows the genetic and phenotypic correlations between traits for KTD. The genetic correlations between SW and other traits were not significant with large SEs. All the other genetic correlations were significant and positive, varied from 0.42 [+ or -] 0.12 (SH and SCS) to 0.98 [+ or -] 0.11 (AMW and SCS). The range of variation of the phenotypic correlations between the seven traits was from 0.14 [+ or -] 0.04 (SW and SCS) to 0.80 [+ or -] 0.02 (WW and MW).
The G x E Interactions
In a similar fashion, the estimates of G x E interaction for traits between RS and KTD were obtained. Model 1b was used for SL, MW, and SCS; model 3b was used for WW and AMW, and model 5b was used for SH and SW. The genetic correlations for the seven traits in the two environments are summarized in Table 5. The genetic correlations were high for SH, SL, MW, and AMW, which signified little interaction between the two environments. The genetic correlations for WW (0.57 [+ or -] 0.36) and SCS (0.55 [+ or -] 0.35) were both <0.8, which suggested the G x E interaction has an impact on them. The genetic correlation for SW was not significant and had a large SE.
Growth Traits at KTD and RS The trait means except for AMW were higher at RS than that at KTD. An analysis of variance showed significant differences for WW and SCS between KTD and RS. Therefore, WW and SCS may be greatly influenced by environmental factors, and the RS area was more conducive to the traits of WW and SCS of Crassostrea gigas.
In this study, the heritability of the seven traits for RS ranged from 0.19 to 0.43, and the heritability of KTD varied from 0.11 to 0.44. The heritability for SH, WW, and MW of RS were larger than that of KTD, whereas the heritability for SL, AMW, and SCS of RS were lower than that of KTD. Kong et al. (2013) obtained the heritability estimates for SH (0.49 [+ or -] 0.25), SL (0.36 [+ or -] 0.19), SW (0.45 [+ or -] 0.23), and WW (0.35 [+ or -] 0.17) in 12-moold Crassostrea gigas. Appleyard et al. (2007) reported the heritability estimates for SH, SW, SL, WW, and MW were medium to high in 12-mo-old C. gigas. The heritability estimates of this study fell in the usual range for SH (Lannan 1972, Li et al. 2011, Wang et al. 2012), SL (Wang et al. 2009, 2012), SW (Wang et al. 2012), WW (Lannan 1972, Langdon et al. 2003, Ernande et al. 2004, Evans & Langdon 2006a, 2006b), and MW (Lannan 1972). Heritability estimates for AMW and SCS have not been reported in C. gigas. Chihiro et al. (2006) indicated that higher SCS was associated with lower mortality and a higher filtering ability in Pinctada fucata. Bougrier et al. (2003) found that higher SCS indicated lower mortality in C. gigas. SCS was an efficient indicator for breeding and culture management of the Japanese pearl oysters P. fucata (Aoki et al. 2010). In this study, the heritability estimate of AMW (RS 0.26 [+ or -] 0.04, KTD 0.30 [+ or -] 0.12) and SCS (RS 0.29 [+ or -] 0.15, KTD 0.44 [+ or -] 0.19) can provide reference data for breeding and culture management in C. gigas.
Genetic and Phenotypic Correlation
The genetic correlations between SW and other traits for KTD were not significant. The genetic correlations between the seven traits for RS and between the other six traits for KTD were all positive and significant, and the values ranged from 0.48 to 0.99 and 0.42 to 0.98, respectively. The phenotypic correlations between the seven traits ranged widely, 0.14-0.84 for RS and 0.14-0.80 for KTD. Kong et al. (2013) reported that the genetic and phenotypic correlations between four traits (SL, SW, SH, and WW) in Crassostrea gigas were 0.55-0.86 and 0.31-0.50, respectively. Appleyard et al. (2007) found significant positive genetic and phenotypic correlations between WW, SL, SW, and SH. Wang et al. (2012) noted that all genetic and phenotypic correlations between pairs of growth-related traits for SL, SW, SH, MW, and WW were positive. If the genetic correlation between the two traits is close to 1 or-1, it indicates that the breeding of one trait can produce a high correlation response to another trait (Gjedrem & Baranski 2009). In this study, there is a high positive genetic correlation between the growth traits, indicating that the selective breeding of one trait can play a synergistic role in the selection of other traits. In addition, it is particularly worth mentioning that high genetic correlation between SCS and AMW was 0.99 for RS and 0.98 for KTD, meaning that a larger SCS would imply a larger AMW in C. gigas. Similarly, Aoki et al. (2010) showed that SCS tended to increase along with AMW in Pinctada fucata.
