Version:	2.1-2
Date:	2024-10-28
Depends:	R (≥ 4.0.0)
Imports:	haplo.stats, mvtnorm, parallel, survival, tidyr, plyr, ggplot2, poisbinom
Suggests:	testthat, knitr, rmarkdown, biomaRt, VariantAnnotation, GenomicRanges, IRanges, S4Vectors, org.Hs.eg.db, TxDb.Hsapiens.UCSC.hg19.knownGene
Title:	SNPs-Based Whole Genome Association Studies
Description:	Functions to perform most of the common analysis in genome association studies are implemented. These analyses include descriptive statistics and exploratory analysis of missing values, calculation of Hardy-Weinberg equilibrium, analysis of association based on generalized linear models (either for quantitative or binary traits), and analysis of multiple SNPs (haplotype and epistasis analysis). Permutation test and related tests (sum statistic and truncated product) are also implemented. Max-statistic and genetic risk-allele score exact distributions are also possible to be estimated. The methods are described in Gonzalez JR et al., 2007 <doi:10.1093/bioinformatics/btm025>.
URL:	https://github.com/isglobal-brge/SNPassoc
License:	GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
Encoding:	UTF-8
VignetteBuilder:	knitr
RoxygenNote:	7.2.2
NeedsCompilation:	no
Packaged:	2024-10-28 16:22:05 UTC; dpelegri
Author:	Victor Moreno [aut], Juan R Gonzalez [aut], Dolors Pelegri [aut, cre]
Maintainer:	Dolors Pelegri <dolors.pelegri@isglobal.org>
Repository:	CRAN
Date/Publication:	2024-10-28 17:30:02 UTC

Bonferroni correction of p values

Description

This function shows the SNPs that are statistically significant after correcting for the number of tests performed (Bonferroni correction) for an object of class "WGassociation"

Usage

 Bonferroni.sig(x, model = "codominant", alpha = 0.05, 
      include.all.SNPs=FALSE)

Arguments

Details

After deciding the genetic model, the function shows the SNPs that are statistically significant at alpha level corrected by the number of performed tests.

Value

A data frame with the SNPs and the p values for those SNPs that are statistically significant after Bonferroni correction

See Also

WGassociation

Examples

data(SNPs)
datSNP<-setupSNP(SNPs,6:40,sep="")
ans<-WGassociation(protein~1,data=datSNP,model="all")
Bonferroni.sig(ans, model="codominant", alpha=0.05, include.all.SNPs=FALSE)

Population substructure

Description

This function estimates an inflation (or deflation) factor, lambda, as indicated in the paper by Devlin et al. (2001) and corrects the p-values using this factor.

Usage

GenomicControl(x, snp.sel)

Arguments

Details

This method is only valid for 2x2 tables. This means that the object of class 'WGassociation' might not have fitted the codominant model.

See reference for further details.

Value

The same object of class 'WGassociation' where the p-values have been corrected for genomic control.

References

B Devlin, K Roeder, and S.A. Bacanu. Unbiased Methods for Population Based Association Studies. Genetic Epidemiology (2001) 21:273-84

See Also

qqpval, WGassociation

Examples

data(SNPs) 
datSNP<-setupSNP(SNPs,6:40,sep="")
res<-WGassociation(casco,datSNP,model=c("do","re","log-add"))

# Genomic Control 
resCorrected<-GenomicControl(res)

SNPs from HapMap project

Description

Information about 9307 SNPs from the HapMap project belonging to 22 chromosomes. Information about two different population is available: European population (CEU) and Yoruba (YRI). The genomic information (names of SNPs, chromosomes and genetic position) is also available in a data frame called 'HapMap.SNPs.pos'.

Usage

data(HapMap)

Format

A data frame with 120 observations on the 9808 variables (SNPs) and one variable called 'group' indicating the population.

Source

HapMap project (http://www.hapmap.org)

Examples

data(HapMap)

SNPs from HapMap project

Description

Information about 9307 SNPs from the HapMap project belonging to 22 chromosomes. Information about two different population is available: European population (CEU) and Yoruba (YRI). The genomic information (names of SNPs, chromosomes and genetic position) is also available in a data frame called 'HapMap.SNPs.pos'.

Usage

data(HapMap.SNPs.pos)

Format

A data frame with 120 observations on the 9808 variables (SNPs) and one variable called 'group' indicating the population.

Source

HapMap project (http://www.hapmap.org)

Examples

    data(HapMap.SNPs.pos)

max-statistic for a 2x3 table

Description

Compute pairwise linkage disequilibrium between genetic markers

Usage

LD(g1, ...)

