Package 'GeneGeneInteR' reference manual

Title:	Tools for Testing Gene-Gene Interaction at the Gene Level
Description:	The aim of this package is to propose several methods for testing gene-gene interaction in case-control association studies. Such a test can be done by aggregating SNP-SNP interaction tests performed at the SNP level (SSI) or by using gene-gene multidimensionnal methods (GGI) methods. The package also proposes tools for a graphic display of the results. <doi:10.18637/jss.v095.i12>.
Authors:	Mathieu Emily [aut, cre], Nicolas Sounac [ctb], Florian Kroell [ctb], Magalie Houee-Bigot [aut]
Maintainer:	Mathieu Emily <[email protected]>
License:	GPL (>= 2)
Version:	1.33.0
Built:	2024-10-30 07:23:18 UTC
Source:	https://github.com/bioc/GeneGeneInteR

CCA (Canonical-Correlation Analysis) based GGI analysis.

Description

CCA.test performs a Gene-Gene Interaction (GGI) analysis based on the difference of canonical correlation between cases and controls.

Usage

CCA.test(Y, G1, G2, n.boot = 500)
CCA.test(Y, G1, G2, n.boot = 500)

Arguments

`Y`	numeric or factor vector with exactly two different values. `Y` is the response variable and should be of length equal to the number of rows of `G1` and `G2` arguments (number of individuals).
`G1`	SnpMatrix object. Must have a number of rows equal to the length of `Y`.
`G2`	SnpMatrix object. Must have a number of rows equal to the length of `Y`.
`n.boot`	positive integer. `n.boot` is the number of bootstrap replicates for estimating variances. By default, this is fixed to 500.

Details

The test statistic is based on the difference between Fisher's transformation of the maximum of the canonical correlations in cases and controls. To calculate the test statistic for the interaction pvalue, CCA.test estimates the variance of the Fisher's transformation of the maximum of the canonical correlations in cases and controls using a bootstrap method.

Value

A list with class "htest" containing the following components:

`statistic`	The value of the statistic CCU.
`p.value`	The p-value for the test.
`estimate`	A vector of the Fisher's transformed maximum canonical correlation coefficient in cases and controls.
`parameter`	The number of boostrap samples used to estimate the p-value.
`null.value`	The value of CCU under the null hypothesis.
`alternative`	a character string describing the alternative.
`method`	a character string indicating the method used.
`data.name`	a character string giving the names of the data.

References

Qianqian Peng, Jinghua Zhao, and Fuzhong Xue. A gene-based method for detecting gene-gene co-association in a case-control study. European Journal of Human Genetics, 18(5) :582-587, May 2010.

Examples

data(gene.pair)
CCA.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair$G2)
data(gene.pair)
CCA.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair$G2)

CLD (Composite Linkage Desequilibrium) based GGI analysis.

Description

CLD.test performs a Gene-Gene Interaction (GGI) analysis based on the difference between the Composite Linkage Disequilibrium measured in cases and controls respectively.

Usage

CLD.test(Y, G1, G2, n.perm = 1000)
CLD.test(Y, G1, G2, n.perm = 1000)

Arguments

`Y`	numeric or factor vector with exactly two different values. `Y` is the response variable and should be of length equal to the number of rows of `G1` and `G2` arguments (number of individuals).
`G1`	SnpMatrix object. Must have a number of rows equal to the length of `Y`.
`G2`	SnpMatrix object. Must have a number of rows equal to the length of `Y`.
`n.perm`	positive integer. `n.perm` is the number of permutations performed to compute the pvalue. By default, `n.perm` is fixed to 1000.

Details

The test statistic is based on Nagao normalized Quadratic Distance (NQD), as described in Rajapakse et al. (2012). The pvalue is calculated using n.perm permutations of Y.

Value

A list with class "htest" containing the following components:

`statistic`	The value of the statistic CLD.
`p.value`	The p-value for the test.
`estimate`	The estimation of CLD
`parameter`	The number of permutations used to estimate the p-value.
`null.value`	The value of CLD under the null hypothesis.
`alternative`	a character string describing the alternative.
`method`	a character string indicating the method used.
`data.name`	a character string giving the names of the data.

