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Heritability estimates for growth in the tropical abalone Haliotis asinina using microsatellites to assign parentage
Aquaculture 259 (2006) 146 – 152 www.elsevier.com/locate/aqua-online
Heritability estimates for growth in the tropical abalone Haliotis asinina using microsatellites to assign parentage Tim Lucas a,c , Michael Macbeth b , Sandie M. Degnan a , Wayne Knibb c , Bernard M. Degnan a,⁎ a
School of Integrative Biology, The University of Queensland, Brisbane QLD 4072, Australia Department of Primary Industries and Fisheries, Animal Research Institute, Brisbane QLD 4105, Australia Department of Primary Industries and Fisheries, Bribie Island Aquaculture Research Centre, Woorim QLD 4507, Australia b
c
Received 1 November 2005; received in revised form 25 April 2006; accepted 24 May 2006
Abstract The tropical abalone Haliotis asinina is a wild-caught and cultured species throughout the Indo-Pacific as well as being an emerging model species for the study of haliotids. H. asinina has the fastest recorded natural growth rate of any abalone and reaches sexual maturity within one year. As such, it is a suitable abalone species for selective breeding for commercially important traits such as rapid growth. Estimating the amount of variation in size that is attributable to heritable genetic differences can assist the development of such a selective breeding program. Here we estimated heritability for growth-related traits at 12 months of age by creating a single cohort of 84 families in a full-factorial mating design consisting of 14 sires and 6 dams. Of 500 progeny sampled, 465 were successfully assigned to their parents based on shared alleles at 5 polymorphic microsatellite loci. Using an animal model, heritability estimates were 0.48 ± 0.15 for shell length, 0.38 ± 0.13 for shell width and 0.36 ± 0.13 for weight. Genetic correlations were > 0.98 between shell parameters and weight, indicating that breeding for weight gains could be successfully achieved by selecting for shell length. © 2006 Elsevier B.V. All rights reserved. Keywords: Gastropod; Abalone; Haliotis asinina; Selective breeding; Microsatellite DNA; Growth
1. Introduction Abalone are marine molluscs that are highly valued for their edible foot muscle. Over the past decade, aquaculture of abalone has become widespread throughout the world, partially in response to over-exploitation of most wild fisheries (Gordon and Cook, 2004). While global supply has not met demand in the past, a massive recent expansion in availability of cultured abalone, particularly in Taiwan ⁎ Corresponding author. Tel.: +61 7 3365 2467; fax: +61 7 3365 1655. E-mail address: [email protected] (B.M. Degnan). 0044-8486/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.aquaculture.2006.05.039
and China, has more than compensated for the decrease in wild catches (Gordon and Cook, 2004). Increasing growth rate of abalone through selective breeding is becoming important in terms of viability of the industry and is a priority for growers (Viana, 2002). The tropical abalone Haliotis asinina is found throughout the Indo-Pacific region and is economically important both as a cultured and wild harvested resource (Singhagraiwan and Doi, 1993; Jarayabhand and Paphavasit, 1996; Capinpin et al., 1998; Gallardo and Salayo, 2003). Although H. asinina has a high proportion of edible meat and fast growth rate relative to other abalone, it is not yet commercially exploited in Australia. The aim
T. Lucas et al. / Aquaculture 259 (2006) 146–152
of this study is to provide more information about the potential of H. asinina in aquaculture and as a candidate for a selective breeding program. Current practices for spawning H. asinina in Australia involve allowing wild-caught broodstock to synchronously spawn approximately every 14 days over a 6 month spawning season (Counihan et al., 2001). This natural spawning pattern allows for specific and large-scale crosses between individual broodstock. While spawning in domesticated stock does not appear to be synchronous (Capinpin et al., 1998), targeted crosses can be undertaken by rearing conditioned broodstock together. H. asinina is a fecund abalone species, producing up to 2 million eggs fortnightly. It reaches sexual maturity within one year, as opposed to 3 or 4 years in most temperate species. These attributes, together with the availability of microsatellite DNA markers for genotyping (Selvamani et al., 2000; 2001), make H. asinina highly amenable to selective breeding research. The environment under which tropical abalone are typically cultured is different to, and less variable than, that in the wild (e.g. no predators, more food). From a genetic perspective, the ‘optimal’ combinations of alleles for survival and growth in culture are unlikely to be fixed in wild populations. This could provide the basis for significant improvement of valuable traits upon domestication (Doyle, 1983). Selective breeding aims to optimise improvements in commercially desirable traits, while avoiding the negative effects of inbreeding and correlated selection response in undesirable traits. The estimation of genetic parameters (heritabilities and genetic correlations) is important for making decisions regarding design and implementation of selective breeding programs (Mgaya, 2000). In most studies of this kind, heritability is calculated by comparing families grown in separate tanks. The infrastructure and maintenance required to conduct such experiments is expensive (Herbinger et al., 1995; de Leon et al., 1998). This can restrict the number of families in an experiment and hence limit the conclusions that can be drawn from it. An alternative is to pool individuals from different families and raise them together in a single tank, using genetic markers to determine parentage. This greatly reduces cost of rearing, although the cost of genotyping can also become a constraint. A common environment creates a more realistic commercial situation where families compete against each other, and allows for a better scrutiny of the genetic effects underlying a trait by reducing the confounding effects of environment (Herbinger et al., 1999, Dupont-Nivet et al., 2002; Vandeputte et al., 2004). Using a full-factorial design, genetic parameters can be accurately estimated, allowing the separation of additive, dominance and maternal components of variation (Vandeputte et al., 2001).