## S3 method for class 'snp'
LD(g1, g2, ...)

## S3 method for class 'setupSNP'
LD(g1, SNPs, ...)


LDplot(x, digits = 3, marker, distance, which = c("D", "D'",
    "r", "X^2", "P-value", "n", " "), ...)

LDtable(x, colorcut = c(0, 0.01, 0.025, 0.05, 0.1, 1), 
       colors = heat.colors(length(colorcut)),
       textcol = "black", digits = 3, show.all = FALSE, 
       which = c("D", "D'", "r", "X^2", "P-value", "n"), 
       colorize = "P-value", cex, ...)

Arguments

Value

None

Author(s)

functions adapted from LD, LDtable and LDplot in package genetics by Gregory Warnes et al. (warnes@bst.rochester.edu)

References

genetics R package by Gregory Warnes et al. (warnes@bst.rochester.edu)

See Also

setupSNP snp

Internal SNPstat functions

Description

Internal SNPassoc functions

Usage

association.fit(var, dep, adj, quantitative, type, level, 
       nIndiv, genotypingRate = 0, ...) 
extractPval(x)
extractPval.i(i,x,pos,models)
SNPHWE(x)
GenotypeRate(x)
haplo.inter.fit(geno, var2, dep, adj = NULL, fam, 
          haplo.freq.min, ...)
crea.lab(x,pos.ini,cex,dist)
orderChromosome(x)
togeno(f,sep=sep,lab=lab)
expandsetupSNP(o)
pvalTest(dataX,Y,quantitative,type,genotypingRate)
modelTest(X,Y,quantitative,type,genotypingRate)
assoc(y,x,test="lrt",quantitative)
trim(s)
interleave(..., append.source=TRUE, sep=": ", drop=FALSE)
## Default S3 method:
codominant(o)
## Default S3 method:
dominant(o)
## Default S3 method:
recessive(o)
## Default S3 method:
overdominant(o)
## Default S3 method:
additive(o)

Details

These are not to be called by the user

Value

No return value, internal calls

SNPs in a case-control study

Description

SNPs data.frame contains selected SNPs and other clinical covariates for cases and controls in a case-control study

SNPs.info.pos data.frame contains the names of the SNPs included in the data set 'SNPs' including their chromosome and their genomic position

Usage

data(SNPs)

Format

'SNPs' data.frame contains the following columns:

Source

Data obtained from our department. The reference and details will be supplied after being published.

SNPs in a case-control study

Description

SNPs data.frame contains selected SNPs and other clinical covariates for cases and controls in a case-control study

SNPs.info.pos data.frame contains the names of the SNPs included in the data set 'SNPs' including their chromosome and their genomic position

Usage

data(SNPs.info.pos)

Format

'SNPs.info.pos' data.frame contains the following columns: A data frame with 35 observations on the following 3 variables.

snp

name of SNP

chr

name of chromosome

pos

genomic position

Source

Data obtained from our department. The reference and details will be supplied after being published.

Descriptive sample size and percentage

Description

This function computes sample size and percentage for each category of a categorical trait (e.g. case-control status) for each genotype (or combination of genotypes).

Usage

Table.N.Per(var, dep, subset = !is.na(var))

Arguments

Value

See Also

Table.mean.se

Examples

data(SNPs)
#sample size and percentage of cases and controls for each genotype 
Table.N.Per(SNPs$snp10001,SNPs$casco)

# The same table for a subset (males)
Table.N.Per(SNPs$snp10001,SNPs$casco,SNPs$sex=="Male")

# The same table assuming a dominant model
Table.N.Per(dominant(snp(SNPs$snp10001,sep="")),SNPs$casco,SNPs$sex=="Male")

Descriptive sample size, mean, and standard error

Description

This function computes sample size, mean and standard error of a quantitative trait for each genotype (or combination of genotypes)

Usage

Table.mean.se(var, dep, subset = !is.na(var))

Arguments

Value

See Also

Table.N.Per

Examples

data(SNPs)
# sample size, mean age and standard error for each genotype
Table.mean.se(SNPs$snp10001,SNPs$protein)

# The same table for a subset (males)
Table.mean.se(SNPs$snp10001,SNPs$protein,SNPs$sex=="Male")

# The same table assuming a dominant model
Table.mean.se(dominant(snp(SNPs$snp10001,sep="")),SNPs$protein,SNPs$sex=="Male")

Whole genome association analysis

Description

This function carries out a whole genome association analysis between the SNPs and a dependent variable (phenotype) under five different genetic models (inheritance patterns): codominant, dominant, recessive, overdominant and log-additive. The phenotype may be quantitative or categorical. In the second case (e.g. case-control studies) this variable must be of class 'factor' with two levels.

