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PRIMED_GSR_data_model.json
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PRIMED_GSR_data_model.json
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{
"name": "PRIMED GSR Data Model",
"description": "Data model for Genomic Summary Results in the PRIMED consortium",
"version": "2.0",
"tables": [
{
"table": "association_analysis",
"required": "CONDITIONAL (association_file)",
"columns": [
{
"column": "association_analysis_id",
"primary_key": true,
"description": "unique identifier for a gwas in primed",
"data_type": "string",
"notes": "PRIMED upload workflow would generate this"
},
{
"column": "gsr_source",
"required": true,
"description": "Information about source of GSR data. Include additional details in README",
"data_type": "string",
"examples": ["GWAS Catalog", "dbGaP", "Colorado Biobank ", "PRIMED"],
"notes": "free text short label; useful for unreleased GSR"
},
{
"column": "gsr_source_url",
"description": "URL of source (if applicable)",
"data_type": "string",
"examples": "https://pan.ukbb.broadinstitute.org/"
},
{
"column": "gwas_catalog_study_id",
"description": "Study accession identifier for GSR downloaded from GWAS Catalog",
"data_type": "string",
"examples": "GCST90029118",
"notes": "identifier in GCSTXXXXXX format"
},
{
"column": "dbgap_analysis_accession",
"description": "Analysis accession identifier for GSR downloaded from dbGaP",
"data_type": "string",
"examples": "pha003690.1",
"notes": "identifier in phaXXXXXX.v format"
},
{
"column": "pubmed_id",
"description": "Pubmed ID identifier of the publication reporting the GWAS",
"data_type": "string",
"examples": "33568819",
"notes": "PMID identifier"
},
{
"column": "first_author",
"description": "Last name and initials of the first author",
"data_type": "string"
},
{
"column": "publication_url",
"description": "External link to the publication",
"data_type": "string",
"notes": "URL of publication"
},
{
"column": "release_date",
"description": "Date on which the GWAS was released publicy",
"data_type": "date",
"notes": "e.g. on dbGaP or GWAS Catalog"
},
{
"column": "consent_code",
"required": true,
"description": "consent abbreviation (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4721915/table/pgen.1005772.t001/?report=objectonly)",
"data_type": "string",
"notes": "NRES is the code for no restrictions on data use (i.e. open access)"
},
{
"column": "upload_date",
"required": true,
"description": "Date GSR was uploaded to PRIMED AnVIL workspace",
"data_type": "date",
"examples": "2023-03-21",
"notes": "YYYY-MM-DD format"
},
{
"column": "contributor_contact",
"required": true,
"description": "email of the PRIMED contributor who can be contacted for data related questions",
"data_type": "string",
"notes": "recommended by WG. helpful when GSR is not publicly released and is from a Biobank or some other source"
},
{
"column": "trait",
"required": true,
"description": "reported trait",
"data_type": "string",
"examples": ["body mass index (BMI)", "coronary artery disease", "LDL"],
"notes": "free text trait name"
},
{
"column": "trait_type",
"required": true,
"description": "type of trait",
"data_type": "enumeration",
"enumerations": ["quantitative", "binary", "categorical", "ordinal", "time_to_event", "count"],
"notes": "determines which columns we need to check for in the data file"
},
{
"column": "trait_unit",
"required": true,
"description": "unit of measurement for the reported trait (before transformation)",
"data_type": "string",
"notes": "Report unit as something like 'binary', 'indicator', or 'categorical' for non-quantitative traits"
},
{
"column": "trait_transformation",
"required": true,
"description": "transformation applied to the reported trait",
"data_type": "string",