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Despite the proven track record of selective breeding as a method of increasing profit for farmers in agriculture, global research into aquaculture breeding programs has been slow (Lymbery et al., 2000; Gjedrem, 2002; Mair, 2002). Only a small number of studies have reported heritability of growth rate in haliotids (Hara and Kikuchi, 1992; Kawahara et al., 1997; Jonasson et al., 1999; Mgaya, 2000). All suggested that a selective breeding program will increase growth rate. The most comprehensive of these studies reported heritability in H. rufescens of 0.34 at 24 months (Jonasson et al., 1999). Two studies have measured actual response to selection of between 10 and 21% per generation (Hara and Kikuchi, 1992; Kawahara et al., 1997). The work presented here estimates heritability for H. asinina at 12 months age, using microsatellites to assign parentage from a single tank which housed 84 families. 2. Materials and methods 2.1. Experimental design To test variation in growth performance between families, a full-factorial mating design was implemented. Our design followed Vandeputte et al. (2004), but employed fewer parents (14 sires and 6 dams). Using this design, variation between sires and dams can be evaluated whilst accounting for interaction and dam effects. A design with more sires than dams is usually most informative, with an optimal number of dams estimated at only two for maximum statistical power (Dupont-Nivet et al., 2002). In this study six dams were used to prevent interaction effects from masking heritable genetic differences between progeny. 2.2. Spawning Wild broodstock were collected from Heron Island, Queensland and maintained at the Bribie Island Aquaculture Research Centre, where they were individually tagged and housed in a flow-through seawater system and fed to satiation with the red algae Gracillaria edulis. Eggs and sperm were procured (as described in Jebreen et al., 2000; Counihan et al., 2001) from 14 males and 6 females from one synchronous natural spawning event in February, 2003. A suspension of eggs from each female was divided into 14 equal portions and fertilised separately by sperm from each of the 14 males, before being rinsed and pooled together for larval culturing. All initial fertilisations were performed within a one minute time frame, before increasing the concentration of sperm to ensure that no viable eggs remained unfertilised. Samples were taken to count fertilisation success before mixing occurred.
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Although the exact numbers of eggs in each fertilisation were not counted, the volume of egg suspension was kept even between fertilisations. One female broodstock (dam 6) spawned a substantially lower number of eggs than the other dams. 2.3. Animal rearing Eggs from the 6 dams were hatched and reared in separate containers until 84 h post fertilisation. Approximately equal numbers of competent veliger larvae from dams 1–5 were added to all of the available larvae from dam 6, and these were mixed and settled on biofilmcovered plates in a single raceway (see Jackson et al., 2005 for details on larval rearing and settlement). A small amount of the crustose coralline alga Mastophora pacifica was used to induce settlement. Abalone were weaned onto formulated feed at approximately 2 months post settlement, and fed at least 4 times per week (Jackson et al., 2001). Artificial lighting was used for 12 h per day, and water temperature was maintained at 26 °C or higher throughout the experiment. 2.4. Sampling Epipodial clips were taken from all tagged broodstock and stored in 70% ethanol for DNA analysis. After twelve months, 500 cultured abalone were chosen in a random manner from the raceway. Wet weight, shell length and shell width were measured for each individual, and an epipodial clip was taken and stored in 70% ethanol for DNA analysis. 2.5. Parentage assignment DNA extracted from each of the broodstock and their 500 progeny was used to amplify polymorphic microsatellite loci as described in Selvamani et al. (2001). Five loci per individual were assayed — Hau3E, Hau2J, Hau10, Hau13 and Hau2K (Selvamani et al., 2000). Allele sizes were determined using a MEGABASE capillary sequencer and analysed using Fragment Profiler (Amersham Biosciences). Parentages were assigned manually by matching allele combinations of progeny with those of putative parents. Any individual whose full parentage was unclear was excluded from further analysis. 2.6. Data analyses A chi-squared test was performed to evaluate the equity of survival of progeny from sires and dams. Dam 6 was excluded from this analysis because her con-
tribution, although not quantified, was clearly much smaller than the others. Based on the observation that the majority of eggs (> 99.5%) were successfully fertilised, the assumption was made that numbers of eggs fertilised by each male were initially equal. Contributions from dams 1–5 were also assumed to be equal, based on calibration of veliger numbers prior to settlement. Data were assessed for normality from inspection of residuals, and as a result wet weight was log transformed. Variance components were initially estimated using a sire–dam model: yijk ¼ Si þ Dj þ Iij þ eijk :
ðmodel 1Þ
Observation y from sire i, dam j and animal within sire and dam k, was predicted from the random genetic effect of the ith sire (Si) plus random genetic effect of the jth dam (Dj) plus the interaction between the ith sire and the jth dam (Iij) and the residual error (eijk). Heritability was estimated from sire and dam variances using (4Vs / Vp) and (4Vd / Vp) respectively, where Vd = dam variance, Vs = sire variance, and Vp = phenotypic variance. The common environmental effects of the dams (c2) were estimated as h2d − h2s. To evaluate common environmental effect (c2) assuming equal heritability for both sires and dams, the following animal model was applied: yijk ¼ Aijk þ Cj þ Iij þ eijk :
ðmodel 2Þ
Observation y, was predicted from the random genetic effect of the ijkth animal (Aijk), the random common environment effect C of the jth dam (Cj), the interaction of Table 1 Number of progeny assigned to each of the 84 crosses, based on microsatellite genotyping Dam 1 Dam 2 Dam 3 Dam 4 Dam 5 Dam 6 Total Sire 1 Sire 2 Sire 3 Sire 4 Sire 5 Sire 6 Sire 7 Sire 8 Sire 9 Sire 10 Sire 11 Sire 12 Sire 13 Sire 14 Total
1 6 7 3 3 6 4 4 1 4 8 0 5 7⁎ 59
11⁎ 13 23⁎ 15 12⁎⁎ 5 12⁎⁎ 30⁎ 11 6 5 2 13 17⁎ 175
9 7 11 7 4 8 6 9 3 7 10 4 2⁎ 10 97
5 3 2 3 2 6 3⁎ 1 2 1 5 0 1 2 36
2⁎ 7⁎ 10⁎⁎ 6⁎ 6 10 4 7⁎⁎ 5 12 6⁎ 2 3⁎ 9⁎ 89
1 0 0 0 1 0 0 1 0 0 3⁎ 1⁎ 1 1 9
28 34 51 33 27 35 29 52 22 30 38 9 25 43
⁎ and ⁎⁎ indicate one or two individuals, respectively, in the largest 5% progeny (shell length) category.
