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Last data update: 2014.03.03

R: Genome-wide significance for a case-control GWA scan

emp.ccfast

R Documentation

Genome-wide significance for a case-control GWA scan

Description

Genome-wide significance for a case-control GWA scan. Analysis function is ccfast.

Usage

emp.ccfast(y, data, snpsubset, idsubset, times = 200, quiet=FALSE,
		bcast = 10)

Arguments

All arguments are the same as in and passed intact to the ccfast. See help for this function.

`y`	character name of the vector of case-control status. Cases are denoted as 1 and controls as 0.
`data`	An object of `gwaa.data-class`
`snpsubset`	Index, character or logical vector with subset of SNPs to run analysis on. If missing, all SNPs from `data` are used for analysis.
`idsubset`	Index, character or logical vector with subset of IDs to run analysis on. If missing, all people from `data` are used for analysis.
`times`	If more then one, the number of replicas to be used in derivation of empirical genome-wide significance. See `emp.qtscore`, which calls qtscore with times>1 for details
`quiet`	do not print warning messages
`bcast`	If the argument times > 1, progress is reported once in bcast replicas

Details

In the analysis of empirical significance, first time the function ccfast is called and result object is saved. Later, the function ccfast is called times times with replace=FALSE in order to generate the distribution under the null. Each call, minimal P-value is extracted and compared with original P-values. For a particular SNP, empirical P-value is obtained as a proportion of times minimal Ps from resampled data was less then the original P.

The list elements effB, effAB and effBB are the ones obtained from the analysis of the original (not permuted) data set

Value

Object of class scan.gwaa-class

Author(s)

Yurii Aulchenko

Examples

require(GenABEL.data)
data(srdta)
a<-ccfast("bt",data=srdta,snps=c(500:800))
plot(a)
# this does not make sense, as the whole experiment must be analysed, not a small region!
# also, times = 20 is way too small (should be at least 200)
b<-emp.ccfast("bt",data=srdta,snps=c(500:800),bcast=10, times = 20)
plot(b)
# compare qvalues and empirical P
qv<-qvaluebh95(a[,"P1df"])$qval
qv
b[,"P1df"]
plot(qv,b[,"P1df"],xlim=c(0,1),ylim=c(0,1))
abline(a=0,b=1)

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)

R is free software and comes with ABSOLUTELY NO WARRANTY.
You are welcome to redistribute it under certain conditions.
Type 'license()' or 'licence()' for distribution details.

R is a collaborative project with many contributors.
Type 'contributors()' for more information and
'citation()' on how to cite R or R packages in publications.

Type 'demo()' for some demos, 'help()' for on-line help, or
'help.start()' for an HTML browser interface to help.
Type 'q()' to quit R.

> library(GenABEL)
Loading required package: MASS
Loading required package: GenABEL.data
> png(filename="/home/ddbj/snapshot/RGM3/R_CC/result/GenABEL/emp.ccfast.Rd_%03d_medium.png", width=480, height=480)
> ### Name: emp.ccfast
> ### Title: Genome-wide significance for a case-control GWA scan
> ### Aliases: emp.ccfast
> ### Keywords: htest
> 
> ### ** Examples
> 
> require(GenABEL.data)
> data(srdta)
> a<-ccfast("bt",data=srdta,snps=c(500:800))
Warning in ccfast("bt", data = srdta, snps = c(500:800)) :
  11 people (out of 2500 ) excluded as not having cc status

> plot(a)
> # this does not make sense, as the whole experiment must be analysed, not a small region!
> # also, times = 20 is way too small (should be at least 200)
> b<-emp.ccfast("bt",data=srdta,snps=c(500:800),bcast=10, times = 20)
Warning in ccfast(y = y, data = data, snpsubset = snpsubset, idsubset = idsubset,  :
  11 people (out of 2500 ) excluded as not having cc status

