2.7 Download ================ | Link: https://bio.liclab.net/scvmap/download All downloadable data provides users with a centralized access portal aimed at facilitating their access to research resources. We have integrated the following comprehensive datasets: (i) scATAC-seq data; (ii) Fine-mapping results; (iii) The trait--relevant score (TRS) of each single-cell generated by g-chromVAR and SCAVENGE methods; (iv) Results of gene and TF related analysis; (v) Gene regulation annotation data. 2.7.1 Download TRS data for each sample ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. image:: ../img/download/overview.png Below are the detailed download instructions. 2.7.1.1 Overview of scATAC-seq data: ``txt`` file """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" ==== =========================== ============================================================================================================================================================== # Column name Description ==== =========================== ============================================================================================================================================================== 1 f_sample_id The unique identifier of the single-cell sample, used for database operations. 2 f_gse_id GSE ID 3 f_genome The reference genome of the single-cell sample. 4 f_geo_id GEO ID 5 f_label The unique identifier for the single-cell sample, used as the file name during data processing. 6 f_pmid PMID 7 f_species The species information of the single-cell sample. All data belongs to humans. 8 f_tissue_type The tissue type of the single-cell sample. 9 f_sequencing_type The sequencing type of the single-cell sample. 10 f_health_type The health type of the single-cell sample. 11 f_health_type_description Detailed information on the health type of the single-cell sample. 12 f_description Detailed information on the content of the single-cell sample. 13 f_source The source name of the single-cell sample. 14 f_source_url The link to the source of the single-cell sample. 15 f_counts_layer The layer name of the counts matrix stored in the Seurat object of the single-cell sample. 16 f_sample_exist The single-cell sample contains multiple sample information. 17 f_cell_count The number of cells in the single-cell sample. 18 f_cell_type_count The number of cell types in the single-cell sample. 19 f_index The unique index identifier of the single-cell sample has no meaning and is only used for sorting. 20 f_time An indicator variable for whether this single-cell sample contains cell annotation information for age/day/time. 1 indicates presence, 0 indicates absence. 21 f_sex An indicator variable for whether this single-cell sample contains cell annotation information for sex. 1 indicates presence, 0 indicates absence. 22 f_drug An indicator variable for whether this single-cell sample contains cell annotation information for drug resistance. 1 indicates presence, 0 indicates absence. ==== =========================== ============================================================================================================================================================== .. note:: When downloading files, some browsers will directly open the ``txt`` file and need to save the file by right-click. .. image:: ../img/download/txt_download.png 2.7.1.2 scATAC-seq data: ``H5AD`` file """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" Read the information of ``sample_id_1``. .. code-block:: python :linenos: >>> data AnnData object with n_obs × n_vars = 36721 × 414680 obs: 'n_fragment', 'frac_dup', 'frac_mito', 'tsse', 'doublet_probability', 'doublet_score', 'barcode', 'n_genes', 'n_counts', 'cell_type', 'UMAP1', 'UMAP2', 'barcodes' var: 'count', 'selected', 'chr', 'start', 'end', 'n_cells' uns: 'doublet_rate', 'macs3', 'params', 'project_name', 'project_version', 'reference_sequences', 'scrublet_sim_doublet_score', 'step' obsm: 'fragment_paired' >>> >>> >>> data.var count selected chr start end n_cells index chr1:237500-238000 316.0 True chr1 237500 238000 296 chr1:238000-238500 316.0 True chr1 238000 238500 296 chr1:540500-541000 222.0 True chr1 540500 541000 217 chr1:541000-541500 222.0 True chr1 541000 541500 217 chr1:713500-714000 10773.0 True chr1 713500 714000 10145 ... ... ... ... ... ... ... chrX:155232500-155233000 246.0 True chrX 155232500 155233000 225 chrX:155233500-155234000 200.0 True chrX 155233500 155234000 186 chrX:155234000-155234500 200.0 True chrX 155234000 155234500 186 chrX:155260000-155260500 603.0 True chrX 155260000 155260500 563 chrX:155260500-155261000 603.0 True chrX 155260500 155261000 563 [414680 rows x 6 columns] >>> >>> >>> data.obs n_fragment frac_dup frac_mito tsse doublet_probability doublet_score barcode n_genes n_counts cell_type UMAP1 UMAP2 barcodes index AAACGAAAGAACGACC-1 24764 0.613793 0.0 14.751286 0.102154 0.095522 AAACGAAAGAACGACC-1 46094 49528 Tumor 4 10.567199 -4.781785 AAACGAAAGAACGACC-1 AAACGAAAGAATACTG-1 2506 0.389822 0.0 14.333112 0.185441 0.001557 AAACGAAAGAATACTG-1 4809 5012 Myeloid 1.443223 13.324852 AAACGAAAGAATACTG-1 AAACGAAAGACACGGT-1 4923 0.478827 0.0 23.241852 0.124562 0.040230 AAACGAAAGACACGGT-1 9438 9846 Treg -1.004199 -7.261578 AAACGAAAGACACGGT-1 AAACGAAAGACCCTAT-1 3674 0.443755 0.0 21.428571 0.172410 0.007480 AAACGAAAGACCCTAT-1 7059 7348 B -5.697628 13.187097 AAACGAAAGACCCTAT-1 AAACGAAAGAGGTACC-1 7178 0.488674 0.0 20.920746 0.152831 0.018101 AAACGAAAGAGGTACC-1 13666 14356 CD8 TEx -5.956334 -3.010488 AAACGAAAGAGGTACC-1 ... ... ... ... ... ... ... ... ... ... ... ... ... ... TTTGTGTTCGAGGCTC-1 4853 0.432597 0.0 17.623604 0.179749 0.004054 TTTGTGTTCGAGGCTC-1 9306 9706 Treg 1.477226 -8.637981 TTTGTGTTCGAGGCTC-1 TTTGTGTTCGGGTCCA-1 5016 0.492256 0.0 24.892704 0.174884 0.006297 TTTGTGTTCGGGTCCA-1 9551 10032 Treg 2.348910 -6.036977 TTTGTGTTCGGGTCCA-1 TTTGTGTTCGTCCCAT-1 12915 0.498855 0.0 15.457507 0.122509 0.042428 TTTGTGTTCGTCCCAT-1 24172 25830 CD8 TEx -8.256992 -3.043979 TTTGTGTTCGTCCCAT-1 TTTGTGTTCTCTTCCT-1 5429 0.461569 0.0 19.229330 0.173898 0.006765 TTTGTGTTCTCTTCCT-1 10422 10858 Treg 2.174267 -8.784227 TTTGTGTTCTCTTCCT-1 TTTGTGTTCTGCCGAG-1 3275 0.425842 0.0 16.528926 0.151769 0.018755 TTTGTGTTCTGCCGAG-1 6310 6550 Naive CD8 T -0.882584 1.916430 TTTGTGTTCTGCCGAG-1 [36721 rows x 13 