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the ith sire and the jth dam (Iij) and the residual error (eijk). As the sire by dam interaction (Iij), and the dam environmental effects (Cj), were not significantly different from zero, their effects were taken out to produce a simplified model: yijk ¼ Aijk þ eijk d
ðmodel 3Þ
Genetic and phenotypic correlations and their standard errors were calculated between traits log wet weight, shell width and shell length using ASREML (Gilmour et al., 2002). Selection response was calculated using the formula R = ih2σP where R = response to selection, i = intensity of selection (number of standard deviations from the mean), h2 = heritability estimate and σP = phenotypic standard deviation (Falconer and Mackay, 1996). An arbitrary selection intensity value of 5% was chosen to estimate selection response for shell length, shell width and log (weight) based on heritability estimates from model 3. 3. Results 3.1. Survival Hatch success was close to 100% in all 84 families, and survival rates prior to settlement were close to 100% for all females. One female (dam 6) spawned a particularly low number of eggs and hence yielded a smaller, unquantified contribution of competent larvae than the other five dams. 465 of the 500 sampled progeny were successfully assigned to 75 of the 84 possible families. For 35 progeny, poor allelic amplification of one or more loci resulted in insufficient resolution to unambiguously assign offspring to a single parental pair. All broodstock were represented by at least 22 progeny, except for dam 6 and sire 12 (Table 1).
Table 2 Model 1 results, separating components of variance based on a sire– dam model yijk = Si + Dj + Iij + eijk
Sire variance (Vs) Dam variance (Vd) Interaction variance (VI) Error variance V(e) Phenotypic variance (Vp) h2s = 4Vs / Vp h2d = 4Vd / Vp d2 dominance = 4VI / Vp c2 dam environment = h2d − h2s
Shell width
Shell length
Log (wet weight)
0.368 0.583 0.109 3.75 4.81 ± 0.54 0.31 ± 0.17 0.49 ± 0.33 0.09 ± 0.12 0.19 ± 0.40
1.64 4.5 0.36 16.31 22.8 ± 3.5 0.29 ± 0.16 0.79 ± 0.45 0.06 ± 0.11 0.50 ± 0.52
0.039 0.089 0.007 0.518 0.65 ± 0.08 0.24 ± 0.14 0.54 ± 0.35 0.04 ± 0.11 0.31 ± 0.40
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Table 3 Model 2 analysis separating components of variance based on an animal model yijk = Aijk + Cj + Iij + eijk
Animal variance (Va) Dam environment variance (Vc) Interaction variance (VI) Error variance (Ve) Phenotypic variance (Vp) h2 heritability = Va / Vp c2 dam environmental effect d2 dominance genetic effect
Shell width
Shell Length
Log (wet weight)
1.47 0.22
6.57 2.86
0.155 0.05
0.11 3.02 4.81 ± 0.55 0.31 ± 0.17 0.05 ± 0.10 0.09 ± 0.12
0.37 13.03 22.8 ± 3.5 0.29 ± 0.16 0.13 ± 0.13 0.064 ± 0.11
0.007 0.44 0.65 ± 0.08 0.24 ± 0.14 0.08 ± 0.10 0.04 ± 0.11
Chi-squared tests indicated that survival was significantly affected by dam (chi-square =122.2, df= 4, P <0.001) and by sire (chi-square = 50.15, df= 13, P < 0.001). 3.2. Growth Growth rates of progeny were consistent through the 12 month growth period, although substantially slower than those reported in previous studies (Singhagraiwan and Doi, 1993; McNamara and Johnson, 1995; Capinpin and Corre, 1996). At 12 months age, average shell length was 18.1 mm (s.d. 4.6 mm) and average weight was 1.47 g (s.d. 1.1 g). 3.3. Heritability Using the sire–dam model (model 1, Table 2) dam variance was very high, although low sample size for dams meant that common environmental dam effect (c2) was large, and accurate dam estimates were not possible. Using a univariate animal model (Table 3), to more accurately evaluate c2, assuming equal heritability for both sires and dams, neither the c2 nor dominance (d2) effects for shell width, shell length and log (wet weight) were significantly different from zero. These components of the model were thus excluded for further analysis. Heritability estimates using the final animal model (model 3) are shown in Table 4. Shell length gave the
Table 4 Phenotypic (above diagonal) and genetic correlations (below diagonal) with heritabilities in the diagonal, using a simplified animal model ( model 3, yijk = Aijk + eijk)
Shell width Shell length Log (wet weight)
Shell width
Shell length
Log (wet weight)
0.38 ± 0.13 0.96 ± 0.02 0.99 ± 0.1
0.96 ± 0.006 0.48 ± 0.15 0.99 ± 0.01
0.97 ± 0.004 0.96 ± 0.005 0.36 ± 0.13
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highest heritability, although wet weight and shell width were also highly heritable. Phenotypic and genetic correlations under the animal model were ≥0.96 for all trait combinations. Using a selection intensity of 5%, projected improvement in one year old shell length per generation was estimated to be 25%. This equates to an increase in weight of approximately 56% based on the relationship between length and weight in this study. The largest 23 animals (~5%) were the progeny of a broad range of parents, representing 19 families including all 6 dams and 11 of the 14 sires (Table 1). 4. Discussion 4.1. Parentage assignment In this study 465 out of 500 samples were successfully assigned to both parents using just 5 microsatellite loci. The 35 unassigned progeny were the result of technical limitations (poor amplification of some loci) rather than low allelic diversity. Selvamani et al. (2001) demonstrated that Haliotis asinina larvae could be assigned readily with a small number of these polymorphic microsatellite loci. That study revealed that differential fertilization occurred when sperm from more than one male was added to eggs. Based on that observation, we employed the highly controlled breeding practice of undertaking individual crosses – 84 in total – to ensure that the genetic diversity of the original broodstock had the best chance of being represented in the progeny. 89% of the parental combinations were represented in the progeny after one year of culture. 4.2. Survival Although larval survival was very high in this study, the number of individuals retrieved after 12 months was not evenly distributed among families (Table 1). This is despite the standardisation of larvae numbers across dams 1–5 (dam 6 had low numbers from the beginning). Chi-squared tests indicated that both sire and dam affect post-larval survival, and that the effect of the dam is stronger. This may reflect non-genetic differences between dams, such as age or experience in the wild, which could potentially affect egg quality, and hence survival of progeny. Similar differential survival among families has been observed in a study on common carp (Vandeputte et al., 2004). We did not determine if the differential survival was related to differential settlement or post-settlement survival. A large component of this variation in survival may be due to differences in settlement success, which have been shown to be affected by parentage in a small scale analysis (Jackson et al., 2005).