References

Indika Rajapakse, Michael D. Perlman, Paul J. Martin, John A. Hansen, and Charles Kooperberg. Multivariate detection of gene-gene interactions. Genetic Epidemiology, 36(6) :622-630, 2012.

Examples

data(gene.pair)
CLD.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair$G2)
data(gene.pair)
CLD.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair$G2)

Multiple genes dataset

Description

A case-control dataset containing the genotypes of 312 SNPs from 17 genes in a total of 429 patients (266 individuals affected by Rheumatoid Arthritis and 163 Health controls)

Usage

data(data.SNP)
data(data.SNP)

Format

A list with 3 objects:

snpX: SnpMatrix object with 312 SNPs and 429 individuals.
genes.info: a data frame where each SNP is described by its rs ID, its position and the gene it belongs to.
Y: Factor vector of length 429 with 2 levels ("Health Control or "Rheumatoid"). Y correspond to the case-control status response variable.

Value

A dataset containing the genotypes of 312 SNPs from 17 genes in a total of 429 patients.

Source

All three objects were taken from NCBI web site and are part of the GSE39428 series Chang X., et al. Investigating a pathogenic role for TXNDC5 in tumors. Int. J. Oncol., 43(6): 1871-84, 2013.

Gene-based Gene-Gene Interaction analysis by combining SNP-SNP interaction tests

Description

gates.test, minP.test, tTS.test and tProd.test aim at performing gene-gene interaction analysis based on SNP-SNP interaction tests. The following methods are used to combine SNP-SNP interaction tests into a single Gene-Gene Interaction p-value:

Minimum p-value in minP.test function
Gene Association Test with Extended Simes procedure in gates.test
Truncated Tail Strength in tTS.test function
Truncated p-value Product in tProd.test function

Usage

gates.test(Y, G1, G2, alpha = 0.05, me.est = c("ChevNy", "Keff", "LiJi","Galwey"))
gates.test(Y, G1, G2, alpha = 0.05, me.est = c("ChevNy", "Keff", "LiJi","Galwey"))

Arguments

`Y`	numeric or factor vector with exactly two different values. `Y` is the response variable and should be of length equal to the number of rows of `G1` and `G2` arguments (number of individuals).
`G1`	SnpMatrix object. Must have a number of rows equal to the length of `Y`.
`G2`	SnpMatrix object. Must have a number of rows equal to the length of `Y`.
`alpha`	numeric value in [0, 1]. Threshold for GATES method when estimating the number of effective tests Me with Keff method.
`me.est`	character string for GATES method. `me.est` corresponds to the method for estimating the number of effective tests. Must be one of the following: "ChevNy", "Keff", "LiJi", "Galwey" (See details).

Details

In a first step, all methods start by applying a logistic regression model to test all pairs of SNPs between the two genes G1 and G2. If G1 has $m1$ SNPs and G2 $m2$ SNPs, a total of $m1 * m2$ SNP-SNP tests are performed. In a second step, the $m1 * m2$ SNP-SNP tests are combined according to their covariance matrix $\Sigma$ . $\Sigma$ is computed as described in the method developped in Emily (2016). The covariance $\Sigma$ is used in each method as follows:

minP test - minP test considered the significant of the observed minimum p-value. Significance is computed by integrating the multivariate normal distribution with covariance $\Sigma$ as proposed in Conneelly and Boehnke (2008).
GATES test - The p-value for GATES is the minimum p-value obtained after a multiple testing correction of the SNP-SNP interaction p-values. Correction for multiple testing is defined as

$me * p[i]/me[i]$

where $me$ is the effective number of independant tests, $p[i]$ is the i-th top p-values and $me[i]$ is the effective number of independant test among the top i p-values. Many methods exist to estimate $me$ and $me[i]$ terms:
- Cheverud-Nyholt method (Cheverud, 2001 and Nyholt, 2004)
- Keff method (Moskovina and Schmidt, 2008)
- Li & Ji method (Li and Ji, 2005)
- Galwey method (Galwey, 2009)
Details of each method can be found in the references.
tTS test - tTS test does not consider only the strongest signal but all signals that are inferior to a given threshold $\tau$ . For these p-values, the weighted sum of

