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Abstract

Intrapopulation recurrent selection (IRS) has provento be a promising breeding method in eucalyptus, main-ly through being easier to carry out when compared toreciprocal recurrent selection (RRS). However, therecombination strategies in IRS that have not yet beencompared. Thus, the purpose of this study was to verifythe efficiency of different recombination methods in IRS.To do so, computer simulation was used considering dif-ferent heritabilities (0.1, 0.5, 1.0), different initial allelicfrequencies (0.2, 0.8) and allelic interactions withoutdominance and with complete dominance. The initialpopulation consisted of 1000 individuals, which wereselected at random for the beginning of cycle zero. Theseindividuals were interbred two by two. Three selectionstrategies were carried out and, consequently, threerecombination methods: recombine the best individualsselected within the best progenies; the best individualsphenotypically selected regardless of their genealogy; orselection in the mean value of the best progenies select-ed. It was observed that recombination of the best indi-viduals regardless of their genealogy and of the bestindividuals within the best progenies provided for gainssuperior to recombination having only the mean of theprogenies as reference. The average degree of domi-

nance and the heritability of the trait should be consid-ered at the time of choosing the method of selection fol-lowed by recombination.

Key words: Forest breeding, Quantitative Genetics, MonteCarlo, Breeding Strategies, Genetic Gain.

Introduction

Eucalyptus cultivation plays an important role inBrazilian agribusiness through the use of your produc-tion of charcoal, cellulose, posts, poles, lamination, lum-ber and other items (RESENDE et al., 2011). This successis due to genetic breeding, which, together with theareas of nutrition, management and forest conservation,have led to transformation of the country into theworld’s largest producer of eucalyptus in a short periodof time (VILLARI, 2010).

Until recently, the strategy of obtaining clones waspredominantly by means of selection of individuals oncommercial plantations. Initially this was highly suc-cessful; however, “resampling” in the same populationdoes not allow future gains (GONÇALVES et al., 2001). Forthat reason, the need was envisaged for promoting pop-ulational breeding before the selection of individuals forcloning.

Proposals for use of Reciprocal Recurrent Selection(RRS) have been presented (RESENDE et al., 2011). How-ever, RSS is time consuming, laborious and conditionedon the existence of expressive dominance in the traitunder selection. Moreover, RRS is static because twopopulations are selected with a view towards theimprovement of heterosis between them. If the involve-ment of another species or other individuals is required,

Computer Simulation for the Evaluation of Recombination Strategies in Intrapopulation Recurrent Selection in Eucalyptus

By G. B. ABREU1),*), D. F. FERREIRA2), M. A. P. RAMALHO2), F. H. R. B. TOLEDO3) and J. S. DE SOUSA BUENO FILHO2)

(Received 14th August 2012)

1) Embrapa Cocais, Av. Santos Dumont Anil, 18, 65046-660, SãoLuis MA.

2) Universidade Federal de Lavras, Caixa Postal 3037, 37200-000, Lavras MG.

3) ESALQ Av. Pádua Dias, 11, 13418-900, Piracicaba SP.*) Corresponding author: GUILHERME BARBOSA ABREU. EmbrapaCocais, Av. São Luís Rei de França Conjunto Eldorado, 4,65065-470 São Luís MA – Brazil. E-Mail: guilherme.abreu@embrapa.br

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their inclusion in the process is problematic. On theother hand, and since heterosis is not very pronouncedbetween eucalyptus species (BOUVET et al., 2009),Intrapopulation Recurrent Selection (IRS) is an excel-lent option in breeding of this specie. Through use ofIRS, the base population may be synthetic, i.e., originat-ing from crossing of duly evaluated clones/individualsbelonging to one or more species. That way, the synthet-ic population may join conditions for selection of sometraits at the same time.

In conducting IRS in eucalyptus, the literature basi-cally proposes two alternatives for recombination. Oneof them involves interbreeding of the best individualswithin the best progenies. This procedure, although eas-ily applied, has some drawbacks of a practical order dueto many clearings in the field, hindering recombinationbetween the selected individuals. In addition, if theselection is early, there is no way to prove its effective-ness since most of the trees are cut down. Anotheroption would be the use of remnant seeds. In this case,after early selection, plants of the best progenies derivedfrom remnant seeds would be grown in an isolatedgroup. That way, at the time of flowering, the experi-ment would already be more years along and the perfor-mance of the selected progenies could be confirmed(PEREIRA et al., 1997).

