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Genetic parameters for economically important traits in yellowtail kingfis

Seriola lalandi

Paul Whatmore, Nguyen Hong Nguyen, Adam Miller, Rob Lamont,

Dan Powell, Trent D' Antignana, Erin Bubner, Abigail Elizur, Wayne Knibb

PII: S0044-8486(13)00111-7

DOI: doi: 10.1016/j.aquaculture.2013.03.002

Reference: AQUA 630583

To appear in: Aquaculture

Received date: 1 September 2012

Revised date: 27 February 2013

Accepted date: 3 March 2013

Please cite this article as: Whatmore, Paul, Nguyen, Nguyen Hong, Miller, Adam, Lam-

ont, Rob, Powell, Dan, D'

economically important traits in yellowtail kingfish Seriola lalandi, Aquaculture (2013),doi: 10.1016/j.aquaculture.2013.03.002

This is a PDF file of an unedited manuscript that has been accepted for publication.As a service to our customers we are providing this early version of the manuscript.The manuscript will undergo copyediting, typesetting, and review of the resulting proofbefore it is published in its final form. Please note that during the production processerrors may be discovered which could affect the content, and all legal disclaimers thatapply to the journal pertain.

Knibb, Wayne, Genetic parameters forAntignana, Trent, Bubner, Erin, Elizur, Abigail,

http://dx.doi.org/10.1016/j.aquaculture.2013.03.002http://dx.doi.org/10.1016/j.aquaculture.2013.03.002http://dx.doi.org/10.1016/j.aquaculture.2013.03.002

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Genetic parameters for economically important traits in yellowtail kingfish

Seriola lalandi

Paul Whatmorea,b, Nguyen Hong Nguyena, Adam Millerc, Rob Lamonta, Dan Powella, Trent

D'Antignanac, Erin Bubnerd, Abigail Elizura, Wayne Knibba*

aThe University of the Sunshine Coast, Maroochydore, Queensland 4558, Australia

bThe Australian Seafood Cooperative Research Centre.

cClean Seas Tuna Limited, 7 North Quay Boulevard, Port Lincoln, SA 5606, Australia.

dSchool of Biological Science, Lincoln Marine Science Centre, Flinders University, PO Box

2023, Port Lincoln, South Australia 5606, Australia

*Corresponding author: Tel: +61 7 5430 2831; e-mail: wknibb@usc.edu.au

ABSTRACT

The aim of the present study was to estimate genetic parameters for body and carcass

traits, visual condition score, and deformity in yellowtail kingfish Seriola lalandi, an

emerging aquaculture species in Australia. These novel data and genetic parameters are

required to solve the problem of how to conduct efficient selection in this and related

species. Analyses were performed on a total of 400 data records collected from a yellowtail

kingfish breeding population at Cleanseas Tuna Ltd. farm. They were progeny of 22 full- and

half-sib families (eight sires and six dams). Six newly developed and four published

microsatellite markers were used to construct the pedigree. Genetic parameters were

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estimated using average information algorithm in ASReml with a multiple trait model. Fixed

effects included sex, seal bite and deformity status. Random effects were the additive

genetics of individual animal, and maternal and common environmental effects (i.e., dam-

tank effect arising from a short period of separate rearing of offspring that came from two

different broodstock tanks). The estimates of heritability for body and carcass traits were

moderate (h2 = 0.15 to 0.30, s.e. ranging from 0.09 to 0.19). Fillet fat content showed an

unusually high heritability (0.940.21) with a standard animal model, but was only moderate

(0.41 0.26) when tank and dam were included as random effects. The estimate for

condition score was 0.15 0.11, whereas the heritability for deformity was close to zero (h2

= 0.02). The genetic correlations between body and carcass (fillet weight and fillet yield)

traits were high and positive (0.57 to 0.94, s.e. 0.05 to 0.46). Genetic correlations between

body traits and condition score were moderate to high and positive (i.e. favourable). These

results suggest that selection for high growth would result in concomitant increase in fillet

weight, a carcass trait of paramount importance. It is concluded that there is substantial

potential for genetic improvement of economically important traits especially growth

performance and fillet weight in the current population of yellowtail kingfish.

Keywords: Seriola lalandi, kingfish; heritability; genetic correlation; fillet; fat content;

selection.