$tTS=\sum (1-p[i]*(m1*m2+1)/i)$

is computed and represents the test statistic. The p-value is calculated using an empirical distribution of $tTS$ obtained by simulating multivariate normal statistics with a covariance $\Sigma$ as proposed by Jiang et al. (2011).
TProd test - The procedure is similar to $tTS$ with $tProd$ defined as

$tProd=\prod p[i].$

See Zaykin et al. (2002) for details.

Value

A list with class "GGItest" containing the following components:

`statistic`	The value of the statistic: the p-value kept as the minimum after GATES correction)
`p.value`	Tue p-value for the test
`estimate`	The estimation of the GATES p-value.
`alternative`	a character string describing the alternative.
`method`	a character string indicating the method used.
`data.name`	a character string giving the names of the data.

References

M. Emily AGGrEGATOr: A Gene-based GEne-Gene interActTiOn test for case-control association studies, Statistical Application in Genetics and Molecular Biology, 15(2): 151-171, 2016.
L. Ma, A.G. Clark and A. Keinan Gene-Based Testing Of Interactions in Association Studies of Quantitative Traits. PLoS Genetics 9(2):e1003321, 2013.
V. Moskvina and K.M. Schmidt On multiple-testing correction in genome-wide association studies. Genetic Epidemiology, 32(6): 567-573, 2008.
J. Li and L. Ji. Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity 95: 221-227, 2005.
N.W. Galwey. A new measure of the effective number of tests, a practical tool for comparing families of non-independent significance tests. Genetic Epidemiology 33(7): 559-568, 2009.
J.M. Cheverud. A simple correction for multiple comparisons in interval mapping genome scans. Heredity. 87(1):52-58, 2001.
D.R. Nyholt. A Simple Correction for Multiple Testing for Single-Nucleotide Polymorphisms in Linkage Disequilibrium with Each Other. American journal of human genetics. 74(4): 765-769, 2004.
K.N. Conneely and M. Boehnke. So many correlated tests, so little time! rapid adjustment of p values for multiple correlated tests. The American Journal of Human Genetics, 81: 1158-1168, 2008
B. Jiang, X. Zhang, Y. Zuo and G. Kang. A powerful truncated tail strength method for testing multiple null hypotheses in one dataset. Journal of Theoretical Biology 277: 67-73, 2011.
D.V. Zaykin, L.A. Zhivotovsky, P.H. Westfall and B.S. Weir. Truncated product method for combining P-values. Genetic epidemiology 22: 170-185, 2002.

Examples

data(gene.pair)
## Estimation of the interaction between a pair of gene by using the Keff method with alpha=0.05.
gates.test(gene.pair$Y, gene.pair$G1, gene.pair$G2, me.est = "Keff",alpha=0.05)
data(gene.pair)
## Estimation of the interaction between a pair of gene by using the Keff method with alpha=0.05.
gates.test(gene.pair$Y, gene.pair$G1, gene.pair$G2, me.est = "Keff",alpha=0.05)

GBIGM (Gene-based information gain method) for GGI analysis.

Description

GBIGM.test performs a Gene-Gene Interaction (GGI) analysis by contrasting the information entropy between cases and controls.

Usage

GBIGM.test(Y, G1, G2, n.perm = 1000)
GBIGM.test(Y, G1, G2, n.perm = 1000)

Arguments

`Y`	numeric or factor vector with exactly two different values. `Y` is the response variable and should be of length equal to the number of rows of `G1` and `G2` arguments (number of individuals).
`G1`	SnpMatrix object. Must have a number of rows equal to the length of `Y`.
`G2`	SnpMatrix object. Must have a number of rows equal to the length of `Y`.
`n.perm`	positive integer. `n.perm` is the number of permutations performed to compute the pvalue. By default, this is fixed to 1000.