"examples": "none, log, inverse normal, winsorization, standardization, other",
"notes": "put 'none' if no transformation applied"
},
{
"column": "trait_definition",
"required": true,
"description": "a brief description of how the trait was measured or defined; additional details can be provided in the README",
"data_type": "string",
"notes": "e.g. ICD codes used to identify cases/phenotypes in EHR data"
},
{
"column": "covariates",
"required": true,
"description": "Covariates included in the association analysis for trait adjustment",
"data_type": "string",
"multi_value_delimiter": "|",
"examples": "age | sex | ancestry PCs",
"notes": "put 'none' if no covariates"
},
{
"column": "concept_id",
"description": "OMOP concept_id",
"data_type": "string"
},
{
"column": "mapped_trait",
"description": "Experimental Factor Ontology (EFO) term the trait was mapped to",
"data_type": "string",
"notes": "mapped_trait value used in GWAS Catalog"
},
{
"column": "reference_assembly",
"required": true,
"description": "Reference genome assembly that the submitted data is mapped to",
"data_type": "enumeration",
"enumerations": ["GRCh38", "GRCh37", "NCBI36", "NCBI35", "NCBI34"],
"examples": "GRCh38"
},
{
"column": "dbsnp_build_version",
"description": "dbSNP build for the rsIDs included in GSR files",
"data_type": "string"
},
{
"column": "n_variants",
"required": true,
"description": "Total number of variants in the analysis results across all chromosomes",
"data_type": "integer",
"examples": "1000000",
"notes": "This will be used for a QC step to check data integrity of submitted data"
},
{
"column": "min_MAF_filter",
"description": "minimum minor allele frequency filter",
"data_type": "float",
"examples": "0.01"
},
{
"column": "min_MAC_filter",
"description": "minimum minor allele count filter",
"data_type": "integer",
"examples": "20"
},
{
"column": "genotyping_technology",
"required": true,
"description": "The genotyping technology used for detecting variants",
"data_type": "enumeration",
"enumerations": ["genome-wide array", "WGS", "exome array", "WES", "other array"],
"multi_value_delimiter": "|"
},
{
"column": "genotyping_platform",
"required": true,
"description": "Genotyping platform description including manufacturer, array name, sequencer name",
"data_type": "string",
"multi_value_delimiter": "|",
"examples": ["Illumina GSA", "Illumina Omni-2.5", "Illumina HiSeq", "Affymetrix"],
"notes": "put 'unavailable' if unknown"
},
{
"column": "is_imputed",
"required": true,
"description": "Indicator of whether the analysis was performed using imputed genotypes or dosages",
"data_type": "boolean",
"enumerations": ["TRUE", "FALSE"],
"examples": true
},
{
"column": "imputation_reference_panel",
"required": "CONDITIONAL (is_imputed = TRUE)",
"description": "Reference panel use for imputation",
"data_type": "enumeration",
"enumerations": ["1000 Genomes", "HRC", "TOPMed", "Other"],
"examples": "TOPMed",
"notes": "Put 'Other' if not one of the common reference panels listed. Can include further information in imputation_reference_panel_detail or README"
},
{
"column": "imputation_reference_panel_detail",
"required": "CONDITIONAL (is_imputed = TRUE)",
"description": "Details of the imputation reference panel; e.g. version number or name of panel when imputation_reference_panel = 'Other'",
"data_type": "string",
"examples": "TOPMed r2",
"notes": "version number or name of 'other' include N/A"
},
{
"column": "imputation_quality_filter",
"required": "CONDITIONAL (is_imputed = TRUE)",
"description": "minimum imputation quality value (e.g. Rsq, info) for filtering imputed variants",
"data_type": "float",
"examples": "0.3",
"notes": "If no filter, enter value of 0"
},
{
"column": "n_samp",
"required": true,
"description": "Total sample size in the analysis",