4.3. Heritability of growth rate Three models were used to calculate heritability in this study. The sire/dam model (Table 2) estimates components of variance from both the sire and dam. Although estimates were obtained using this approach, the number of dams was very low, and hence the error was very high, particularly for common environment effect (c2). Since it was not possible to accurately estimate dam components of variation using the sire/dam analysis, we used an animal model which assumes equal heritability for parents regardless of gender. The animal model is considered more accurate because it evaluates additive genetic variance from both maternal and paternal sources simultaneously. Neither common environmental effects (c2) nor dominance genetic effects were significantly different from zero, and were therefore not included in the final animal model (model 3). The higher chi-square values for survival and variance for growth rate compared with sires indicate that there may be some environmental or genetic effect associated with dams, although our experimental design did not allow us to accurately explore these factors with only six dam replicates. Although conditions in this study were designed to reflect a commercial situation, slower than expected growth rates probably reflect sub-optimal rearing conditions. This may have inflated the heritability estimates if competition for resources occurred (Vandeputte et al., 2004), although sub-optimal rearing conditions may also decrease variance between families by increasing variation within families (Falconer and Mackay, 1996). A larger-scale study investigating realised heritability in a more optimal commercial environment is therefore recommended. Under present conditions, heritability was highest for shell length (h2 = 0. 48 ± 0.15). This is higher than previously reported estimates for red abalone H. rufescens of 0.34 (Jonasson et al., 1999). A selective breeding program would mainly be interested in weight improvement (h2 = 0.36 ± 0.13), which is more closely related to market value than shell length. Because of the high genetic correlation between the two traits (Table 4), it is likely that improvement in weight could be made by selecting for shell length. Heritability estimates in this study are a useful guide for industry. Nonetheless, it is important to acknowledge that these estimates have large error values associated with them, and that heritability will change according to both the broodstock population and the growing environment (Falconer and Mackay, 1996). Predicted gains of 25% per generation (based on an animal model and 5% selection intensity) indicate that large gains in size
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might be possible, initially. Our predicted gains are similar to those measured by Hara and Kikuchi (1992) who found a 63% increase in shell length in a 3rd generation of selection in the pacific abalone H. discus hannai. It is likely, though, that gains would be reduced in subsequent generations of high intensity selection, as gene frequencies change and phenotypic variance is reduced (Falconer and Mackay, 1996). This study suggests that mass selection would be a viable option to consider. In our sample, the largest 23 animals (5%) represented 19 families including all 6 dams and 11 of the 14 males (Table 1). Maximising the diversity of families in this ‘selected’ group reduces the likelihood of inbreeding becoming a problem. To ensure family diversity is maximised, individual crosses need to be undertaken to minimise differential fertilization (Selvamani et al., 2001). The present study suggests that microsatellite technology can facilitate the recovery of families each generation. More trials will be required to estimate the impact of high intensity selection using mass selection on rates of inbreeding. Acknowledgements The authors thank the staff of Bribie Island Aquaculture Research Centre for their assistance. The research was supported by an Australian Research Council grant to B.M.D. References Capinpin, E.C., Corre, K.G., 1996. Growth rate of the Philippine abalone, Haliotis asinina fed an artificial diet and macroalgae. Aquaculture 144, 81–89. Capinpin, E.C., Encena, V.C., Bayona, N.C., 1998. Studies on the reproductive biology of the Donkey's ear abalone, Haliotis asinina Linne. Aquaculture 166, 141–150. Counihan, R.T., McNamara, D.C., Souter, D.C., Jebreen, E.J., Preston, N.P., Johnson, C.R., Degnan, B.M., 2001. Pattern, synchrony and predictability of spawning of the tropical abalone Haliotis asinina from Heron Reef, Australia. Marine Ecology. Progress Series 213, 193–202. de Leon, F.J.G., Canonne, M., Quillet, E., Bonhomme, F., Chatain, B., 1998. The application of microsatellite markers to breeding programmes in the sea bass, Dicentrarchus labrax. Aquaculture 159, 303–316. Doyle, R.W., 1983. An approach to the quantitative analysis of domestication selection in aquaculture. Aquaculture 33, 167–185. Dupont-Nivet, M., Vandeputte, M., Chevassus, B., 2002. Optimization of factorial mating designs for inference on heritability in fish species. Aquaculture 204, 361–370. Falconer, D.S., Mackay, T.F.C., 1996. Introduction to quantitative genetics. Longman Group. Gallardo, W.G., Salayo, N.D., 2003. Abalone culture—a new business opportunity. SEAFDEC Asian Aquaculture 1–28.