1. IntroductionThe yellowtail kingfish, Seriola lalandi, is an emerging aquaculture finfish species in

Australia, with a current production approaching four thousand tons at a value of 20 million

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dollars/year (Booth et al., 2010) although aquaculture production on Seriola species in Japan

is very large, accounting for most of Japans finfish aquaculture (Statistics Department,

Ministry of Agriculture, Forestry and Fisheries, Japan 2002). In South Australia, where the

majority of production occurs, it is the second most commercially valuable aquaculture

industry (Fernandes and Tanner, 2008). There is increasing demand for the species in both

domestic and export markets with culture technologies being well developed. Yellowtail

kingfish is a premium quality product and is marketed as whole live fish or fresh and frozen

fillets, cutlets and loins. In the Japanese sashimi market yellowtail kingfish is an extremely

valuable product after tuna. However, with steadily reducing catches and quotas for bluefin

tuna, the value and demand for yellowtail kingfish will continue to grow (Love and

Langenkamp, 2003). Up to the present, quantitative genetic basis of these traits are not

known in yellowtail kingfish and there are no reports on genetic associations between body

and carcass traits and flesh quality in this species. Genetic associations between

performance and fitness related traits (condition score and deformity) were also not

available in the literature for this species (Seriola lalandi). As yellowtail kingfish are group

spawners and produce small larvae (about 4mm at hatching), physical tagging of offspring is

unfeasible. DNA markers function as naturally occurring biological tags that can be used in

lieu of physical tags, with the added benefit of enabling other biological observations to be

made, such as management of genetic diversity and inbreeding of stocks (Frost et al., 2006).

The application of genetic markers for parentage allocation and pedigree reconstruction

have been practiced in other aquaculture species (e.g., Ferguson and Danzmann, 1998; Hara

and Sekino, 2003; Sekino et al., 2005; Jerry et al., 2004; Vandeputte et al., 2004; Ninh et al.,

2011). The technology has long been recognized as an effective tool to reconstruct the

pedigrees of group spawned and communally reared aquaculture populations, allowing high

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selection intensity in genetic improvement programmes (Doyle and Herbinger, 1995), to

account for environmental effects common to full-sibs (Rodzen et al., 2004) and to achieve a

high selection responses (Ninh et al., 2011).

In the present study, we used a total of 10 microsatellites to construct the pedigrees for the

breeding population of yellowtail kingfish from which genetic parameters were estimated.

Specifically we report: i) heritability for body and carcass (fillet weight and fillet yield) traits

including fat content, condition score and deformity, and ii) genetic relationships among

traits studied. The main aim of this study was to understand the quantitative genetic basis

of traits in yellowtail kingfish such as whole and fillet weight, traits for which the market

pays for, to start a formal breeding program for this species.

2. Materials and methods2.1Experimental locationThe reproduction, larval rearing, fingerling production and grow-out were conducted at

Clean Seas Tuna Ltd., Arno Bay (33 56.202' S 136 34.500' E), South Australia. Cages were

towed from Arno Bay to Port Lincoln, South Australia (latitude 34 73' S, longitude 135 86'

E) for harvest which is a distance of about 115 km.

2.2Animal materialsThe original broodstock were eight wild males and six wild females, presumed to be 6-10

years old and estimated to be >15kg each, caught in the Spencer Gulf (i.e., are presumed to

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come from the same population) and were held into two broodstock tanks at Arno Bay and

all broodstock contributed to the offspring. The two tanks spawned a daily average of

1,290,000 and 974,000 eggs. Broodstock were kept at a density of 3-5 kg/m3, fed

commercial diets, and mass spawned on several consecutive nights between 19th and 30th

October 2011. The larvae were stocked in hatchery rearing tanks (8 14 m3) at a density of

90/l, and fed rotifers (Artemia) from day 2 until day 15, Artemia was introduced at day 11

and fed until day 25, fish were weaned onto pellet diet around day 22-25. During larval

rearing, water temperature was maintained at about 24.5oC and 12h photoperiod. The

survival rate from hatching to fingerlings varied from 6 to 14%. Fish continued to be reared

in the hatchery until around day 70-80 when they reached 6-8 g when they were transferred

to a sea cage, where they were on-grown to harvest (average body weight of 3.1 kg 0.35;

427 days post spawning) using standard industry practices. At harvest, four hundred fish

were randomly sampled from the sea cage with approximately 40,000 fish and transported

to the processing factory in Port Lincoln in large, ice-filled containers. They were the G1

progeny of 22 full-sibs and half-sibs families. As there were eight sires and six dams,

typically each sire or dam had three to four half-sib groups, ranging from two to 50 offspring

per group.

Phenotyping: Measurements and tissue sampling was completed at the processing factory in

Port Lincoln and at the laboratory at the Lincoln Marine Science Centre. Four hundred

animals were taken for measuring but some data were not available because of seal bites

and availability from the factory. Measurements included weights, lengths (mouth to caudal

fork), and condition score (an arbitrary measure to grade the general appearance of the fish

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on a 1 to 5 scale, with 1 being the poorest condition and 5 the best condition; a fish that

scored 5 had long, wide and deep body shape, appeared to have a heavy body weight for its

length and no deformity, by contrast, a fish that scored 1 had deformity, and short and thin

body shape).

Deformity was visually recorded and categorised as lower jaw, nasal erosion or operculum,

which were the prevalent deformities found in these fish. In the present study, deformity

was defined as a binary trait (coded as 1 for fish which had either low jaw, nasal erosion or

operculum, and coded as 0 if these clinical signs were absent).

