# Fitness function from contribution theory Simple function for fitness where an index of traits is weighted by the group relationship. ```r ocsFunC(Q, SNP, solution, wtf=NULL, wbaf="base", ...) ``` ## Arguments - `Q`: A QTL matrix for solutions. See details. - `SNP`: A SNP marker matrix for the QTLs if the QTLs are individuals. - `solution`: an object of RRsol-class. - `wtf`: A vector of trait weights for the solution object. See details. - `wbaf`: A character value to indicate which type of weights should be used for the beneficial-allele frequencies of the different solutions (Q). Accepted values are; 1) 'base' to use 1-benef.freq.base.pop as proposed originaly by Covarrubias-Pazaran and Pita (2026), 2) 'alpha' to use the average allelic effects, 3) 'none' to only multuply by one which is equivalent to not using weights. - `...`: additional arguments to pass. ## Details A function of the type: sum( freq.solution * (1 - freq.base.pop) ) the frequency of beneficial alleles is computed for each solution in Q and then it is weighted by 1 - freq.base.pop which is the frequency of beneficial alleles in the base population. ## Returns - **$res**: a vector of values ## References Giovanny Covarrubias-Pazaran (2024). evola: a simple evolutionary algorithm for complex problems. To be submitted to Bioinformatics. ## Examples ```r # mickey mouse example Q=matrix(sample(0:1,32,replace = TRUE),nrow=4,ncol=8) # possible solutions SNP=matrix(sample(0:2,80,replace = TRUE),nrow=8,ncol=10) # SNPs of 8 individuals with 10 markers solution = matrix(rnorm(10)) # marker effects ocsFunC(SNP=SNP,Q=Q, solution=solution) # sum of frequency of positive alleles ## Not run: # using the function in an optimal contribution data(DT_technow, package="enhancer") DT <- DT_technow DT$occ <- 1; DT$occ[1]=0 Md <- apply(Md_technow,2,as.numeric) rownames(Md) <- rownames(Md_technow) Mf <- apply(Mf_technow,2,as.numeric) rownames(Mf) <- rownames(Mf_technow) M <- rbind(Md,Mf) A <- A.matr(M) A <- A[DT$hy,DT$hy] library(rrBLUP) mixm = mixed.solve(y=DT$GY, Z=M) solution = matrix(mixm$u[colnames(M)]) sum(freqPosAllele(M,solution[,1])) # starting frequencies # run the genetic algorithm # we assig a weight to x'Dx of (20*pi)/180=0.34 res<-evolafit(formula = c(GY, occ)~hy, dt= DT, # constraints: if sum is greater than this ignore constraintsUB = c(Inf,100), # constraints: if sum is smaller than this ignore constraintsLB= c(-Inf,-Inf), # weight the traits for the selection b = c(1,0), # population parameters nCrosses = 100, nProgeny = 10, recombGens=1, nChr=1, mutRateAllele=0, # coancestry parameters D=A, lambda= (20*pi)/180 , nQtlStart = 90, # selection parameters propSelBetween = 0.5, propSelWithin =0.5, fitnessf = ocsFunC, SNP=M, solution=solution, nGenerations = 20) Q <- pullQtlGeno(res$pop, simParam = res$simParam, trait=1); Q <- Q/2 best = bestSol(res$pop)[,"fitness"] qa = (Q %*% DT$GY)[best,]; qa qAq = Q[best,] %*% A %*% Q[best,]; qAq sum(Q[best,]) # total # of inds selected sum(freqPosAllele(diag(Q[best,])%*%M,solution[,1])) evolmonitor(res) plot(DT$GY, col=as.factor(Q[best,]), pch=(Q[best,]*19)+1) pareto(res) ## End(Not run) ``` ## See Also `evolafit` -- the core function of the package

ocsFunC function