Elsevier

Aquaculture

Volume 265, Issues 1–4, 1 May 2007, Pages 61-69
Aquaculture

Optimisation of marker assisted selection for abalone breeding programs

https://doi.org/10.1016/j.aquaculture.2007.02.016Get rights and content

Abstract

A computer simulation model was used to optimise strategies for marker assisted selection with best linear unbiased prediction (MBLUP) for quantitative traits in a selective breeding program for the southern Australian abalone industry. The simulation model assumed five marker loci associated with genes affecting growth rate explained 50% of the genetic variance for this trait. Use of markers associated with improved disease resistance was also modelled. Parameters and simulation scheme used are described. The simulations accounted for the possibility of early induction of reproductive condition to reduce generation intervals and for selective genotyping. Affects on profitability were calculated using estimates from the industry of income and expenses for a typical farm and accounting for the costs associated with marker testing. While the extra genetic gains from MAS were in the order of 15%, if the cost of genotyping each marker per animal was $10 AUS, then MBLUP for growth was only profitable when 75 progeny per family were genotyped. Early selection (halving the generation interval) was advantageous for MBLUP and BLUP provided the number of progeny per family was greater 5, as the advantage of two generations of selection more than compensated for the decreased accuracy of selection for early growth. However accurate estimates of parameters, such as the genetic correlation between early growth rate and growth and age at first spawning, would need to be obtained before considering implementing this strategy. The advantage of MBLUP was greater for disease resistance than for growth rate. Selective genotyping, which would greatly reduced genotyping costs of implementing MBLUP to improve disease resistance, did not greatly reduce the additional response achieved with marker assisted selection.

Introduction

Growth rate, or size at harvest, is a key determinant of profitability for the aquaculture of abalone in Australia (Elliot, 2000, Lymbery, 2000). In other aquaculture species, large improvements in growth rate have been achieved through selective breeding (eg. Bentsen et al., 1998, Argue et al., 2002, Gjerde et al., 2004, Fjalestad et al., 2005). Moderate to high estimates of 0.30 to 0.36 for the heritability of growth rate in two species of abalone (Jonasson et al., 1999, Lucas et al., in press) suggest similar gains could be achieved in abalone. Studies in livestock species indicate the rate of genetic gain could be accelerated even further through the use of marker assisted selection (MAS), if markers surrounding quantitative trait loci (QTL) affecting growth rate were available (eg. Meuwissen and Goddard, 1996, Spelman and Garrick, 1998, Hayes and Goddard, 2003). The first linkage map for Haliotis rubra (black lip abalone) is now published, consisting of 122 microsatellite markers in 18 linkage groups, spanning a total sex averaged map length of 694 cM, or one approximately one marker every 5.5 cM (Baranski et al., 2006). Further, a recent genome scan in four full sib families of more than 1000 individuals each identified QTL affecting growth rate in H. rubra (Baranski, 2006). The QTL were estimated to explain approximately 50% of the genetic variance for growth rate, when a correction was made for the upward bias of estimating the QTL effect from the genome scan. These tools will form the basis for applying MAS in Abalone. In this paper, we investigate various MAS strategies for exploiting this information in order to maximise the rate of genetic gain from an abalone breeding program.

Family based breeding programs, where a large number of offspring from each pair of mated broodstock are identified and maintained, have a number of advantages over mass selection based programs for aquaculture species, including better control of inbreeding and potential to improve a wider variety of traits, such as disease resistance and meat quality, through sib selection. When MAS is implemented, an additional advantage of a family line based breeding program is that large family sizes allow accurate estimates of the effects of QTL alleles segregating within each family. This is important because the accuracy of estimating the QTL effects determines the response to MAS (Goddard and Hayes, 2002).