Both sides of skinless fillets were weighed and used to calculate total fillet weight and

percent fillet yield (fillet weight/ slaughter weight). Gonads were bagged and placed on ice

for later visual identification of sex. Approximately 20 g of muscle tissue were bagged and

placed on ice for flesh quality assessment (analysis of fat content).

2.3Chemical analysis of fat

The crude fat content of the flesh was determined with an ethyl acetate extraction method

based on the Norwegian Standard method (NS 9402 E) (NSA, 1994). We sampled tissue from

approximately the same region in each animal; approximately 30mm inferior and anterior

from the dorsal fin. This region showed the least seasonal variation and corresponds with

the Mowi cut, which is a standard cut used in examining fat content in salmonids

(Bremner, 2010).

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2.4Marker development2.4.1 Primer design: For this study we used four published microsatellite primer pairs(references following) and developed primers for six additional microsatellite loci from S.

lalandi transcriptome sequences. Primers for 25 published microsatellites from two other

Seriola species (S. quinqueradiata and S. dumerili) were tested on S. lalandibroodstock and

four candidates were selected based on polymorphism and scoring accuracy. These four

microsatellites were Sequ38 (Ohara et al., 2003), Sdu21 (Renshaw et al. 2006), Sdu32 and

Sdu46 (Renshaw et al., 2007). These were tested and validated using the protocols outlined

by Renshaw et al. (2007).

Transcriptome sequences from S. lalandi liver and digestive systems were generated using

the Roche 454 FLX next generation sequencing platform at the Australian Genome Research

Facility. Microsatellite sequences were identified using the QDD pipeline (Meglecz et al.,

2010) which includes primer design using Primer3 (Rozen and Skaletsky, 2000). Of the

41,765 reads generated, 250 contigs containing microsatellites were identified, from which

70 were found to have a suitable number of repeat motifs (over 20) and suitable flanking

regions on which to design primers. Of these 70 microsatellite sequences, 31 candidate

primer pairs with similar Tm (around 60C) and minimal dimer interaction were selected for

further testing. Forward primers for these 31 microsatellites were tagged with the M13(21)

universal sequence 5-TGT AAA ACG ACG GCC AGT -3 to enable two-stage labelling with

the use of an additional fluorescent dye labelled M13(21) primer. Primers were optimised

by testing under a range of thermal and reaction parameters and a final six of the most

specific and polymorphic microsatellites were selected for production. These loci in addition

to the four published microsatellites are Sel001, Sel002, Sel008, Sel011, Sel017 and Sel019.

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A total of six loci developed from our laboratory in addition to four published loci made up a

total of 10 microsatellites for parentage assignments. The thermal parameters for primers

with M13 sequences were: 94C for 3 minutes, 20 cycles of 94C for 30 seconds, 60C for 30

seconds (decreasing by 0.5C for each cycle), 72C for 45 seconds, 20 cycles of 94C for 30

seconds, 50C for 30 seconds, 72C for 45 sec, 72C for 10 minutes. For the primers without

M13 sequences the parameters were: 94C for 3 minutes, 40 cycles of 94C for 30 seconds,

52C for 30 seconds, 72C for 45 seconds, 72C for 10 minutes. Considering all 10 loci, the

average number of alleles detected was 102.10, the average observed heterozygosity was

0.70.05 and the polymorphic information content average was 0.66.06.

2.4.2 DNA extraction and PCR Reactions: DNA was extracted from caudal fin clips frombroodstock and muscle tissue from G1 animals using a Qiagen DNeasy Blood and tissue kit.

Microsatellite markers were amplified using a two-stage touchdown protocol with an initial

20 cycles running at an annealing temperature of 60C reducing by 0.5C every cycle, and

then another 20 cycles run at 50C. This protocol is specifically optimised for M13 tailed

primers. PCR reactions were performed using 1.25 l of 10x reaction buffer, 1 l of 2.5mM

dNTPs, 0.75 l of 25mM MgCl2, 0.05 l of Taq and 1 l of genomic DNA. For reactions using

M13 tailed primers, three primers are included: equimolar reverse primer and M13

fluorescently labelled primers, and a M13 tailed forward primer that has 25% concentration

of the other two primers (Schuelke, 2000). For our reactions this was 0.5 l of both the M13

fluorescently tagged primer and the reverse primer, and 0.125 l of the M13 tailed forward

primer. To reach a final reaction volume of 13 l, 7.825 l of DNAse free water was added.

