# Calculate Genomic Relatedness Matrix (GRM) for honeybees from Identical By State genomic data Level 0 function that returns Genomic Relatedness Matrix (GRM) for honeybees from Identical By State genomic data (bi-allelic SNP represented as allele dosages) following the method for the sex X chromosome (Druet and Legarra, 2020) ```r calcBeeGRMIbs(x, sex, alleleFreq = NULL) calcBeeAlleleFreq(x, sex) ``` ## Arguments - `x`: `matrix` of genotypes represented as allele dosage coded as 0, 1, or 2 in females (queens or workers) and as 0 or 1 in males (fathers or drones); individuals are in rows and sites are in columns; no missing values are allowed (this is not checked - you will get NAs!) - `sex`: character vector denoting sex for individuals with genotypes in `x` - `"F"` for female and `"M"` for male - `alleleFreq`: numeric, vector of allele frequencies for the sites in `x`; if `NULL`, then `calcBeeAlleleFreq` is used ## Returns matrix of genomic relatedness coefficients ## Functions * `calcBeeAlleleFreq()`: Calculate allele frequencies from honeybee genotypes ## Examples ```r founderGenomes <- quickHaplo(nInd = 3, nChr = 1, segSites = 100) SP <- SimParamBee$new(founderGenomes) SP$setTrackRec(TRUE) SP$setTrackPed(isTrackPed = TRUE) basePop <- createVirginQueens(founderGenomes) drones <- createDrones(x = basePop[1], nInd = 1000) droneGroups <- pullDroneGroupsFromDCA(drones, n = 1, nDrones = nFathersPoisson) colony <- createColony(basePop[2]) colony <- cross(x = colony, drones = droneGroups[[1]]) colony <- buildUp(x = colony, nWorkers = 6, nDrones = 3) geno <- getSegSiteGeno(colony, collapse = TRUE) sex <- getCasteSex(x = colony, collapse = TRUE) GRM <- calcBeeGRMIbs(x = geno, sex = sex) # You can visualise this matrix with the function image() from the package 'Matrix' #Look at the diagonal at the relationship matrix x <- diag(GRM) hist(x) summary(x) #Look at the off-diagonal at the relationship matrix x <- GRM[lower.tri(x = GRM, diag = FALSE)] hist(x) summary(x) # Compare relationship between castes ids <- getCasteId(colony) idQueen <- ids$queen idWorkers <- ids$workers idDrones <- ids$drones # Queen vs others GRM[idQueen, idWorkers] GRM[idQueen, idDrones] # Workers vs worker GRM[idWorkers, idWorkers] # Workers vs drones GRM[idWorkers, idDrones] # Calculating allele frequencies ourselves (say, to "shift" base population) aF <- calcBeeAlleleFreq(x = geno, sex = sex) hist(aF) GRM2 <- calcBeeGRMIbs(x = geno, sex = sex, alleleFreq = aF) stopifnot(identical(GRM2, GRM)) # You can also create relationships with pooled genomes pooledGenoW <- getPooledGeno(getWorkersSegSiteGeno(colony), type = "mean", sex = getCasteSex(colony, caste="workers")) queenGeno <- getQueenSegSiteGeno(colony) # Compute relationship between pooled workers genotype and the queen calcBeeGRMIbs(x = rbind(queenGeno, pooledGenoW), sex = c("F","F")) # You can now compare how this compare to relationships between the queen # individual workers! ``` ## References Druet and Legarra (2020) Theoretical and empirical comparisons of expected and realized relationships for the X-chromosome. Genetics Selection Evolution, 52:50 tools:::Rd_expr_doi("10.1186/s12711-020-00570-6")

calcBeeGRMIbs function