# Instruction of POLMM-GENE method POLMM-GENE implements region-based association tests for ordinal categorical phenotypes, adjusting for sample relatedness. It is well-suited for analyzing rare variants in large-scale biobank data, and effectively controls type I error rates while maintaining statistical power. ```r GRAB.POLMM.Region() ``` ## Details For single-variant tests, see `GRAB.POLMM`. See `GRAB.POLMM` for details on step 1. Additional Control Parameters for GRAB.Region() with POLMM : * `showInfo` (logical, default: FALSE): Whether to print PCG iteration information for debugging. * `tolPCG` (numeric, default: 0.001): Tolerance for PCG in region testing. * `maxiterPCG` (integer, default: 100): Maximum PCG iterations in region testing. Results are saved to four files : 1. `OutputFile`: Region-based test results (SKAT-O, SKAT, Burden p-values). 2. `paste0(OutputFile, ".markerInfo")`: Marker-level results for rare variants (MAC \>= `min_mac_region`) included in region tests. 3. `paste0(OutputFile, ".otherMarkerInfo")`: Information for excluded markers (ultra-rare variants or failed QC). 4. `paste0(OutputFile, ".infoBurdenNoWeight")`: Summary statistics for burden tests without weights. Region-level results (`OutputFile`) columns: - **Region**: Region identifier from `GroupFile`. - **nMarkers**: Number of rare variants with MAF < cutoff and MAC >= `min_mac_region`. - **nMarkersURV**: Number of ultra-rare variants with MAC < `min_mac_region`. - **Anno.Type**: Annotation type from `GroupFile`. - **MaxMAF.Cutoff**: Maximum MAF cutoff used for variant selection. - **pval.SKATO**: SKAT-O test p-value. - **pval.SKAT**: SKAT test p-value. - **pval.Burden**: Burden test p-value. Marker-level results (`paste0(OutputFile, ".markerInfo")`) columns: - **Region**: Region identifier. - **ID**: Marker identifier. - **Info**: Marker information in format CHR:POS:REF:ALT. - **Anno**: Annotation from `GroupFile`. - **AltFreq**: Alternative allele frequency. - **MAC**: Minor allele count. - **MAF**: Minor allele frequency. - **MissingRate**: Proportion of missing genotypes. - **IndicatorVec**: Marker status indicator (1 = rare variant included, 3 = ultra-rare variant included). - **StatVec**: Score test statistic. - **altBetaVec**: Effect size estimate. - **seBetaVec**: Standard error of effect size estimate. - **pval0Vec**: Unadjusted p-value. - **pval1Vec**: SPA-adjusted p-value. - **posRow**: Position row index. Other marker info (`paste0(OutputFile, ".otherMarkerInfo")`) columns: - **ID**: Marker identifier. - **Annos**: Annotation from `GroupFile`. - **Region**: Region identifier. - **Info**: Marker information in format CHR:POS:REF:ALT. - **Anno**: Annotation category. - **AltFreq**: Alternative allele frequency. - **MAC**: Minor allele count. - **MAF**: Minor allele frequency. - **MissingRate**: Proportion of missing genotypes. - **IndicatorVec**: Status indicator (0 or 2 for excluded markers). Burden test summary (`paste0(OutputFile, ".infoBurdenNoWeight")`) columns: - **region**: Region identifier. - **anno**: Annotation type. - **max_maf**: Maximum MAF cutoff. - **sum**: Sum of genotypes. - **Stat**: Score test statistic. - **beta**: Effect size estimate. - **se.beta**: Standard error of effect size estimate. - **pvalue**: P-value for burden test. ## Examples ```r GenoFileStep1 <- system.file("extdata", "simuPLINK.bed", package = "GRAB") GenoFileStep2 <- system.file("extdata", "simuPLINK_RV.bed", package = "GRAB") SparseGRMFile <- system.file("extdata", "SparseGRM.txt", package = "GRAB") GroupFile <- system.file("extdata", "simuPLINK_RV.group", package = "GRAB") OutputFile <- file.path(tempdir(), "resultPOLMMregion.txt") PhenoFile <- system.file("extdata", "simuPHENO.txt", package = "GRAB") PhenoData <- data.table::fread(PhenoFile, header = TRUE) PhenoData$OrdinalPheno <- factor(PhenoData$OrdinalPheno, levels = c(0, 1, 2)) # Step 1 obj.POLMM <- GRAB.NullModel( OrdinalPheno ~ AGE + GENDER, data = PhenoData, subjIDcol = "IID", method = "POLMM", traitType = "ordinal", GenoFile = GenoFileStep1, SparseGRMFile = SparseGRMFile, control = list(tolTau = 0.2, tolBeta = 0.1) ) # Step 2 GRAB.Region(obj.POLMM, GenoFileStep2, OutputFile, GroupFile = GroupFile, SparseGRMFile = SparseGRMFile, MaxMAFVec = "0.01,0.005" ) head(data.table::fread(OutputFile)) head(data.table::fread(paste0(OutputFile, ".markerInfo"))) head(data.table::fread(paste0(OutputFile, ".otherMarkerInfo"))) head(data.table::fread(paste0(OutputFile, ".infoBurdenNoWeight"))) ``` ## References Bi et al. (2023). Scalable mixed model methods for set-based association studies on large-scale categorical data analysis and its application to exome-sequencing data in UK Biobank. tools:::Rd_expr_doi("10.1016/j.ajhg.2023.03.010")

GRAB.POLMM.Region function