columns] 2.7.1.3 The result data of method g-ChromVAR: ``H5AD`` file """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" Read the information of ``sample_id_1 + FINEMAP``. | ``obs``: Cell | ``var``: Trait or disease | ``X``: Z-score .. code-block:: python :linenos: >>> data AnnData object with n_obs × n_vars = 36721 × 15805 obs: 'f_sample_id', 'f_barcodes', 'f_cell_type', 'f_sample', 'f_umap_x', 'f_umap_y', 'f_tsse', 'f_index', 'f_cell_type_index' var: 'f_trait_id', 'f_trait_code', 'f_source_genome', 'f_trait_abbr', 'f_trait', 'f_variant_count' >>> >>> data.var f_trait_id f_trait_code f_source_genome f_trait_abbr f_trait f_variant_count f_trait_id trait_id_826 trait_id_826 CAUSALdb_Appendicitis_PE06234_672 hg19 Appendicitis_PE06234 Appendicitis 13 trait_id_2146 trait_id_2146 CAUSALdb_COE_FG02496_3096 hg19 COE_FG02496 Cancer of esophagus 2 trait_id_3466 trait_id_3466 CAUSALdb_EHKPCAORROACYBNITLY_FG00466_5927 hg19 EHKPCAORROACYBNITLY_FG00466 Ever had known person concerned about, or reco... 1 trait_id_1156 trait_id_1156 CAUSALdb_BNT_F900340_4465 hg19 BNT_F900340 Benign neoplasm: Testis 1 trait_id_1816 trait_id_1816 CAUSALdb_CI_FG00089_4526 hg19 CI_FG00089 Carrot intake 21 ... ... ... ... ... ... ... trait_id_15801 trait_id_15801 UKBB_Worrier_43 hg19 Worrier Worrier 5683 trait_id_15802 trait_id_15802 UKBB_Worry_Too_Long_85 hg19 Worry_Too_Long Worry too long after embarrassment 3225 trait_id_15803 trait_id_15803 UKBB_eBMD_6 hg19 eBMD Estimated heel bone mineral density 37155 trait_id_15804 trait_id_15804 UKBB_eGFR_15 hg19 eGFR Estimated glomerular filtration rate (serum cr... 35955 trait_id_15805 trait_id_15805 UKBB_eGFRcys_3 hg19 eGFRcys Estimated glomerular filtration rate (cystain C) 37319 [15805 rows x 6 columns] >>> >>> data.obs f_sample_id f_barcodes f_cell_type f_sample f_umap_x f_umap_y f_tsse f_index f_cell_type_index index AAACGAAAGAACGACC-1 sample_id_1 AAACGAAAGAACGACC-1 Tumor 4 GSE129785 10.567199 -4.781785 14.751286 1 0 AAACGAAAGAATACTG-1 sample_id_1 AAACGAAAGAATACTG-1 Myeloid GSE129785 1.443223 13.324852 14.333112 2 0 AAACGAAAGACACGGT-1 sample_id_1 AAACGAAAGACACGGT-1 Treg GSE129785 -1.004199 -7.261578 23.241852 3 0 AAACGAAAGACCCTAT-1 sample_id_1 AAACGAAAGACCCTAT-1 B GSE129785 -5.697628 13.187097 21.428571 4 0 AAACGAAAGAGGTACC-1 sample_id_1 AAACGAAAGAGGTACC-1 CD8 TEx GSE129785 -5.956334 -3.010488 20.920746 5 0 ... ... ... ... ... ... ... ... ... ... TTTGTGTTCGAGGCTC-1 sample_id_1 TTTGTGTTCGAGGCTC-1 Treg GSE129785 1.477226 -8.637981 17.623604 36717 4065 TTTGTGTTCGGGTCCA-1 sample_id_1 TTTGTGTTCGGGTCCA-1 Treg GSE129785 2.348910 -6.036977 24.892704 36718 4066 TTTGTGTTCGTCCCAT-1 sample_id_1 TTTGTGTTCGTCCCAT-1 CD8 TEx GSE129785 -8.256992 -3.043979 15.457507 36719 3897 TTTGTGTTCTCTTCCT-1 sample_id_1 TTTGTGTTCTCTTCCT-1 Treg GSE129785 2.174267 -8.784227 19.229330 36720 4067 TTTGTGTTCTGCCGAG-1 sample_id_1 TTTGTGTTCTGCCGAG-1 Naive CD8 T GSE129785 -0.882584 1.916430 16.528926 36721 2767 [36721 rows x 9 columns] >>> >>> data.X.todense() matrix([[ 0. , 0. , 0. , ..., 1.34798235, 0.13897425, 0.46950752], [ 0. , 0. , 0. , ..., -0.27093183, -0.28416698, 0.2759976 ], [ 0. , 0. , 0. , ..., -0.6249468 , 0.11480793, -1.2071487 ], ..., [ 0. , 0. , 0. , ..., -0.40784247, 0.35490693, -0.85452906], [ 0. , 0. , 0. , ..., 0.50343663, 0.07536454, 0.42840868], [ 0. , 0. , 0. , ..., -0.82765052, 0.20382107, 0.89792407]]) 2.7.1.4 The result data of method SCAVENGE: ``H5AD`` file """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" Read the information of ``sample_id_1 + FINEMAP``. | ``obs``: Cell | ``var``: Trait or disease | ``X``: TRS .. code-block:: python :linenos: >>> data AnnData object with n_obs × n_vars = 36721 × 15805 obs: 'f_sample_id', 'f_barcodes', 'f_cell_type', 'f_sample', 'f_umap_x', 'f_umap_y', 'f_tsse', 'f_index', 'f_cell_type_index' var: 'f_trait_id', 'f_trait_code', 'f_source_genome', 'f_trait_abbr', 'f_trait', 'f_variant_count' >>> >>> >>> data.X.todense() matrix([[0. , 0. , 0. , ..., 0.11992209, 0.26094234, 0.35693139], [0. , 0. , 0. , ..., 0.50589785, 2.59232072, 1.68724861], [0. , 0. , 0. , ..., 0.10034563, 0.40161146, 0.31860852], ..., [0. , 0. , 0. , ..., 0.03006235, 0.37951727, 0.08840483], [0. , 0. , 0. , ..., 0.09616686, 0.52534063, 0.47852776], [0. , 0. , 0. , ..., 0.21577299, 0.47587153, 0.39203965]]) >>> 2.7.2 Download fine-mapping result data for each sample ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. image:: ../img/download/trait.png Below are the detailed download instructions. 2.7.2.1 Overview of fine-mapping result data: ``xlsx`` file """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" ==== ==================== ============================================================================================================================================================ # Column name Description ==== ==================== ============================================================================================================================================================ 1 f_trait_id The unique identifier of the trait used for searching in the database. 2 f_trait_index The unique identifier of the trait, used for sorting in the database, corresponds one-to-one with 'f_trait_id'. 3 f_trait_code The unique identifier of the trait, used as the file name for the file processing procedure. 4 f_trait_abbr The abbreviation form of the trait. 5 f_trait Detailed information for the trait. 6 f_type The trait is classified as one of the types of "disease", "drug", "compound", "health", "subject", "treatment", "symptom", "indicator" or "other". 7 f_icd10 ICD-10 8 f_category Major categories in ICD-10 9 f_sub_category Subcategories in ICD-10 10 f_three_category The third category in ICD-10 11 f_source_id Unique ID of the trait source cohort. 12 f_source_name Name of the trait source cohort. 13 f_source_genome Reference genome of trait source cohort. (Reference genome of the trait before LiftOver) 14 f_variant_count The number of variant in the trait before LiftOver. 15 f_variant_pp_sum The total PP value of variant in the trait before LiftOver. 16 f_hg19_count The number of variant in the trait based on hg19 as a background reference genome. 17 f_hg38_count The number of variant in the trait based on hg38 as a background reference genome. 