The G x E Interactions
Significant G x E interactions have been reported in fish species (Iwamoto et al. 1986, Dunham et al. 1990, Eguia & Doyle 1992, Imsland et al. 2000, Jonassen et al. 2000, Imsland et al. 2005) and other bivalve species such as hard clam (Rawson & Hilbish 1991), pearl oyster (Kvingedal et al. 2008), and eastern oyster (Newkirk 1978). In Crassostrea gigas, Evans and Langdon (2006b) concluded that the G x E interactions on body weight, survival, and yield were all significant. Degremont et al. (2005) reported that significant G x E interactions were observed for yield and survival but not for growth. Andrew et al. (2007) estimated that the genetic correlations for weight between environments were all significantly high. Robertson (1959) suggested that a genotype-environment correlation <0.8 could be used as a rule of thumb, indicating when G x E correlation is present. In the study, the genetic correlations were all significant except for SW, which had a large SE, so the correlation could not be accurately calculated. The significant genetic correlations were high for SH, SL, MW, and AMW (>0.8), indicating that selection in one environment tended to indicate a similar genetic response in the other environment. The genetic correlations for WW and SCS were both low (<0.8), which meant that the effect of the G x E interactions should be considered. In addition, an analysis of variance also indicated that the results for WW and SCS were significantly different at RS and KTD, RS > KTD. Therefore, the G x E interaction have an important impact on the selective breeding of WW and SCS, which can be used to design a site-specific breeding program.
This study was financially supported by the National Natural Science Foundation of China (31402298) and by the Earmarked Fund for Agriculture Seed Improvement Project of Shandong Province of China (2017LZGC009), as well as supported by the Scholar of Yellow River delta of Dongying Government of China. We thank Elsevier for its linguistic assistance during the preparation of this manuscript.
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RENPING JI, (1,2) WEIJUN WANG, (1*) QIANG ZHAO, (3) BIN LI, (4) GUOHUA SUN, (1) HUANJUN LI (4) AND JIANMIN YANG (1*)
(1) Ludong University, 186 Middle Hongqi Road, Yantai 264025, China; (2) Government Service Centre of Yantai, 46 Caishi Street, Yantai 264000, China; (3) Fisheries Research Institute of Yantai, 26 Yinhai Road, Yantai 264003, China; (4) Shandong Marine Resource and Environment Research Institute, 216 Yangtze River Road, Yantai 264006, China
(*) Corresponding authors. E-mails: email@example.com or firstname.lastname@example.org