Usage

   WGassociation(formula, data, model = c("all"), 
                 quantitative = is.quantitative(formula, data),
                 genotypingRate = 80, level = 0.95, ...)

Arguments

Details

This function assesses the association between the response variable included in the left side in the 'formula' and the SNPs included in the 'data' argument adjusted by those variables included in the right side of the 'formula'. Different genetic models may be analyzed using 'model' argument.

Value

An object of class 'WGassociation'.

'summary' returns a summary table by groups defined in info (genes/chromosomes).

'WGstats' returns a detailed output, similar to the produced by association.

'pvalues' and 'print' return a table of p-values for each genetic model for each SNP. The first column indicates whether a problem with genotyping is present.

'plot' produces a plot of p values in the -log scale. See plot.WGassociation for further details.

'labels' returns the names of the SNPs analyzed.

The functions 'codominat', 'dominant', 'recessive', 'overdominant' and 'additive' are used to obtain the p values under these genetic models.

See examples for further illustration about all previous issues.

References

JR Gonzalez, L Armengol, X Sole, E Guino, JM Mercader, X Estivill, V Moreno. SNPassoc: an R package to perform whole genome association studies. Bioinformatics, 2007;23(5):654-5.

See Also

getSignificantSNPs association WGstats setupSNP plot.WGassociation

Examples

data(SNPs)
datSNP<-setupSNP(SNPs,6:40,sep="")
ansAll<-WGassociation(protein~1,data=datSNP,model="all")

# In that case the formula is not required. You can also write:
# ansAll<-WGassociation(protein,data=datSNP,model="all")


#only codominant and log-additive
ansCoAd<-WGassociation(protein~1,data=datSNP,model=c("co","log-add"))

#for printing p values
print(ansAll)
print(ansCoAd)

#for obtaining a matrix with the p palues
pvalAll<-pvalues(ansAll)
pvalCoAd<-pvalues(ansCoAd)

# when all models are fitted and we are interested in obtaining 
# p values for different genetic models

# codominant model
pvalCod<-codominant(ansAll)

# recessive model
pvalRec<-recessive(ansAll)

# and the same for additive, dominant or overdominant


#summary
summary(ansAll)

#for a detailed report
WGstats(ansAll)

#for plotting the p values
plot(ansAll)

Association analysis between a single SNP and a given phenotype

Description

This function carries out an association analysis between a single SNP and a dependent variable (phenotype) under five different genetic models (inheritance patterns): codominant, dominant, recessive, overdominant and log-additive. The phenotype may be quantitative or categorical. In the second case (e.g. case-control studies) this variable must be of class 'factor' with two levels.

Usage

association(formula, data, model=c("all"), model.interaction=
       c("codominant"), subset, name.snp = NULL, quantitative = 
       is.quantitative(formula,data), genotypingRate= 0, 
       level = 0.95, ...)

Arguments

Details

This function should be called by the user when we are interested in analyzing an unique SNP. It is recommended to use WGassociation function when more than one SNP is studied.

Value

For each genetic model (codominant, dominant, recessive, overdominant, and log-additive) the function gives a matrix with sample size and percentages for each genotype, the Odds Ratio and its 95% confidence interval (taking the most frequent homozygous genotype as the reference), the p-value corresponding to the likelihood ratio test obtained from a comparison with the null model, and the Akaike Information Criterion (AIC) of each genetic model. In the case of analyzing a quantitative trait, the function returns a matrix with sample size, mean and standard errors for each genotype, mean difference and its 95% confidence interval with respect to the most frequent homozygous genotype, the p-value obtained from an overall gene effect and the Akaike Information Criterion (AIC) of each genetic model.

When an interaction term (a categorical covariate with an SNP) is included in the model, three different tables are given. The first one correponds to the full interaction matrix where the ORs (or mean differences if a quantitative trait is analyzed) are expressed with respect to the non variant genotype and the first category of the covariate. The other two tables show the ORs and their 95% confidence intervals for both marginal models. P values for interaction and trend are also showed in the output.

References

JR Gonzalez, L Armengol, X Sole, E Guino, JM Mercader, X Estivill, V Moreno. SNPassoc: an R package to perform whole genome association studies. Bioinformatics, 2007;23(5):654-5.

Iniesta R, Guino E, Moreno V. Statistical analysis of genetic polymorphisms in epidemiological studies. Gac Sanit. 2005;19(4):333-41.

Elston RC. Introduction and overview. Statistical methods in genetic epidemiology. Stat Methods Med Res. 2000;9:527-41.

