GenoScan (version 0.1)

GenoScan.VCF.chr: Scan a VCF file to study the association between an quantitative/dichotomous outcome variable and a region or whole chromosome by score type statistics allowing for multiple functional annotation scores.

Description

Once the preliminary work is done by "GenoScan.prelim()", this function scan a target region or chromosome, and output results for all windows as well as an estimated significance threshold. For genome-wide scan, users can scan each chromosome individually, then the genome-wide significance threshold can be obtained by combining chromosome-wise thresholds:

alpha=1/(1/alpha_1+1/alpha_2+...+1/alpha_22).

Usage

GenoScan.VCF.chr(result.prelim,vcf.filename,chr,pos.min=NULL,pos.max=NULL,
Gsub.id=NULL,annot.filename=NULL,cell.type=NULL,MAF.weights='beta',
test='combined',window.size=c(5000,10000,15000,20000,25000,50000),
MAF.threshold=1,impute.method='fixed')

Arguments

result.prelim

The output of function "GenoScan.prelim()"

vcf.filename

A character specifying the directory (including the file name) of the vcf file.

chr

Chromosome number.

pos.min

Minimum position of the scan. The default is NULL, where the scan starts at the first base pair.

pos.max

Maximum position of the scan. The default is NULL, where the scan ends at the last base pair, according to the chromosome sizes at:

http://hgdownload.cse.ucsc.edu/goldenPath/hg19/bigZips/hg19.chrom.sizes.

Gsub.id

The subject id corresponding to the genotype matrix, an n dimensional vector. This is used to match phenotype with genotype. The default is NULL, where the subject id in the vcf file is used.

annot.filename

A character specifying the directory (including the file name) of functional annotations. Currently GenoScan supports GenoNet scores across 127 tissues/cell types, which can be downloaded at:

http://www.openbioinformatics.org/annovar/download/GenoNetScores/

cell.type

A character specifying the tissue/cell type integrated in the analysis, in addition to standard dispersion and/or burden tests. The default is NULL, where no functional annotation is included. If cell.type='all', GenoNet scores across all 127 tissues/cell types are incorperated.

MAF.weights

Minor allele frequency based weight. Can be 'beta' to up-weight rare variants or 'equal' for a flat weight. The default is 'beta'.

test

Can be 'dispersion', 'burden' or 'combined'. The test is 'combined', both dispersion and burden tests are applied. The default is 'combined'.

window.size

Candidate window sizes in base pairs. The default is c(5000,10000,15000,20000,25000,50000). Note that extemely small window size (e.g. 1) requires large sample size.

MAF.threshold

Threshold for minor allele frequency. Variants above MAF.threshold are ignored. The default is 1.

impute.method

Choose the imputation method when there is missing genotype. Can be "random", "fixed" or "bestguess". Given the estimated allele frequency, "random" simulates the genotype from binomial distribution; "fixed" uses the genotype expectation; "bestguess" uses the genotype with highest probability.

Value

window.summary

Results for all windows. Each row presents a window.

M

Estimated number of effective tests.

threshold

Estimated threshold, 0.05/M.

%% ~Describe the value returned %% If it is a LIST, use %% \item{comp1 }{Description of 'comp1'} %% \item{comp2 }{Description of 'comp2'} %% ...

Examples

# NOT RUN {
# load example vcf file from package "seqminer"
vcf.filename = system.file("vcf/all.anno.filtered.extract.vcf.gz", package = "seqminer")

# simulated outcomes, covariates and inidividual id.
Y<-as.matrix(rnorm(3,0,1))
X<-as.matrix(rnorm(3,0,1))
id<-c("NA12286", "NA12341", "NA12342")

# fit null model
result.prelim<-GenoScan.prelim(Y,X=X,id=id,out_type="C",B=5000)

# scan the vcf file
result<-GenoScan.VCF.chr(result.prelim,vcf.filename,chr=1,pos.min=196621007,pos.max=196716634)


## this is how the actual genotype matrix from package "seqminer" looks like
example.G <- t(readVCFToMatrixByRange(vcf.filename, "1:196621007-196716634",annoType='')[[1]])

# }