Details

First, the conditional entropy and information gain rate are computed for each gene G1 and G2. In a second step, information gain rate for the gene pair (G1,G2) is computed. A p-value is estimated using permutations of Y. More details can be found in Li et al. (2015).

Value

A list with class "htest" containing the following components:

`statistic`	The value of the statistic $DeltaR1,2$ .
`p.value`	The p-value for the test.
`estimate`	The estimation of $DeltaR1,2$ .
`parameter`	The number of permutations used to estimate the p-value.
`alternative`	a character string describing the alternative.
`method`	a character string indicating the method used.
`data.name`	a character string giving the names of the data.

References

J. Li, et al.. A gene-based information gain method for detecting gene-gene interactions in case-control studies. European Journal of Human Genetics, 23 :1566-1572, 2015.

Examples

data(gene.pair)
GBIGM.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair$G2,n.perm=500)
data(gene.pair)
GBIGM.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair$G2,n.perm=500)

A case-control dataset with two genes

Description

A case-control dataset containing the genotypes of 8 SNPs within GC gene (object G1) and 4 SNPs within PADI2 gene (object G2). The dataset includes 247 individuals affected by Rheumatoid Arthritis (RA) and 202 individuals not affected by RA.

Usage

data(gene.pair)
data(gene.pair)

Format

A list with 3 objects:

Y: A factor of disease status with two levels: HealthControl or RheumatoidArthritis.
G1: A SnpMatrix object with 8 SNPs.
G2: A SnpMatrix object with 4 SNPs.

Value

A dataset containing the genotypes of 8 SNPs within GC gene (object G1) and 4 SNPs within PADI2 gene (object G2).

Source

All three objects were taken from NCBI web site and are part of the GSE39428 series Chang X., et al. Investigating a pathogenic role for TXNDC5 in tumors. Int. J. Oncol., 43(6): 1871-84, 2013.

Gene-Gene Interaction Analysis of a set of genes

Description

GGI allows the search for Gene-Gene Interactions by testing all possible pairs of genes in a set of genes.

Usage

GGI(Y, snpX, genes.length = NULL, genes.info = NULL, method = c("minP","PCA", "CCA",
 "KCCA","CLD","PLSPM","GBIGM","GATES", "tTS", "tProd"), ...)
GGI(Y, snpX, genes.length = NULL, genes.info = NULL, method = c("minP","PCA", "CCA",
 "KCCA","CLD","PLSPM","GBIGM","GATES", "tTS", "tProd"), ...)

Arguments

`Y`	numeric, integer, character or factor vector with exactly two different values. `Y` is the response variable and should be of length equal to the number of rows of `snpX` (number of individuals).
`snpX`	SnpMatrix object. Must have a number of rows equal to the length of `Y`. See details.
`genes.length`	(optional) a numeric vector. `gene.length` is the length (in columns/SNP) of each gene.
`genes.info`	(optional) a data frame. `genes.info` must have four columns named `Genenames`, `SNPnames`, `Position` and `Chromosome`. Each row describes a SNP and missing values are not allowed.
`method`	a string matching one of the following: PCA, CCA, KCCA, CLD, PLSPM, GBIGM, minP, GATES, tTS or tProd. Only one method can be parsed.
`...`	Other optional arguments to be passed to the functions associated with the method chosen. See more in elementary methods help.

Details

This function is a wrapper for all Gene-Gene Interaction analysis methods and drive the overall analysis: splitting the dataset in gene matrices and starting elementary analysis for each pair of genes.

SNPs from the same gene are assumed to be ordered along the chromosome. See selectSnps.

If genes.lenght is provided, it contains the number of SNPs of each gene. For example, if genes.length is the vector: c(20, 35, 15), then gene 1 will be interpreted as the set of the first 20 columns/SNPs of snpX, gene 2 will be interpreted as the following 35 columns/SNP, etc. Each gene declared is considered contiguous with the one before and after it. genes.length can be named if you want the returned matrix to have dimensions named after those. If no names are given then generic names are generated following the pattern Gene.n (n being the gene's index) .