See Also

WGassociation

Examples

data(SNPs)

# first, we create an object of class 'setupSNP'
datSNP<-setupSNP(SNPs,6:40,sep="")

# case-control study, crude analysis
association(casco~snp10001, data=datSNP)

# case-control study, adjusted by sex and arterial blood pressure
association(casco~sex+snp10001+blood.pre, data=datSNP)


# quantitative trait, crude analysis
association(log(protein)~snp10001,data=datSNP)
# quantitative trait, adjusted by sex
association(log(protein)~snp10001+sex,data=datSNP)


#
# Interaction analysis
#

# Interaction SNP and factor
association(log(protein)~snp10001*sex+blood.pre, data=datSNP, 
          model="codominant")

# Interaction SNP and SNP (codominant and codominant)
association(log(protein)~snp10001*factor(snp10002)+blood.pre, 
          data=datSNP, model="codominant")

# Interaction SNP and SNP (dominant and recessive)
association(log(protein)~snp10001*factor(recessive(snp100019))+blood.pre, 
          data=datSNP, model="dominant")

SNP data on asthma case-control study

Description

data.frame with 51 SNPs and 6 epidemiological variables: country, gender, age, bmi, smoke ans case/control status.

Usage

data("asthma")

Format

The asthma data frame has 1578 rows (individuals) and 57 columns (variables) of data from a genetic study on asthma.

Value

An data.frame object.

Examples

data(asthma)
dim(asthma)
str(asthma)

Get gene symbol from a list of SNPs

Description

Get gene symbol from a list of SNPs

Usage

getGeneSymbol(
  x,
  snpCol = 1,
  chrCol = 2,
  posCol = 3,
  db = TxDb.Hsapiens.UCSC.hg19.knownGene
)

Arguments

Value

a data.frame having initial information and gene symbol

Examples

    
        snps = c('rs58108140','rs189107123','rs180734498','rs144762171')
        chr = c('chr1','chr1','chr1','chr1')
        pos = c(10583, 10611, 13302, 13327)
        
        x <- data.frame(snps, chr, pos )
        
        getGeneSymbol(x)
    

Get Latex output

Description

Create Latex output from association analyses

Usage

getNiceTable( x )

Arguments

Value

The R output of specific association analyses exported into LaTeX

Extract significant SNPs from an object of class 'WGassociation'

Description

Extract significant SNPs from an object of class 'WGassociation' when genomic information is available

Usage

getSignificantSNPs(x, chromosome, model, sig = 1e-15)

Arguments

Value

A list with the following components:

...

See Also

WGassociation

Examples

data(resHapMap)
# resHapMap contains the results for a log-additive genetic model

# to get the significant SNPs for chromosome 12
getSignificantSNPs(resHapMap,chromosome=12)
# to get the significant SNPs for chromosome 5
getSignificantSNPs(resHapMap,5)
# to get the significant SNPs for chromosome X at level 1e-8
getSignificantSNPs(resHapMap,5,sig=1e-8)

Haplotype interaction with a covariate

Description

This function computes the ORs (or mean differences if a quantitative trait is analyzed) and their 95% confidence intervals corresponding to an interaction between the haplotypes and a categorical covariate

Usage

haplo.interaction(formula, data, SNPs.sel, quantitative = 
  is.quantitative(formula, data), haplo.freq.min = 0.05, ...)

Arguments

Details

The function estimates the haplotypes for the SNPs indicated in the 'SNPs.sel' argument. Then, usign 'haplo.glm' function (from 'haplo.stats' library) estimates the interaction between these haplotypes and the covariate indicated in the formula by means of 'interaction' function.

Value

Three different tables are given. The first one corresponds to the full interaction matrix where the ORs (or mean differences if a quantitative trait is analyzed) are expressed with respect to the most frequent haplotype and the first category of the covariate. The other two tables show the ORs (or mean differences if a quantitative trait is analyzed) and their 95% confidence intervals for both marginal models. P values for interaction are also showed in the output.

Examples

# not Run
library(SNPassoc)
library(haplo.stats)

data(SNPs)
datSNP<-setupSNP(SNPs,6:40,sep="")
res <- haplo.interaction(log(protein)~int(sex), data=datSNP,
                    SNPs.sel=c("snp100019","snp10001","snp100029"))
res

Collapsing (or recoding) genotypes into different categories (generally two) depending on a given genetic mode of inheritance

Description

codominant function recodifies a variable having genotypes depending on the allelic frequency in descending order.

dominant, recessive, and overdominant functions collapse the three categories of a given SNP into two categories as follows: Let 'AA', 'Aa', and 'aa' be the three genotypes. After determining the most frequent allele (let's suppose that 'A' is the major allele) the functions return a vector with to categories as follows. dominant: 'AA' and 'Aa-aa'; recessive: 'AA-Aa' and 'aa'; overdominant: 'AA-aa' vs 'Aa'.

additive function creates a numerical variable, 1, 2, 3 corresponding to the three genotypes sorted out by descending allelic frequency (this model is referred as log-additive).