With the ease of artificial hybridizations in eucalyptus(ASSIS et al., 1993; ASSIS et al., 2005), the use of full sibsis viable, in which recombination is directed and there-fore more efficient than free interbreeding. In addition,the best individuals of the best progenies may be clonedfor more intensive evaluation in clone tests, as well asallowing various copies of the same tree to be obtainedfor recombination. The clonal test may be evaluatedearly and this allows one to recombine only the proge-nies/individuals whose clones are really the best. In thissituation, recombination is much more efficient because,in addition to the crosses of the best progenies/clonesbeing directed, the decision in respect to the individualto be cloned is performed with greater accuracy.

The question that arises is in regard to the bestoption: recombine the individual itself that was cloned,

or interbreed the progenies that generated the best indi-viduals. In recombination, the question also arises if theperformance of the progeny should be considered or ifonly the information of the individual that was selectedshould be used. An answer to these questions is practi-cally impossible under field conditions due to the timeand space necessary to achieve a conclusive answer. Thealternative is the use of computer simulation because itallows the generation of various population types and,above all, it makes an evaluation of the medium andlong term response viable (SHELBOURNE et al., 2007).

Therefore, the objective of this study was to verify, bymeans of computational simulation, the best recombina-tion strategy in intrapopulation recurrent selection ofeucalyptus, using full sibs progenies.

Material and Methods

Computer simulation was used to determine the bestmethod of recombination in intrapopulation recurrentselection based on a population. Different allelic fre-quencies, heritabilities and types of allelic interactionswere considered. Each configuration proposed inadvance was repeated 100 times. The scenarios aredetailed below.

Populations of 1000 individuals (n) were simulatedconsidering traits with polygenic control of 100 loci (g) ofequal and independent effects. The genetic model pro-posed did not consider epistasis. The frequencies of thefavorable allele (p), in the population of cycle 0, were 0.2and 0.8 in the mean of the g genes considered. The Ballele was considered as favorable and the b as unfavor-able. The additive value (a) was considered as 1. Thus,when the allelic interaction of the complete dominancetype was considered, the value of ‘d’ was also 1 (one) andwithout dominance ‘d’ was 0 (zero).

That said, the phenotypic value (VFi) of the individuali was obtained by VFi = Gi + e, where Gi is the sum of the100 genes considered and e is the error term. The geno-typic variance was obtained as being the variance of thegenotypic values of the individuals of the base popula-tion. The environmental deviation, or error term (e), was

Table 1. – Configurations used in simulation considering different heritabilities(h2), allelic frequencies and average degree of dominance.

1 Average degree of dominance: 0 (zero) = without dominance, 1 (one) = com-plete dominance.

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obtained as of a normal distribution with zero mean andstandard deviation according to pre-established heri-tabilities (h2 = 0.10; 0.50; and 1.00).

The different configurations, that is, the variations inthe average degree of dominance, heritability and allelicfrequency of the initial population are presented inTable 1.

Selection and recombination of the individuals of thecycle zero

By means of demosntration, it was shown details ofthe simulation process. It was used only functions of thebase package of the R software, basically the pseudo-random numbers generators, following statistics distrib-utions (R DEVELOPMENT CORE TEAM, 2010).

In the simulation implementation of the initial popu-lation, given the allele frequency, it was used the betaand uniform distribution. g values were generated fromboth distributions for each n individuals. If the uniformvalue is smaller than the square of beta value, the locireceive the recessive genotype (-a, or genotype bb), if theuniform value is among the square of beta value and 2times beta value multiplied by one minus beta value,the loci receive the heterozigous value (d, or genotypeBb). The values of the uniform greater than thesereceive the genotype BB (or a value). The sum of thegenotype values by each individual is the total geneticvalue (G). The variance between the Gs was taken asgenetic variance. Normal values with zero mean andstandard deviations according to the heritabilitie simu-late were generated and added to genotipic value result-ing in fenotipic values. After all, for each one of n indi-viduals, in g loci, it is ensured that the genotipic values,by loci, are independent and are in Hardy-Heinbergequilibrium. That process was implemented in R lan-guage as:gen <- matrix(0, ncol = g, nrow = n)

# create the matriz to store genotypes

for(i in 1:n){

f <- rbeta(g, p * 10, 10 * (1 - p))