The following methods are available to perform the interaction test for a single pair of genes:

Principal Components Analysis method (PCA) PCA.test - PCA is performed on both genes and resulting principal components are used to fit a logistic regression model with interaction between and a second logistic regression model without interaction term. The interaction between the two genes is then tested using a likelihood ratio test between the two logistic regression models (see Li et al. 2009).
Canonical Correlation Analysis (CCA) CCA.test - The maximum of canonical correlation between the two genes is computed for each group (cases and controls). The difference between the two transformed values (Fisher transformation) is used to test for interaction between genes (see Peng et al. 2010).
Kernel Canonical Correlation Analysis (KCCA) KCCA.test - This method is similar to the CCA method where the canonical correlations are computed using Kernel method (see Yuan et al., 2012 and Larson et al., 2013).
Composite Linkage Disequilibrium (CLD) CLD.test - CLD is based on the difference of the covariance matrices between the two genes computed for cases and controls. The covariance is estimated via the Composite Linkage Disequilibrium and a method based on Nagao normalized Quadratic Distance is used to compute the test statistic (see Rajapakse et al., 2012).
Partial Least Square Path Modeling (PLSPM) PLSPM.test - A network of statistical relations between latent and manifest variables is built. The difference between the path coefficients is used to compute the test statistic (see Zhang et al., 2013).
Gene-Based Information Gain Method (GBIGM) GBIGM.test - Entropies and Information Gain Ratio are used to compute a measure of the co-association between two genes (see Li et al., 2015).
Minimum p-value test (minP) minP.test - Given two genes, G1 with $m1$ SNPs and G2 with $m2$ SNPs, all SNP-SNP interactions are first tested using a logistic regression model, thus generated a set of $m1*m2$ p-values. The significance of the minimum p-value is evaluated using multivariate normal distribution that accounts for the covariance between the tests statistics at the SNP level (see Emily, 2016).
Gene Association Test using Extended Simes procedure (GATES) gates.test - Given two genes, G1 with $m1$ SNPs and G2 with $m2$ SNPs, all SNP-SNP interactions are first tested using a logistic regression model, thus generated a set of $m1*m2$ p-values. P-values are then corrected for multiple testing using an extension of the Simes procedure that take into account the correlation between the tests statistic via the number of effective tests (see Li. et al., 2011).
Truncated Tail Strength test (tTS) tTS.test - Given two genes, G1 with $m1$ SNPs and G2 with $m2$ SNPs, all SNP-SNP interactions are first tested using a logistic regression model, thus generated a set of $m1*m2$ p-values. All p-values below a user defined threshold are weighted and summed up to provide the tTS test statistic (see Jiang et al., 2011).
Truncated p-value Product test (tProd) tProd.test - Similar to tTS but with a different p-values transformation (see Zaykin, 2002)

Missing values are not allowed and trying to parse an incomplete SnpMatrix object as an argument will result in an error. Imputation can be performed prior to the analysis with the imputeSnpMatrix function.

Value

A list with class "GGInetwork" containing the following components:

`statistic`	a symmetric `matrix` of size $G*G$ where $G$ is the number of genes studied. The general term of the matrix is the statistic of the interaction between the two genes.
`p.value`	a symmetric `matrix` of size $G*G$ where $G$ is the number of genes studied. The general term of the matrix is the p-value of the interaction between the two genes.
`df`	(Only for `method="PCA"`). a symmetric `matrix` of size $G*G$ where $G$ is the number of genes studied. The general term of the matrix is the degrees of freedom of the interaction test.
`method`	The method used to perform the Gene-Gene interaction test.
`parameter`	A list of the parameters used to perform the Gene-Gene Interaction test.

