# SEL.HAP: Haplotype selection and extension along the genome Perform genome-wide haplotype selection and extension using the CHAP-GWAS framework. The function scans along each chromosome, builds local haplotype segments, and adaptively extends them based on association evidence with the phenotype. ```r SEL.HAP(GEN, YFIX, KIN, nHap, p.threshold, PAR) ``` ## Arguments - `GEN`: Genotype matrix with rows corresponding to markers and columns corresponding to individuals. The first two columns give chromosome (`chr`) and physical position (`pos`); the remaining columns contain alleles for each individual (e.g. `"A"`, `"C"`, `"G"`, `"T"`), one allele per haplotype copy. - `YFIX`: A matrix or data.frame with phenotype in the first column and fixed-effect covariates (e.g. intercept, PCs) in the remaining columns, one row per individual. - `KIN`: A list of kinship matrices, each of dimension $n \times n$, where $n$ is the number of individuals. - `nHap`: Initial haplotype window size (number of consecutive markers). - `p.threshold`: P-value threshold for haplotype extension. - `PAR`: Optional variance component parameters passed to `RANDOM()`. If `NULL`, they are estimated internally. ## Returns A list of three matrices summarizing: * FINAL[[1]] : initial haplotype segments * FINAL[[2]] : extended haplotype segments * FINAL[[3]] : final selected segments after extension ## Examples ```r ## Minimal example with small simulated data (alleles encoded as A/C/G/T) set.seed(1) ## Number of individuals and markers n_ind <- 200 n_mark <- 50 ## Construct a simple GEN matrix: ## first two columns: chromosome and position ## each individual is represented by two allele columns (A1/A2) chr <- rep(1, n_mark) pos <- seq_len(n_mark) * 100 alleles <- c("A", "C", "G", "T") geno <- matrix(NA_character_, nrow = n_mark, ncol = 2 * n_ind) for (m in seq_len(n_mark)) { a <- sample(alleles, 2, replace = FALSE) # biallelic per marker geno[m, ] <- sample(a, 2 * n_ind, replace = TRUE) } colnames(geno) <- as.vector(rbind( paste0("id", seq_len(n_ind), "_A1"), paste0("id", seq_len(n_ind), "_A2") )) GEN <- cbind(chr, pos, geno) ## Phenotype + intercept as fixed effect y <- rnorm(n_ind) X <- cbind(1, rnorm(n_ind)) # intercept + one covariate YFIX <- cbind(y, X) ## Simple kinship: identity matrix KIN <- list(diag(n_ind)) ## Run SEL.HAP with a small initial window and mild threshold res <- SEL.HAP(GEN, YFIX, KIN, nHap = 2, p.threshold = 0.05, PAR = NULL) ## Inspect the structure of the result (three matrices) str(res) ```

SEL.HAP function