18 f_hg19_pp_sum The total PP value of variant in the trait based on hg19 as a background reference genome. 19 f_hg38_pp_sum The total PP value of variant in the trait based on hg38 as a background reference genome. 20 f_cohort The cohort for collecting the trait. 21 f_author The author of the origin of the trait. 22 f_mesh_id MESH ID 23 f_mesh_term MESH TERM 24 f_meta_id META ID 25 f_popu Experimental population 26 f_pmid PMID 27 f_n_case Case size 28 f_n_control Control size 29 f_sample_size Sample size 30 f_filter Each trait is retained, with a value of 1 for all. 31 f_index The unique index identifier given in the same source cohort has no meaning and is only used to distinguish different traits in the same source cohort. 32 f_url The link to download the source of each trait. ==== ==================== ============================================================================================================================================================ 2.7.2.2 Fine-mapping result data """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" 1. ``txt`` file (``Download`` field) This file was formed through uniform processing after the original download. ==== ==================== ==================================================================================================== # Column name Description ==== ==================== ==================================================================================================== 1 trait_code unique identifier of the trait, used as the file name for the file processing procedure 2 chr chromosome in the reference genome coordinate of the source cohort 3 position position of variant in the reference genome coordinate of the source cohort 4 variant unique variant identifier 5 rsId rsID identifier 6 allele1 reference allele in the reference genome coordinate of the source cohort 7 allele2 alternative allele in the reference genome coordinate of the source cohort. (This allele is the effect allele.) 8 maf allele frequency of the minor allele in cohort 9 af allele frequency of allele2 (alt) 10 beta marginal association effect size from linear mixed model/effect size GWAS 11 se standard error on marginal association effect size from linear mixed model/standard error GWAS 12 p_value p-value GWAS 13 chisq test statistic for marginal association 14 z_score original z-score 15 pp posterior probability of association from fine-mapping (FINEMAP or SuSiE) 16 beta_posterior posterior expectation of true effect size 17 sd_posterior posterior standard deviation of true effect size 18 trait_abbr abbreviation for the trait 19 trait detailed information for the trait 20 index Unique index identifiers based on trait or disease variants are meaningless and can be used to identify the uniqueness of variants. ==== ==================== ==================================================================================================== .. note:: When collecting fine-mapping result data, some data may not include all columns, and a small number of columns may have null values. Of course, the four columns of "chr", "position", "pp", and "trait" are definitely included. 2. ``bed`` file (``Download (LiftOver)`` field) scVMAP provides variant coordinates under different reference genomes. ==== ==================== ==================================================================================================================================== # Column name Description ==== ==================== ==================================================================================================================================== 1 None chromosome in hg19/hg38 coordinates 2 None (start) position of variant in hg19/hg38 coordinates 3 None (end) position of variant in hg19/hg38 coordinates 4 None rsID identifier 5 None posterior probability of association from fine-mapping (FINEMAP or SuSiE) 6 None abbreviation for the trait 7 None Unique index identifiers based on trait or disease variants are meaningless and can be used to identify the uniqueness of variants. ==== ==================== ==================================================================================================================================== .. note:: This format of data is suitable for performing overlay operations with enhancer data, etc. .. note:: The download name is the same regardless of the method or reference genome selected, so please be aware of this. 2.7.3 Download other data ^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^^ .. image:: ../img/download/other_data.png 2.7.3.1 Fine-mapping result data: ``tar.gz`` file """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" Here is the complete download for Part ``2.7.2 Download fine-mapping result data for each sample``. | Fine-mapping result data (FINEMAP/SuSiE) (source): ``txt`` file (``Download`` field) | Repeat display once: ==== ==================== ==================================================================================================== # Column name Description ==== ==================== ==================================================================================================== 1 trait_code unique identifier of the trait, used as the file name for the file processing procedure 2 chr chromosome in the reference genome coordinate of the source cohort 3 position position of variant in the reference genome coordinate of the source cohort 4 variant unique variant identifier 5 rsId rsID identifier 6 allele1 reference allele in the reference genome coordinate of the source cohort 7 allele2 alternative allele in the reference genome coordinate of the source cohort. (This allele is the effect allele.) 