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Heritability estimates for growth in the tropical abalone Haliotis asinina using microsatellites to assign parentage Tim Lucas a,c , Michael Macbeth b , Sandie M. Degnan a , Wayne Knibb c , Bernard M. Degnan a,⁎ a
School of Integrative Biology, The University of Queensland, Brisbane QLD 4072, Australia Department of Primary Industries and Fisheries, Animal Research Institute, Brisbane QLD 4105, Australia Department of Primary Industries and Fisheries, Bribie Island Aquaculture Research Centre, Woorim QLD 4507, Australia b
c
Received 1 November 2005; received in revised form 25 April 2006; accepted 24 May 2006
Abstract The tropical abalone Haliotis asinina is a wild-caught and cultured species throughout the Indo-Pacific as well as being an emerging model species for the study of haliotids. H. asinina has the fastest recorded natural growth rate of any abalone and reaches sexual maturity within one year. As such, it is a suitable abalone species for selective breeding for commercially important traits such as rapid growth. Estimating the amount of variation in size that is attributable to heritable genetic differences can assist the development of such a selective breeding program. Here we estimated heritability for growth-related traits at 12 months of age by creating a single cohort of 84 families in a full-factorial mating design consisting of 14 sires and 6 dams. Of 500 progeny sampled, 465 were successfully assigned to their parents based on shared alleles at 5 polymorphic microsatellite loci. Using an animal model, heritability estimates were 0.48 ± 0.15 for shell length, 0.38 ± 0.13 for shell width and 0.36 ± 0.13 for weight. Genetic correlations were > 0.98 between shell parameters and weight, indicating that breeding for weight gains could be successfully achieved by selecting for shell length. © 2006 Elsevier B.V. All rights reserved. Keywords: Gastropod; Abalone; Haliotis asinina; Selective breeding; Microsatellite DNA; Growth
1. Introduction Abalone are marine molluscs that are highly valued for their edible foot muscle. Over the past decade, aquaculture of abalone has become widespread throughout the world, partially in response to over-exploitation of most wild fisheries (Gordon and Cook, 2004). While global supply has not met demand in the past, a massive recent expansion in availability of cultured abalone, particularly in Taiwan ⁎ Corresponding author. Tel.: +61 7 3365 2467; fax: +61 7 3365 1655. E-mail address: [email protected] (B.M. Degnan). 0044-8486/$ - see front matter © 2006 Elsevier B.V. All rights reserved. doi:10.1016/j.aquaculture.2006.05.039
and China, has more than compensated for the decrease in wild catches (Gordon and Cook, 2004). Increasing growth rate of abalone through selective breeding is becoming important in terms of viability of the industry and is a priority for growers (Viana, 2002). The tropical abalone Haliotis asinina is found throughout the Indo-Pacific region and is economically important both as a cultured and wild harvested resource (Singhagraiwan and Doi, 1993; Jarayabhand and Paphavasit, 1996; Capinpin et al., 1998; Gallardo and Salayo, 2003). Although H. asinina has a high proportion of edible meat and fast growth rate relative to other abalone, it is not yet commercially exploited in Australia. The aim
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of this study is to provide more information about the potential of H. asinina in aquaculture and as a candidate for a selective breeding program. Current practices for spawning H. asinina in Australia involve allowing wild-caught broodstock to synchronously spawn approximately every 14 days over a 6 month spawning season (Counihan et al., 2001). This natural spawning pattern allows for specific and large-scale crosses between individual broodstock. While spawning in domesticated stock does not appear to be synchronous (Capinpin et al., 1998), targeted crosses can be undertaken by rearing conditioned broodstock together. H. asinina is a fecund abalone species, producing up to 2 million eggs fortnightly. It reaches sexual maturity within one year, as opposed to 3 or 4 years in most temperate species. These attributes, together with the availability of microsatellite DNA markers for genotyping (Selvamani et al., 2000; 2001), make H. asinina highly amenable to selective breeding research. The environment under which tropical abalone are typically cultured is different to, and less variable than, that in the wild (e.g. no predators, more food). From a genetic perspective, the ‘optimal’ combinations of alleles for survival and growth in culture are unlikely to be fixed in wild populations. This could provide the basis for significant improvement of valuable traits upon domestication (Doyle, 1983). Selective breeding aims to optimise improvements in commercially desirable traits, while avoiding the negative effects of inbreeding and correlated selection response in undesirable traits. The estimation of genetic parameters (heritabilities and genetic correlations) is important for making decisions regarding design and implementation of selective breeding programs (Mgaya, 2000). In most studies of this kind, heritability is calculated by comparing families grown in separate tanks. The infrastructure and maintenance required to conduct such experiments is expensive (Herbinger et al., 1995; de Leon et al., 1998). This can restrict the number of families in an experiment and hence limit the conclusions that can be drawn from it. An alternative is to pool individuals from different families and raise them together in a single tank, using genetic markers to determine parentage. This greatly reduces cost of rearing, although the cost of genotyping can also become a constraint. A common environment creates a more realistic commercial situation where families compete against each other, and allows for a better scrutiny of the genetic effects underlying a trait by reducing the confounding effects of environment (Herbinger et al., 1999, Dupont-Nivet et al., 2002; Vandeputte et al., 2004). Using a full-factorial design, genetic parameters can be accurately estimated, allowing the separation of additive, dominance and maternal components of variation (Vandeputte et al., 2001).