"data_type": "integer",
"notes": "When different markers have different sample sizes, e.g, due to missing genotypes, use max sample size across markers"
},
{
"column": "n_case",
"required": "CONDITIONAL (trait_type = binary)",
"description": "Number of cases",
"data_type": "integer",
"notes": "When different markers have different sample sizes, e.g, due to missing genotypes, use max number of cases across markers"
},
{
"column": "n_ctrl",
"required": "CONDITIONAL (trait_type = binary)",
"description": "Number of controls",
"data_type": "integer",
"notes": "When different markers have different sample sizes, e.g, due to missing genotypes, use max number of controls across markers"
},
{
"column": "n_effective",
"required": true,
"description": "effective sample size = n_samp for quantitative traits = 4/(1/n_case + 1/n_ctrl) for binary traits",
"data_type": "float",
"notes": "When different markers have different sample sizes, e.g, due to missing genotypes, use max sample size across markers"
},
{
"column": "proportion_female",
"description": "proportion of female participants",
"data_type": "float",
"examples": "0.64"
},
{
"column": "age_mean",
"description": "mean age of study participants",
"data_type": "float"
},
{
"column": "age_median",
"description": "median age of study participants",
"data_type": "float"
},
{
"column": "age_min",
"description": "minimum age of study participants",
"data_type": "float"
},
{
"column": "age_max",
"description": "maximum age of study participants",
"data_type": "float"
},
{
"column": "age_mean_case",
"description": "mean age of study cases",
"data_type": "float"
},
{
"column": "age_median_case",
"description": "median age of study cases",
"data_type": "float"
},
{
"column": "age_min_case",
"description": "minimum age of study cases",
"data_type": "float"
},
{
"column": "age_max_case",
"description": "maximum age of study cases",
"data_type": "float"
},
{
"column": "age_mean_ctrl",
"description": "mean age of study controls",
"data_type": "float"
},
{
"column": "age_median_ctrl",
"description": "median age of study controls",
"data_type": "float"
},
{
"column": "age_min_ctrl",
"description": "minimum age of study controls",
"data_type": "float"
},
{
"column": "age_max_ctrl",
"description": "maximum age of study controls",
"data_type": "float"
},
{
"column": "cohorts",
"required": true,
"description": "A list of cohorts that collected the samples.",
"data_type": "string",
"multi_value_delimiter": "|"
},
{
"column": "is_meta_analysis",
"required": true,
"description": "Is the analysis a meta-analysis? Include additional details (e.g. what groups comprised the meta-analysis) in README",
"data_type": "boolean",
"examples": ["TRUE", "FALSE"]
},
{
"column": "population_descriptor",
"required": true,
"description": "the concept or classification scheme used to categorize people into populations for this analysis",
"data_type": "string",
"examples": "reported ancestry"
},
{
"column": "population_labels",
"required": true,
"description": "name given to a population that describes or classifies it according to the dimension along which it was identified",
"data_type": "string",
"multi_value_delimiter": "|",
"examples": "Chinese Americans | European Americans | African Americans"
},
{
"column": "population_proportions",
"description": "proportion of participants from each population in the same order mapping to the values in the population_labels variable",
"data_type": "string",
"multi_value_delimiter": "|",
"examples": "0.7 | 0.1 | 0.2"
},
{
"column": "countries_of_recruitment",
"description": "Reported countries of recruitment",
"data_type": "string",
"multi_value_delimiter": "|",
"examples": "Ghana | Kenya | Nigeria"
},
{
"column": "countries_of_birth",
"description": "Reported countries of birth",
