xSNP2nGenes: Function to define nearby genes given a list of SNPs

In Pi: Leveraging Genetic Evidence to Prioritise Drug Targets at the Gene and Pathway Level

Description

xSNP2nGenes is supposed to define nearby genes given a list of SNPs within certain distance window. The distance weight is calcualted as a decaying function of the gene-to-SNP distance.

Usage

xSNP2nGenes(
data,
distance.max = 2e+05,
decay.kernel = c("rapid", "slow", "linear", "constant"),
decay.exponent = 2,
GR.SNP = c("dbSNP_GWAS", "dbSNP_Common", "dbSNP_Single"),
GR.Gene = c("UCSC_knownGene", "UCSC_knownCanonical"),
include.TAD = c("none", "GM12878", "IMR90", "MSC", "TRO", "H1", "MES",
"NPC"),
verbose = TRUE,
RData.location = "http://galahad.well.ox.ac.uk/bigdata",
guid = NULL
)

Arguments

data	an input vector containing SNPs. SNPs should be provided as dbSNP ID (ie starting with rs). Alternatively, they can be in the format of 'chrN:xxx', where N is either 1-22 or X, xxx is genomic positional number; for example, 'chr16:28525386'
distance.max	the maximum distance between genes and SNPs. Only those genes no far way from this distance will be considered as seed genes. This parameter will influence the distance-component weights calculated for nearby SNPs per gene
decay.kernel	a character specifying a decay kernel function. It can be one of 'slow' for slow decay, 'linear' for linear decay, and 'rapid' for rapid decay. If no distance weight is used, please select 'constant'
decay.exponent	a numeric specifying a decay exponent. By default, it sets to 2
GR.SNP	the genomic regions of SNPs. By default, it is 'dbSNP_GWAS', that is, SNPs from dbSNP (version 146) restricted to GWAS SNPs and their LD SNPs (hg19). It can be 'dbSNP_Common', that is, Common SNPs from dbSNP (version 146) plus GWAS SNPs and their LD SNPs (hg19). Alternatively, the user can specify the customised input. To do so, first save your RData file (containing an GR object) into your local computer, and make sure the GR object content names refer to dbSNP IDs. Then, tell "GR.SNP" with your RData file name (with or without extension), plus specify your file RData path in "RData.location". Note: you can also load your customised GR object directly
GR.Gene	the genomic regions of genes. By default, it is 'UCSC_knownGene', that is, UCSC known genes (together with genomic locations) based on human genome assembly hg19. It can be 'UCSC_knownCanonical', that is, UCSC known canonical genes (together with genomic locations) based on human genome assembly hg19. Alternatively, the user can specify the customised input. To do so, first save your RData file (containing an GR object) into your local computer, and make sure the GR object content names refer to Gene Symbols. Then, tell "GR.Gene" with your RData file name (with or without extension), plus specify your file RData path in "RData.location". Note: you can also load your customised GR object directly
include.TAD	TAD boundary regions are also included. By default, it is 'none' to disable this option. Otherwise, inclusion of a TAD dataset to pre-filter SNP-nGene pairs (i.e. only those within a TAD region will be kept). TAD datasets can be one of "GM12878" (lymphoblast), "IMR90" (fibroblast), "MSC" (mesenchymal stem cell) ,"TRO" (trophoblasts-like cell), "H1" (embryonic stem cell), "MES" (mesendoderm) and "NPC" (neural progenitor cell). Explanations can be found at http://dx.doi.org/10.1016/j.celrep.2016.10.061
verbose	logical to indicate whether the messages will be displayed in the screen. By default, it sets to true for display
RData.location	the characters to tell the location of built-in RData files. See xRDataLoader for details
guid	a valid (5-character) Global Unique IDentifier for an OSF project. See xRDataLoader for details

Value

a data frame with following columns:

• Gene: nearby genes

• SNP: SNPs

• Dist: the genomic distance between the gene and the SNP

• Weight: the distance weight based on the genomic distance

• Gap: the genomic gap between the gene and the SNP (in the form of 'chr:start-end')

• TAD: if applied, it can be 'Excluded' or the TAD boundary region (in the form of 'chr:start-end') that the genomic interval falls into. Also if SNP within the gene body, Gap and TAD will be SNP location (in the form of 'chr:start-end')

Note

For details on the decay kernels, please refer to xVisKernels

See Also

xSNPlocations, xRDataLoader, xGR, xSymbol2GeneID

Examples

RData.location <- "http://galahad.well.ox.ac.uk/bigdata"
## Not run: 

# a) provide the seed SNPs with the significance info
## load ImmunoBase
ImmunoBase <- xRDataLoader(RData.customised='ImmunoBase',
RData.location=RData.location)
## get lead SNPs reported in AS GWAS and their significance info (p-values)
gr <- ImmunoBase$AS$variant
data <- names(gr)

# b) define nearby genes
df_nGenes <- xSNP2nGenes(data=data, distance.max=200000,
decay.kernel="slow", decay.exponent=2, RData.location=RData.location)

# c) define nearby genes (considering TAD boundary regions in GM12878)
df_nGenes <- xSNP2nGenes(data=data, distance.max=200000,
decay.kernel="slow", decay.exponent=2, include.TAD='GM12878',
RData.location=RData.location)

## End(Not run)

Pi documentation built on Nov. 26, 2020, 2:01 a.m.