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Despite the proven track record of selective breeding as a method of increasing profit for farmers in agriculture, global research into aquaculture breeding programs has been slow (Lymbery et al., 2000; Gjedrem, 2002; Mair, 2002). Only a small number of studies have reported heritability of growth rate in haliotids (Hara and Kikuchi, 1992; Kawahara et al., 1997; Jonasson et al., 1999; Mgaya, 2000). All suggested that a selective breeding program will increase growth rate. The most comprehensive of these studies reported heritability in H. rufescens of 0.34 at 24 months (Jonasson et al., 1999). Two studies have measured actual response to selection of between 10 and 21% per generation (Hara and Kikuchi, 1992; Kawahara et al., 1997). The work presented here estimates heritability for H. asinina at 12 months age, using microsatellites to assign parentage from a single tank which housed 84 families. 2. Materials and methods 2.1. Experimental design To test variation in growth performance between families, a full-factorial mating design was implemented. Our design followed Vandeputte et al. (2004), but employed fewer parents (14 sires and 6 dams). Using this design, variation between sires and dams can be evaluated whilst accounting for interaction and dam effects. A design with more sires than dams is usually most informative, with an optimal number of dams estimated at only two for maximum statistical power (Dupont-Nivet et al., 2002). In this study six dams were used to prevent interaction effects from masking heritable genetic differences between progeny. 2.2. Spawning Wild broodstock were collected from Heron Island, Queensland and maintained at the Bribie Island Aquaculture Research Centre, where they were individually tagged and housed in a flow-through seawater system and fed to satiation with the red algae Gracillaria edulis. Eggs and sperm were procured (as described in Jebreen et al., 2000; Counihan et al., 2001) from 14 males and 6 females from one synchronous natural spawning event in February, 2003. A suspension of eggs from each female was divided into 14 equal portions and fertilised separately by sperm from each of the 14 males, before being rinsed and pooled together for larval culturing. All initial fertilisations were performed within a one minute time frame, before increasing the concentration of sperm to ensure that no viable eggs remained unfertilised. Samples were taken to count fertilisation success before mixing occurred.
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Although the exact numbers of eggs in each fertilisation were not counted, the volume of egg suspension was kept even between fertilisations. One female broodstock (dam 6) spawned a substantially lower number of eggs than the other dams. 2.3. Animal rearing Eggs from the 6 dams were hatched and reared in separate containers until 84 h post fertilisation. Approximately equal numbers of competent veliger larvae from dams 1–5 were added to all of the available larvae from dam 6, and these were mixed and settled on biofilmcovered plates in a single raceway (see Jackson et al., 2005 for details on larval rearing and settlement). A small amount of the crustose coralline alga Mastophora pacifica was used to induce settlement. Abalone were weaned onto formulated feed at approximately 2 months post settlement, and fed at least 4 times per week (Jackson et al., 2001). Artificial lighting was used for 12 h per day, and water temperature was maintained at 26 °C or higher throughout the experiment. 2.4. Sampling Epipodial clips were taken from all tagged broodstock and stored in 70% ethanol for DNA analysis. After twelve months, 500 cultured abalone were chosen in a random manner from the raceway. Wet weight, shell length and shell width were measured for each individual, and an epipodial clip was taken and stored in 70% ethanol for DNA analysis. 2.5. Parentage assignment DNA extracted from each of the broodstock and their 500 progeny was used to amplify polymorphic microsatellite loci as described in Selvamani et al. (2001). Five loci per individual were assayed — Hau3E, Hau2J, Hau10, Hau13 and Hau2K (Selvamani et al., 2000). Allele sizes were determined using a MEGABASE capillary sequencer and analysed using Fragment Profiler (Amersham Biosciences). Parentages were assigned manually by matching allele combinations of progeny with those of putative parents. Any individual whose full parentage was unclear was excluded from further analysis. 2.6. Data analyses A chi-squared test was performed to evaluate the equity of survival of progeny from sires and dams. Dam 6 was excluded from this analysis because her con-
tribution, although not quantified, was clearly much smaller than the others. Based on the observation that the majority of eggs (> 99.5%) were successfully fertilised, the assumption was made that numbers of eggs fertilised by each male were initially equal. Contributions from dams 1–5 were also assumed to be equal, based on calibration of veliger numbers prior to settlement. Data were assessed for normality from inspection of residuals, and as a result wet weight was log transformed. Variance components were initially estimated using a sire–dam model: yijk ¼ Si þ Dj þ Iij þ eijk :
ðmodel 1Þ
Observation y from sire i, dam j and animal within sire and dam k, was predicted from the random genetic effect of the ith sire (Si) plus random genetic effect of the jth dam (Dj) plus the interaction between the ith sire and the jth dam (Iij) and the residual error (eijk). Heritability was estimated from sire and dam variances using (4Vs / Vp) and (4Vd / Vp) respectively, where Vd = dam variance, Vs = sire variance, and Vp = phenotypic variance. The common environmental effects of the dams (c2) were estimated as h2d − h2s. To evaluate common environmental effect (c2) assuming equal heritability for both sires and dams, the following animal model was applied: yijk ¼ Aijk þ Cj þ Iij þ eijk :
ðmodel 2Þ
Observation y, was predicted from the random genetic effect of the ijkth animal (Aijk), the random common environment effect C of the jth dam (Cj), the interaction of Table 1 Number of progeny assigned to each of the 84 crosses, based on microsatellite genotyping Dam 1 Dam 2 Dam 3 Dam 4 Dam 5 Dam 6 Total Sire 1 Sire 2 Sire 3 Sire 4 Sire 5 Sire 6 Sire 7 Sire 8 Sire 9 Sire 10 Sire 11 Sire 12 Sire 13 Sire 14 Total
1 6 7 3 3 6 4 4 1 4 8 0 5 7⁎ 59
11⁎ 13 23⁎ 15 12⁎⁎ 5 12⁎⁎ 30⁎ 11 6 5 2 13 17⁎ 175
9 7 11 7 4 8 6 9 3 7 10 4 2⁎ 10 97
5 3 2 3 2 6 3⁎ 1 2 1 5 0 1 2 36
2⁎ 7⁎ 10⁎⁎ 6⁎ 6 10 4 7⁎⁎ 5 12 6⁎ 2 3⁎ 9⁎ 89
1 0 0 0 1 0 0 1 0 0 3⁎ 1⁎ 1 1 9
28 34 51 33 27 35 29 52 22 30 38 9 25 43
⁎ and ⁎⁎ indicate one or two individuals, respectively, in the largest 5% progeny (shell length) category.