"data_type": "string",
"multi_value_delimiter": "|",
"examples": "Ghana | Kenya | Nigeria"
},
{
"column": "analysis_method",
"required": true,
"description": "The name or description of the method or computational algorithm used for GWAS.",
"data_type": "string",
"multi_value_delimiter": "|",
"examples": ["linear regression", "logistic regression", "LMM", "GLMM", "meta-analysis"]
},
{
"column": "analysis_software",
"description": "The name of the software used for the GWAS",
"data_type": "string",
"multi_value_delimiter": "|",
"examples": ["GCTA", "PLINK", "GENESIS", "SAIGE"]
},
{
"column": "primed_dataset_id",
"description": "For GWAS that used a dataset in primed individual data model indicate its dataset_id",
"data_type": "string",
"multi_value_delimiter": "|",
"examples": "a60fb66cd539ad2c",
"notes": "From PRIMED inventory workspace"
},
{
"column": "analysis_workspace_id",
"description": "workspace identifier for the GWAS that was generated in PRIMED",
"data_type": "string",
"examples": "primed-analysis/PRIMED_ANALYSIS_GERA-topmed-v3-imputation"
}
]
},
{
"table": "association_file",
"columns": [
{
"column": "association_file_id",
"primary_key": true,
"data_type": "string",
"references": "from: md5sum",
"notes": "AnVIL requires entity id with naming convention <table_name>_id"
},
{
"column": "md5sum",
"required": true,
"description": "A valid md5 checksum",
"data_type": "string",
"examples": "49ea8cf53801c7f1e2f11336fb8a29c8",
"notes": "(32-digit hexadecimal number)"
},
{
"column": "association_analysis_id",
"required": true,
"description": "unique identifier for a gwas in primed",
"data_type": "string",
"references": "> association_analysis.association_analysis_id",
"notes": "AnVIL upload workflow would generate this"
},
{
"column": "file_path",
"required": true,
"description": "File path in cloud storage",
"data_type": "string",
"is_bucket_path": true
},
{
"column": "file_type",
"required": true,
"description": "Type of the file",
"data_type": "enumeration",
"enumerations": ["data", "data dictionary", "README"],
"examples": "data"
},
{
"column": "chromosome",
"required": true,
"description": "chromosome included in the GSR data file",
"data_type": "enumeration",
"enumerations": ["ALL", "1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "X", "Y", "MT", "None"],
"examples": "2",
"notes": "'None' for data dictionary or README files"
},
{
"column": "n_variants",
"description": "Count of variants in the GSR data file",
"data_type": "integer",
"examples": "15281216",
"notes": "Leave blank for data dictionary or README files"
}
]
},
{
"table": "association_files_dd",
"columns": [
{
"column": "SNPID",
"description": "chr:pos:ref:alt",
"data_type": "string",
"examples": "13:40983974:G:A"
},
{
"column": "chromosome",
"required": true,
"description": "the chromosome that the variant is located on",
"data_type": "enumeration",
"enumerations": ["1", "2", "3", "4", "5", "6", "7", "8", "9", "10", "11", "12", "13", "14", "15", "16", "17", "18", "19", "20", "21", "22", "X", "Y", "MT"],
"examples": "13"
},
{
"column": "position",
"required": true,
"description": "the base pair location of the variant",
"data_type": "integer",
"examples": "40983974"
},
{
"column": "rsID",
"description": "rs Identifier",
"data_type": "string",
"examples": "rs7329174"
},
{
"column": "strand",
"required": true,
"description": "DNA strand designation",
"data_type": "enumeration",
"enumerations": ["-", "+", "forward", "reverse"],
"examples": "-"
},
{
"column": "effect_allele",
"required": true,
"description": "effect allele of the variant",
"data_type": "string",
"examples": ["A", "I", "ATTCG+"],
"notes": "Full strings encouraged. For long strings, they can be truncated with a '+' appended. I/D allowed for insertions/deletions."
