1d:T7b6,# Simulate genotype data matrix for related and unrelated subjects Generates genotype data for association studies, supporting both unrelated subjects and family-based designs with various pedigree structures. ```r GRAB.SimuGMat( nSub, nFam, FamMode, nSNP, MaxMAF = 0.5, MinMAF = 0.05, MAF = NULL ) ``` ## Arguments - `nSub`: Number of unrelated subjects. If 0, all subjects are related. - `nFam`: Number of families. If 0, all subjects are unrelated. - `FamMode`: Family structure: "4-members", "10-members", or "20-members". See `Details` for pedigree structures. - `nSNP`: Number of genetic markers to simulate. - `MaxMAF`: Maximum minor allele frequency for simulation (default: 0.5). - `MinMAF`: Minimum minor allele frequency for simulation (default: 0.05). - `MAF`: Optional vector of specific MAF values for each marker. If provided, `MaxMAF` and `MinMAF` are ignored. ## Returns List containing: - **GenoMat**: Numeric genotype matrix (subjects × markers) with values 0, 1, 2. - **markerInfo**: Data frame with marker IDs and MAF values. ## Details Genotypes are simulated under Hardy-Weinberg equilibrium with MAF ~ Uniform(MinMAF, MaxMAF). Family Structures: * 4-members : 1+2→3+4 (parents 1,2 → offspring 3,4) * 10-members : 1+2→5+6, 3+5→7+8, 4+6→9+10 * 20-members : Complex multi-generational pedigree with 20 members Total subjects: `nSub + nFam × family_size` ## Examples ```r nSub <- 100 nFam <- 10 FamMode <- "10-members" nSNP <- 10000 OutList <- GRAB.SimuGMat(nSub, nFam, FamMode, nSNP) GenoMat <- OutList$GenoMat markerInfo <- OutList$markerInfo GenoMat[1:10, 1:10] head(markerInfo) ## The following is to calculate GRM MAF <- apply(GenoMat, 2, mean) / 2 GenoMatSD <- t((t(GenoMat) - 2 * MAF) / sqrt(2 * MAF * (1 - MAF))) GRM <- GenoMatSD %*% t(GenoMatSD) / ncol(GenoMat) GRM1 <- GRM[1:10, 1:10] GRM2 <- GRM[100 + 1:10, 100 + 1:10] GRM1 GRM2 ``` ## See Also `GRAB.makePlink` for converting to PLINK format.

GRAB.SimuGMat function