Usage

codominant(o)
dominant(o)
recessive(o)
overdominant(o)
additive(o)

Arguments

Value

A snp object collapsing genotypes into different categories depending on a given genetic mode of inheritance

Examples

data(SNPs)
dominant(snp(SNPs$snp10001,sep=""))
overdominant(snp(SNPs$snp10001,sep=""))

Identify interaction term

Description

This is a special function used for 'haplo.interaction' function. It identifies the variable that will interact with the haplotype estimates. Using int() in a formula implies that the interaction term between this variable and haplotypes is included in 'haplo.glm' function.

Usage

int(x)

Arguments

Value

x

See Also

haplo.interaction

Examples

library(SNPassoc)
library(haplo.stats)

data(SNPs)
datSNP<-setupSNP(SNPs, 6:40, sep = "")
mod <- haplo.interaction(casco~int(sex)+blood.pre, data = datSNP, 
                         SNPs.sel = c("snp10001","snp10004","snp10005"))

Two-dimensional SNP analysis for association studies

Description

Perform a two-dimensional SNP analysis (interaction) for association studies with possible allowance for covariate

Usage

interactionPval(formula, data, quantitative = 
          is.quantitative(formula, data), model = "codominant")

Arguments

Details

The 'interactionPval' function calculates, for each pair of SNPs (i,j), the likelihood underling the null model L0, the likelihood under each of the single-SNP, L(i) and L(j), the likelihood under an additive SNP model La(i,j), and the likelihood under a full SNP model (including SNP-SNP interaction), Lf(i,j).

The upper triangle in matrix from this function contains the p values for the interaction (epistasis) log-likelihood ratio test, LRT, LRTij = -2 (log Lf(i,j) - log La(i,j))

The diagonal contains the p values from LRT for the crude effect of each SNP, LRTii = -2 (log L(i) - log L0)

The lower triangle contains the p values from LRT comparing the two-SNP additive likelihood to the best of the single-SNP models, LRTji = -2 (log La(i,j) - log max(L(i),L(j)))

In all cases the models including the SNPs are adjusted by the covariates indicated in the 'formula' argument. This method is used either for quantitative traits and dicotomous variables (case-control studies).

Value

The 'interactionPval' function returns a matrix of class 'SNPinteraction' containing the p values corresponding to the different likelihood ratio tests above describe.

Methods defined for 'SNPinteraction' objects are provided for print and plot. The plot method uses 'image' to plot a grid of p values. The upper triangle contains the interaction (epistasis) p values from LRT. The content in the lower triangle is the p values from the LRT comparing the additive model with the best single model. The diagonal contains the main effects pvalues from LRT. The 'plot.SNPinteraction' function also allows the user to plot the SNPs sorted by genomic position and with the information about chromosomes as in the 'plotMissing' function.

Note

two-dimensional SNP analysis on a dense grid can take a great deal of computer time and memory.

References

JR Gonzalez, L Armengol, X Sole, E Guino, JM Mercader, X Estivill, V Moreno. SNPassoc: an R package to perform whole genome association studies. Bioinformatics, 2007;23(5):654-5.

See Also

setupSNP

Examples

data(SNPs)
datSNP<-setupSNP(SNPs,6:40,sep="")

ansCod<-interactionPval(log(protein)~sex,datSNP)
print(ansCod)
plot(ansCod)

Print ORs and 95% confidence intervals for an object of class 'haplo.glm'

Description

Print ORs and confidence intervals for an object of class 'haplo.glm'

Usage

intervals(o, level=.95, ...)

Arguments

Value

intervals object with ORs and 95% confidence intervals for an object of class 'haplo.glm'

Examples

# Not Run
library(SNPassoc)
library(haplo.stats)

data(asthma, package = "SNPassoc")

asthma.s <- setupSNP(data=asthma, colSNPs=7:ncol(asthma), sep="")
trait <- asthma.s$casecontrol
snpsH <- c("rs714588", "rs1023555",  "rs898070")
genoH <- make.geno(asthma.s, snpsH)

mod <- haplo.stats:: haplo.glm( trait ~ genoH,           
                                family="binomial", 
                                locus.label=snpsH,
                                allele.lev=attributes(genoH)$unique.alleles,
                                control = haplo.glm.control(haplo.freq.min=0.05))   
intervals(mod)
summary(mod)

Check whether a SNP is Monomorphic

Description

This function verifies when a SNP is Monomorphic

Usage

is.Monomorphic(x) 

Arguments

Value

A logical value TRUE if the SNP is Monomorphic, otherwise a FALSE

Examples

data(SNPs)
is.Monomorphic(SNPs$snp10001)
is.Monomorphic(SNPs$snp100020)
apply(SNPs[,20:30],2,is.Monomorphic)

Create a group of locus objects from some SNPs, assign to 'model.matrix' class.

