snpgdsDiss {SNPRelate} | R Documentation

## Individual dissimilarity analysis

### Description

Calculate the individual dissimilarities for each pair of individuals.

### Usage

    snpgdsDiss(gdsobj, sample.id=NULL, snp.id=NULL, autosome.only=TRUE,
        remove.monosnp=TRUE, maf=NaN, missing.rate=NaN, num.thread=1, verbose=TRUE)

### Arguments

`gdsobj` | an object of class `SNPGDSFileClass`, a SNP GDS file
`sample.id` | a vector of sample id specifying selected samples; if NULL, all samples are used
`snp.id` | a vector of snp id specifying selected SNPs; if NULL, all SNPs are used
`autosome.only` | if `TRUE`, use autosomal SNPs only; if it is a numeric or character value, keep SNPs according to the specified chromosome
`remove.monosnp` | if TRUE, remove monomorphic SNPs
`maf` | to use the SNPs with ">= maf" only; if NaN, no MAF threshold
`missing.rate` | to use the SNPs with "<= missing.rate" only; if NaN, no missing threshold
`num.thread` | the number of (CPU) cores used; if `NA`, detect the number of cores automatically
`verbose` | if TRUE, show information

### Details

The minor allele frequency and missing rate for each SNP passed in `snp.id` are calculated over all the samples in `sample.id`.

The details will be described in future.

### Value

Return a class "snpgdsDissClass":

`sample.id` | the sample ids used in the analysis
`snp.id` | the SNP ids used in the analysis
`diss` | a matrix of individual dissimilarity

### Author(s)

Xiuwen Zheng

### References

Zheng, Xiuwen. 2013. Statistical Prediction of HLA Alleles and Relatedness Analysis in Genome-Wide Association Studies. PhD dissertation, the department of Biostatistics, University of Washington.

Weir BS, Zheng X. SNPs and SNVs in Forensic Science. 2015. Forensic Science International: Genetics Supplement Series.

### See Also

`snpgdsHCluster`

### Examples

    # open an example dataset (HapMap)
    genofile <- snpgdsOpen(snpgdsExampleFileName())

    pop.group <- as.factor(read.gdsn(index.gdsn(
        genofile, "sample.annot/pop.group")))
    pop.level <- levels(pop.group)

    diss <- snpgdsDiss(genofile)
    hc <- snpgdsHCluster(diss)

    # close the genotype file
    snpgdsClose(genofile)

    # split
    set.seed(100)
    rv <- snpgdsCutTree(hc, label.H=TRUE, label.Z=TRUE)

    # draw dendrogram
    snpgdsDrawTree(rv, main="HapMap Phase II",
        edgePar=list(col=rgb(0.5,0.5,0.5, 0.75), t.col="black"))

[Package SNPRelate version 1.14.0 Index]