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2.4.3 Genotyping and analysis: All genotyping was performed at the University of theSunshine Coast on an Applied Biosystems 3500 Genetic Analyzer (Life Technologies). The G5

standard dye set was used on this platform (FAM-blue, NED-yellow, VIC-green, PET-red)

with GeneScan LIZ 600 V2.0 (orange) as the size marker. Samples were prepared for use on

this instrument as per the manufacturers instructions. Genotypes were scored using

Genemarker software (SoftGenetics, USA). Only clear, high quality and easy to score peaks

were accepted, with lower quality results discarded and/or repeated, and accuracy of

markers was further validated by repeat genotyping of 100 samples. Of the 100 genotypes

were that repeated, including a repeated PCR step, all match exactly at all 10 loci which

suggest a high accuracy of scoring. This was done to minimise genotyping error and produce

high confidence results. Validation of individual microsatellite loci was performed by using

MICROCHECKER (van Oosterhout et al., 2004) to check for null alleles, large allele dropout

and scoring errors. Linkage disequilibrium between loci and deviation from Hardy-Weinberg

Equilibrium (HWE) at each locus was calculated using GENEPOP 4.1 (Raymond and Rousset,

1995). Parentage assignment was completed using Cervus software (Kalinowski et al., 2007),

and validated by comparing all broodstock genotypes to designated cohort of 90 G1

individuals known to be offspring from one of the two production broodstock tanks. Over

98% of these G1 animals was correctly assigned to their known parental group. Confidence

scores for parentage assignments were calculated from likelihood-odds ratios and only

assignments scoring over either 80% or 95% confidence were included in the pedigree,

those below 80% were excluded. In total, 84% of parental assignments scored greater than

a 95% confidence, and 8% scored between 80% and 95% confidence, thus 92% scores over

80% confidence. Those scoring between 80% and 95% confidence were confirmed by

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visually scoring individual alleles. The pedigree comprised only one generation of offspring

and parents.

Genotyping and molecular data analysis were conducted at University of the Sunshine Coast

(USC) in Sippy Downs, Queensland, Australia.

2.5Statistical analysis2.5.1 Linear mixed model: Uni- and multi-trait linear mixed models were applied toestimate heritability and correlations for all the traits studied, respectively. The ASReml

package was used (Gilmour et al., 2009). In a matrix notation, the model is written as the

following:

y =Xb +Za + Wc+ e

where yis a vector of observations for traits studied, X,Zand Ware the incidence matrices

related to the fixed, additive genetic and common full-sib effects, and b is the vector of fixed

effects including sex (female and male), seal bite (fish were bitten by seal or not) and

deformity status (yes or no), a is the additive genetic effect of individual animal for traits

studied, and c is the vector of common full-sib effects [i.e., dam nested within spawning

batch (tank), c2]. Due to a short period of about two-week early rearing before communal

grow-out of all families was practised, the c2 effect was included as the second random term

in the model. The c2 effect was however not significant. When it was added to the model,

there were no (or only trivial) changes in logarithmic likelihood (LogL) value to all traits

including fat content. Therefore, all traits observed were also analysed with a model that

omitted the c2 effect.

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Under the full model, heritabilities for traits were calculated as2

22

P

Ah

, where the

phenotypic variance ( 2P

) was the sum of the additive genetic variance ( 2A

), maternal and

common environmental variance ( 2C

) and the residual variance ( 2e

), i.e.

2222

eCAP . The estimated heritability for deformity was transformed from observed

(0, 1) scale to the underlying liability scale using the classical formula of Dempster and

Lerner (1950) as follows.

2

22 )1(

zpphh oL

where 2Lh is the estimate of heritability on the underlying normal scale;2

Oh is the estimate of

heritability from the linear model with binomial observations; p is the fraction of deformity

with observations of 1; and z is the height of the ordinate at the truncation point for an area

ofp under the normal curve.

Genetic and phenotypic correlations were estimated from a series of bivariate and trivariate

linear mixed model with the fixed and random effects as described above. ASReml allows to

specify different random effects for different traits in the model. For traits that had zero c2

estimates (from univariate analysis), this effect was omitted in multi-variate models.

Correlations were calculated as the covariance divided by the product of the standard

deviations of traits:2

2

2

1

12

r where12

was the estimated additive genetic or

phenotypic covariance between the two traits, and 21

and 22

are the additive genetic or

phenotypic variances of traits 1 and 2, respectively.

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2.5.2 Generalized linear mixed model (GLMM): In addition to standard linear mixed modelas described above, deformity was also analysed by GLMM sire model. Under logit model,

the assumption was that the data followed a binomial distribution, and the probability can

be obtained by logistic function ( p = ex /(1 +ex)) where p is the probability of fish deformity

recorded at harvest andxis a linear predictor. The effects fitted in the model included the

effects of sex (j= female and male) and seal bite (the fish were bitten by seal or not). With

GLMM sire model, heritability was calculated using the variance of the logit link function,

which implies that residuals have a mean of 0 and a variance of2/3.

3

42

22

22

es

sh

where 12 e

The mathematical form of the generalized linear mixed model with a logit link function used

to estimate variance components for deformity is as the followings:

)1

log(ijklm

ijklm

p

p

= + Bi+ Sk+ al+ eijklm

where Bi, and Skare the fixed effects of seal bite and sex respectively, al is the random effect

of sire and eijkl is the error term of the model, and p is the probability of fish deformity

recorded at harvest.