# beta distributio for alelles frequencies foreach loci

n01 <- runif(g, 0, 1) # uniform distributionfor genotype values for each loci

const <- vector(‘numeric’, g) # vector to storegenotype value for each loci

for(j in 1:g){

# ‘laco’ — apply the segregation rule for AAAa or aa

# if uniform value < p^2, loci recieve ‘r’(recessive)

# if uniform value between p^2 e 2p(1-p) locirecieve ‘h’ (heterozigous)

# ir uniform value > (1-p)^2 loci recieve ‘d’(dominant)

if(n01[j] < f[j]^2) const[j] <- ‘d’ else

if(n01[j] < (f[j]^2 + 2 * f[j] * (1 -f[j])))const[j]<- ‘h’ else

const[j] <- ‘r’

}

gen[i,] <- const # store genotype of each indi-vidual of population

}

The selection of 20 individuals in the base populationwas performed at random and afterwards they were

recombined two by two, generating 190 progenies. Eachprogeny consisted of 20 individuals. The values werestored, identifying their genealogy, in other words,which individuals (in the base population) were theirparents.

To cross two individuals it was used the follow segre-gation rule: the loci that both parents were homozygous,dominant or recessive, the genotype value of the son wastaken as homozygous, dominant or recessive, respec -tively. Loci that one parent was homozygous recessiveand other homozygous dominant the son receive the heterozygote value. If both parents were heterozygote,the son loci values were a sample of the multinomialdistribution with probabilities taken as: a=0.25; d=0.5;and -a=0.25. And for loci with one homozygous parent(recessive or dominant) and the other heterozygous par-ent, the distribution used was binomial with probabilityof success equal to 0.5 (a, or -a), depending on the par-ents genotype. This implementation in R is:rule <- function(father, mother){

# arguments of the function

if ( length(father) != length(mother) ) stop

# father and mother with different number ofloci

son <- vector(‘character’, length(father)) #son vector

pm <- (father == mother) # search where fatheris equal mother

# father ‘d’ x mother ‘d’, son ‘d’

son[(pm & father == ‘d’)] <- ‘d’

# father ‘r’ x mother ‘r’ son ‘r’

son[(pm & father == ‘r’)] <- ‘r’

# father ‘h’ x mother ‘h’ (son .25:.5:.25)

hh <- (pm & father == ‘h’)

# search where father and mother are ‘h’

if (sum(hh) > 0) son[hh] <- c(‘d’, ‘h’, ‘r’)[apply(rmultinom(sum(hh), 1, c(.25, .5, .25)),2, which.max)]

# ‘d’ x ‘r’ OR ‘r’ x ‘d’ (son ‘h’)

son[((father == ‘d’ & mother == ‘r’) | (father== ‘r’ & mother == ‘d’))] <- ‘h’

# ‘d’ x ‘h’ OR ‘h’ x ‘d’ (son .5 ‘d’ .5 ‘h’)

hd <- (father == ‘h’ & mother == ‘d’) | (father== ‘d’ & mother == ‘h’)

if (sum(hd) > 0) son[hd] <- c(‘d’,‘h’)[rbinom(sum(hd),1,.5)+1]

# ‘h’ x ‘r’ OR ‘r’ x ‘h’ (son .5 ‘r’ .5 ‘h’)

hr <- (father == ‘h’ & mother == ‘r’) | (father== ‘r’ & mother == ‘h’)

if (sum(hr) > 0) son[hr] <- c(‘r’,‘h’)[rbinom(sum(hr), 1, .5)+1]

return(son) # return of the function (genotypeof the son)

}

Selection of progenies in cycle zero and recombinationstrategies

After the formation of the progenies of full sibs, threeselection strategies of individuals were adopted for thenext cycle of recurrent selection: a) phenotypic selection:selection of the 20 best individuals regardless of theirgenealogy; b) selection among and within: selection ofthe best individuals within the best progenies. In thiscase, the superior individual was selected within the 20best progenies; c) selection considering only the mean ofthe best progenies. In this strategy, the 20 best proge-

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nies were selected and the 20 individuals of each proge-ny were recombined. Full diallel was performed amongthe progenies, such that each individual participated inonly one cross.

At the time of recombination, the individuals selectedin the phenotypic selection and in the selection amongand within were recombined two by two, generating atthe end 190 progenies of full sibs with 20 individualswith each progeny, making for a total of 3800 individu-als.

In recombination of the best progenies, full diallel wascarried out among the 20 best progenies, also generating190 progenies of full sibs. For that purpose, 20 individu-als were used from each progeny and each one of themparticipated only once in each cross.