8 maf allele frequency of the minor allele in cohort 9 af allele frequency of allele2 (alt) 10 beta marginal association effect size from linear mixed model/effect size GWAS 11 se standard error on marginal association effect size from linear mixed model/standard error GWAS 12 p_value p-value GWAS 13 chisq test statistic for marginal association 14 z_score original z-score 15 pp posterior probability of association from fine-mapping (FINEMAP) 16 beta_posterior posterior expectation of true effect size 17 sd_posterior posterior standard deviation of true effect size 18 trait_abbr abbreviation for the trait 19 trait detailed information for the trait 20 index Unique index identifiers based on trait or disease variants are meaningless and can be used to identify the uniqueness of variants. ==== ==================== ==================================================================================================== | Fine-mapping result data (FINEMAP/SuSiE) (hg19/hg38): ``bed`` file (``Download (LiftOver)`` field) | Repeat display once: ==== ==================== ==================================================================================================== # Column name Description ==== ==================== ==================================================================================================== 1 None chromosome in hg19/hg38 coordinates 2 None (start) position of variant in hg19/hg38 coordinates 3 None (end) position of variant in hg19/hg38 coordinates 4 None rsID identifier 5 None posterior probability of association from fine-mapping (FINEMAP or SuSiE) 6 None abbreviation for the trait 7 None Unique index identifiers based on trait or disease variants are meaningless and can be used to identify the uniqueness of variants. ==== ==================== ==================================================================================================== 2.7.3.2 Differential gene data: ``txt`` file """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" | Differential Genes data (Cell type): ``tar.gz`` file This file contains differential gene data for all cell types of single-cell samples. Of course, it is after passing the threshold. ==== ==================== ==================================================================================================== # Column name Description ==== ==================== ==================================================================================================== 1 f_sample_id unique identifier of scATAC-seq sample 2 f_cell_type cell type 3 f_gene gene name 4 f_score score 5 f_adjusted_p_value adjusted p value 6 f_log2_fold_change Log2(Fold change) 7 f_p_value P-value ==== ==================== ==================================================================================================== | Differential Genes data (Age/Sex/Drug resistance): ``txt`` file This file contains differential gene data for all cell types of single-cell samples. Of course, it is after passing the threshold. ==== ==================== ==================================================================================================== # Column name Description ==== ==================== ==================================================================================================== 1 f_sample_id unique identifier of scATAC-seq sample 2 f_type_value Corresponds to the values under the `f_type` field. 3 f_gene gene name 4 f_score score 5 f_adjusted_p_value adjusted p value 6 f_log2_fold_change Log2(Fold change) 7 f_p_value P-value 7 f_type Age, gender, or drug resistance information. ==== ==================== ==================================================================================================== .. note:: You need to download the complete data without threshold filtering, and enter the details page of the sample to download the ``H5AD`` file. Example: `sample_id_1 `_ .. image:: ../img/download/difference_gene_h5ad.png | ``obs``: gene | ``var``: cell type | ``X``: score | ``layers``: adjusted p value, Log2(Fold change), P-value .. code-block:: python :linenos: >>> data AnnData object with n_obs × n_vars = 33501 × 20 obs: 'n_cells' var: 'cell_type', 'size' uns: 'diff_genes' layers: 'adjusted_p_value', 'log2_fold_change', 'p_value' >>> >>> data.var cell_type size cell_type B B 404 CD8 TEx CD8 TEx 3898 Effector CD8 T Effector CD8 T 1153 Endothelial Endothelial 562 Fibroblasts Fibroblasts 1325 Memory CD8 T Memory CD8 T 4965 Myeloid Myeloid 732 NK1 NK1 418 NK2 NK2 1207 Naive CD4 T Naive CD4 T 4059 Naive CD8 T Naive CD8 T 2768 Plasma B Plasma B 335 Tfh Tfh 4138 Th1 Th1 338 Th17 Th17 1842 Treg Treg 4068 Tumor 1 Tumor 1 757 Tumor 2 Tumor 2 875 Tumor 3 Tumor 3 1687 Tumor 4 Tumor 4 1190 >>> >>> data.obs n_cells AP006222.2 296 ENSG00000286448 296 ENSG00000230021 14992 ENSG00000228327 10389 LINC01409 10389 ... ... TMLHE 4231 SPRY3 5205 VAMP7 7748 IL9R 5738 ENSG00000270726 395 [33501 rows x 1 columns] >>> >>> data.X array([[-16.08996773, 16.2977314 , -3.94544339, ..., 22.60018349, 65.58148956, 41.31241226], [ -9.23847771, 38.57592773, -28.23983192, ..., -8.53127384, 16.334095 , 46.58874512], [ -9.22247505, 38.53868484, -28.31791878, ..., -8.08869743, 16.5304184 , 46.68078613], ..., [ -0.73027158, 34.58570862, 42.81091309, ..., -33.24862289, -56.29743958, -51.4512825 ], [ 12.86117554, -13.21335506, -1.77498877, ..., -29.03244019, -39.19504929, -43.00321579], [-16.56791496, -32.8029213 , 2.89613366, ..., 38.49712753, 32.102005 , -17.40989685]]) >>> 2.7.3.3 Differential TF data: ``txt`` file """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" This file contains differential TF data for all cell types of single-cell samples. Of course, it is after passing the threshold. ==== ==================== ==================================================================================================== # Column name Description ==== ==================== ==================================================================================================== 1 f_sample_id unique identifier of scATAC-seq sample 2 f_cell_type cell type 3 f_tf transcription factor name 4 f_tf_id unique identifier of transcription factor 5 f_p_value P-value 6 f_adjusted_p_value adjusted p value 7 f_log2_fold_change Log2(Fold change) ==== ==================== ==================================================================================================== .. note:: You need to download the complete data without threshold filtering, and enter the details page of the sample to download the ``H5AD`` file. Example: `sample_id_1 `_ .. image:: ../img/download/difference_tf_h5ad.png | ``obs``: TF | ``var``: cell type | ``X``: P-value | ``layers``: adjusted p value, Log2(Fold change) .. code-block:: python :linenos: >>> data AnnData object with n_obs × n_vars = 1165 × 20 obs: 'id', 'name' var: 'cell_type', 'size' layers: 'adjusted_p_value', 'log2_fold_change' >>> >>> data.obs id name index AC023509.3+M02872_2.00 AC023509.3+M02872_2.00 AC023509.3 AC138696.1+M04597_2.00 