},
{
"column": "other_allele",
"required": true,
"description": "the other allele to the effect allele",
"data_type": "string",
"examples": "G"
},
{
"column": "ref_allele",
"description": "reference allele",
"data_type": "string",
"examples": "G"
},
{
"column": "alt_allele",
"description": "alternate allele",
"data_type": "string",
"examples": "A"
},
{
"column": "effect_allele_freq",
"required": true,
"description": "effect allele frequency of the variant",
"data_type": "float",
"examples": "0.22"
},
{
"column": "eaf_case",
"description": "effect allele frequency in cases for binary traits",
"data_type": "float",
"examples": "0.22"
},
{
"column": "eaf_ctrl",
"description": "effect allele frequency in controls for binary traits",
"data_type": "float",
"examples": "0.22"
},
{
"column": "mac",
"description": "minor allele count",
"data_type": "integer",
"examples": "55"
},
{
"column": "p_value",
"required": true,
"description": "p-value",
"data_type": "float",
"examples": "0.00000001"
},
{
"column": "p_value_log10",
"description": "negative log10 of p-value",
"data_type": "float",
"examples": "7.3"
},
{
"column": "beta",
"required": true,
"description": "estimated effect size",
"data_type": "float",
"examples": "-0.064",
"notes": "reported on log odds scale for binary traits"
},
{
"column": "se",
"required": true,
"description": "standard error of beta",
"data_type": "float",
"examples": "0.03"
},
{
"column": "beta_ci_lower",
"description": "95% confidence interval lower bound for beta",
"data_type": "float",
"examples": "-0.02"
},
{
"column": "beta_ci_upper",
"description": "95% confidence interval upper bound for beta",
"data_type": "float",
"examples": "0.08"
},
{
"column": "odds_ratio",
"required": "CONDITIONAL (trait_type = binary)",
"description": "odds ratio for binary traits",
"data_type": "float",
"examples": "1.5"
},
{
"column": "OR_ci_lower",
"description": "95% confidence interval lower bound for OR",
"data_type": "float",
"examples": "1.36"
},
{
"column": "OR_ci_upper",
"description": "95% confidence interval upper bound for OR",
"data_type": "float",
"examples": "1.64"
},
{
"column": "direction_of_effect",
"required": "CONDITIONAL (is_meta_analysis = TRUE)",
"description": "direction of effect",
"data_type": "enumeration",
"enumerations": ["-", "+"],
"examples": "-"
},
{
"column": "n_samp",
"description": "sample size",
"data_type": "integer",
"examples": "1500",
"notes": "Optional. If it's the same value for all variants then not necessary to include (since captured in the analysis table). If values differ across variants, then encouraged to include."
},
{
"column": "n_case",
"description": "number of cases for binary traits",
"data_type": "integer",
"examples": "500",
"notes": "Optional. If it's the same value for all variants then not necessary to include (since captured in the analysis table). If values differ across variants, then encouraged to include."
},
{
"column": "n_ctrl",
"description": "number of controls for binary traits",
"data_type": "integer",
"examples": "750",
"notes": "Optional. If it's the same value for all variants then not necessary to include (since captured in the analysis table). If values differ across variants, then encouraged to include."
},
{
"column": "n_effective",
"description": "effective sample size = n_samp for quantitative traits = 4/(1/n_case + 1/n_ctrl) for binary traits",
"data_type": "float",
"notes": "Optional. If it's the same value for all variants then not necessary to include (since captured in the analysis table). If values differ across variants, then encouraged to include."
},
{
"column": "is_imputed",
"description": "logical for whether or not the variant is imputed",
"data_type": "boolean",
"examples": ["TRUE", "FALSE"]
},
{
"column": "imputation_quality_score",
"required": "CONDITIONAL (is_imputed = TRUE)",
"description": "imputation quality score for an imputed variant",
"data_type": "float",
"examples": "0.9",
"notes": "Description of the score will be provided in data dictionary"
},
{
"column": "heterogeneity_p_value",
"required": "CONDITIONAL (is_meta_analysis = TRUE)",
"description": "p-value for test of effect size heterogeneity",
"data_type": "float"
},
{
"column": "heterogeneity_I2",
"description": "heterogeneity I^2 statistic value",
"data_type": "float"
}
]
},
{
"table": "ancestry_analysis",
"required": "CONDITIONAL (genetic_ancestry_file)",