Estimates of gain with selection in the different methods

These three recombination strategies were carried outindependently for 20 selection cycles of a common popu-lation base. The strategy that generated the best cloneswas verified at the end. This was evaluated by estimat-ing the following parameters in each cycle: phenotypicmean, phenotypic variance, genetic variance and num-ber of fixed alleles. For better visualization, charts weregenerated with the mean values of the 100 simulations.

At the end of the 20 cycles, the gain achieved with theselection of the different recombination strategies wasestimated using the mean values of all the progenies ofeach cycle. To do so, the difference between the pheno-

typic value of the last cycle and of the initial cycle wasestimated. This value was divided by the number ofcycles to obtain the phenotypic gain achieved per cycle.

Results and Discussion

In computer simulation, the first premise is the needof consistency in the proposition. According to FERREIRA(2001), the programmer must use validation processesso that the simulated system may operate within simi-lar conditions of the real system and verify if the resultsgenerated by simulation are in accordance. To demon-strate this fact, the mean values of heritability in thestrategy of selection and recombination of the best indi-viduals, regardless of the genealogy (phenotypic selec-tion), are shown (Table 2). It may be observed that theestimates of heritabilities obtained were practicallyidentical to the values established a priori in the simu-lation. It was observed that in the configurations inwhich the heritability is high, and especially when theinitial allelic frequency is high, fixation of the allelesoccurs rapidly and, consequently, there is no way to esti-mate heritability.

Another observation regarding to use of simulation isthe number of times the process is repeated. The litera-ture reports a range from 10 (WANG et al., 2004) to 500repeats (WANG et al., 2003) in simulation studies onplant breeding. In this study, 100 times was chosen dueto the time and computational resources spent in eachconfiguration. As previously mentioned, with 100 simu-

Table 2. – Estimates of heritabilities in the recombination strategy of the best individu-als regardless of genealogy, without dominance.

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lations, the mean value of heritability expected and pre-dicted was practically equal, showing that the numberof replications of simulation per configuration was suffi-cient.

Heritability estimates available in the literature forvolume of wood are varied; nevertheless, the values atthe individual level are normally less than 0.4 or 40%(FLOYD et al., 2003). For other traits, as for example,wood quality, the heritability estimates are also vari-able, however, with magnitude normally greater thanthat of volume (APIOLAZA et al., 2005; TOLFO et al., 2005).For that reason, in order to have a greater range of situ-ations, the choice was made to consider heritabilityranging from 0.1 to 1.0.

Another question is the number of selection cyclesevaluated. Considering that an IRS cycle in eucalyptustakes eight years, the use of 20 selective cycles wouldresult in 160 years, which evidently is a more than suffi-cient period to evaluate any breeding strategy.

Recurrent selection is a cyclical breeding process withthe goal of growth in the mean value without expressivereduction in genetic variability, so as to continue obtain-ing gains with selection (HALLAUER, 1999). The effect ofselection cycles on the estimate of genetic variance maybe observed in Figures 1 and 2. It is worth highlightingthat in most situations there was no expressive differ-ence between the methods of phenotypic selection andselection among and within progenies. For that reason,

Figure 1. – Genetic variance in the different recurrent selection cycles with average degree of domi-nance equal to zero and: (a) allelic frequency: 0.2 and heritability: 0.1; (b) allelic frequency: 0.8 and heritability: 0.1; (c) allelic frequency: 0.2 and heritability: 0.5; (d) allelic frequency: 0.8 and heritability:0.5; (e) allelic frequency: 0.2 and heritability: 1.0; (f) allelic frequency: 0.8 and heritability: 1.0.

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the lines of these two selection and recombination meth-ods overlap in the charts. First, it may be seen that thebehavior of genetic variance in the different selectivecycles varied very little in terms of the type of recombi-nation performed. In all of them, there was reduction ingenetic variance, as was expected if selection is efficient.Observe, however, that the reduction of variance isrelated to heritability and the initial allelic frequency.With low heritability and low allelic frequency, evenafter 20 selective cycles, with a not very large effectivesize, genetic variance changed very little with selection.There was a tendency for it to be greater with recombi-nation of progenies. With the increase of heritability,above all for high initial allelic frequency, since the fixa-tion of alleles must be quicker, genetic variance is morerapidly reduced. Proof of this last observation may be

obtained by means of the number of fixed loci withfavorable alleles during selective cycles.

