README for BCX2 summary stat files on GRASP ####################################################################################################################################################################### file name convention: TRAIT_ANCESTRY_METHOD_date_GC.gz TRAIT: BASO, EOS, HCT, HGB, LYM, MCH, MCHC, MCV, MONO, MPV, NEU, PLT, RBC, RDW, WBC ANCESTRY: AA (African), EA (European), EAS, (East Asian), HA (Hispanic/Latino), SA (South Asian), Trans (trans-ethnic) METHOD: GWAMA fixed effects (https://genomics.ut.ee/en/tools/gwama) for ancestry-specific meta-analyses and MRMEGA (https://genomics.ut.ee/en/tools/mr-mega) for trans-ethnic meta-analysis, BoltLMM (https://data.broadinstitute.org/alkesgroup/BOLT-LMM/) for SA and MPV_HA that have one single cohort date: 20190214 (analysis date) GC: with GC corrected p-value (lambda>1) or lambda estimate (lambda<=1) Cohort level analyses: We tested an additive genetic model of association between genotype imputation dosage and inverse normal transformed residuals (obtained from regression of blood-cell phenotypes adjusted for sex, age, squared age and principal components) using linear mixed effects model. Variant filters: imputation Info score <= 0.4 and minor allele count (MAC) <= 5, for large cohorts UKBB_EA, GERA_EA and WHI, the MAC filter is <= 20 Ancestry-specific meta-analyses: fixed effect model implemented in GWAMA v2.2.2 (Magi and Morris, 2010) Trans-ethnic meta-analysis: Meta-Regression of Multi-Ethnic Genetic Association (MRMEGA v0.1.5, Magi et al., 2017) 1. Chen M-H et al. (2020) Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations. Cell. 2020 Sep 3;182(5):1198-1213.e14. doi: 10.1016/j.cell.2020.06.045. PMID: 32888493; PMCID: PMC7480402. 2. Vuckovic et al. (2020) The Polygenic and Monogenic Basis of Blood Traits and Diseases. Cell. 2020 Sep 3;182(5):1214-1231.e11. doi: 10.1016/j.cell.2020.08.008. PMID: 32888494; PMCID: PMC7482360. ####################################################################################################################################################################### Header descriptions in GWAMA meta-analysis files (AA, EA, EAS, and HA) rs_number - unique marker identification across input files (chromosome:position_allele1_allele2, with alleles listed in lexicographical order or based on indel length) reference_allele - Effect allele other_allele - Non effect allele eaf - effect allele frequency beta - Overall effect size/beta value for meta-analysis se - standard error beta_95L - Lower 95% CI for beta beta_95U - Upper 95% CI for beta z - Z-score p-value - Meta-analysis p-value _-log10_p-value - Absolut value of logarithm of meta-analysis p-value to the base of 10. q_statistic - Cochran's heterogeneity statistic q_p-value - Cochran's heterogeneity statistic's p-value i2 - Heterogeneity index I2 by Higgins et al 2003 n_studies - Number of studies with marker present n_samples - Number of samples with marker present effects - Summary of effect directions ('+' - positive effect of reference allele, '-' - negative effect of reference allele,'?' - missing data) chi-squared_GCcorr - GC corrected chi-square stat p-value_GCcorr - GC corrected p-value Lambda - GC lambda estimate ####################################################################################################################################################################### Header descriptions in MR-MEGA meta-analysis files (trans-ethnic meta-analyses) MarkerName - unique marker identification across input files (Chromosome:Position_allele1_allele2, with alleles listed in lexicographical order or based on indel length) Chromosome - Marker chromosome Position - Marker position (base pair), build 37 EA - Effect allele NEA - Non effect allele EAF - average effect allele frequency (weighted by the samplesize of each input file) Nsample - total number of samples Ncohort - total number of cohorts, where the marker was present Effects - effect direction across cohorts (+ positive effect of effect allele, '-' - negative effect of effect allele,'?' - missing data) beta_0 - effect of intercept meta-regression se_0 - standard error of intercept meta-regression beta_1 - effect of first PC of meta-regression se_1 - standard error of the effect of first PC of meta-regression beta_2 - effect of second PC of meta-regression se_2 - standard error of the effect of second PC of meta-regression beta_3 - effect of fourth PC of meta-regression se_3 - standard error of the effect of fourth PC of meta-regression beta_4 - effect of fourth PC of meta-regression se_4 - standard error of the effect of fourth PC of meta-regression chisq_association - chisq value of the association ndf_association - number of degrees of freedom of the association P.value_association - p-value of the association chisq_ancestry_het - chisq value of the heterogeneity due to different ancestry ndf_ancestry_het - ndf of the heterogeneity due to different ancestry P.value_ancestry_het - p-value of the heterogeneity due to different ancestry chisq_residual_het - chisq value of the residual heterogeneity ndf_residual_het - ndf of the residual heterogeneity P.value_residual_het - p-value of the residual heterogeneity lnBF - log of Bayes factor Comments - reason why marker was not analysed (blank for our analysis) chi-squared_association_GCcorr: GC corrected chisq value of the association p-value_association_GCcorr: GC corrected p-value of the association chi-squared_ancestry_het_GCcorr: GC corrected chisq value of the heterogeneity due to different ancestry p-value_ancestry_het_GCcorr: GC corrected p-value of the heterogeneity due to different ancestry ####################################################################################################################################################################### Header descriptions in BoltLMM analysis files (SAS and MPV_HA) cptid- Chromosome:Position ID STRAND - strand, + or - EFFECT_ALLELE - effect allele OTHER_ALLELE - non-effect allele EAF - effect allele frequency IMPUTATION - imputation rsq (metric of imputation quality) BETA - effect size SE - standard error of effect size PVAL - p-value N - number of samples MAC - minor allele count Unique_ID- unique marker identification across input files (Chromosome:Position_allele1_allele2, with alleles listed in lexicographical order or based on indel length) CHR - Marker chromosome BP- Marker position (base pair), build 37 chi-squared_GCcorr: GC corrected chi-square statistic p-value_GCcorr: GC corrected p-value ####################################################################################################################################################################### File name sample size BASO_AA_GWAMA_20190214_GC.gz 11502 BASO_EA_GWAMA_20190214_GC.gz 474001 BASO_EAS_GWAMA_20190214_GC.gz 81042 BASO_HA_GWAMA_20190214_GC.gz 2969 BASO_SAS_BoltLMM_20190214_GC.gz 8149 BASO_Trans_MRMEGA_20190214_GC.gz 577663 EOS_AA_GWAMA_20190214_GC.gz 11615 EOS_EA_GWAMA_20190214_GC.gz 474237 EOS_EAS_GWAMA_20190214_GC.gz 86890 EOS_HA_GWAMA_20190214_GC.gz 2966 EOS_SAS_BoltLMM_20190214_GC.gz 8142 EOS_Trans_MRMEGA_20190214_GC.gz 583850 HCT_AA_GWAMA_20190214_GC.gz 15134 HCT_EA_GWAMA_20190214_GC.gz 562259 HCT_EAS_GWAMA_20190214_GC.gz 142940 HCT_HA_GWAMA_20190214_GC.gz 9301 HCT_SAS_BoltLMM_20190214_GC.gz 8189 HCT_Trans_MRMEGA_20190214_GC.gz 737823 HGB_AA_GWAMA_20190214_GC.gz 15133 HGB_EA_GWAMA_20190214_GC.gz 563946 HGB_EAS_GWAMA_20190214_GC.gz 149861 HGB_HA_GWAMA_20190214_GC.gz 9302 HGB_SAS_BoltLMM_20190214_GC.gz 8189 HGB_Trans_MRMEGA_20190214_GC.gz 746431 LYM_AA_GWAMA_20190214_GC.gz 13477 LYM_EA_GWAMA_20190214_GC.gz 524923 LYM_EAS_GWAMA_20190214_GC.gz 89266 LYM_HA_GWAMA_20190214_GC.gz 7541 LYM_SAS_BoltLMM_20190214_GC.gz 8163 LYM_Trans_MRMEGA_20190214_GC.gz 643370 MCH_AA_GWAMA_20190214_GC.gz 11974 MCH_EA_GWAMA_20190214_GC.gz 486823 MCH_EAS_GWAMA_20190214_GC.gz 119629 MCH_HA_GWAMA_20190214_GC.gz 3518 MCH_SAS_BoltLMM_20190214_GC.gz 8181 MCH_Trans_MRMEGA_20190214_GC.gz 630125 MCHC_AA_GWAMA_20190214_GC.gz 12766 MCHC_EA_GWAMA_20190214_GC.gz 491553 MCHC_EAS_GWAMA_20190214_GC.gz 126151 MCHC_HA_GWAMA_20190214_GC.gz 3518 MCHC_SAS_BoltLMM_20190214_GC.gz 8185 MCHC_Trans_MRMEGA_20190214_GC.gz 642173 MCV_AA_GWAMA_20190214_GC.gz 14222 MCV_EA_GWAMA_20190214_GC.gz 544127 MCV_EAS_GWAMA_20190214_GC.gz 121047 MCV_HA_GWAMA_20190214_GC.gz 9298 MCV_SAS_BoltLMM_20190214_GC.gz 8188 MCV_Trans_MRMEGA_20190214_GC.gz 696882 MONO_AA_GWAMA_20190214_GC.gz 13471 MONO_EA_GWAMA_20190214_GC.gz 521594 MONO_EAS_GWAMA_20190214_GC.gz 88929 MONO_HA_GWAMA_20190214_GC.gz 7543 MONO_SAS_BoltLMM_20190214_GC.gz 7043 MONO_Trans_MRMEGA_20190214_GC.gz 639696 MPV_AA_GWAMA_20190214_GC.gz 10737 MPV_EA_GWAMA_20190214_GC.gz 460935 MPV_HA_BoltLMM_20190214_GC.gz 2810 MPV_SAS_BoltLMM_20190214_GC.gz 8189 MPV_Trans_MRMEGA_20190214_GC.gz 484042 NEU_AA_GWAMA_20190214_GC.gz 13476 NEU_EA_GWAMA_20190214_GC.gz 519288 NEU_EAS_GWAMA_20190214_GC.gz 78744 NEU_HA_GWAMA_20190214_GC.gz 7542 NEU_SAS_BoltLMM_20190214_GC.gz 8165 NEU_Trans_MRMEGA_20190214_GC.gz 627215 PLT_AA_GWAMA_20190214_GC.gz 15171 PLT_EA_GWAMA_20190214_GC.gz 542827 PLT_EAS_GWAMA_20190214_GC.gz 145648 PLT_HA_GWAMA_20190214_GC.gz 9367 PLT_SAS_BoltLMM_20190214_GC.gz 8188 PLT_Trans_MRMEGA_20190214_GC.gz 721201 RBC_AA_GWAMA_20190214_GC.gz 14222 RBC_EA_GWAMA_20190214_GC.gz 545203 RBC_EAS_GWAMA_20190214_GC.gz 150708 RBC_HA_GWAMA_20190214_GC.gz 9302 RBC_SAS_BoltLMM_20190214_GC.gz 8189 RBC_Trans_MRMEGA_20190214_GC.gz 727624 RDW_AA_GWAMA_20190214_GC.gz 13519 RDW_EA_GWAMA_20190214_GC.gz 531774 RDW_HA_GWAMA_20190214_GC.gz 8526 RDW_SAS_BoltLMM_20190214_GC.gz 8166 RDW_Trans_MRMEGA_20190214_GC.gz 563352 WBC_AA_GWAMA_20190214_GC.gz 15061 WBC_EA_GWAMA_20190214_GC.gz 562243 WBC_EAS_GWAMA_20190214_GC.gz 151807 WBC_HA_GWAMA_20190214_GC.gz 9368 WBC_SAS_BoltLMM_20190214_GC.gz 8188 WBC_Trans_MRMEGA_20190214_GC.gz 746667