The optimal contribution selection method and the simulated annealing algorithm were used to maximize the genetic gain and reduce inbreeding in fish breeding populations. This study considered the following mating designs: full factorial (3 x 3); full factorial (6 x 6) and nested (6 males x 18 females). A quantitative trait based on a strictly additive and polygenic model was simulated. Two levels for the number of genotyped offspring (360 or 720) and three levels of heritability (0.1; 0.3; 0.5) were assumed. The best results in terms of DF and DG were obtained with the full factorial design (6 x 6) and considering a trait with a high heritability. The optimal family size was found at 20 fish per mating

D'Agaro, E.

Haley, Chris

Lanari, D.

Woolliams, John

English

D. Lanari

C. S. Haley

J. A. Woolliams

E. D'Agaro

Directory of Open Access Journals

Italian Journal of Animal Science

Optimizing mating schemes in fish breeding

Crossref

The optimal contribution selection method and the simulated annealing algorithm were used to maximize the genetic gain and reduce inbreeding in fish breeding populations. This study considered the following mating designs: full factorial (3 x 3); full factorial (6 x 6) and nested (6 males x 18 females). A quantitative trait based on a strictly additive and polygenic model was simulated. Two levels for the number of genotyped offspring (360 or 720) and three levels of heritability (0.1; 0.3; 0.5) were assumed. The best results in terms of Delta F and Delta G were obtained with the full factorial design (6 x 6) and considering a trait with a high heritability. The optimal family size was found at 20 fish per mating