In the case of average degree of dominance equal toone, in other words, full dominance, it may be observedthat genetic variance is more slowly exhausted (Figure2). To explain this, it is enough to use one gene with twoalleles as an example. With full dominance, the homozy-gote with the two dominant alleles and the heterozygotehave the same genetic value (1), while the homozygotewith recessive alleles has a value of -1. Genetic variancein this case is 1.33. But in the absence of dominance, thegenetic value of the dominant homozygote is one (1), theheterozygote is zero (0) and the recessive homozygoteminus one (-1) and the genetic variance comes to be (1).From this example, it is clear that in order to maintain

Figure 2. – Genetic variance in the different recurrent selection cycles with average degree of domi-nance equal to one and: (a) allelic frequency: 0.2 and heritability: 0.1; (b) allelic frequency: 0.8 and heritability: 0.1; (c) allelic frequency: 0.2 and heritability: 0.5; (d) allelic frequency: 0.8 and heritability:0.5; (e) allelic frequency: 0.2 and heritability: 1.0; (f) allelic frequency: 0.8 and heritability: 1.0.

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genetic variance throughout the selective cycles, theaverage degree of dominance must be considered.

The interaction of dominance hampers selection ofsuperior individuals since the descendents of this indi-vidual may have behavior inferior to it (RAMALHO et al.,2012). This fact may be observed in Table 3 becausecomparing the configurations, changing only the aver-age degree of dominance, the gain achieved is alwaysgreater when the absence of dominance is considered.

When one trait is controlled by various genes, in otherwords, a quantitative trait, the probability of joining allthe favorable alleles in one genotype in only one inter-breeding cycle is very small (RAMALHO et al., 2012). Toget around this problem, recurrent selection proves to be

an efficient breeding method because the alleles come togradually accumulate with advancement in the selectioncycles. In the literature there are various recurrentselection methods for the growing of eucalyptus that arepromising (RESENDE, 2002; SOUZA JÚNIOR, 2001). KERR etal. (2004), comparing different recurrent selection meth-ods by means of computer simulation, arrived at theconclusion that the use of intrapopulation recurrentselection based on a synthetic population was the mostefficient method for increasing the frequencies of favor-able alleles.

Among the innumerable advantages of using IRS, thepossibility of introducing new genotypes in any selectivecycle stands out, with this being a good strategy for

Figure 3. – Number of fixed loci in the different recurrent selection cycles with average degree of domi-nance equal to zero and: (a) allelic frequency: 0.2 and heritability: 0.1; (b) allelic frequency: 0.8 and heritability: 0.1; (c) allelic frequency: 0.2 and heritability: 0.5; (d) allelic frequency: 0.8 and heritability:0.5; (e) allelic frequency: 0.2 and heritability: 1.0; (f) allelic frequency: 0.8 and heritability: 1.0.

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maintaining genetic variation. It is not possible to dothis when using RRS, once more showing that intrapop-ulation recurrent selection should be preferred in genet-ic breeding of eucalyptus.

There are some reports in the literature saying thatwhen recurrent selection is made in perennial species,as is the case of eucalyptus, remnant seeds (recombina-tion of the best progenies) may be used for recombina-tion (SOUZA JÚNIOR, 2001). In the case in which the aver-age degree of dominance was considered to be zero, andallelic frequency equal to 0.2 and heritability equal to0.5 and 1 (Figure 3c, 3e), the method of recombination ofthe best progenies was the least efficient, because 20selection cycles were not enough to fix all the 100 loci,

different from the other methods of recombination. Withdegree of dominance equal to one, no strategy was suffi-cient to fix all the favorable loci in 20 selection cycles(Figure 4).

To achieve success in a breeding program, there mustbe sufficient genetic variance to select the best individu-als (BARNARDO, 2010). Nevertheless, this variance mustbe associated with high averages for the genetic gain tobe associated with populations or clones that are reallyadvantageous to those who use them. When the bestprogenies are recombined, it is observed that geneticvariation in most cases is of greater magnitude whencompared with the other two recombination methodsproposed in this study. However, the phenotypic mean,

Figure 4. – Number of fixed loci in the different recurrent selection cycles with average degree of domi-nance equal to one and: (a) allelic frequency: 0.2 and heritability: 0.1; (b) allelic frequency: 0.8 and heritability: 0.1; (c) allelic frequency: 0.2 and heritability: 0.5; (d) allelic frequency: 0.8 and heritability:0.5; (e) allelic frequency: 0.2 and heritability: 1.0; (f) allelic frequency: 0.8 and heritability: 1.0.

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likewise in most cases, was less, once more showing thatthe recombination method of the best individuals of theprogenies is better (Figures 5a, 5c, 5d, 5e, 5f, 6c and 6d).