TABLE 1. The construction for full-sib families of Crassostrea gigas. Full-sib families Chinese F4 population [female]1 [female]2 [female]3 Chinese F4 population [male]l 22 (1), 7 (2) - - [male]2 14,32 1,3 - [male]3 - 4,9 12,2 Japanese F4 population [male]4 - - 27,4 [male]5 - - - [male]6 14,16 - - Korea F4 population [male]7 - 2,2 - [male]8 - - 15,3 [male]9 - - - Full-sib families Japanese F4 population [female]4 [female]5 [female]6 Chinese F4 population - 49,18 - - - 12,20 - - - Japanese F4 population 15,40 - - 51,13 31,13 - - 17,40 10,18 Korea F4 population - - 18,3 - - - 68,5 - - Full-sib families Korea F4 population [female]7 [female]8 [female]9 Chinese F4 population - - 19,68 - - - 18,7 - - Japanese F4 population - 43,4 - - - 26,11 - - - Korea F4 population 20,15 - - 24,10 72,10 - - 22,8 27,1 The number of offspring from the parentage assignment for total 54 full-sib families of set 0527 (1) and set 0612 (2) in C. gigas. TABLE 2. Comparison of the seven traits in both RS and KTD area in Crassostrea gigas ([+ or -] SD). Mean SH/mm SL/mm SW/mm RS 83.53 [+ or -] 12.43 53.91 [+ or -] 7.80 28.69 [+ or -] 4.71 KTD 81.39 [+ or -] 13.26 52.02 [+ or -] 7.08 28.02 [+ or -] 4.64 Mean AMW/g WW/g MW/g RS 1.08 [+ or -] 0.43 64.69 [+ or -] 8.17 (*) 11.60 [+ or -] 3.09 KTD 1.08 [+ or -] 0.37 57.79 [+ or -] 7.38 (*) 10.15 [+ or -] 2.67 Mean SCS/kg*N RS 25.00 [+ or -] 3.88 (*) KTD 21.57 [+ or -] 4.51 (*) TABLE 3. Phenotypic (above diagonal) and genetic correlations (below diagonal) with the heritability on the diagonal ([+ or -]SD) for RS area. SH SW SH 0.38 [+ or -] 0.18 0.25 [+ or -] 0.05 (***) SW 0.91 [+ or -] 0.20 0.19 [+ or -] 0.12 SL 0.95 [+ or -] 0.14 (***) 0.87 [+ or -] 0.16 (***) WW 0.96 [+ or -] 0.05 (***) 0.99 [+ or -] 0.13 (***) MW 0.95 [+ or -] 0.07 (***) 0.98 [+ or -] 0.20 (***) AMW 0.53 [+ or -] 0.31 (*) 0.76 [+ or -] 0.29 (**) SCS 0.48 [+ or -] 0.31 (*) 0.75 [+ or -] 0.29 (**) SL WW SH 0.41 [+ or -] 0.05 (***) 0.70 [+ or -] 0.03 (***) SW 0.32 [+ or -] 0.06 (***) 0.54 [+ or -] 0.04 (***) SL 0.25 [+ or -] 0.08 0.62 [+ or -] 0.03 (***) WW 0.90 [+ or -] 0.14 (***) 0.43 [+ or -] 0.19 MW 0.88 [+ or -] 0.17 (***) 0.99 [+ or -] 0.02 (***) AMW 0.58 [+ or -] 0.17 (***) 0.78 [+ or -] 0.17 (***) SCS 0.66 [+ or -] 0.27 (**) 0.66 [+ or -] 0.24 (**) MW AMW SH 0.63 [+ or -] 0.04 (***) 0.33 [+ or -] 0.05 (***) SW 0.44 [+ or -] 0.04 (***) 0.34 [+ or -] 0.05 (***) SL 0.53 [+ or -] 0.04 (***) 0.37 [+ or -] 0.05 (***) WW 0.84 [+ or -] 0.02 (***) 0.60 [+ or -] 0.04 (***) MW 0.43 [+ or -] 0.20 0.61 [+ or -] 0.04 (***) AMW 0.64 [+ or -] 0.25 (**) 0.26 [+ or -] 0.14 SCS 0.67 [+ or -] 0.24 (**) 0.99 [+ or -] 0.22 (***) SCS SH 0.15 [+ or -] 0.06 (***) SW 0.14 [+ or -] 0.05 (**) SL 0.15 [+ or -] 0.05 (**) WW 0.24 [+ or -] 0.05 (***) MW 0.27 [+ or -] 0.05 (***) AMW 0.25 [+ or -] 0.05 (***) SCS 0.29 [+ or -] 0.15 Phenotypic and genetic correlation estimates that differed from 0 at the P < 0.05, 0.01, and 0.001 levels of significance are indicated with (*), (**), and (***), respectively. TABLE 4. Phenotypic (above diagonal) and genetic correlations (below diagonal) with the heritability on the diagonal ([+ or -]SD) for KTD area. SH SW SH 0.12 [+ or -] 0.09 0.24 [+ or -] 0.05 (***) SW 0.57 [+ or -] 0.54ns 0.19 [+ or -] 0.08 SL 0.97 [+ or -] 0.20 (***) 0.95 [+ or -] 2.77 ns WW 0.88 [+ or -] 0.14 (***) 0.32 [+ or -] 0.46 ns MW 0.94 [+ or -] 0.20 (***) 0.53 [+ or -] 0.62 ns AMW 0.56 [+ or -] 0.33 (*) 0.58 [+ or -] 0.44 ns SCS 0.42 [+ or -] 0.12 (***) 0.34 [+ or -] 0.50 ns SL WW SH 0.36 [+ or -] 0.05 (***) 0.67 [+ or -] 0.04 (***) SW 0.29 [+ or -] 0.05 (***) 0.54 [+ or -] 0.04 (***) SL 0.30 [+ or -] 0.15 0.64 [+ or -] 0.03 (***) WW 0.74 [+ or -] 0.23 (***) 0.37 [+ or -] 0.17 MW 0.65 [+ or -] 0.40 (*) 0.90 [+ or -] 0.21 (***) AMW 0.87 [+ or -] 0.15 (**) 0.45 [+ or -] 0.28 (*) SCS 0.83 [+ or -] 0.19 (***) 0.65 [+ or -] 0.32 (*) MW AMW SH 0.60 [+ or -] 0.04 (***) 0.56 [+ or -] 0.33 (*) SW 0.44 [+ or -] 0.04 (***) 0.32 [+ or -] 0.05 (***) SL 0.50 [+ or -] 0.04 (***) 0.51 [+ or -] 0.05 (***) WW 0.80 [+ or -] 0.02 (***) 0.56 [+ or -] 0.04 (***) MW 0.11 [+ or -] 0.09 0.58 [+ or -] 0.04 (***) AMW 0.67 [+ or -] 0.11 (***) 0.30 [+ or -] 0.12 SCS 0.67 [+ or -] 0.33 (*) 0.98 [+ or -] 0.11 (***) SCS SH 0.21 [+ or -] 0.08 (**) SW 0.14 [+ or -] 0.04 (**) SL 0.37 [+ or -] 0.05 (***) WW 0.44 [+ or -] 0.05 (***) MW 0.41 [+ or -] 0.05 (***) AMW 0.50 [+ or -] 0.05 (***) SCS 0.44 [+ or -] 0.19 Phenotypic and genetic correlation estimates that differed from 0 at the P < 0.05, 0.01, and 0.001 levels of significance are indicated with (*), (**), and (***), respectively. TABLE 5. G x E interaction of the seven traits between RS and KTD area exhibited genetic correlations ([+ or -]SD). Trait Genetic correlation SH 0.88 [+ or -] 0.15 (***) SW 0.78 [+ or -] 0.58 ns SL 0.99 [+ or -] 0.20 (**) WW 0.57 [+ or -] 0.36 (*) MW 0.92 [+ or -] 0.50 (*) AMW 0.96 [+ or -] 0.36 (**) SCS 0.55 [+ or -] 0.35 (*) Genetic correlation estimates which differed from 0 at the P< 0.05, 0.01, and 0.001 levels of significance are indicated with (*), (**), and (***), respectively.
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|Author:||Ji, Renping; Wang, Weijun; Zhao, Qiang; Li, Bin; Sun, Guohua; Li, Huanjun; Yang, Jianmin|
|Publication:||Journal of Shellfish Research|
|Date:||Aug 1, 2019|
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