Description

This function prepares the CRITICAL element corresponding to matrix of genotypes necessary to be included in 'haplo.glm' function.

Usage

make.geno(data, SNPs.sel)

Arguments

Value

the same as 'setupGeno' function, from 'haplo.stats' library, returns

See Also

snp

Examples

## Not run:
data(SNPs)
# first, we create an object of class 'setupSNP'
datSNP<-setupSNP(SNPs,6:40,sep="")
geno<-make.geno(datSNP,c("snp10001","snp10002","snp10003"))
## End(Not run)

max-statistic for a 2x3 table

Description

Computes the asymptotic p-value for max-statistic for a 2x3 table

Usage

maxstat(x, ...)

## Default S3 method:
maxstat(x, y, ...)

## S3 method for class 'table'
maxstat(x, ...)

## S3 method for class 'setupSNP'
maxstat(x, y, colSNPs=attr(x,"colSNPs"), ...)

## S3 method for class 'matrix'
maxstat(x, ...)

Arguments

Value

A matrix with the chi-square statistic for dominant, recessive, log-additive and max-statistic and its asymptotic p-value.

References

Gonzalez JR, Carrasco JL, Dudbridge F, Armengol L, Estivill X, Moreno V. Maximizing association statistics over genetic models (2007). Submitted

Sladek R, Rocheleau G, Rung J et al. A genome-wide association study identifies novel risk loci for type 2 diabetes (2007). Nature 445, 881-885

See Also

setupSNP

Examples

# example from Sladek et al. (2007) for the SNP rs1111875 
 tt<-matrix(c(77,298,310,122,316,231),nrow=2,ncol=3,byrow=TRUE)
 maxstat(tt)

 data(SNPs)
 maxstat(SNPs$casco,SNPs$snp10001) 
 myDat<-setupSNP(SNPs,6:40,sep="")
 maxstat(myDat,casco)

 

Extract odds ratios, 95% CI and pvalues

Description

Extract odds ratios, 95

Usage

 odds(x, model=c("log-additive", "dominant", "recessive", "overdominant", "codominant"), 
      sorted=c("no","p-value","or"))

Arguments

Value

A matrix with OR 95% CI (lower, upper) and P value for the selected model. For codominant model, the OR and 95%CI are given for heterozygous and homozigous.

References

JR Gonzalez, L Armengol, X Sole, E Guino, JM Mercader, X Estivill, V Moreno. SNPassoc: an R package to perform whole genome association studies. Bioinformatics, 2007;23(5):654-5.

Examples

 data(SNPs)
 datSNP<-setupSNP(SNPs,6:40,sep="")
 ans<-WGassociation(casco~1,data=datSNP,model="all")
 odds(ans)

Permutation test analysis

Description

This function extract the p values for permutation approach performed using scanWGassociation function

Usage

   permTest(x, method="minimum", K)

Arguments

Details

This function extract the p values from an object of class 'WGassociation'. This object migth be obtained using the funcion called 'scanWGassociation' indicating the number of permutations in the argument 'nperm'.

Value

An object of class 'permTest'.

'print' returns a summary indicating the number of SNPs analyzed, the number of valid SNPs (those non-Monomorphic and that pass the calling rate), the p value after Bonferroni correction, and the p values based on permutation approach. One of them is based on considering the empirical percentil for the minimum p values, and the another one on assuming that the minimum p values follow a beta distribution.

'plot' produces a plot of the empirical distribution for the minimum p values (histogram) and the expected distribution assuming a beta distribution. The corrected p value is also showed in the plot.

See examples for further illustration about all previous issues.

References

Dudbridge F, Gusnanto A and Koeleman BPC. Detecting multiple associations in genome-wide studies. Human Genomics, 2006;2:310-317.

Dudbridge F and Koeleman BPC. Efficient computation of significance levels for multiple associations in large studies of correlated data, including genomewide association studies. Am J Hum Genet, 2004;75:424-435.

JR Gonzalez, L Armengol, X Sole, E Guino, JM Mercader, X Estivill, V Moreno. SNPassoc: an R package to perform whole genome association studies. Bioinformatics, 2007;23(5):654-5.

