studiesAndLeadVariantsForGeneByL2G {otargen}

Retrieve "locus-to-gene" (L2G) model summary data for a gene.

Description

The "locus-to-gene" (L2G) model derives features to prioritize likely causal genes at each GWAS locus based on genetic and functional genomics features. The main categories of predictive features are:

• Distance: Distance from credible set variants to the gene.

• Molecular QTL colocalization: Colocalization with molecular QTLs.

• Chromatin interaction: Interactions, such as promoter-capture Hi-C.

• Variant pathogenicity: Pathogenicity scores from VEP (Variant Effect Predictor).

Usage

studiesAndLeadVariantsForGeneByL2G(gene, l2g = NA, pvalue = NA, vtype = NULL)

Arguments

gene

Character: Gene ENSEMBL ID (e.g. ENSG00000169174) or gene symbol (e.g. PCSK9). This argument can take a list of genes too.

l2g

Numeric: Locus-to-gene (L2G) cutoff score. (Default: NA)

pvalue

Character: P-value cutoff. (Default: NA)

vtype

Character: Most severe consequence to filter the variant types, including "intergenic_variant", "upstream_gene_variant", "intron_variant", "missense_variant", "5_prime_UTR_variant", "non_coding_transcript_exon_variant", "splice_region_variant". (Default: NULL)

Details

The function also provides additional filtering parameters to narrow the results based following parameters (see below)

Value

Returns a data frame containing the input gene ID and its data for the L2G model. The table consists of the following columns:

• yProbaModel: Numeric. L2G score.

• yProbaDistance: Numeric. Distance.

• yProbaInteraction: Numeric. Chromatin interaction.

• yProbaMolecularQTL: Numeric. Molecular QTL.

• yProbaPathogenicity: Numeric. Pathogenicity.

• pval: Numeric. P-value.

• beta.direction: Character. Beta direction.

• beta.betaCI: Numeric. Beta confidence interval.

• beta.betaCILower: Numeric. Lower bound of the beta confidence interval.

• beta.betaCIUpper: Numeric. Upper bound of the beta confidence interval.

• odds.oddsCI: Numeric. Odds ratio confidence interval.

• odds.oddsCILower: Numeric. Lower bound of the odds ratio confidence interval.

• odds.oddsCIUpper: Numeric. Upper bound of the odds ratio confidence interval.

• study.studyId: Character. Study ID.

• study.traitReported: Character. Reported trait.

• study.traitCategory: Character. Trait category.

• study.pubDate: Character. Publication date.

• study.pubTitle: Character. Publication title.

• study.pubAuthor: Character. Publication author.

• study.pubJournal: Character. Publication journal.

• study.pmid: Character. PubMed ID.

• study.hasSumstats: Logical. Indicates if the study has summary statistics.

• study.nCases: Integer. Number of cases in the study.

• study.numAssocLoci: Integer. Number of associated loci.

• study.nTotal: Integer. Total number of samples in the study.

• study.traitEfos: Character. Trait EFOs.

• variant.id: Character. Variant ID.

• variant.rsId: Character. Variant rsID.

• variant.chromosome: Character. Variant chromosome.

• variant.position: Integer. Variant position.

• variant.refAllele: Character. Variant reference allele.

• variant.altAllele: Character. Variant alternate allele.

• variant.nearestCodingGeneDistance: Integer. Distance to the nearest coding gene.

• variant.nearestGeneDistance: Integer. Distance to the nearest gene.

• variant.mostSevereConsequence: Character. Most severe consequence.

• variant.nearestGene.id: Character. Nearest gene ID.

• variant.nearestCodingGene.id: Character. Nearest coding gene ID.

• ensembl_id: Character. Ensembl ID.

• gene_symbol: Character. Gene symbol.

Examples

## Not run: 
result <- studiesAndLeadVariantsForGeneByL2G(genes = c("ENSG00000163946",
     "ENSG00000169174", "ENSG00000143001"), l2g = 0.7)
result <- studiesAndLeadVariantsForGeneByL2G(genes = "ENSG00000169174",
     l2g = 0.6, pvalue = 1e-8, vtype = c("intergenic_variant", "intron_variant"))
result <- studiesAndLeadVariantsForGeneByL2G(genes = "TMEM61")

## End(Not run)