AC138696.1+M04597_2.00 AC138696.1 AHR+M09817_2.00 AHR+M09817_2.00 AHR AIRE+M09375_2.00 AIRE+M09375_2.00 AIRE ALX1+M05327_2.00 ALX1+M05327_2.00 ALX1 ... ... ... ZSCAN4+M02919_2.00 ZSCAN4+M02919_2.00 ZSCAN4 ZSCAN5+M04460_2.00 ZSCAN5+M04460_2.00 ZSCAN5 ZSCAN5C+M08390_2.00 ZSCAN5C+M08390_2.00 ZSCAN5C ZSCAN9+M04466_2.00 ZSCAN9+M04466_2.00 ZSCAN9 ZZZ3+M01272_2.00 ZZZ3+M01272_2.00 ZZZ3 [1165 rows x 2 columns] >>> >>> data.X array([[1.01662951e-01, 1.74660328e-01, 2.50931395e-01, ..., 6.34538848e-02, 7.25013930e-02, 5.10951651e-05], [2.07562180e-01, 1.93983057e-01, 2.10357488e-01, ..., 3.01950908e-01, 3.46950746e-01, 8.56932171e-02], [2.40413032e-01, 9.76634287e-02, 6.66147596e-01, ..., 2.68301581e-01, 1.75328527e-02, 1.26211337e-03], ..., [4.38363454e-01, 1.43397437e-01, 4.24778841e-01, ..., 7.15759727e-03, 5.41759614e-02, 9.35845828e-12], [4.86767592e-01, 1.47841135e-01, 5.32381338e-01, ..., 2.74014131e-01, 1.13489445e-05, 6.38005942e-11], [1.61418404e-01, 3.23724955e-01, 4.50586827e-02, ..., 2.66768124e-01, 7.84328678e-02, 4.08885306e-07]]) >>> 2.7.3.4 MAGMA result data: ``tar.gz`` file """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" The result data of enriched genes for traits or diseases through MAGMA. | MAGMA result data (Annotation) (hg19/hg38): ``Annotation`` | MAGMA result data (Analysis) (hg19/hg38): ``Gene analysis -raw data`` 2.7.3.4.1 ``Annotation``: ``txt`` file (After decompression) ********************************************************************************* ==== ==================== ==================================================================================================== # Column name Description ==== ==================== ==================================================================================================== 1 trait_id unique identifier of trait or disease 2 gene_id unique identifier of gene 3 gene gene name 4 rsId rsID identifier ==== ==================== ==================================================================================================== .. note:: The user needs to obtain the ``genes.annot`` file after MAGMA runs and needs to enter the details page to obtain it. Example: `trait_id_894 `_ .. image:: ../img/download/magma_annotation.png Click ``View`` .. image:: ../img/download/magma_annotation_view.png 2.7.3.4.1 ``Gene analysis -raw data``: ``txt`` file (After decompression) ********************************************************************************* ==== ==================== ==================================================================================================== # Column name Description ==== ==================== ==================================================================================================== 1 trait_id unique identifier of trait or disease 2 gene_id unique identifier of gene 3 gene gene name 4 chr chromosome code 5 start starting boundary of gene annotation on chromosomes 6 end ending boundary of gene annotation on chromosomes 7 n_snps The number of SNPs not annotated to this gene based on previous SNP QC exclusion. 8 z_score z-value 9 p_value p-value ==== ==================== ==================================================================================================== .. note:: The user needs to obtain the ``genes.out`` file after MAGMA runs and needs to enter the details page to obtain it. Example: `trait_id_894 `_ .. image:: ../img/download/magma_analysis.png 2.7.3.5 HOMER result data: ``tar.gz`` file """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" | HOMER result data (hg19/hg38): ``txt`` file (After decompression) ==== ==================== ==================================================================================================== # Column name Description ==== ==================== ==================================================================================================== 1 f_trait_id unique identifier of trait or disease 2 f_motif_name unique identifier of gene 3 f_tf TF name 4 f_consensus consensus 5 f_p_value p-value 6 f_q_value q-value ==== ==================== ==================================================================================================== .. note:: Users need to download complete data without threshold filtering and enter the details page to download the file. Example: `trait_id_894 `_ .. image:: ../img/download/homer.png Click on the link symbol button. .. image:: ../img/download/homer_link.png 2.7.3.6 Gene enrichment analysis results: ``tar.gz`` file """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" | Gene enrichment for differential genes: ``txt`` file (After decompression) | Gene enrichment results of traits (hg19/hg38): ``txt`` file (After decompression) 2.7.3.6.1 Gene enrichment for differential genes ********************************************************************************* File name: ``{Sample ID}_gene_enrichment_data.txt`` ==== ==================== ==================================================================================================== # Column name Description ==== ==================== ==================================================================================================== 1 f_gene_set Gene set (GO_Biological_Process_2023, GO_Cellular_Component_2023, GO_Molecular_Function_2023 and GWAS_Catalog_2023) 2 f_term gene enrichment term 3 f_overlap percentage of gene set overlap 4 f_p_value p-value 5 f_adjusted_p_value adjusted p-value 6 f_odds_ratio odds ratio 7 f_combined_score combined score 8 f_gene overlap genes 9 f_count count of overlapping genes 10 f_cell_type cell type ==== ==================== ==================================================================================================== 2.7.3.6.2 Gene enrichment results of traits (hg19/hg38) ********************************************************************************* File name: ``{Trait label}_gene_enrichment_trait_data.txt`` ==== ======================= ==================================================================================================== # Column name Description ==== ======================= ==================================================================================================== 1 trait_id unique identifier of trait or disease 2 Gene_set Gene set (GO_Biological_Process_2023, GO_Cellular_Component_2023, GO_Molecular_Function_2023 and GWAS_Catalog_2023) 3 Term gene enrichment term 4 Overlap percentage of gene set overlap 5 P-value p-value 6 Adjusted P-value adjusted p-value 7 Old P-value old p-value 8 Old Adjusted P-value old adjusted p-value 9 Odds Ratio odds ratio 10 Combined Score combined score 11 Genes overlap genes ==== ======================= ==================================================================================================== .. note:: A very small number of traits or diseases contain too few fine-mapped variants, resulting in a lack of gene enrichment results. 