"columns": [
{
"column": "ancestry_analysis_id",
"primary_key": true,
"description": "unique identifier for an analysis in primed",
"data_type": "string",
"notes": "PRIMED upload workflow would generate this"
},
{
"column": "gsr_source",
"required": true,
"description": "Information about source of GSR data. Include additional details in README",
"data_type": "string",
"examples": ["dbGaP", "Colorado Biobank ", "PRIMED"],
"notes": "free text short label; useful for unreleased GSR"
},
{
"column": "gsr_source_url",
"description": "URL of source (if applicable)",
"data_type": "string"
},
{
"column": "dbgap_analysis_accession",
"description": "Analysis accession identifier for GSR downloaded from dbGaP",
"data_type": "string",
"examples": "pha003690.1",
"notes": "identifier in phaXXXXXX.v format"
},
{
"column": "pubmed_id",
"description": "Pubmed ID identifier of the publication",
"data_type": "string",
"examples": "33568819",
"notes": "PMID identifier"
},
{
"column": "first_author",
"description": "Last name and initials of the first author",
"data_type": "string"
},
{
"column": "publication_url",
"description": "External link to the publication",
"data_type": "string",
"notes": "URL of publication"
},
{
"column": "release_date",
"description": "Date on which the analysis was released publicy",
"data_type": "date",
"notes": "e.g. on dbGaP or GWAS Catalog"
},
{
"column": "consent_code",
"required": true,
"description": "consent abbreviation (https://www.ncbi.nlm.nih.gov/pmc/articles/PMC4721915/table/pgen.1005772.t001/?report=objectonly)",
"data_type": "string",
"notes": "NRES is the code for no restrictions on data use (i.e. open access)"
},
{
"column": "upload_date",
"required": true,
"description": "Date GSR was uploaded to PRIMED AnVIL workspace",
"data_type": "date",
"examples": "2023-03-21",
"notes": "YYYY-MM-DD format"
},
{
"column": "contributor_contact",
"required": true,
"description": "email of the PRIMED contributor who can be contacted for data related questions",
"data_type": "string",
"notes": "recommended by WG. helpful when GSR is not publicly released and is from a Biobank or some other source"
},
{
"column": "reference_assembly",
"required": true,
"description": "Reference genome assembly that the submitted data is mapped to",
"data_type": "enumeration",
"enumerations": ["GRCh38", "GRCh37", "NCBI36", "NCBI35", "NCBI34"],
"examples": "GRCh38"
},
{
"column": "dbsnp_build_version",
"description": "dbSNP build for the rsIDs included in GSR files",
"data_type": "string"
},
{
"column": "n_variants",
"required": true,
"description": "Total number of variants in the analysis results across all chromosomes",
"data_type": "integer",
"examples": "1000000",
"notes": "This will be used for a QC step to check data integrity of submitted data"
},
{
"column": "min_MAF_filter",
"description": "minimum minor allele frequency filter",
"data_type": "float",
"examples": "0.01"
},
{
"column": "min_MAC_filter",
"description": "minimum minor allele count filter",
"data_type": "integer",
"examples": "20"
},
{
"column": "LD_max_r2_filter",
"description": "maximum r^2 for SNPs kept in LD pruning",
"data_type": "float",
"examples": "0.1"
},
{
"column": "genotyping_technology",
"required": true,
"description": "The genotyping technology used for detecting variants",
"data_type": "enumeration",
"enumerations": ["genome-wide array", "WGS", "exome array", "WES", "other array"],
"multi_value_delimiter": "|"
},
{
"column": "genotyping_platform",
"required": true,
"description": "Genotyping platform description including manufacturer, array name, sequencer name",
"data_type": "string",
"multi_value_delimiter": "|",
"examples": ["Illumina GSA", "Illumina Omni-2.5", "Illumina HiSeq", "Affymetrix"],
"notes": "put 'unavailable' if unknown"
},
{
"column": "is_imputed",
"required": true,
"description": "Indicator of whether the analysis was performed using imputed genotypes or dosages",
"data_type": "boolean",
"enumerations": ["TRUE", "FALSE"],
"examples": true
},
{
"column": "imputation_reference_panel",
"required": "CONDITIONAL (is_imputed = TRUE)",
"description": "Reference panel use for imputation",
"data_type": "enumeration",
"enumerations": ["1000 Genomes", "HRC", "TOPMed", "Other"],
"examples": "TOPMed",
"notes": "Put 'Other' if not one of the common reference panels listed. Can include further information in imputation_reference_panel_detail or README"