While at present we have only detected QTL for growth in abalone, future genome scans should have QTL for disease resistance traits, given the importance of disease resistance in other aquaculture breeding programs (eg. Fjalestad et al., 1993, Nell and Hand, 2003, Argue et al., 2002, Henryon et al., 2005). Meuwissen and Goddard (1996) suggested that increases in genetic gain from marker assisted selection (MAS) will be greatest for traits that can not be measured on a breeding candidate (eg. disease resistance or meat quality traits which require infection or death of the animal for measurement and for which records must be collected from relatives of the selection candidates). While Meuwissen and Goddard (1996) investigated the value of MAS in livestock populations, there have been no reports investigating the use of MAS in aquaculture family based breeding programs. A key difference here is the possibility to exploit the much larger full-sibling family sizes possible with aquaculture species.

In this study, we have simulated a family based abalone breeding program similar to the one underway in Australian abalone, and where markers linked to QTL explaining 50% of the genetic variance in growth are available. As disease resistance is likely to gain increasing importance in the future, we have investigated a situation where a genome scan with similar power to the one described above was performed for disease resistance, so that QTL explaining 50% of the genetic variance for disease resistance were available. Detecting QTL conferring resistance to abalone diseases will depend on the ability to perform challenge tests for such diseases, this has been done for the abalone pathogens Vibrio parahaemolyticus (Cheng et al., 2004) and the Withering syndrome causing Rickettsiales-like prokaryote (WS-RLP) (Braid et al., 2005). The aim was to evaluate the increase in genetic gain from using MAS for different traits in the breeding program, and to determine the optimum number of individuals to genotype and phenotype to maximise the rate of genetic gain. The MAS modelled here exploited only within family linkage disequilibrium between QTL and markers, the extent of which is determined only by the distance of the QTL from the marker. MAS assisted selection exploiting across population linkage disequilbrium would require a denser marker map than is currently available.

The use of markers linked to QTL can provide accurate estimation of breeding values for animals prior to accurate phenotypic information being available. Further, there has been considerable research into maturation and induced spawning of abalone (eg. Grubert and Ritar, 2004, Grubert and Ritar, 2005, Litaay and De Silva, 2000, Moss et al., 1995, Shepherd, 1976, Uki and Kikuchi, 1984). Accordingly, we have also simulated schemes with early selection and more rapid turnover of generations.

Finally, the major cost in implementing a MAS scheme for disease resistance will be genotyping the full-sibs of the selection candidates, the individuals challenged for disease, in order to estimate the effect of the QTL alleles within each family. We evaluate a selective genotyping strategy which greatly reduces the cost of implementing MAS for disease resistance.

Section snippets

Simulation for growth rate

We first weighed 1996 farmed H. rubra of 2.5 years of age. This gave a mean of 109 g and a phenotypic standard deviation of 25 g (see Baranski, 2006 for a description). Given a value of 0.31 for heritability of growth, this gives a total additive genetic variance of 199 g2. In our simulations, we considered 99.5 g2 of this to be contributed by 5 QTL and the rest by a polygenic effect. Fig. 1 depicts the simulation scheme. We simulated growth rate as a quantitative trait in a population of

Results

For all traits, the gains from both BLUP and MBLUP increased as the number of progeny per family increased, reflecting increased selection intensity. When the trait simulated was growth rate, the advantage of MBLUP over BLUP increased as the number of progeny per family increased (Fig. 2 and Table 1). The increase from MAS is due to the increased accuracy of calculating breeding values, which in turn is proportional to the amount of genetic variance explained by the QTL and the accuracy of

Discussion

When growth rate was the trait simulated, the advantage of marker assisted section over non-marker assisted selection increased as more progeny per family were genotyped. This was true regardless of age at selection.

Two generations of selection on early growth rate resulted in greater genetic gains than one generation of selection on growth rate when the number of progeny per family was more than 5. However the validity of this result depends on the heritabilities of growth rate and early

Acknowledgments

This research was conducted while the authors were recipients of the grant from the Fisheries Research and Development Corporation (project 2002/202) and Fisheries Victoria. Thanks to Shane McLinden of the Australian Abalone Growers Association for providing economic figures from the industry.

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