Edinburgh Research Explorer

     Edinburgh Research Explorer                                      Optimizing mating schemes in fish breedingCitation for published version:D'Agaro, E, Woolliams, J, Haley, C & Lanari, D 2007, 'Optimizing mating schemes in fish breeding' ItalianJournal of Animal Science, ???volume??? 6, ???pages??? 795-796., 10.4081/ijas.2007.1s.795Digital Object Identifier (DOI):10.4081/ijas.2007.1s.795Link:Link to publication record in Edinburgh Research ExplorerDocument Version:Publisher final version (usually the publisher pdf)Published In:Italian Journal of Animal SciencePublisher Rights Statement:Copyright 2007 PAGEPress®General rightsCopyright for the publications made accessible via the Edinburgh Research Explorer is retained by the author(s)and / or other copyright owners and it is a condition of accessing these publications that users recognise andabide by the legal requirements associated with these rights.Take down policyThe University of Edinburgh has made every reasonable effort to ensure that Edinburgh Research Explorercontent complies with UK legislation. If you believe that the public display of this file breaches copyright pleasecontact openaccess@ed.ac.uk providing details, and we will remove access to the work immediately andinvestigate your claim.Download date: 20. Feb. 2015ITAL.J.ANIM.SCI. VOL. 6 (SUPPL. 1), 795-796, 2007 795Table 1. Parameters of the closed nucleus scheme.Number of selection candidates per generation 0 or 180 or 360 or 720Number of generations 10Number of replicated simulations 100Mating schemes factorial   3 x 3;  6 x 6; nested 6 (males) x 18 (females)Heritability 0.1 or 0.3 or 0.5Optimizing mating schemes in fish breedingE. D’Agaro1, J. A. Woolliams2, C. S. Haley2, D. Lanari11 Dipartimento di Scienze Animali. Università di Udine, Italy2 Roslin Institute. Edinburgh, UKCorresponding author: Edo D’Agaro. Dipartimento di Scienze Animali, Università di Udine, Via S. Mauro 2,Pagnacco (UD) - Tel. +39 0432650110 - Fax: +39 0432660614 - Email: dagaro@dspa.uniud.itABSTRACT: The optimal contribution selection method and the simulated annealing algorithm were used to maximizethe genetic gain and reduce inbreeding in fish breeding populations. This study considered the following mating designs:full factorial (3 x 3); full factorial (6 x 6) and nested (6 males x 18 females). A quantitative trait based on a strictly addi-tive and polygenic model was simulated. Two levels for the number of genotyped offspring (360 or 720) and three levelsof heritability (0.1; 0.3; 0.5) were assumed. The best results in terms of ∆F and ∆G were obtained with the full factorialdesign (6 x 6) and considering a trait with a high heritability. The optimal family size was found at 20 fish per mating.Key words: Simulation, Fish, optimum contribution, Annealing algorithm.INTRODUCTION – The design of optimal mating schemes is a mean to improve fish performance worldwide. Duringthe last two decades, breeding strategies and techniques addressing both genetic improvement has been well docu-mented and applied in several countries (Sonesson et al., 2005). Nevertheless, in Italy, selective breeding has not beenwidely practiced in fish farming. Recent advances in fish breeding involve comparisons and optimization of matingdesigns using advanced analytical models as the optimum contribution selection (Meuwissen, 1997) and the simulatedannealing algorithm (Press et al., 1989). The high reproductive potential in fish species allows high genetic gains byapplying high selection intensities. This means that a very small number of individuals are used to generate successivegenerations and hence the rate of inbreeding can be high (Sonesson et al., 2005). The detrimental effects of inbreedingare well documented in fish. In recent years, many selection and mating strategies have been proposed to restrictinbreeding in selection programmes (Sonesson et al., 2005). In this study, a stochastic simulation was used to optimizemating schemes using different genetic parameters and under restricted inbreeding in fish breeding.MATERIAL AND METHODS – A fish population under artificial selection was modelled by stochastic (Monte Carlo)simulation using Mathlab software. Selection was applied for a single trait measured on both sexes and based on  esti-mated breeding values (EBVs) using the ASREML2 statistical package. Generation were discrete (equal number ofsires and dams were selected at each generation). The trait under selection was assumed to be determined by an infi-nite number of unlinked additive loci, each with an infinitesimal effect. The trait  was considered to be standardized, sothe initial phenotypic variance is unity. Phenotypes of unrelated base population animals (generation 0) were generat-ed as the sum of a normally distributed environmental and genetic effects. Phenotipic values of the offspring born everygeneration were generated as: P = µ + (σA RND(0,1)S + σA RND(0,1)D )/2 + σE RND(0,1) + (0.5(1-(Fs+Fd)/2 ))1/2 σA*RND(0,1) where σA (1) = σA (0) /(1+kh2(1)) and k=(0.5)(km+kf); ky= iy(iy-xy) y=male or female. Selection was directionalupwards and by truncation. Total number of offspring born per generation and numbers of selected  males andfemales were constant (10 or 20 offspring per mating) over generations and varied according to the mating schemes.The simulated breeding schemes are  described in Table 1.PROC. 17th NAT. CONGR. ASPA, ALGHERO, ITALY796 ITAL.J.ANIM.SCI. VOL. 6 (SUPPL. 1), 795-796, 2007The method of Meuwissen (1997) was used to select animals. This method maximises the genetic level of the nextgeneration of animals Gt+1 = c’EBVt where ct is a vector of genetic contributions of selected candidates to genera-tion t+1 and EBVs is a vector of  best linear unbiased prediction (BLUP) estimates of candidates in generation t.Rates of inbreeding are controlled by constraining the average coancestry to Ct+1 =c’Atct/2  where At is a (n x n) rela-tionship matrix among n selection candidates Ct+1 = 1-(1-∆Ft)t and ∆Fd is the desidered rate of inbreeding. For opti-mizing of  ct, it was also necessary to constrain the sum  the contributions of males (females) to 1/2. In order to obtainthe optimal ct that maximize Gt+1 Lagrangian multipliers were used. An additional restriction was to select onlyone full sibs per family. The minimum coancestry mating (reduce the average relationship of sires and dams andtherefore also the inbreeding of their progeny is minimized) was obtained by applying the simulated annealingalgorithm according to Press et al. (1989)RESULTS AND CONCLUSIONS – The rate of inbreeding (∆F) and genetic gain (∆G) for the three mating designsare reported in Table 2.The full factorial design gives the best results in terms of  ∆F and ∆G (1.85 and 0.94 or 1.80 and 0.69) for a max-imal number of sires and dams (6 x 6), higher size of families per mating used for selection (720 offspings) andhigher heritability (0.3 or 0.5). According to Sorensen et al. (2005) the superiority of the factorial mating com-pared to hierarchical scheme can be explained in terms of  the different genetic structure of  populations obtainedshowing, in the factorial design, small full-sibs families, more paternal half-sibs and a group of maternal half-sibs. At a lower heritability (0.1)  the nested design become competitive with the full factorial mating (6 x 6). Inconclusion, the optimal design to estimate additive effects in fish seems to be a full factorial mating with full-sibsfamilies of 20 animals.REFERENCES – Meuwissen, T.H.E., 1997. Maximizing the response of selection. with a predefined rate ofinbreeding. J. Anim.Sci. 75:924-940. Press, W.H., Flannery B.P., Teukolsky, S.A., Vetterling, W.T., 1989.Minimization or maximization of function. In: Numerical recipes. The act of scientific computing. University ofCambridge Press, pp. 274-334. Sonesson, A.K., Gjerde, B., Meuwissen, T.H.E., 2005. Truncation selection of BLUP-EBV and phenotypic values in fish breeding schemes. Aquaculture 243:61-68. Sorensen, A.C., Berg, P., Woolliams,J.A., 2005. The advantage of  factorial mating under selection is uncovered by deterministically predicted rates ofinbreeding. Genet. Sel. Evol. 37:57-81.Table 2. Rate of inbreeding (∆F)(x100) and genetic gain (DG))(σp) for different matingschemes and genetic parameters.N. of offsprings per family10 20Heritability 0.1 0.3 0.5 0.1 0.3 0.5Full factorial (3 x  3)∆F 3.96 5.00 4.55 4.50 4.70 4.65∆G 0.29 0.60 0.75 0.37 0.60 0.97Full factorial (6 x  6)∆F 2.25 2.20 2.10 2.05 1.80 1.85∆G 0.38 0.64 0.76 0.47 0.69 0.94Nested (6males x 18 females)∆F 2.60 2.25 2.36 2.06 2.90 2.05∆G 0.43 0.72 0.86 0.57 0.66 0.89

http://doaj.org/search?source=%7B%22query%22%3A%7B%22bool%22%3A%7B%22must%22%3A%5B%7B%22term%22%3A%7B%22id%22%3A%229679fecd70544ecca2ad8884cb24c87d%22%7D%7D%5D%7D%7D%7D

Optimizing mating schemes in fish breeding

Abstract

Similar works

Full text

Available Versions

Directory of Open Access Journals

Crossref

Edinburgh Research Explorer