References

M. Emily. AGGrEGATOr: A Gene-based GEne-Gene interActTiOn test for case-control association studies, Statistical Application in Genetics and Molecular Biology, 15(2): 151-171, 2016.
J. Li et al. Identification of gene-gene interaction using principal components. BMC Proceedings, 3 (Suppl. 7): S78, 2009.
Qianqian Peng, Jinghua Zhao, and Fuzhong Xue. A gene-based method for detecting gene-gene co-association in a case-control study. European Journal of Human Genetics, 18(5) :582-587, 2010.
Yuan, Z. et al. (2012): Detection for gene-gene co-association via kernel canonical correlation analysis, BMC Genetics, 13, 83.
Larson, N. B. et al. (2013): A kernel regression approach to gene-gene interaction detection for case-control studies, Genetic Epidemiology, 37, 695-703.
Indika Rajapakse, Michael D. Perlman, Paul J. Martin, John A. Hansen, and Charles Kooperberg. Multivariate detection of gene-gene interactions. Genetic Epidemiology, 36(6):622-630, 2012.
X. Zhang et al. A PLSPM-based test statistic for detecting gene-gene co-association in genome-wide association study with case-control design. PLoS ONE, 8(4):e62129, 2013.
J. Li, et al.. A gene-based information gain method for detecting gene-gene interactions in case-control studies. European Journal of Human Genetics, 23 :1566-1572, 2015.
M.X. Li et al. GATES: A Rapid and Powerful Gene-Based Association Test Using Extended Simes Procedure, American Journal of Human Genetics, 88(3): 283-293, 2011.
B. Jiang, X. Zhang, Y. Zuo and G. Kang. A powerful truncated tail strength method for testing multiple null hypotheses in one dataset. Journal of Theoretical Biology 277: 67-73, 2011.
D.V. Zaykin, L.A. Zhivotovsky, P.H. Westfall and B.S. Weir. Truncated product method for combining P-values. Genetic epidemiology 22: 170-185, 2002.

Examples

## Not run: 
## Dataset is included in the package
ped <- system.file("extdata/example.ped", package="GeneGeneInteR")
info <- system.file("extdata/example.info", package="GeneGeneInteR")
posi <- system.file("extdata/example.txt", package="GeneGeneInteR")

## Importation of the genotypes
data.imported <- importFile(file=ped, snps=info, pos=posi, pos.sep="\t")
## Filtering of the data: SNPs with MAF < 0.05 or p.value for HWE < 1e-3 or SNPs with 
## call.rate < 0.9 are removed. 
data.scour <- snpMatrixScour(snpX=data.imported$snpX,genes.info=data.imported$genes.info,min.maf=0.05,
                              min.eq=1e-3,call.rate=0.9)
## Imputation of the missing genotypes
data.imputed <- imputeSnpMatrix(data.scour$snpX, genes.info = data.scour$genes.info)

## End(Not run)
## Equivalent loading of the genotypes
load(system.file("extdata/dataImputed.Rdata", package="GeneGeneInteR"))

## Importation of the phenotype
resp <- system.file("extdata/response.txt", package="GeneGeneInteR")
Y  <- read.csv(resp, header=FALSE)

## estimation of the interaction between the 17 genes with the CLD method -- can take a few minutes
## Not run: 
GGI.res <- GGI(Y=Y, snpX=data.imputed$snpX, genes.info=data.imputed$genes.info,method="CLD")

## End(Not run)

## estimation of the interaction between 12 among the 17 genes with the default PCA method 
## Selection of 12 genes among 17
dta <- selectSnps(data.imputed$snpX, data.imputed$genes.info, c("bub3","CDSN","Gc","GLRX",
                  "PADI1","PADI2","PADI4","PADI6","PRKD3","PSORS1C1","SERPINA1","SORBS1"))
GGI.res <- GGI(Y=Y, snpX=dta$snpX, genes.info=dta$genes.info,method="PCA")

## Not run: 
## Dataset is included in the package
ped <- system.file("extdata/example.ped", package="GeneGeneInteR")
info <- system.file("extdata/example.info", package="GeneGeneInteR")
posi <- system.file("extdata/example.txt", package="GeneGeneInteR")