Deformity was also analysed using probit threshold model. The statistical model was the

same as described for logit model. However, the probit link function )(1 ip is used,

with inverse link

dxe

x

i2

2

2

1)( , where is the cumulative normal density

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function, and pi denotes the probability of deformity for fish i. Under the probit threshold

model, the heritability was calculated as

1

42

22

s

sh

3. Results3.1Descriptive statisticsThe number of actual observations, means, standard deviations and coefficients of variation

for traits studied is given in Table 1. The body weight of the fish at harvest averaged 3.1 kg,

corresponding to a fillet weight of 1.8 kg and fillet yield of 61%. The fillet fat content of

yellowtail kingfish fillet sampled from approximately the same region (about 30mm inferior

and anterior from the dorsal fin) was 4.9%. The coefficient of variation (CV) for fat content

was 43.3%, markedly greater than that for body and fillet traits (10-15%). The proportion of

deformities in this population was about 20%.

3.2Fixed effectsThe fixed effects included in statistical models for the analysis of traits studied were sex,

seal bite and deformity. The least square means for sex, seal bite and deformity status are

shown in Table 2. Of particular interest is that in yellowtail kingfish sex dimorphism does not

exist at the size evaluated (3.1 kg). Between-sex differences were not statistically significant

for all traits studied.

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The effect of seal bite was statistically significant for carcass traits only (i.e., fillet weight,

fillet yield and fat content). On the other hand, deformity had significant effect ( P < 0.05 to

0.001) on the majority of growth and carcass performance traits in yellowtail kingfish,

except for fillet yield (Table 2). Deformed fish grew slower and had smaller fillet weights and

lower visual condition score than normal (non-deformed) counterparts.

3.3HeritabilitiesSingle trait analysis of heritabilities, and maternal and common environmental variances in

proportion to phenotypic variances (c2) is presented in Table 3. The estimates of heritability

for body and carcass traits including fillet fat content were moderate (0.15 to 0.41).

Condition score was heritable (0.15). Overall, the estimates for heritability for all traits were

associated with high standard errors (0.03 to 0.26), likely due to the limited sample size and

shallowness of the pedigree. Heritability for deformity obtained from both nonlinear

(generalized) mixed model using logit and probit link functions and standard animal mixed

were low, close to zero.

The c2 were low, only 5% for body weight, but were essentially zero for other traits. One

exception was the moderate c2 for fat content. The heritability of 0.94 for fat content from

the model without maternal and common environmental effect hence may be

overestimated.

Among body and carcass traits, multivariate analyses increased albeit marginally heritability

estimates for body and fillet weight relative to single trait models, 0.30 vs. 0.26 and 0.34 vs.

0.31, respectively (results not shown). However these differences were not significant.

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3.4Correlations among body and fillet traits

Table 4 presents phenotypic and genetic correlations among body and carcass traits.

Measurements of body traits (weight and length) were genetically highly correlated (rg=

0.57 0.28). The genetic correlations between fillet weight and body measurements were

also very high (close to unity, 0.91 to 0.96), indicating that both growth and fillet weight can

be simultaneously improved. Fillet weight showed slightly greater correlations with body

weight than with fork length (0.96 vs. 0.91), suggesting that harvest weight is the most

effective selection criterion to improve both growth and fillet weight in practical breeding

programs. The genetic correlation of fillet yield with body weight were moderate (0.57), but

those with fork length were not significant different from zero (-0.19 and 0.24, respectively).

There was also a moderate and positive genetic correlation between fillet yield and fillet

weight (rg = 0.72). Breeding objectives for increased fillet weight are thus expected to result

in improvement in fillet yield.

Overall, condition score show favourable phenotypic and genetic correlations with body and

carcass traits.

3.5Correlations between fillet fat content and body and carcass traitsPhenotypic and genetic correlations of body and carcass traits with muscle fat content are

shown in Table 5. At both phenotypic and genetic levels, all the estimates were moderately

positive and significant, except for the genetic correlations between length and fillet yield

with fat content that were not significant due to their high standard errors.

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4. Discussion4.1Carcass characteristics of yellowtail kingfishThe fillet yield of yellowtail kingfish Seriola lalandiin our study (61%) falls in the high range

reported for marine species in the literature, from 43 to 69% (e.g. Navarro et al., 2009). In

the present study we report skinless fillet yield which is higher than the average value of the

same species reported in other studies (Burke, 2011), and remarkably higher than other

fishes such as tilapia (Nguyen et al., 2010b; Gjerde et al., 2012) or equivalent to rainbow

trout and Atlantic salmon (Kause et al., 2007; Powell et al., 2008; Le Boucher et al., 2011).

The fat content of 4.9% in the yellowtail kingfish population in our present study is generally

in line with other reports for Seriola lalandi(e.g., Bowyer et al., 2012). Several studies (e.g.,

Tobin et al., 2006) report that fat content varies largely with fish tissues (fillet cuts, adipose

and intestinal tissues, viscera or liver). Within a fillet, the distribution of fat also differs, that

is, higher fat content in anterior ventral than posterior dorsal sections (Burke, 2011). The

muscle tissue sampled in the present study showed the least variation in fat content.

