Loading required namespace: GenomicFiles Using local VCF. File already tabix-indexed. Finding empty VCF columns based on first 10,000 rows. Dropping 1 duplicate column(s). 1 sample detected: PROT-a-2725 Constructing ScanVcfParam object. VCF contains: 10,283,233 variant(s) x 1 sample(s) Reading VCF file: multi-threaded (4 threads) Dropping 1 duplicate column(s). Dropping 1 duplicate column(s). Dropping 1 duplicate column(s). Dropping 1 duplicate column(s). Renaming ID as SNP. VCF file has -log10 P-values; these will be converted to unadjusted p-values in the 'P' column. No INFO (SI) column detected. Standardising column headers. First line of summary statistics file: SNP chr BP end REF ALT FILTER AF ES LP SE P Summary statistics report: - 10,265,284 rows - 10,265,264 unique variants - 407 genome-wide significant variants (P<5e-8) - 22 chromosomes Checking for multi-GWAS. Checking for multiple RSIDs on one row. Inferring genome build. Loading SNPlocs data. Loading reference genome data. Preprocessing RSIDs. Validating RSIDs of 10,000 SNPs using BSgenome::snpsById... Loading required package: BiocGenerics Attaching package: ‘BiocGenerics’ The following objects are masked from ‘package:stats’: IQR, mad, sd, var, xtabs The following objects are masked from ‘package:base’: anyDuplicated, aperm, append, as.data.frame, basename, cbind, colnames, dirname, do.call, duplicated, eval, evalq, Filter, Find, get, grep, grepl, intersect, is.unsorted, lapply, Map, mapply, match, mget, order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank, rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply, union, unique, unsplit, which.max, which.min Loading required package: S4Vectors Loading required package: stats4 Attaching package: ‘S4Vectors’ The following objects are masked from ‘package:base’: expand.grid, I, unname BSgenome::snpsById done in 27 seconds. Loading SNPlocs data. Loading reference genome data. Preprocessing RSIDs. Validating RSIDs of 10,000 SNPs using BSgenome::snpsById... BSgenome::snpsById done in 34 seconds. Inferred genome build: GRCH37 Checking SNP RSIDs. Checking for merged allele column. Checking A1 is uppercase Checking A2 is uppercase Checking for incorrect base-pair positions Ensuring all SNPs are on the reference genome. Loading SNPlocs data. Loading reference genome data. Preprocessing RSIDs. Validating RSIDs of 10,265,264 SNPs using BSgenome::snpsById... BSgenome::snpsById done in 267 seconds. Found 884,444 Indels. These won't be checked against the reference genome as it does not contain Indels. WARNING If your sumstat doesn't contain Indels, set the indel param to FALSE & rerun MungeSumstats::format_sumstats() Checking for correct direction of A1 (reference) and A2 (alternative allele). There are 14 SNPs where neither A1 nor A2 match the reference genome. These will be removed. Sorting coordinates with 'data.table'. Writing in tabular format ==> /rds/general/project/neurogenomics-lab/ephemeral/MAGMA_Files_Public/data/GWAS_munged/prot-a-2725/logs/alleles_dont_match_ref_gen.tsv Writing uncompressed instead of gzipped to enable tabix indexing. Converting full summary stats file to tabix format for fast querying... Reading header. Ensuring file is bgzipped. Tabix-indexing file. Removing temporary .tsv file. There are 51 SNPs where A1 doesn't match the reference genome. These will be flipped with their effect columns. Reordering so first three column headers are SNP, CHR and BP in this order. Reordering so the fourth and fifth columns are A1 and A2. Checking for missing data. WARNING: 39 rows in sumstats file are missing data and will be removed. Sorting coordinates with 'data.table'. Writing in tabular format ==> /rds/general/project/neurogenomics-lab/ephemeral/MAGMA_Files_Public/data/GWAS_munged/prot-a-2725/logs/missing_data.tsv Writing uncompressed instead of gzipped to enable tabix indexing. Converting full summary stats file to tabix format