See Also

scanWGassociation

Examples

library(SNPassoc)

data(asthma, package = "SNPassoc")
asthma.s <- setupSNP(data=asthma, colSNPs=7:ncol(asthma), sep="")

ans <- WGassociation(casecontrol, data=asthma.s)

Function to plot -log p values from an object of class 'WGassociation'

Description

Function to plot -log p values from an object of class 'WGassociation'

Usage

## S3 method for class 'WGassociation'
plot(x, ...)

Arguments

Details

A panel with different plots (one for each mode of inheritance) are plotted. Each of them represents the -log(p value) for each SNP. Two horizontal lines are also plotted. One one them indicates the nominal statistical significance level whereas the other one indicates the statistical significance level after Bonferroni correction.

Value

No return value, just the plot

References

JR Gonzalez, L Armengol, X Sole, E Guino, JM Mercader, X Estivill, V Moreno. SNPassoc: an R package to perform whole genome association studies. Bioinformatics, 2007;23(5):654-5.

See Also

association setupSNP WGassociation

Examples

library(SNPassoc)

data(asthma, package = "SNPassoc")
asthma.s <- setupSNP(data=asthma, colSNPs=7:ncol(asthma), sep="")

ans <- WGassociation(casecontrol, data=asthma.s)

plot(ans)

Plot of missing genotypes

Description

Plot a grid showing which genotypes are missing

Usage

plotMissing(x, print.labels.SNPs = TRUE, 
        main = "Genotype missing data", ...) 

Arguments

Details

This function uses 'image' function to plot a grid with black pixels where the genotypes are missing.

Value

No return value, just the plot

See Also

setupSNP

Examples

 data(SNPs)
 data(SNPs.info.pos) 
 ans<-setupSNP(SNPs,colSNPs=6:40,sep="")
 plotMissing(ans)
 
 # The same plot with the SNPs sorted by genomic position and 
 # showing the information about chromosomes

 ans<-setupSNP(SNPs,colSNPs=6:40,sort=TRUE,SNPs.info.pos,sep="") 
 plotMissing(ans)

Functions for inspecting population substructure

Description

This function plots ranked observed p values against the corresponding expected p values in -log scale.

Usage

qqpval(p, pch=16, col=4, ...)

Arguments

Value

No return value, just the plot

See Also

GenomicControl, WGassociation

Examples

data(SNPs)
datSNP<-setupSNP(SNPs,6:40,sep="")
res<-WGassociation(casco,datSNP,model=c("do","re","log-add"))

# observed vs expected p values for recessive model
qqpval(recessive(res))

Get related samples

Description

Get related samples

Usage

related(x)

Arguments

Value

A matrix with related individuals.

Examples

  library(SNPassoc)
  data(SNPs)

SNPs from HapMap project

Description

Information about 9307 SNPs from the HapMap project belonging to 22 chromosomes. Information about two different population is available: European population (CEU) and Yoruba (YRI). The genomic information (names of SNPs, chromosomes and genetic position) is also available in a data frame called 'HapMap.SNPs.pos'.

Usage

data(resHapMap)

Format

A data frame with 120 observations on the 9808 variables (SNPs) and one variable called 'group' indicating the population.

Source

HapMap project (http://www.hapmap.org)

Examples

    data(resHapMap)

Whole genome association analysis

Description

This function is obsolete due to some problems with gfotran compiler. Use 'WGassociation' function instead or send an e-mail to the maintainer for receiving a version including this function

Convert columns in a dataframe to class 'snp'

Description

setupSNP Convert columns in a dataframe to class 'snp'

summary.setupSNP gives a summary for an object of class 'setupSNP' including allele names, major allele frequencie, an exact thest of Hardy-Weinberg equilibrium and percentage of missing genotypes

Usage

setupSNP(data, colSNPs, sort = FALSE, info, sep = "/", ...)

Arguments

Value

a dataframe of class 'setupSNP' containing converted SNP variables. All other variables will be unchanged.

References

JR Gonzalez, L Armengol, X Sole, E Guino, JM Mercader, X Estivill, V Moreno. SNPassoc: an R package to perform whole genome association studies. Bioinformatics, 2007;23(5):654-5.