4.2HeritabilitiesThe extent of genetic variation in body traits and fillet weight for yellowtail kingfish in our

study is also consistent with those reported for sea bream (Knibb, 2000), rainbow trout

(Kause et al., 2005), Atlantic salmon (Gjerde and Gjedrem, 1984), tilapias (Nguyen et al.,

2007; Ponzoni et al., 2011; Bentsen et al., 2012), common carp (Ninh et al., 2011) and

crustaceans species (Gitterleet al., 2005; Kenway et al., 2006; Jurezet al., 2007; Hung, D.,

pers. comm.). The high standard errors associated with our estimates were likely due to the

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limited sample size, single generation data, mass spawning practices as well as impacts of

other environmental factors during rearing and growout. Though the data were somewhat

limited (n = 400), the animals did come from a number of half-sib families and the genetic

parameters estimates were usually statistically significant. Overall, our estimates together

with the moderate to high heritabilities across farmed aquaculture species indicates that

improvement for body traits can be achieved through conventional selection in properly

designed breeding program.Our current estimate of heritability for fillet yield in yellowtail kingfish was 0.19 0.11, in

good agreement with the results reported by Nguyenet al. (2010b) in GIFT tilapia where h2

for fillet yield was at 0.250.07. Some other studies, however, showed low heritability

estimates for fillet yield or carcass ratio traits (0.03-0.05) such as in striped catfish

Pangasianodon hypophthalmus (Sang et al., 2012), gilthead seabream (Navarro et al., 2009),

rainbow trout and Atlantic salmon (Gjerde and Gjedrem, 1984).

In the present population of yellowtail kingfish fillet fat content is highly heritable, showing

potential for genetic improvement of this trait. The estimates of heritability for fat content

range from 0.17 to 0.33 in salmonids (Gjerde and Schaeffer, 1989; Iwamoto et al., 1990; Rye

and Gjerde 1996; Kause et al., 2002; Tobin et al., 2006; Niera et al., 2004; Quinton et al.,

2005; Viera et al., 2007; Powell et al., 2008) and from 0.06 to 0.48 in tilapia (Nguyen et al.,

2010a and b; Hamzah, A. per. comm.). The number of fillet samples analysed for fat content

in our study is generally comparable with those studies reported in the literature (e.g., n=

514 in tilapia reported by Nguyen et al., 2010a).

On both observed and underlying liability scales, the heritability for deformity is not

significantly different from zero. Perhaps since our measures of deformity include a range of

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traits, a low or negligible heritability is expected. The heritability on the underlying scale for

skeletal deformities is 0.25 0.03 for European seabass (Bardon et al., 2009; Karahan et al.,

2012), 0.36 0.14 for Atlantic salmon (Gjerde et al., 2005), 0.64 for sire and 0.36 for dam

components of variance on the underlying liability scale in another salmon study (McKay

and Gjerde, 1986), and 0.27 in cod (Kolstad et al., 2006). These results show that there is

additive genetic component for deformity which would allow scope for selection and

improvement through conventional selective breeding approach.

4.3Common environmental effectsThe common environmental differences between full-sib and half-sib families had significant

effects on growth performance on farmed aquaculture species (e.g., Winkelman and

Peterson, 1994). The extent of common environmental effects on body traits generally

depend on mating strategy, rearing environment, data and family structure, and they are

not easily separated from maternal genetic and non-genetic components unless there is a

good depth in the pedigree and ideal data structure. In our present study we demonstrated

that by the application of early communal rearing of all families pooled as soon as hatching,

the common environmental effects (c2) was minimized. The logarithmic likelihood ratio test

showed that the c2 effect was not significant for all body traits (Chi-square test with one

degree of freedom, P > 0.05). Ninh et al. (2011) also report a close to zero c2 in communal

early rearing of a common carp population in Vietnam. Similar findings were reported in

other species (Fishback et al., 2002; Vandeputte et al., 2004; Kocour et al., 2007).

4.4Correlations

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The high positive genetic correlations among body traits and fillet weight in this current

study suggests that all of the above body traits were closely genetically correlated and are

likely to be controlled by similar sets of genes. Hence, any one of these traits tested could

be used, on its own or simultaneously, to improve overall growth performance of the

animals without a requirement for making different measurements. However, harvest

weight is reported to be the best predictor of fillet weight, with R2 = 0.94 and a correlation

between observed and predicted value of 0.97 in tilapia (Nguyen et al., 2008). The genetic

correlation of fillet weight with body weight was higher than for fillet weight with other

body traits (Nguyen et al., 2010b). In practical selection programs live weight is

recommended due to its greater heritability and the ease and accuracy of measurements

relative to other body traits (length or width). Therefore both performance and fillet weight

can be improved simultaneously.

In the present population of yellowtail kingfish, the estimated genetic correlation of fillet

yield with body weight was moderate and positive (0.57 0.28). By contrast in a study of

striped catfish, Sang (2010) found low genetic correlations between fillet yield and body

weight or between fillet yield and fillet weight. Realised correlated response to selection for

body weight was also reported to be negligible for fillet yield in this species or in tilapia

where the fish were slaughtered at an average weight of 500 g (Nguyen et al., 2010b).

