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Last data update: 2014.03.03

R: Select a subset of SNPs

ASP.Selection

R Documentation

Select a subset of SNPs

Description

Select a subset of SNPs using discrimination method for affected sib pairs

Usage

 ASP.Selection(Tem_Gen, Index_Gen, IBD, k = log(10000))

Arguments

`Tem_Gen`	Genotypes of controls (denoted by the number of alternative allele)
`Index_Gen`	Genotypes of index cases
`IBD`	IBD states for each affected sib pair
`k`	Selection threshold (by default log(1e4))

Details

Take the genotypes of controls and index cases and the IBD states. Give the score statistics, discrimination statistics and the subset of selected SNPs with the chosen threshold.

Value

List of 4 vectors with components:

`score`	The values of the score statistic for each SNPs
`stat`	The values of discrimination statistic comparing each SNPs with the most associated SNP
`SNP_subset`	The indexes (numbers of columns) of selected SNPs
`SNPname_subset`	The names (names of columns) of selected SNPs

Author(s)

Claire Dandine-Roulland

References

Dandine-Roulland, Claire and Perdry, Herve. Where is the causal variant? On the advantage of the family design over the case-control design in genetic association studies. Submitted to Eur J Hum Genet

Examples

data(ASPData)
ASP.Selection(ASPData$Control, ASPData$Index, ASPData$IBD )

Results


R version 3.3.1 (2016-06-21) -- "Bug in Your Hair"
Copyright (C) 2016 The R Foundation for Statistical Computing
Platform: x86_64-pc-linux-gnu (64-bit)

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> library(ASPBay)
Loading required package: hexbin
Loading required package: Rcpp
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/ASPBay/ASP.Selection.Rd_%03d_medium.png", width=480, height=480)
> ### Name: ASP.Selection
> ### Title: Select a subset of SNPs
> ### Aliases: ASP.Selection
> ### Keywords: Selection Affected sib pairs
> 
> ### ** Examples
> 
> data(ASPData)
> ASP.Selection(ASPData$Control, ASPData$Index, ASPData$IBD )
$score
      SNP1       SNP2       SNP3       SNP4       SNP5       SNP6       SNP7 
10.5697498 -2.6353276 -0.7334053 -0.7617181  6.3666141 -2.4239793 -0.3127143 
      SNP8       SNP9      SNP10      SNP11      SNP12      SNP13      SNP14 
-2.5229966  9.2774756 -3.7236812 -0.9274910  9.4499055 11.2370354  9.4499055 
     SNP15      SNP16      SNP17      SNP18      SNP19      SNP20      SNP22 
11.2370354 -0.9820782 -0.9820782 -1.5493967 -0.4868593 -0.1776845 -0.6855409 

$dis_stat
     SNP1      SNP2      SNP3      SNP4      SNP5      SNP6      SNP7      SNP8 
 14.55135 119.32601 125.73308 125.69075  85.73719 120.39529 126.17317 119.90545 
     SNP9     SNP10     SNP11     SNP12     SNP13     SNP14     SNP15     SNP16 
 40.19941 112.40516 125.41072  36.97025   0.00000  36.97025   0.00000 125.30649 
    SNP17     SNP18     SNP19     SNP20     SNP22 
125.30649 123.87033 126.03393 126.23939 125.80100 

$SNP_subset
[1] 13 15

$SNPnames_subset
[1] "SNP13" "SNP15"

> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>