50%100%> plot(b)
> # compare qvalues and empirical P
> qv<-qvaluebh95(a[,"P1df"])$qval
> qv
  [1] 0.9826648 0.9826648 0.9826648 0.9826648 0.9826648 0.9837415 0.9826648
  [8] 0.9935462 0.9826648 0.9826648 0.9826648 0.9826648 0.9826648 0.9826648
 [15] 0.9826648 0.9826648 0.9826648 0.9826648 0.9602959 0.9826648 0.9602959
 [22] 0.9826648 0.9973161 0.9826648 0.9826648 0.9826648 0.9935462 0.9826648
 [29] 0.9826648 0.9826648 0.9973161 0.9826648 0.9973161 0.9826648 0.9826648
 [36] 0.9602959 0.9826648 0.9826648 0.9602959 0.9826648 0.9602959 0.9602959
 [43] 0.9826648 0.9973161 0.9826648 0.9826648 0.9935462 0.9826648 0.9826648
 [50] 0.9826648 0.9826648 0.9826648 0.9973161 0.9826648 0.9602959 0.9602959
 [57] 0.9602959 0.9837415 0.9826648 0.9973161 0.9602959 0.9602959 0.9837415
 [64] 0.9826648 0.9826648 0.9826648 0.9826648 0.9602959 0.9602959 0.9602959
 [71] 0.9826648 0.9826648 0.9973161 0.9602959 0.9602959 0.9826648 0.9826648
 [78] 0.9973161 0.9826648 0.9826648 0.9826648 0.9826648 0.9826648 0.9826648
 [85] 0.9602959 0.9826648 0.9899597 0.9973161 0.9850907 0.9973161 0.9826648
 [92] 0.9720685 0.9826648 0.9826648 0.9837415 0.9826648 0.9826648 0.9826648
 [99] 0.9973161 0.9826648 0.9826648 0.9826648 0.9973161 0.9826648 0.9826648
[106] 0.9899597 0.9826648 0.9826648 0.9826648 0.9826648 0.9973161 0.9973161
[113] 0.9973161 0.9850907 0.9985882 0.9826648 0.9973161 0.9834113 0.9973161
[120] 0.9826648 0.9826648 0.9973161 0.9602959 0.9602959 0.9602959 0.9826648
[127] 0.9973161 0.9973161 0.9602959 0.9826648 0.9826648 0.9826648 0.9826648
[134] 0.9826648 0.9602959 0.9602959 0.9826648 0.9826648 0.9826648 0.9602959
[141] 0.9602959 0.9602959 0.9826648 0.9826648 0.9826648 0.9826648 0.9935462
[148] 0.9955128 0.9935462 0.9973161 0.9826648 0.9935462 0.9826648 0.9826648
[155] 0.9826648 0.9602959 0.9826648 0.9602959 0.9602959 0.9826648 0.9826648
[162] 0.9826648 0.9720685 0.9826648 0.9826648 0.9826648 0.9826648 0.9826648
[169] 0.9826648 0.9602959 0.9826648 0.9826648 0.9826648 0.9973161 0.9973161
[176] 0.9602959 0.9826648 0.9602959 0.9602959 0.9826648 0.9720685 0.9826648
[183] 0.9826648 0.9826648 0.9973161 0.9602959 0.9826648 0.9826648 0.9973161
[190] 0.9826648 0.9826648 0.9826648 0.9973161 0.9826648 0.9899597 0.9712952
[197] 0.9602959 0.9602959 0.9602959 0.9602959 0.9826648 0.9602959 0.9826648
[204] 0.9826648 0.9826648 0.9826648 0.9837415 0.9826648 0.9826648 0.9826648
[211] 0.9826648 0.9826648 0.9973161 0.9826648 0.9826648 0.9973161 0.9826648
[218] 0.9826648 0.9973161 0.9602959 0.9826648 0.9602959 0.9985882 0.9826648
[225] 0.9826648 0.9602959 0.9602959 0.9602959 0.9826648 0.9826648 0.9602959
[232] 0.9602959 0.9826648 0.9826648 0.9826648 0.9826648 0.9826648 0.9973161
[239] 0.9973161 0.9602959 0.9826648 0.9826648 0.9826648 0.9826648 0.9826648
[246] 0.9826648 0.9837415 0.9973161 0.9602959 0.9973161 0.9826648 0.9826648
[253] 0.9602959 0.9602959 0.9826648 0.9826648 0.9602959 0.9826648 0.9602959
[260] 0.9973161 0.9826648 0.9826648 0.9826648 0.9826648 0.9826648 0.9826648
[267] 0.9826648 0.9602959 0.9602959 0.9973161 0.9826648 0.9602959 0.9837415
[274] 0.9826648 0.9826648 0.9826648 0.9602959 0.9826648 0.9826648 0.9602959
[281] 0.9826648 0.9826648 0.9602959 0.9602959 0.9826648 0.9826648 0.9973161
[288] 0.9602959 0.9826648 0.9602959 0.9826648 0.9837415 0.9826648 0.9901093
[295] 0.9602959 0.9602959 0.9826648 0.9602959 0.9973161 0.9602959 0.9826648
> b[,"P1df"]
  [1] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
 [16] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
 [31] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
 [46] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
 [61] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
 [76] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
 [91] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
[106] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
[121] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
[136] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
[151] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
[166] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
[181] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
[196] 1.00 1.00 1.00 1.00 1.00 1.00 0.85 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
[211] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
[226] 1.00 1.00 0.85 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
[241] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
[256] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
[271] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
[286] 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00 1.00
[301] 1.00
> plot(qv,b[,"P1df"],xlim=c(0,1),ylim=c(0,1))
> abline(a=0,b=1)
> 
> 
> 
> 
> 
> dev.off()
null device 
          1 
>