Hence, direct improvement of fillet yield in farmed aquaculture species is predicted to be

difficult because ratios have been long known to lead statistical hurdles. This is due to the

likely disproportionate nature by which selection pressure is exerted on component traits

(Gunsett et al., 1987; Simm et al., 1987). However, we may be able to overcome some of

these issues relating to ratios by selection on fillet weight adjusted for body weight. In this

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case indirect improvement in fillet yield to selection for increased body weight may be

expected as calculated using selection index theory by Nguyen et al. (2010b).

The positive genetic correlations of body and fillet weight with fat content (0.92 and 0.98

respectively) indicated that selection for increased growth can result in increased fat

content. The reported genetic correlation between fat and body weight was at the high end

ranges, from 0.11 to 0.80, reported in salmonids (Neira et al., 2004; Quinton et al., 2005;

Powell et al., 2008; Vieira et al., 2007) and from 0.16 0.40 reported in tilapia (Hamzah, A.

pers. comm). The great range of values may reflect slightly different practices in the

methods. In any case, such a large range makes it difficult to determine if our values are

typical or not. In this study, we examine total fat content from the same region of the fillet,

whereas different measurements of fat are reported in the literature. For instance in a

rainbow trout (Oncorhynchus mykiss) breeding program, Kause et al. (2007) measured

muscle (dissected above the lateral line of the fish) and body lipid (samples of un-gutted

fish) as two different traits. Across studies, the percentages of fat deposits in fish fillet and

visceral weight were different. Therefore, fillet fat contents should be measured as a

genetically different trait from the fat deposits at other body locations. Gjerde and Schaeffer

(1989), Kause et al. (2002) and Tobin et al. (2006) also proposed that fat deposits at

different body locations should be measured as genetically different traits. In the present

study, only fillet fat content was examined due to the high costs of chemical analysis and

labours involved (fats of other body compartments were not analysed). The present

estimates of the genetic correlation of body weight with fat content, are generally in line

with other fish species in which either fillet or whole body fat contents were measured

(0.36, Iwamoto et al., 1990; 0.73, Neira et al., 2004; 0.31, Quinton et al., 2005; 0.80, Powell

et al., 2008; 0.11, Vieira et al., 2007).

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In our study, the genetic correlations between body traits and deformity were weak (0.09 to

0.34) and not significant due to their large standard errors (results not shown). In Atlantic

cod, Kolstard et al. (2006) also reported a low genetic correlation between growth and

various measures of deformities. The genetic relationship between growth and deformity

reported in the literature vary with species (positive in European sea bass, Karahan et al.

2012, but negative in Atlantic salmon, Gjerde et al., 2005), history of populations, and

rearing phases (Vehvilainen et al., 2012) to which the fish subject. In practical selective

breeding programs for farmed aquaculture species, this trait (deformity) should be closely

monitored.

Although our study showed that deformity had a low heritability and no significant

correlation with other traits, this character probably should be included in any recording

scheme, the breeding objective and the selection index to ensure the sustainability of long

term genetic improvement programs for yellowtail kingfish. In farmed animals, high

performing individuals resulted from long term selection programs tend to prone to

diseases such as mastitis in dairy cattle, leg weakness in pigs or egg defects in layers chicken.

Both allocation resource and selection theories (Beilhharz et al., 1993) suggests that fitness

related traits may show a tendency to decline due to selection for other traits and due to

inbreeding depression (Goddard, 2009). They thus merit further considerations in practical

selective breeding programs to meet future demands of the aquaculture industry.

In summary, there is large potential for genetic improvement to increase productivity for

production traits in yellowtail kingfish in Australia. Among these traits, body and fillet

weights are of particular interest because the fish are priced on the basis of their wet body

weight or fillet weight. Fillet weight can be indirectly improved through selection for

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increased growth due to the strong positive genetic correlation between the traits.

Alternatively a linear index combining measurements of available body dimensions can be

applied (Nguyen et al., 2010b). For some species such as tuna, fat can be a factor in the

value of a fish. Should there be consumer and or market demand to increase the fat content

of kingfish, it may be possible to achieve this with genetic selection. To enable routine data

recording on a large scale for genetic evaluation and selection in kingfish, cheap and non-

invasive measurements of fats on live animals should be considered such as using fat-

meters, near infrared spectroscopy or computerised tomography (Cozzorino and Murray,

2012).

5. ConclusionsGenetic properties of muscle fat content, condition score and deformity and their genetic

associations with body and carcass traits are reported for the first time in yellowtail kingfish.

Selection for improving body weight, fillet weight and fat content could be effective as

evidenced by their moderate heritability. Genetic correlations among these traits (body

weight, fillet and fat) are positive and high, i.e. desirable. Our present results provide a basic

genetic inheritance for traits of economic importance, and thus should be useful for the

design and conduct of practical selective breeding programs in yellowtail kingfish. Due to

the limited number of parents and families involved in the present study, frequent update

of the genetic parameters would be necessary to guide future breeding programs in this

population of yellowtail kingfish.