2.7.3.7 Gene regulation/V2G annotation data: """"""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""""" scVMAP provides gene regulation annotation data for five types of epigenome data. 2.7.3.7.1 Common SNP: ``txt`` file (After decompression) ********************************************************************************* ==== ================== ================================================================================================================ # Column name Description ==== ================== ================================================================================================================ 1 chr chromosome 2 position position 3 rsId rsID identifier 4 ref reference allele in the reference genome coordinate of the source cohort 5 alt alternative allele in the reference genome coordinate of the source cohort. (This allele is the effect allele.) ==== ================== ================================================================================================================ .. code-block:: shell :linenos: $ head dbsnp_common_snp_hg38.txt chr position rsId ref alt chr1 10177 rs367896724 A AC chr1 10352 rs555500075 T TA chr1 10616 rs376342519 CCGCCGTTGCAAAGGCGCGCCG C chr1 11012 rs544419019 C G chr1 11063 rs561109771 T G chr1 13110 rs540538026 G A chr1 13116 rs62635286 T G chr1 13118 rs62028691 A G chr1 13273 rs531730856 G C 2.7.3.7.2 eQTL: ``txt`` file (After decompression) ********************************************************************************* ==== ================== ================================================================================================================= # Column name Description ==== ================== ================================================================================================================= 1 chr chromosome 2 position position 3 ref reference allele in the reference genome coordinate of the source cohort 4 alt alternative allele in the reference genome coordinate of the source cohort. (This allele is the effect allele.) 5 gene_name gene name 6 tss_distance The distance between SNP and gene transcription start site (TSS). 7 af allele frequency of alternative allele (alt) 8 pval_nominal p-value 9 tissue_type tissue type ==== ================== ================================================================================================================= .. code-block:: shell :linenos: $ head gtex_v10_eqtl_hg38.txt chr position ref alt gene_name tss_distance af pval_nominal tissue_type chr1 766455 T C LINC01409 -12292 0.047058824 1.7230692640469627e-10 Vagina chr1 766938 C T LINC01409 -11809 0.047058824 7.331238896267609e-10 Vagina chr1 771358 T G LINC01409 -7389 0.047058824 3.298544072962652e-12 Vagina chr1 771398 G A LINC01409 -7349 0.67058825 2.133429762259741e-05 Vagina chr1 775571 G T LINC01409 -3176 0.047058824 3.298544072962652e-12 Vagina chr1 777550 T C LINC01409 -1197 0.05 9.539419071495843e-12 Vagina chr1 777751 A AT LINC01409 -996 0.05 9.539419071495843e-12 Vagina chr1 778534 A G LINC01409 -213 0.05 9.539419071495843e-12 Vagina chr1 778639 A G LINC01409 -108 0.08235294 2.6823764300049156e-08 Vagina 2.7.3.7.3 Risk SNP: ``txt`` file (After decompression) ********************************************************************************* ==== ================== =============================================================================================================== # Column name Description ==== ================== =============================================================================================================== 1 chr chromosome 2 pos position 3 rsId rsID identifier 4 ref reference allele in the reference genome coordinate of the source cohort 5 alt alternative allele in the reference genome coordinate of the source cohort. (This allele is the effect allele.) 6 p p-value 7 Trait trait 8 Population population 9 PMID PMID ==== ================== =============================================================================================================== .. code-block:: shell :linenos: $ head gwasatlas_v20191115_risk_snp_hg38.txt chr pos rsID ref alt p Trait Population PMID chr1 43718521 rs11420276 G GT 6.452e-13 Attention deficit hyperactivity disorder EUR 30478444 chr1 96136884 rs1222063 A G 3.068e-08 Attention deficit hyperactivity disorder EUR 30478444 chr3 20627579 rs4858241 G T 8.172e-09 Attention deficit hyperactivity disorder EUR 30478444 chr4 31149834 rs28411770 C T 1.152e-08 Attention deficit hyperactivity disorder EUR 30478444 chr5 88558577 rs4916723 A C 1.807e-08 Attention deficit hyperactivity disorder EUR 30478444 chr5 88919777 rs304132 A G 3.047e-08 Attention deficit hyperactivity disorder EUR 30478444 chr7 114418676 rs34291892 C CA 1.585e-08 Attention deficit hyperactivity disorder EUR 30478444 chr8 34495092 rs74760947 A G 1.393e-08 Attention deficit hyperactivity disorder EUR 30478444 chr10 104987596 rs11591402 A T 1.76e-08 Attention deficit hyperactivity disorder EUR 30478444 2.7.3.7.4 Enhancer (SEA v3): ``txt`` file (After decompression) ********************************************************************************* ==== ===================== ==================================================================================================== # Column name Description ==== ===================== ==================================================================================================== 1 chr chromosome 2 start start position of enhancer 3 end end position of enhancer 4 associated_gene reference allele in the reference genome coordinate of the source cohort 5 cell_tissue_type cell type/tissue type 6 recognition_factor recognition factor (eg. h3k27ac) 7 sequence_region sequence