Dataset
CRISPRi + scRNAseq
README (see README_scCRISPRi_HUDEP_Ilboudo_etal.txt) and count table (see scCRISPRi_HUDEP_Ilboudo_etal.txt) for “CRISPRi + scRNAseq for regulators of fetal hemoglobin in HUDEP-2 (Ilboudo et al.)”
Atrial fibrillation and sinus rhythm
Left atrial appendage bulk RNAseq count tables of atrial fibrillation and sinus rhythm patients from a European dominant cohort (CTSN) and East Asian cohort (Harbin).
| CTSN | |
| counts.ctsn.tsv | meta.ctsn.tsv |
| Harbin | |
| counts.harbin.tsv | meta.harbin.tsv |
| LINC01629_KD | |
| counts.LINC01629_KD.tsv | meta.LINC01629_KD.tsv |
Blood pressure pLoF variants (UKB)
GWAS summary statistics of pLoF variants association with systolic and diastolic blood pressure in UKB reported in this article:
- Association analyses of predicted loss-of-function variants prioritized 15 genes as blood pressure regulators, Canadian Journal of Cardiology 2023
| Systolic blood pressure | Diastolic blood pressure | |
| WES pLoF 200k discovery UKB cohort (additive and recessive) | file | file |
| WES pLoF 300k replication UKB cohort (additive and recessive) | file | file |
| WES pLoF whole 500k UKB cohort (additive and recessive) | file | file |
| WES pLoF whole 500k UKB cohort (gene-based test) | file | file |
Blood Cell Consortium (BCX)
Summary association data (see readme_BCX2_meta_analyses.txt), fine-mapping results (see readme_BCX2_CS95_VEP.txt) and g-chromVar enrichment results (see readme_BCX2_gchromVAR_5Sept2019.txt) reported in the following article:
- Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations, Cell 2020
| MR-MEGA | GWAMA | ||||||
| Trans-ethnic | Trans-ethnic | European | East-Asian | African | Hispanics/Latinos | ||
| Red blood cells | red blood cell count (RBC count) | file | file | file | file | file | file |
| hemoglobin concentration (HGB) | file | file | file | file | file | file | |
| hematocrit (HCT) | file | file | file | file | file | file | |
| mean corpuscular hemoglobin (MCH) | file | file | file | file | file | file | |
| mean corpuscular volume (MCV) | file | file | file | file | file | file | |
| mean corpuscular hemoglobin concentration (MCHC) | file | file | file | file | file | file | |
| RBC distribution width (RDW) | file | file | file | N.A. | file | file | |
| White blood cells | total white blood cell count (WBC count) | file | file | file | file | file | file |
| neutrophil count (Neutro) | file | file | file | file | file | file | |
| lymphocyte count (Lympho) | file | file | file | file | file | file | |
| monocyte count (Mono) | file | file | file | file | file | file | |
| basophil count (Baso) | file | file | file | file | file | file | |
| eosinophil count (Eosin) | file | file | file | file | file | file | |
| Platelets | platelet count (PLT count) | file | file | file | file | file | file |
| mean platelet volume (MPV) | file | file | file | file | file | file | |
| Fine-mapping results (95% credible sets) | BCX2_CR95_VEP.zip |
| G-chromVar enrichment results | CS.trans_all.PP01.strong_atac_overlap.tsv |
| CS.trans_subancestry.PP01.strong_atac_overlap.tsv |
Summary ExomeChip association data (for more information, see readme2.txt) reported in the following article:
- Rare coding variants pinpoint genes that control human hematological traits, PLoS Genet 2017
| Red blood cells | red blood cell count (RBC count) | meta_rare_rbc.txt.gz (3.3MB) |
| hemoglobin concentration (HGB) | meta_rare_hgb.txt.gz (3.4MB) | |
| hematocrit (HCT) | meta_rare_hct.txt.gz (3.4MB) | |
| mean corpuscular hemoglobin (MCH) | meta_rare_mch.txt.gz (3.3MB) | |
| mean corpuscular volume (MCV) | meta_rare_mcv.txt.gz (3.3MB) | |
| mean corpuscular hemoglobin concentration (MCHC) | meta_rare_mchc.txt.gz (3.3MB) | |
| RBC distribution width (RDW) | meta_rare_rdw.txt.gz (3.3MB) | |
| White blood cells | total white blood cell count (WBC count) | meta_rare_wbc.txt.gz (3.4MB) |
| neutrophil count (Neutro) | meta_rare_neutro.txt.gz (3.4MB) | |
| lymphocyte count (Lympho) | meta_rare_lympho.txt.gz (3.3MB) | |
| monocyte count (Mono) | meta_rare_mono.txt.gz (3.3MB) | |
| basophil count (Baso) | meta_rare_bas.txt.gz (3.2MB) | |
| eosinophil count (Eosin) | meta_rare_eos.txt.gz (3.2MB) | |
| Platelets | platelet count (PLT count) | meta_rare_plt.txt.gz (3.4MB) |
| mean platelet volume (MPV) | meta_rare_mpv.txt.gz (3.2MB) |
Summary ExomeChip association data (for more information, see readme.txt) reported in the following articles:
- Exome Genotyping Identifies Pleiotropic Variants Associated with Red Blood Cell Traits, Am J Hum Genet. 2016
- Large-Scale Exome-wide Association Analysis Identifies Loci for White Blood Cell Traits and Pleiotropy with Immune-Mediated Diseases, Am J Hum Genet. 2016
- Platelet-Related Variants Identified by Exomechip Meta-analysis in 157,293 Individuals, Am J Hum Genet. 2016
| African ancestry | European ancestry | All ancestries | |
| Red blood cell | BCX_RBC_AA.tar.gz (56.1MB) | BCX_RBC_EA.tar.gz (61.1MB) | BCX_RBC_ALL.tar.gz (67.3MB) |
| White blood cell | BCX_WBC_AA.tar.gz (43.4MB) | BCX_WBC_EA.tar.gz (48.3MB) | BCX_WBC_ALL.tar.gz (56.5MB) |
| Platelet | BCX_PLT_AA.tar.gz (15.8MB) | BCX_PLT_EA.tar.gz (17.3MB) | BCX_PLT_ALL.tar.gz (19.1MB) |
Software
CRISPR Off-Target Tool
This script aims at finding off-targets in CRISPR/Cas9 experiments given a list of single-guide RNAs (sgRNAs).
- Version 2.0.5: [Documentation] [R scripts]
- Version 1.02: [Documentation] [R script]
Supplementary tables for Lessard S, Francioli L, Alfoldi J, Tardif JC, Ellinor PT, MacArthur DG, Lettre G, Orkin SH, and Canver MC. Human genetic variation alters CRISPR/Cas9 on- and off-targeting specificity at therapeutically implicated loci [PNAS 2017]
- Supplementary data. List of individual aggregate off-target scores for samples from the 1,000 Genomes Project and their local scores. CFD: cutting frequency determination.
- Supplementary data. List of individual aggregate off-target scores for samples from the French Canadian dataset and their local scores. CFD: cutting frequency determination.
KnockoutQT
This script (knockoutQT.rb) aims to find gene knockouts (KOs) and test their association with a given quantitative phenotype.