## Importation of the genotypes
data.imported <- importFile(file=ped, snps=info, pos=posi, pos.sep="\t")
## Filtering of the data: SNPs with MAF < 0.05 or p.value for HWE < 1e-3 or SNPs with 
## call.rate < 0.9 are removed. 
data.scour <- snpMatrixScour(snpX=data.imported$snpX,genes.info=data.imported$genes.info,min.maf=0.05,
                              min.eq=1e-3,call.rate=0.9)
## Imputation of the missing genotypes
data.imputed <- imputeSnpMatrix(data.scour$snpX, genes.info = data.scour$genes.info)

## End(Not run)
## Equivalent loading of the genotypes
load(system.file("extdata/dataImputed.Rdata", package="GeneGeneInteR"))

## Importation of the phenotype
resp <- system.file("extdata/response.txt", package="GeneGeneInteR")
Y  <- read.csv(resp, header=FALSE)

## estimation of the interaction between the 17 genes with the CLD method -- can take a few minutes
## Not run: 
GGI.res <- GGI(Y=Y, snpX=data.imputed$snpX, genes.info=data.imputed$genes.info,method="CLD")

## End(Not run)

## estimation of the interaction between 12 among the 17 genes with the default PCA method 
## Selection of 12 genes among 17
dta <- selectSnps(data.imputed$snpX, data.imputed$genes.info, c("bub3","CDSN","Gc","GLRX",
                  "PADI1","PADI2","PADI4","PADI6","PRKD3","PSORS1C1","SERPINA1","SORBS1"))
GGI.res <- GGI(Y=Y, snpX=dta$snpX, genes.info=dta$genes.info,method="PCA")

Imports SNPs information from pedfile, PLINK, VCF (4.0) file, or genotypes imputed by IMPUTE2.

Description

importFile generates a SnpMatrix object on the basis of diallelic object contained in a file, and creates also a data frame containing information about positions of the SNPs on the genome.

Usage

importFile(file, pos, pos.sep = "\t", ...)
importFile(file, pos, pos.sep = "\t", ...)

Arguments

`file`	String containing the path of the file to import.
`pos`	(optional) Path of a csv file, character vector or numeric vector containing informations for each SNP about chromosome, gene names, snp names and positions.
`pos.sep`	(optional) String to be passed to `read.csv` function as `sep` argument. Default is tab (\t).
`...`	Additionnal arguments to be passed to the reading file function.

Details

As input information, importFile takes the full paths of the files to be imported. Files are then read with read.pedfile, read.plink, vcf2sm, or read.impute, depending on the file extension (pedfile, plink, vcf or impute2). For a pedfile, importFile also reads the ".info" file associated (this must be in the same directory as the ".ped". Similarly, for a PLINK, there must be 3 files with extensions ".bed" (passed as argument file), ".bim" and ".fam". A VCF file must be with the associated ".tbi" file.

If the file is a vcf file, two additional arguments have to done :

gr instance of GRanges.
nmetacol numeric defining the number of columns used in each record as locus-level metadata.

Pos argument is optional. If it's not given, the data frame with position information is filled with NAs, except for SNP names which are imported from the column names of the SnpMatrix, and eventually positions and chromosomes if the file imported is a pedfile or a PLINK. Else, the pos argument can be either the path to a csv file, a character vector with elements of the form chr:position, or a numeric vector with only the positions. Additionnaly, SNP names can be precised as names of the vector. If you choose the csv file path, be sure that the columns are named as follows : Chromosome, Genenames, SNPnames, Position.

Value

A list of two objects :

`snpX`	a SnpMatrix
`genes.info`	a data frame with 4 columns, and one row per SNP. The columns are Chromosome, Genenames, SNPnames and position.