region (coding or noncoding) 8 se_id SE ID of SEA ==== ===================== ==================================================================================================== .. code-block:: shell :linenos: $ head sea_v3_enhancer_hg38.txt chr start end associated_gene cell_tissue_type recognition_factor sequence_region se_id chr10 88384139 88389120 RNLS 22Rv1 h3k27ac coding 442 chr13 20117533 20129315 LINC01072 22Rv1 h3k27ac noncoding 443 chr11 9056277 9061918 SCUBE2 22Rv1 h3k27ac coding 444 chr5 44537047 44541439 LINC02224 22Rv1 h3k27ac noncoding 445 chr9 112327808 112339994 PTBP3 22Rv1 h3k27ac coding 446 chr4 138896634 138913955 LOC105377448 22Rv1 h3k27ac noncoding 447 chr2 180254341 180260431 CWC22 22Rv1 h3k27ac coding 448 chrX 66898375 66921461 EDA2R 22Rv1 h3k27ac coding 449 chr7 12709011 12717389 ARL4A 22Rv1 h3k27ac coding 450 2.7.3.7.5 Enhancer (SEdb v2): ``txt`` file (After decompression) ********************************************************************************* ==== ===================== ==================================================================================================== # Column name Description ==== ===================== ==================================================================================================== 1 chr chromosome 2 start start position of enhancer 3 end end position of enhancer 4 sample_id sample ID of SEdb 5 se_id SE ID of SEdb 6 cell_source source 7 cell_type cell type 8 tissue_type tissue type 9 cell_state cell state ==== ===================== ==================================================================================================== .. code-block:: shell :linenos: $ head sedb_v2_enhancer_hg38.txt chr start end sample_id se_id cell_source cell_type tissue_type cell_state chr6 32968553 32969528 SE_00_0001 TE_00_000100001 Roadmap Tissue Adipose adipose-tissue chr19 3404076 3405134 SE_00_0001 TE_00_000100002 Roadmap Tissue Adipose adipose-tissue chr22 17638273 17639305 SE_00_0001 TE_00_000100003 Roadmap Tissue Adipose adipose-tissue chr7 100428402 100429667 SE_00_0001 TE_00_000100004 Roadmap Tissue Adipose adipose-tissue chr19 6273122 6274837 SE_00_0001 TE_00_000100005 Roadmap Tissue Adipose adipose-tissue chr17 77128730 77140351 SE_00_0001 TE_00_000100006 Roadmap Tissue Adipose adipose-tissue chr6 33313122 33314294 SE_00_0001 TE_00_000100007 Roadmap Tissue Adipose adipose-tissue chr7 5555574 5556788 SE_00_0001 TE_00_000100008 Roadmap Tissue Adipose adipose-tissue chr7 143380426 143381762 SE_00_0001 TE_00_000100009 Roadmap Tissue Adipose adipose-tissue 2.7.3.7.6 Super enhancer (dbSUPER): ``txt`` file (After decompression) ********************************************************************************* ==== ===================== ==================================================================================================== # Column name Description ==== ===================== ==================================================================================================== 1 chr chromosome 2 start start position of enhancer 3 end end position of enhancer 4 se_id SE ID of SEdb 5 cell_type_type cell type/tissue type ==== ===================== ==================================================================================================== .. code-block:: shell :linenos: $ head dbsuper_super_enhancer_hg38.txt chr start end se_id cell_type_type chr6 32580146 32643038 SE_10156 CD19 Primary chr14 105557581 105606092 SE_10157 CD19 Primary chr14 105677864 105749363 SE_10158 CD19 Primary chr6 167078442 167154502 SE_10159 CD19 Primary chr21 44137096 44181452 SE_10160 CD19 Primary chr5 150398244 150436858 SE_10161 CD19 Primary chr2 88831594 88886476 SE_10162 CD19 Primary chr6 33006818 33032650 SE_10163 CD19 Primary chr2 136114080 136141217 SE_10164 CD19 Primary 2.7.3.7.7 Super enhancer (SEA v3): ``txt`` file (After decompression) ********************************************************************************* ==== ===================== ==================================================================================================== # Column name Description ==== ===================== ==================================================================================================== 1 chr chromosome 2 start start position of enhancer 3 end end position of enhancer 4 associated_gene reference allele in the reference genome coordinate of the source cohort 5 cell_tissue_type cell type/tissue type 6 recognition_factor recognition factor (eg. h3k27ac) 7 sequence_region sequence region (coding or noncoding) 8 se_id SE ID ==== ===================== ==================================================================================================== .. code-block:: shell :linenos: $ head sea_v3_super_enhancer_hg38.txt chr start end associated_gene cell_tissue_type recognition_factor sequence_region se_id chr6 110617715 110700931 CDK19 22Rv1 h3k27ac coding 1 chr7 92030110 92091121 AKAP9 22Rv1 h3k27ac coding 2 chr11 59005426 59074536 LOC283194 22Rv1 h3k27ac noncoding 3 chr5 71599725 71707973 MCCC2 22Rv1 h3k27ac coding 4 chr21 6360657 6375827 CBS 22Rv1 h3k27ac coding 5 chr12 101602935 101625047 MYBPC1 22Rv1 h3k27ac coding 6 chr10 37145277 37199659 ANKRD30A 22Rv1 h3k27ac coding 7 chr6 138221168 138289554 ARFGEF3 22Rv1 h3k27ac coding 8 chr16 52550656 52582081 CASC16 22Rv1 h3k27ac noncoding 9 2.7.3.7.8 Super enhancer (SEdb v2): ``txt`` file (After decompression) ********************************************************************************* ==== ===================== ==================================================================================================== # Column name Description ==== ===================== ==================================================================================================== 1 chr chromosome 2 start start position of enhancer 3 end end position of enhancer 4 sample_id sample ID of SEdb 5 se_id SE ID of SEdb 6 cell_source source 7 cell_type cell type 8 tissue_type tissue type 9 cell_state cell state ==== ===================== ==================================================================================================== .. code-block:: shell :linenos: $ head sedb_v2_super_enhancer_hg38.txt chr start end sample_id se_id cell_source cell_type tissue_type cell_state chr1 100008001 100081709 SE_02_1036 