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the ith sire and the jth dam (Iij) and the residual error (eijk). As the sire by dam interaction (Iij), and the dam environmental effects (Cj), were not significantly different from zero, their effects were taken out to produce a simplified model: yijk ¼ Aijk þ eijk d
ðmodel 3Þ
Genetic and phenotypic correlations and their standard errors were calculated between traits log wet weight, shell width and shell length using ASREML (Gilmour et al., 2002). Selection response was calculated using the formula R = ih2σP where R = response to selection, i = intensity of selection (number of standard deviations from the mean), h2 = heritability estimate and σP = phenotypic standard deviation (Falconer and Mackay, 1996). An arbitrary selection intensity value of 5% was chosen to estimate selection response for shell length, shell width and log (weight) based on heritability estimates from model 3. 3. Results 3.1. Survival Hatch success was close to 100% in all 84 families, and survival rates prior to settlement were close to 100% for all females. One female (dam 6) spawned a particularly low number of eggs and hence yielded a smaller, unquantified contribution of competent larvae than the other five dams. 465 of the 500 sampled progeny were successfully assigned to 75 of the 84 possible families. For 35 progeny, poor allelic amplification of one or more loci resulted in insufficient resolution to unambiguously assign offspring to a single parental pair. All broodstock were represented by at least 22 progeny, except for dam 6 and sire 12 (Table 1).
Table 2 Model 1 results, separating components of variance based on a sire– dam model yijk = Si + Dj + Iij + eijk
Sire variance (Vs) Dam variance (Vd) Interaction variance (VI) Error variance V(e) Phenotypic variance (Vp) h2s = 4Vs / Vp h2d = 4Vd / Vp d2 dominance = 4VI / Vp c2 dam environment = h2d − h2s
Shell width
Shell length
Log (wet weight)
0.368 0.583 0.109 3.75 4.81 ± 0.54 0.31 ± 0.17 0.49 ± 0.33 0.09 ± 0.12 0.19 ± 0.40
1.64 4.5 0.36 16.31 22.8 ± 3.5 0.29 ± 0.16 0.79 ± 0.45 0.06 ± 0.11 0.50 ± 0.52
0.039 0.089 0.007 0.518 0.65 ± 0.08 0.24 ± 0.14 0.54 ± 0.35 0.04 ± 0.11 0.31 ± 0.40
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Table 3 Model 2 analysis separating components of variance based on an animal model yijk = Aijk + Cj + Iij + eijk
Animal variance (Va) Dam environment variance (Vc) Interaction variance (VI) Error variance (Ve) Phenotypic variance (Vp) h2 heritability = Va / Vp c2 dam environmental effect d2 dominance genetic effect
Shell width
Shell Length
Log (wet weight)
1.47 0.22
6.57 2.86
0.155 0.05
0.11 3.02 4.81 ± 0.55 0.31 ± 0.17 0.05 ± 0.10 0.09 ± 0.12
0.37 13.03 22.8 ± 3.5 0.29 ± 0.16 0.13 ± 0.13 0.064 ± 0.11
0.007 0.44 0.65 ± 0.08 0.24 ± 0.14 0.08 ± 0.10 0.04 ± 0.11
Chi-squared tests indicated that survival was significantly affected by dam (chi-square =122.2, df= 4, P <0.001) and by sire (chi-square = 50.15, df= 13, P < 0.001). 3.2. Growth Growth rates of progeny were consistent through the 12 month growth period, although substantially slower than those reported in previous studies (Singhagraiwan and Doi, 1993; McNamara and Johnson, 1995; Capinpin and Corre, 1996). At 12 months age, average shell length was 18.1 mm (s.d. 4.6 mm) and average weight was 1.47 g (s.d. 1.1 g). 3.3. Heritability Using the sire–dam model (model 1, Table 2) dam variance was very high, although low sample size for dams meant that common environmental dam effect (c2) was large, and accurate dam estimates were not possible. Using a univariate animal model (Table 3), to more accurately evaluate c2, assuming equal heritability for both sires and dams, neither the c2 nor dominance (d2) effects for shell width, shell length and log (wet weight) were significantly different from zero. These components of the model were thus excluded for further analysis. Heritability estimates using the final animal model (model 3) are shown in Table 4. Shell length gave the
Table 4 Phenotypic (above diagonal) and genetic correlations (below diagonal) with heritabilities in the diagonal, using a simplified animal model ( model 3, yijk = Aijk + eijk)
Shell width Shell length Log (wet weight)
Shell width
Shell length
Log (wet weight)
0.38 ± 0.13 0.96 ± 0.02 0.99 ± 0.1
0.96 ± 0.006 0.48 ± 0.15 0.99 ± 0.01
0.97 ± 0.004 0.96 ± 0.005 0.36 ± 0.13
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highest heritability, although wet weight and shell width were also highly heritable. Phenotypic and genetic correlations under the animal model were ≥0.96 for all trait combinations. Using a selection intensity of 5%, projected improvement in one year old shell length per generation was estimated to be 25%. This equates to an increase in weight of approximately 56% based on the relationship between length and weight in this study. The largest 23 animals (~5%) were the progeny of a broad range of parents, representing 19 families including all 6 dams and 11 of the 14 sires (Table 1). 4. Discussion 4.1. Parentage assignment In this study 465 out of 500 samples were successfully assigned to both parents using just 5 microsatellite loci. The 35 unassigned progeny were the result of technical limitations (poor amplification of some loci) rather than low allelic diversity. Selvamani et al. (2001) demonstrated that Haliotis asinina larvae could be assigned readily with a small number of these polymorphic microsatellite loci. That study revealed that differential fertilization occurred when sperm from more than one male was added to eggs. Based on that observation, we employed the highly controlled breeding practice of undertaking individual crosses – 84 in total – to ensure that the genetic diversity of the original broodstock had the best chance of being represented in the progeny. 89% of the parental combinations were represented in the progeny after one year of culture. 4.2. Survival Although larval survival was very high in this study, the number of individuals retrieved after 12 months was not evenly distributed among families (Table 1). This is despite the standardisation of larvae numbers across dams 1–5 (dam 6 had low numbers from the beginning). Chi-squared tests indicated that both sire and dam affect post-larval survival, and that the effect of the dam is stronger. This may reflect non-genetic differences between dams, such as age or experience in the wild, which could potentially affect egg quality, and hence survival of progeny. Similar differential survival among families has been observed in a study on common carp (Vandeputte et al., 2004). We did not determine if the differential survival was related to differential settlement or post-settlement survival. A large component of this variation in survival may be due to differences in settlement success, which have been shown to be affected by parentage in a small scale analysis (Jackson et al., 2005).