},
{
"column": "imputation_reference_panel_detail",
"required": "CONDITIONAL (is_imputed = TRUE)",
"description": "Details of the imputation reference panel; e.g. version number or name of panel when imputation_reference_panel = 'Other'",
"data_type": "string",
"examples": "TOPMed r2",
"notes": "version number or name of 'other' include N/A"
},
{
"column": "imputation_quality_filter",
"required": "CONDITIONAL (is_imputed = TRUE)",
"description": "minimum imputation quality value (e.g. Rsq, info) for filtering imputed variants",
"data_type": "float",
"examples": "0.3",
"notes": "If no filter, enter value of 0"
},
{
"column": "n_samp",
"required": true,
"description": "Total sample size in the analysis",
"data_type": "integer",
"notes": "When different markers have different sample sizes, e.g, due to missing genotypes, use max sample size across markers"
},
{
"column": "cohorts",
"required": true,
"description": "A list of cohorts that collected the samples.",
"data_type": "string",
"multi_value_delimiter": "|"
},
{
"column": "population_descriptor",
"required": true,
"description": "the concept or classification scheme used to categorize people into populations for this analysis",
"data_type": "string",
"examples": "reported ancestry"
},
{
"column": "population_labels",
"required": true,
"description": "name given to a population that describes or classifies it according to the dimension along which it was identified",
"data_type": "string",
"multi_value_delimiter": "|",
"examples": "Chinese Americans | European Americans | African Americans"
},
{
"column": "population_proportions",
"description": "proportion of participants from each population in the same order mapping to the values in the population_labels variable",
"data_type": "string",
"multi_value_delimiter": "|",
"examples": "0.7 | 0.1 | 0.2"
},
{
"column": "countries_of_recruitment",
"description": "Reported countries of recruitment",
"data_type": "string",
"multi_value_delimiter": "|",
"examples": "Ghana | Kenya | Nigeria"
},
{
"column": "countries_of_birth",
"description": "Reported countries of birth",
"data_type": "string",
"multi_value_delimiter": "|",
"examples": "Ghana | Kenya | Nigeria"
},
{
"column": "analysis_method",
"required": true,
"description": "The name or description of the method or computational algorithm used for genetic ancestry analysis.",
"data_type": "string",
"multi_value_delimiter": "|",
"examples": ["linear regression", "logistic regression", "LMM", "GLMM", "meta-analysis"]
},
{
"column": "analysis_software",
"description": "The name of the software used for the genetic anestry analysis",
"data_type": "string",
"multi_value_delimiter": "|",
"examples": ["GCTA", "PLINK", "GENESIS", "SAIGE"]
},
{
"column": "primed_dataset_id",
"description": "For analyses that used a dataset in primed individual data model indicate its dataset_id",
"data_type": "string",
"multi_value_delimiter": "|",
"examples": "a60fb66cd539ad2c",
"notes": "From PRIMED inventory workspace"
},
{
"column": "analysis_workspace_id",
"description": "workspace identifier for the analysis that was generated in PRIMED",
"data_type": "string",
"examples": "primed-analysis/PRIMED_ANALYSIS_GERA-topmed-v3-imputation"
}
]
},
{
"table": "ancestry_file",
"columns": [
{
"column": "ancestry_file_id",
"primary_key": true,
"data_type": "string",
"references": "from: md5sum",
"notes": "AnVIL requires entity id with naming convention <table_name>_id"
},
{
"column": "md5sum",
"required": true,
"description": "A valid md5 checksum",
"data_type": "string",
"examples": "49ea8cf53801c7f1e2f11336fb8a29c8",
"notes": "(32-digit hexadecimal number)"
},
{
"column": "ancestry_analysis_id",
"required": true,
"description": "unique identifier for an analysis in primed",
"data_type": "string",
"references": "> ancestry_analysis.ancestry_analysis_id",
"notes": "AnVIL upload workflow would generate this"
},
{
"column": "file_path",
"required": true,
"description": "File path in cloud storage",
"data_type": "string",
"is_bucket_path": true
},
{
"column": "file_type",
"required": true,
"description": "Type of the file",
"data_type": "string",
"examples": ["SNP loadings", "allele frequencies"]
},
{
"column": "n_variants",
"description": "Count of variants in the GSR data file",
"data_type": "integer",
"examples": "15281216"
}
]
}
]
}