for fast querying... Reading header. Ensuring file is bgzipped. Tabix-indexing file. Removing temporary .tsv file. Checking for duplicate columns. Checking for duplicate SNPs from SNP ID. Found 9 Indels. These won't be checked for duplicates based on RS ID as there can be multiples. WARNING If your sumstat doesn't contain Indels, set the indel param to FALSE & rerun MungeSumstats::format_sumstats() Checking for SNPs with duplicated base-pair positions. Found 9 Indels. These won't be checked for duplicates based on base-pair position as there can be multiples. WARNING If your sumstat doesn't contain Indels, set the indel param to FALSE & rerun MungeSumstats::format_sumstats() Checking for duplicated rows. INFO column not available. Skipping INFO score filtering step. Filtering SNPs, ensuring SE>0. Ensuring all SNPs have N<5 std dev above mean. Checking for bi-allelic SNPs. 220,066 SNPs are non-biallelic. These will be removed. Sorting coordinates with 'data.table'. Writing in tabular format ==> /rds/general/project/neurogenomics-lab/ephemeral/MAGMA_Files_Public/data/GWAS_munged/prot-a-2725/logs/snp_bi_allelic.tsv Writing uncompressed instead of gzipped to enable tabix indexing. Converting full summary stats file to tabix format for fast querying... Reading header. Ensuring file is bgzipped. Tabix-indexing file. Removing temporary .tsv file. Computing Z-score from P using formula: `sign(BETA)*sqrt(stats::qchisq(P,1,lower=FALSE)` Warning: When method is an integer, must be >0. 1,566,833 SNPs (17.2%) have FRQ values > 0.5. Conventionally the FRQ column is intended to show the minor/effect allele frequency. The FRQ column was mapped from one of the following from the inputted summary statistics file: FRQ, EAF, FREQUENCY, FRQ_U, F_U, MAF, FREQ, FREQ_TESTED_ALLELE, FRQ_TESTED_ALLELE, FREQ_EFFECT_ALLELE, FRQ_EFFECT_ALLELE, EFFECT_ALLELE_FREQUENCY, EFFECT_ALLELE_FREQ, EFFECT_ALLELE_FRQ, A1FREQ, A1FRQ, A2FREQ, A2FRQ, ALLELE_FREQUENCY, ALLELE_FREQ, ALLELE_FRQ, AF, MINOR_AF, EFFECT_AF, A2_AF, EFF_AF, ALT_AF, ALTERNATIVE_AF, INC_AF, A_2_AF, TESTED_AF, AF1, ALLELEFREQ, ALT_FREQ, EAF_HRC, EFFECTALLELEFREQ, FREQ.A1.1000G.EUR, FREQ.A1.ESP.EUR, FREQ.ALLELE1.HAPMAPCEU, FREQ.B, FREQ1, FREQ1.HAPMAP, FREQ_EUROPEAN_1000GENOMES, FREQ_HAPMAP, FREQ_TESTED_ALLELE_IN_HRS, FRQ_A1, FRQ_U_113154, FRQ_U_31358, FRQ_U_344901, FRQ_U_43456, POOLED_ALT_AF, AF_ALT, AF.ALT, AF-ALT, ALT.AF, ALT-AF, A2.AF, A2-AF, AF.EFF, AF_EFF, AF_EFF As frq_is_maf=TRUE, the FRQ column will not be renamed. If the FRQ values were intended to represent major allele frequency, set frq_is_maf=FALSE to rename the column as MAJOR_ALLELE_FRQ and differentiate it from minor/effect allele frequency. Sorting coordinates with 'data.table'. Sorting coordinates with 'data.table'. Writing in tabular format ==> /rds/general/project/neurogenomics-lab/ephemeral/MAGMA_Files_Public/data/GWAS_munged/prot-a-2725/prot-a-2725.tsv Writing uncompressed instead of gzipped to enable tabix indexing. Converting full summary stats file to tabix format for fast querying... Reading header. Ensuring file is bgzipped. Tabix-indexing file. Removing temporary .tsv file. Summary statistics report: - 9,108,474 rows (88.7% of original 10,265,284 rows) - 9,108,474 unique variants - 360 genome-wide significant variants (P<5e-8) - 22 chromosomes Done munging in 19.059 minutes. Successfully finished preparing sumstats file, preview: Reading header. SNP CHR BP A1 A2 END FILTER FRQ BETA LP SE 1: rs575272151 1 11008 C G 11008 PASS 0.08306 -0.0608 0.620001 0.0518 2: rs531730856 1 13273 G C 13273 PASS 0.13251 -0.0374 0.410000 0.0434 3: rs554760071 1 13483 G C 13483 PASS 0.00504 -0.0243 0.050000 0.1959 4: rs541940975 1 14604 A G 14604 PASS 0.18665 0.0423 0.599999 0.0370 5: rs199856693 1 14933 G A 14933 PASS 0.04746 -0.0496 0.330000 0.0677 P Z 1: 0.2398827 -1.1752799 2: 0.3890451 -0.8613503 3: 0.8912509 -0.1367215 4: 0.2511892 1.1474657 5: 0.4677351 -0.7261691