SE_02_103600569 NCBI GEO/SRA Cell line Mammary gland HCC70_XY018 chr1 100015493 100079709 SE_02_1429 SE_02_142900169 NCBI GEO/SRA Cell line Blood GM12878_WT chr1 1000160 1006599 SE_02_0988 SE_02_098800774 NCBI GEO/SRA Cell line Blood K562_EPZ chr1 1000180 1006408 SE_02_1080 SE_02_108000734 NCBI GEO/SRA Cell line Muscle JR1 shCtrl chr1 100026929 100040607 SE_00_0009 SE_00_000900816 Roadmap Primary cell Blood CD8-positive-alpha-beta-T-cell chr1 100027783 100040448 SE_00_0027 SE_00_002700801 Roadmap Primary cell Blood natural-killer-cell chr1 100028493 100040305 SE_02_0707 SE_02_070700751 NCBI GEO/SRA Cell line Pancreas BxPC3 WT chr1 100028934 100040097 SE_02_0022 SE_02_002200606 NCBI GEO/SRA Primary cell Blood CD8donorA chr1 100033978 100061969 SE_02_1468 SE_02_146800857 NCBI GEO/SRA Cell line Blood HUDEP-2_WT 2.7.3.7.9 3D chromatin interaction: ``bed`` file (After decompression) ********************************************************************************* ==== ===================== ==================================================================================================== # Column name Description ==== ===================== ==================================================================================================== 1 None chromosome (Interaction1) 2 None start position of enhancer (Interaction1) 3 None end position of enhancer (Interaction1) 4 None chromosome (Interaction2) 5 None start position of enhancer (Interaction2) 6 None end position of enhancer (Interaction2) 7 None Source/Interaction ID 8 None Method 9 None Tissue/cell type 10 None Cell line ==== ===================== ==================================================================================================== .. code-block:: shell :linenos: $ head 3D_hg19.bed chr1 37883731 37885731 chr1 38374488 38376488 3D_4DGenome_001 3C Kidney 293Trex chr1 68019395 68021395 chr1 68444820 68446820 3D_4DGenome_001 3C Kidney 293Trex chr1 94005332 94007332 chr1 94477646 94479646 3D_4DGenome_001 3C Kidney 293Trex chr1 9762548 9762685 chr1 9882283 9883893 3D_OncoBase_084 EpiTensor Kidney Kidney chr1 9848832 9851345 chr1 9882283 9883893 3D_OncoBase_084 EpiTensor Kidney Kidney chr1 98991643 98992662 chr1 99114108 99115246 3D_OncoBase_084 EpiTensor Kidney Kidney chr1 99114108 99115246 chr1 99125090 99125899 3D_OncoBase_084 EpiTensor Kidney Kidney chr1 98991643 98992662 chr1 99125090 99125899 3D_OncoBase_084 EpiTensor Kidney Kidney chr1 99181550 99181760 chr1 99182450 99183081 3D_OncoBase_084 EpiTensor Kidney Kidney chr1 99125090 99125899 chr1 99193746 99195271 3D_OncoBase_084 EpiTensor Kidney Kidney 2.7.3.7.10 MPRA: ``csv`` file ********************************************************************************* | Download source: https://mpravardb.rc.ufl.edu/ ==== ================== ==================================================================================================== # Column name Description ==== ================== ==================================================================================================== 1 chr chromosome 2 pos position of variant 3 ref reference allele in the reference genome coordinate of the source cohort 4 alt alternative allele in the reference genome coordinate of the source cohort. (This allele is the effect allele.) 5 genome reference genome 6 rsid rsID identifier 7 disease trait/disease 8 cellline cell line 9 Description description 10 log2FC Log2(Fold change) 11 pvalue P value 12 fdr FDR 13 MPRA_study MPRA study ==== ================== ==================================================================================================== .. code-block:: shell :linenos: $ head All_MPRA_Data.csv "chr","pos","ref","alt","genome","rsid","disease","cellline","Description","log2FC","pvalue","fdr","MPRA_study" "1",2440958,"A","G","hg38","rs6688934","Schizophrenia","SH-SY5Y","1,049 SZ and 30 AD variants in 64 SZ loci and 9 AD loci, respectively",NA,0.108571634,0.341634497,"A screen of 1049 schizophrenia and 30 Alzheimer's-associated variants for regulatory potential (Myint et al., 2020)" "1",2441515,"A","G","hg38","rs6673661","Schizophrenia","SH-SY5Y","1,049 SZ and 30 AD variants in 64 SZ loci and 9 AD loci, respectively",NA,0.057599896,0.234108669,"A screen of 1049 schizophrenia and 30 Alzheimer's-associated variants for regulatory potential (Myint et al., 2020)" "1",2443319,"A","G","hg38","rs4648844","Schizophrenia","SH-SY5Y","1,049 SZ and 30 AD variants in 64 SZ loci and 9 AD loci, respectively",NA,0.014320564,0.115533569,"A screen of 1049 schizophrenia and 30 Alzheimer's-associated variants for regulatory potential (Myint et al., 2020)" "1",2444405,"T","G","hg38","rs6687012","Schizophrenia","SH-SY5Y","1,049 SZ and 30 AD variants in 64 SZ loci and 9 AD loci, respectively",NA,0.258798019,0.530956548,"A screen of 1049 schizophrenia and 30 Alzheimer's-associated variants for regulatory potential (Myint et al., 2020)" "1",2448266,"A","G","hg38","rs942820","Schizophrenia","SH-SY5Y","1,049 SZ and 30 AD variants in 64 SZ loci and 9 AD loci, respectively",NA,0.077694104,0.275581292,"A screen of 1049 schizophrenia and 30 Alzheimer's-associated variants for regulatory potential (Myint et al., 2020)" "1",2455662,"C","T","hg38","rs4648845","Schizophrenia","SH-SY5Y","1,049 SZ and 30 AD variants in 64 SZ loci and 9 AD loci, respectively",NA,0.453624774,0.700344436,"A screen of 1049 schizophrenia and 30 Alzheimer's-associated variants for regulatory potential (Myint et al., 2020)" "1",8362616,"T","C","hg38","rs2252865","Schizophrenia","SH-SY5Y","1,049 SZ and 30 AD variants in 64 SZ loci and 9 AD loci, respectively",NA,0.551078425,0.775862448,"A screen of 1049 schizophrenia and 30 Alzheimer's-associated variants for regulatory potential (Myint et al., 2020)" "1",8363450,"A","G","hg38","rs10779702","Schizophrenia","SH-SY5Y","1,049 SZ and 30 AD variants in 64 SZ loci and 9 AD loci, respectively",NA,0.295545372,0.575535724,"A screen of 1049 schizophrenia and 30 Alzheimer's-associated variants for regulatory potential (Myint et al., 2020)" "1",8372076,"C","T","hg38","rs894875","Schizophrenia","SH-SY5Y","1,049 SZ and 30 AD variants in 64 SZ loci and 9 AD loci, respectively",NA,0.543395748,0.774441451,"A screen of 1049 schizophrenia and 30 Alzheimer's-associated variants for regulatory potential (Myint et al., 2020)"