In Figures 5 and 6 are presented the phenotypicmeans throughout the selection cycles. In all the config-urations proposed, there were expressive gains in thethree recombination methods; however, it is clear thatthe strategy of recombination of the best progenies wasthat which obtained the lowest mean values throughoutthe selective cycle, in most cases. Considering averagedegree of dominance equal to zero and low initial allelicfrequency (0.2) (Figures 5a, 5c, 5e), the phenotypicmeans of the recombination strategies of individuals,without consideration of the genealogy (phenotypicselection) and recombination of the best individualswithin the best progenies (selection among and within

progenies) were similar, while recombination of theprogenies with greater mean values was less in all thecycles, regardless of heritability. However, with initialallelic frequency high (0.8), in all cases, recurrent selec-tion was efficient in reaching the maximum phenotypicmean value (all the favorable alleles fixed). However,when the best progenies were recombined, a greaternumber of cycles in recurrent selection was necessary toreach this level (Figures 5b, 5d, 5f).

To evaluate genetic progress in recurrent selection,various estimators may be used. One of them is the gainachieved per cycle. It may be observed in Table 3 that inall the recombination methods proposed, there wasexpressive gain throughout the cycles. It is worth high-lighting that the gain achieved with selection is moreexpressive when the initial allelic frequency is low and

Figure 5. – Phenotypic mean in the different recurrent selection cycles with average degree of domi-nance equal to zero and: (a) allelic frequency: 0.2 and heritability: 0.1; (b) allelic frequency: 0.8 and heritability: 0.1; (c) allelic frequency: 0.2 and heritability: 0.5; (d) allelic frequency: 0.8 and heritability:0.5; (e) allelic frequency: 0.2 and heritability: 1.0; (f) allelic frequency: 0.8 and heritability: 1.0.

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when there is an increase of the heritability of the trait.This occurs apart from the average degree of dominanceand is evident in the recombination of individuals (phe-notypic selection and selection among and within). As insome cases the maximum phenotypic values werereached before completing 20 selective cycles, all theconfigurations proposed in the gain from the selectionwere not considered because these values would beunderestimated.

The methods of recombination of the best individuals,with or without consideration of their genealogy, weregreater than the third method proposed (Table 3). Thus,it is clear that the recombination of the best progenies,in spite of showing gains with selection, was the mostinefficient recombination method in recurrent selection.

However, information of the progenies may or may notbe used to select the best individuals and afterwardsrecombine them.

In the present study, as mentioned, the differencesbetween phenotypic selection, in other words, recombi-nation of the best individuals regardless of genealogy,and selection among and within progenies with theirrespective recombination, were not expressive. Thisprobably occurred because there are progenies withmany individuals of superior genetic constitution withinit which are selected when phenotypic selection is used,and these individuals within few progenies are as goodas the best individuals of the best progenies, since inthis second method, only one individual per progeny isselected. As these two methods did not present differ-

Figure 6. – Phenotypic mean in the different recurrent selection cycles with average degree of domi-nance equal to one and: (a) allelic frequency: 0.2 and heritability: 0.1; (b) allelic frequency: 0.8 and heritability: 0.1; (c) allelic frequency: 0.2 and heritability: 0.5; (d) allelic frequency: 0.8 and heritability:0.5; (e) allelic frequency: 0.2 and heritability: 1.0; (f) allelic frequency: 0.8 and heritability: 1.0.

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ences, combined selection would probably be the mostappropriate selection method because, in this case, anindex is generated that results in a differentiated num-ber of progenies and individuals selected per progeny,while in selection among and within, this number is con-stant.

The methods that recombine the best individuals, con-sidering or not the genealogy, showed the most promis-ing strategies in most cases. However, the averagedegree of dominance and the heritability of the traitmust be considered at the time of choosing the methodof selection followed by recombination. The additionaladvantage of recombination of the best individuals isthat they may be cloned. In this case, a milder selectionintensity may be applied. The clones obtained in thisway would be evaluated and, based on the results, onlythose with greater performance would be recombined.

Conclusion

Recombination of the best individuals, regardless oftheir genealogy, and of the best individuals within thebest progenies provided for greater gains than recombi-nation of the best progenies, in most cases.

The average degree of dominance and the heritabilityof the trait should be considered at the time of choosingthe method of selection followed by recombination.

Acknowledgments

To the CNPq for granting the Research Scholarship tothe author GUILHERME BARBOSA ABREU and to EmbrapaInformática Agropecuária for lending the computer labo-ratory.

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1 Average degree of dominance; 2 Mean heritability; 3 Initial allelic frequency.

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