GWAS summary data accompanying "Multi-Level Characterization of Pleiotropy and Unique Genetic Influences on Cognitive Abilities"

GWAS summary data accompanying "Multi-Level Characterization of Pleiotropy and Unique Genetic Influences on Cognitive Abilities" ********************************************************************************** 1. GWAS of specific cognitive tests******************************************************************************* All GWAS analyses were conducted using Regenie (v 2.1.1; implemented in the DNA Nexus tools library for UK Biobank). *** Column Names:#CHROM : Chromosome number (1–22)GENPOS : Base-pair position (GRCh37)ID : Variant identifier (rsID)ALLELE0 : Reference/non-effect allele (A0)ALLELE1 : Effect allele (A1); BETA is the effect of ALLELE1A1FREQ : Frequency of ALLELE1 in the analyzed sampleINFO : Imputation INFO score (or 1.0 for hard-called genotypes)N : Per-variant sample size used in the association testTEST : Genetic model used (e.g., ADD = additive)BETA : Standardised regression coefficient for ALLELE1 (ADD model).SE : Standard error of BETACHISQ : Chi-square statistic LOG10P : −log10(p-value) GWAS on specific cognitive tests were carried out for:- Matrix pattern reasoning (matrix_all.tsv.gz)- Memory pairs matching (memory_all.tsv.gz)- Reaction time (rt_all.tsv.gz)- Prospective memory (prospective_all.tsv.gz)- Symbol-digit substitution (symbol_all.tsv.gz)- Numeric memory (numeric_all.tsv.gz)- Trail-Making Test B (tmtb_all.tsv.gz)- Verbal-Numerical reasoning (vnr_all.tsv.gz) ********************************************************************************** 2. Common factor GWAS******************************************************************************* g_factor.txt.gz is the output from commonfactorGWAS(), conducted using GenomicSEM (v0.0.5). ********************************************************************************** 3. Network/Conditional GWAS******************************************************************************* The _net_sumstats suffix refers to network/conditional GWAS, which we obtained using the gwasNET() function from the GNA R package (v0.0.1). See: https://github.com/GenomicNetworkAnalysis/GNA/wiki/4.-Running-a-Conditional-GWAS.Columns have been subsetted to create separate summary statistics for each trait. *** Column Names:SNP : rsidCHR : Chromosome number (1–22)BP :Base-pair position (GRCh37)A1FREQ : Effect-allele frequency; corresponds to A1A1 : Effect allele A2 : Other/non-effect alleleBETA : Standardised regression coefficient for A1.SE : Standard error of BETAZ : Z-statistic p : P-value