xSNPscores: Function to score lead or LD SNPs based on the given...

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

Description

xSNPscores is supposed to score a list of Lead SNPs together with the significance level. It can consider LD SNPs and the given threshold of the significant level.

Usage

xSNPscores(
data,
include.LD = NA,
LD.customised = NULL,
LD.r2 = 0.8,
significance.threshold = 5e-05,
score.cap = 10,
verbose = TRUE,
RData.location = "http://galahad.well.ox.ac.uk/bigdata",
guid = NULL
)

Arguments

data	a named input vector containing the significance level for nodes (dbSNP). For this named vector, the element names are dbSNP (starting with rs or in the format of 'chrN:xxx', where N is either 1-22 or X, xxx is number; for example, 'chr16:28525386'), the element values for the significance level (measured as p-value or fdr). Alternatively, it can be a matrix or data frame with two columns: 1st column for dbSNP, 2nd column for the significance level.
include.LD	additional SNPs in LD with Lead SNPs are also included. By default, it is 'NA' to disable this option. Otherwise, LD SNPs will be included based on one or more of 26 populations and 5 super populations from 1000 Genomics Project data (phase 3). The population can be one of 5 super populations ("AFR", "AMR", "EAS", "EUR", "SAS"), or one of 26 populations ("ACB", "ASW", "BEB", "CDX", "CEU", "CHB", "CHS", "CLM", "ESN", "FIN", "GBR", "GIH", "GWD", "IBS", "ITU", "JPT", "KHV", "LWK", "MSL", "MXL", "PEL", "PJL", "PUR", "STU", "TSI", "YRI"). Explanations for population code can be found at http://www.1000genomes.org/faq/which-populations-are-part-your-study
LD.customised	a user-input matrix or data frame with 3 columns: 1st column for Lead SNPs, 2nd column for LD SNPs, and 3rd for LD r2 value. It is designed to allow the user analysing their precalcuated LD info. This customisation (if provided) has the high priority over built-in LD SNPs
LD.r2	the LD r2 value. By default, it is 0.8, meaning that SNPs in LD (r2>=0.8) with input SNPs will be considered as LD SNPs. It can be any value from 0.8 to 1
significance.threshold	the given significance threshold. By default, it is set to NULL, meaning there is no constraint on the significance level when transforming the significance level of SNPs into scores. If given, those SNPs below this are considered significant and thus scored positively. Instead, those above this are considered insigificant and thus receive no score
score.cap	the maximum score being capped. By default, it is set to 10. If NULL, no capping is applied
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:

• SNP: Lead and/or LD SNPs

• Score: the scores for SNPs calculated based on p-values taking into account the given threshold of the significant level

• Pval: the input p-values for Lead SNPs or R2-adjusted p-values for LD SNPs

• Flag: the flag to indicate whether the resulting SNPs are Lead SNPs or LD SNPs

Note

None

See Also

xRDataLoader

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 <- GenomicRanges::mcols(gr)[,c(1,3)]

# b) calculate SNP scores (considering significant cutoff 5e-5)
## without inclusion of LD SNPs
df_SNP <- xSNPscores(data=data, significance.threshold=5e-5,
RData.location=RData.location)
## include LD SNPs (calculated based on European populations)
df_SNP <- xSNPscores(data=data, significance.threshold=5e-5,
include.LD="EUR", RData.location=RData.location)

## End(Not run)

Pi documentation built on Nov. 29, 2021, 3 p.m.