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Acknowledgements

This work was supported financially by the Australian Seafood Cooperative Research Centre

with the Fisheries Research Development Cooperation (project 2010/768 Broodstock

management for Southern Bluefin Tuna and Yellowtail Kingfish). Facilities, fish and

experimental support was provided by Clean Sea Tuna, Inc. We would like to acknowledge

technical input and other advice from Jane Quinn (at USC), Morten Deichmann and Craig

Foster (CST).

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Vieira, V.L., Norris, A., Johnston, I.A., 2007. Heritability of fibre number and size parameters and

their genetic relationship to flesh quality traits in Atlantic salmon (Salmo salarL.).

Aquaculture 272, 100-109.

Winkelman, A., Peterson, R., 1994. Genetic parameters (heritabilities, dominance ratios and genetic

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Table 1a: Means, standard deviations (SD), coefficient of variation (CV, %) for traits studied

Traits Unit N Mean SD CV

WT Kg 385 3.1 0.35 11.4

LG Cm 385 59.7 2.20 3.7

FW Kg 385 1.8 0.28 15.4

FY % 341 60.7 5.06 8.3

FC % 379 4.9 2.14 43.3

CS Score 385 3.8 0.57 15.1

DF % 385 20.0 40.1 200.2

aWT = weight, LG = Length, FW = Fillet weight, FY = Fillet yield, FC = Fillet Fat content, CS =

Visual condition score and DF = Deformity. Some data were not taken in the case of FY when

there was seal bight or when factory operations prevented timely data acquisition.

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Table 2a, b, c: Least squares means of fixed effects

Traits Gender Seal bite Deformity

Female Male Yes No Yes No

WT 3.000.036 2.970.038 2.980.057 3.060.023 3.060.023 3.130.057

LG 59.10.224 59.00.233 59.20.354 59.40.144 58.50.296 60.00.185

FW 1.600.024 1.580.025 1.300.038 1.850.015 1.500.032 1.650.020

FY 53.10.313 53.10.326 43.80.493 60.30.201 51.80.413 52.40.258

FC 4.230.226 4.090.232 3.260.354 4.800.143 3.610.298 4.150.184

CS 3.570.056 3.620.058 -d 3.620.040 3.380.072 3.870.036

DF 17.50.278 16.10.290 11.60.476 23.70.145 - -

aBetween gender difference was not significant for all traits (P > 0.05)

bSeal bite has significant effect (P

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Table 3a,b,c,d: Heritability (h2), and maternal and common environmental effects (c2) for

economically important traits in yellowtail kingfish

Traits Model 1 Model 2 -2(LogLModel2

LogLModel1)h2 SE h2 SE c2 SE

WT 0.26 0.13 0.17 0.16 0.05 0.09 0.21

LG 0.15 0.09 0.15 0.09 0.00 0

FW 0.31 0.15 0.24 0.19 0.04 0.09 0.15

FY 0.19 0.11 0.19 0.11 0.00 0

FC 0.94 0.21 0.41 0.26 0.29 0.20 2.3

CS 0.15 0.11 0.15 0.11 0.00 0

DF1 0.001 0.03 0.001 0.027 0.00 0

DF2 0.023 0.11

DF3 0.021 0.11

aModel 1 (standard animal model): Spawning batch (tank) was not included. bModel 2:

Spawning batch (tank) nested with dam as a random factor in addition to the additive

genetic effect. Chi-square test (i.e. -2LogL difference between models 2 and 1) with one

degree was not significant for all traits (P > 0.05).

cDF1 estimated from linear animal model. The heritability from linear model transformed to

liability scale was 0.0006.

dDF2 estimated from logit threshold sire model

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dDF3 estimated from probit threshold sire model

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Table 4: Phenotypic (above) and genetic (below the diagonal) correlations among body andfillet traits, using model 1Traits WT LG FW FY CS

WT 0.80 0.02 0.92 0.008 0.14 0.06 0.69 0.04

LG 0.57 0.27 0.83 0.03 0.07 0.05 0.45 0.05

FW 0.96 0.05 0.91 0.05 0.47 0.05 0.63 0.04

FY 0.57 0.28 -0.19 0.46 0.72 0.22 0.06 0.07

CS 0.94 0.35 0.23 0.44 0.95 0.19 0.24 0.46

Model 1: Standard animal model with only the additive genetic effect.

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Table 5: Phenotypic (rP) and genetic correlations (rG) of body and carcass traits with fillet fat

content

Traits rP rG

WT 0.52 0.07 0.92 0.13

LG 0.24 0.07 0.37 0.43

FW 0.54 0.07 0.78 0.14

FY 0.24 0.06 0.98 0.20

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Highlights

We found body fat is highly heritable in kingfish.

We report genetic parameter estimates from industrial crops of kingfish.

We report the application of new microsatellites to establish kingfish pedigrees.