4.3. Heritability of growth rate Three models were used to calculate heritability in this study. The sire/dam model (Table 2) estimates components of variance from both the sire and dam. Although estimates were obtained using this approach, the number of dams was very low, and hence the error was very high, particularly for common environment effect (c2). Since it was not possible to accurately estimate dam components of variation using the sire/dam analysis, we used an animal model which assumes equal heritability for parents regardless of gender. The animal model is considered more accurate because it evaluates additive genetic variance from both maternal and paternal sources simultaneously. Neither common environmental effects (c2) nor dominance genetic effects were significantly different from zero, and were therefore not included in the final animal model (model 3). The higher chi-square values for survival and variance for growth rate compared with sires indicate that there may be some environmental or genetic effect associated with dams, although our experimental design did not allow us to accurately explore these factors with only six dam replicates. Although conditions in this study were designed to reflect a commercial situation, slower than expected growth rates probably reflect sub-optimal rearing conditions. This may have inflated the heritability estimates if competition for resources occurred (Vandeputte et al., 2004), although sub-optimal rearing conditions may also decrease variance between families by increasing variation within families (Falconer and Mackay, 1996). A larger-scale study investigating realised heritability in a more optimal commercial environment is therefore recommended. Under present conditions, heritability was highest for shell length (h2 = 0. 48 ± 0.15). This is higher than previously reported estimates for red abalone H. rufescens of 0.34 (Jonasson et al., 1999). A selective breeding program would mainly be interested in weight improvement (h2 = 0.36 ± 0.13), which is more closely related to market value than shell length. Because of the high genetic correlation between the two traits (Table 4), it is likely that improvement in weight could be made by selecting for shell length. Heritability estimates in this study are a useful guide for industry. Nonetheless, it is important to acknowledge that these estimates have large error values associated with them, and that heritability will change according to both the broodstock population and the growing environment (Falconer and Mackay, 1996). Predicted gains of 25% per generation (based on an animal model and 5% selection intensity) indicate that large gains in size
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might be possible, initially. Our predicted gains are similar to those measured by Hara and Kikuchi (1992) who found a 63% increase in shell length in a 3rd generation of selection in the pacific abalone H. discus hannai. It is likely, though, that gains would be reduced in subsequent generations of high intensity selection, as gene frequencies change and phenotypic variance is reduced (Falconer and Mackay, 1996). This study suggests that mass selection would be a viable option to consider. In our sample, the largest 23 animals (5%) represented 19 families including all 6 dams and 11 of the 14 males (Table 1). Maximising the diversity of families in this ‘selected’ group reduces the likelihood of inbreeding becoming a problem. To ensure family diversity is maximised, individual crosses need to be undertaken to minimise differential fertilization (Selvamani et al., 2001). The present study suggests that microsatellite technology can facilitate the recovery of families each generation. More trials will be required to estimate the impact of high intensity selection using mass selection on rates of inbreeding. Acknowledgements The authors thank the staff of Bribie Island Aquaculture Research Centre for their assistance. The research was supported by an Australian Research Council grant to B.M.D. References Capinpin, E.C., Corre, K.G., 1996. Growth rate of the Philippine abalone, Haliotis asinina fed an artificial diet and macroalgae. Aquaculture 144, 81–89. Capinpin, E.C., Encena, V.C., Bayona, N.C., 1998. Studies on the reproductive biology of the Donkey's ear abalone, Haliotis asinina Linne. Aquaculture 166, 141–150. Counihan, R.T., McNamara, D.C., Souter, D.C., Jebreen, E.J., Preston, N.P., Johnson, C.R., Degnan, B.M., 2001. Pattern, synchrony and predictability of spawning of the tropical abalone Haliotis asinina from Heron Reef, Australia. Marine Ecology. Progress Series 213, 193–202. de Leon, F.J.G., Canonne, M., Quillet, E., Bonhomme, F., Chatain, B., 1998. The application of microsatellite markers to breeding programmes in the sea bass, Dicentrarchus labrax. Aquaculture 159, 303–316. Doyle, R.W., 1983. An approach to the quantitative analysis of domestication selection in aquaculture. Aquaculture 33, 167–185. Dupont-Nivet, M., Vandeputte, M., Chevassus, B., 2002. Optimization of factorial mating designs for inference on heritability in fish species. Aquaculture 204, 361–370. Falconer, D.S., Mackay, T.F.C., 1996. Introduction to quantitative genetics. Longman Group. Gallardo, W.G., Salayo, N.D., 2003. Abalone culture—a new business opportunity. SEAFDEC Asian Aquaculture 1–28.
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