See Also

snp

Examples

 data(SNPs)
 myDat<-setupSNP(SNPs,6:40,sep="")


#sorted SNPs and having genomic information
 data(SNPs.info.pos)
 myDat.o<-setupSNP(SNPs,6:40,sep="",sort=TRUE, info=SNPs.info.pos)

# summary
 summary(myDat.o)

# plot one SNP
  plot(myDat,which=2)

SNP object

Description

snp creates an snp object

is returns TRUE if x is of class 'snp'

as attempts to coerce its argument into an object of class 'snp'

reorder change the reference genotype

summary gives a summary for an object of class 'snp' including genotype and allele frequencies and an exact thest of Hardy-Weinberg equilibrium

plot gives a summary for an object of class 'snp' including genotype and allele frequencies and an exact thest of Hardy-Weinberg equilibrium in a plot. Barplot or pie are allowed

[.snp is a copy of [.factor modified to preserve all attributes

Usage

  snp(x, sep = "/", name.genotypes, reorder="common", 
    remove.spaces = TRUE, allow.partial.missing = FALSE) 

  is.snp(x)
 
  as.snp(x, ...)

  ## S3 method for class 'snp'
additive(o)

Arguments

Details

SNP objects hold information on which gene or marker alleles were observed for different individuals. For each individual, two alleles are recorded.

The snp class considers the stored alleles to be unordered , i.e., "C/T" is equivalent to "T/C". It assumes that the order of the alleles is not important.

When snp is called, x is a character vector, and it is assumed that each element encodes both alleles. In this case, if sep is a character string, x is assumed to be coded as "Allele1<sep>Allele2". If sep is a numeric value, it is assumed that character locations 1:sep contain allele 1 and that remaining locations contain allele 2.

additive.snp recodes the SNPs for being analyzed as a linear covariate (codes 0,1,2)

Value

The snp class extends "factor" where the levels is a character vector of possible genotype values stored coded by paste( allele1, "", allele2, sep="/")

References

JR Gonzalez, L Armengol, X Sole, E Guino, JM Mercader, X Estivill, V Moreno. SNPassoc: an R package to perform whole genome association studies. Bioinformatics, 2007;23(5):654-5.

See Also

association

Examples

# some examples of snp data in different formats

dat1  <- c("21", "21", "11", "22", "21",
                    "22", "22", "11", "11", NA)
ans1  <- snp(dat1,sep="")
ans1

dat2 <- c("A/A","A/G","G/G","A/G","G/G",
                    "A/A","A/A","G/G",NA)
ans2  <- snp(dat2,sep="/")
ans2

dat3 <- c("C-C","C-T","C-C","T-T","C-C",
                    "C-C","C-C","C-C","T-T",NA)
ans3 <- snp(dat3,sep="-")
ans3


dat4 <- c("het","het","het","hom1","hom2",
                    "het","het","hom1","hom1",NA)
ans4 <- snp(dat4,name.genotypes=c("hom1","het","hom2"))
ans4


# summary 
summary(ans3)

# plots

plot(ans3)
plot(ans3,type=pie)
plot(ans3,type=pie,label="SNP 10045")

Sort a vector of SNPs by genomic position

Description

This function sorts a vector with the position of SNPs in a data frame using another data frame which contains information about SNPs, their chromosome, and their genomic position

Usage

sortSNPs(data, colSNPs, info)

Arguments

Details

First of all, the function obtains a vector with the SNPs sorted using the data frame with the genomic positions (see 'dataSNPs.pos' argument). Then, the columns which indicate where the information about the genotypes is in our data frame, are sorted using this vector.

This information is useful when WGassociation function is called since it allow the user to summaryze the results with the SNPs sorted by genomic position

Value

a vector indicating the colums where the SNPs are recorded in our data frame ('data' argument), sorted using the genomic positions listed in 'dataSNPs.pos' argument)

Examples

#
# data(SNPs)
# data(SNPs.info.pos)
# colSNPs.order<-sortSNPs(SNPs,c(6:40),SNPs.info.pos)
#

Test for Hardy-Weinberg Equilibrium

Description

Test the null hypothesis that Hardy-Weinberg equilibrium holds in cases, controls and both populations.

print print the information. Number of digits, and significance level can be changed

Usage

tableHWE(x, strata, ...)

Arguments

Details

This function calculates the HWE test for those variables of class 'snp' in the object x of class 'setupSNP'

Value

A matrix with p values for Hardy-Weinberg Equilibrium

Author(s)

This function is based on an R function which computes an exact SNP test of Hardy-Weinberg Equilibrium written by Wigginton JE, Cutler DJ and Abecasis GR available at http://csg.sph.umich.edu/abecasis/Exact/r_instruct.html

References

Wigginton JE, Cutler DJ and Abecasis GR (2005). A note on exact tests of Hardy-Weinberg equilibrium. Am J Hum Genet 76:887-93

See Also

setupSNP

Examples

data(SNPs)
ans<-setupSNP(SNPs,6:40,sep="")
res<-tableHWE(ans)
print(res)
#change the significance level showed in the flag column
print(res,sig=0.001)


#stratified analysis
res<-tableHWE(ans,ans$sex)
print(res)