Examples

## Pedfile from this package.
ped <- system.file("extdata/example.ped", package="GeneGeneInteR")
info <- system.file("extdata/example.info", package="GeneGeneInteR")

## Information about position of the snps
posi <- system.file("extdata/example.txt", package="GeneGeneInteR")

## Importation
data.imported <- importFile(file=ped, snps=info, pos=posi, pos.sep="\t")

#########
## VCF file from GGtools package.
## Not run: 
vref <- system.file("vcf/CEU.exon.2010_09.genotypes.vcf.gz", package="GGtools")
irange <- IRanges::IRanges(10e6,20e6)
gg = GenomicRanges::GRanges(seqnames="1", ranges=irange)
dta <- importFile(file=vref, gr=gg, nmetacol=9L)

## End(Not run)
## Pedfile from this package.
ped <- system.file("extdata/example.ped", package="GeneGeneInteR")
info <- system.file("extdata/example.info", package="GeneGeneInteR")

## Information about position of the snps
posi <- system.file("extdata/example.txt", package="GeneGeneInteR")

## Importation
data.imported <- importFile(file=ped, snps=info, pos=posi, pos.sep="\t")

#########
## VCF file from GGtools package.
## Not run: 
vref <- system.file("vcf/CEU.exon.2010_09.genotypes.vcf.gz", package="GGtools")
irange <- IRanges::IRanges(10e6,20e6)
gg = GenomicRanges::GRanges(seqnames="1", ranges=irange)
dta <- importFile(file=vref, gr=gg, nmetacol=9L)

## End(Not run)

Missing values handling for SnpMatrix object

Description

imputeSnpMatrix is a generic wrapper of snp.imputation and impute.snps functions from snpStats package. This function mimics a Leave-One-Out process where missing SNPs are imputed for an individual based on a model trained on all other individuals.

Usage

imputeSnpMatrix(snpX, genes.info, on.rem = c("SNP", "ind", "none"),
  quiet = FALSE)
imputeSnpMatrix(snpX, genes.info, on.rem = c("SNP", "ind", "none"),
  quiet = FALSE)

Arguments

`snpX`	`snpMatrix` object of which SNPs are to be removed
`genes.info`	A `data.frame` with four columns named `Genenames`, `SNPnames`, `Position` and `Chromosome`. Each row describes a SNP and missing values are not allowed.
`on.rem`	(optional) a character string matching one of the following items: SNP, ind, none. Describes the action taken in case of remaining missing values. See details
`quiet`	(optional) a boolean describing wether or not progress bar should be displayed.

Details

For the ith row in the snpX argument (i.e. the ith individual in the dataset), the following steps are performed:

missing SNPs are detected for individual i
rules are imputed for every missing SNPs using the whole dataset where individual i is removed
SNPs are imputed for individual i

Although, such a process allows the imputation of a large number of missing SNPs, some missing values may remains. In that case, the user can specify the action to be done thanks to the om.rem arguments:

om.rem="none": leave the dataset as it is,
om.rem="SNP": remove all SNPs with remaining missing values,
om.rem="ind": remove all individuals with remaining missing values.

Removing all SNPs is often more parsimonious than removing individuals and allows to get a dataset without any missing values with minimum information-loss.

Value

a list object with two named elements:

`snpX`	a `SnpMatrix` object corresponding to input matrix with imputed values.
`genes.info`	a data frame object corresponding to an updated version of input `genes.info` in case SNP had to be removed.

A warning is printed when SNPs or individuals are removed.

Examples

## Not run: 
ped <- system.file("extdata/example.ped", package="GeneGeneInteR")
info <- system.file("extdata/example.info", package="GeneGeneInteR")
posi <- system.file("extdata/example.txt", package="GeneGeneInteR")
data.imported <- importFile(file=ped, snps=info, pos=posi, pos.sep="\t")

## Example 1 without SNPs filtering
## In this example, 9 SNPs were removed due to remaining missing values.
imputed.snps.1 <- imputeSnpMatrix(data.imported$snpX, genes.info = data.imported$genes.info)


## Example 2 with SNPs filtering priori to the imputation
## Filtering of the data: SNPs with MAF < 0.05 or p.value for HWE < 1e-3 or SNPs with
## call.rate < 0.9 are removed. 
data.scour <- snpMatrixScour(snpX=dta$snpX,genes.info=dta$genes.info,min.maf=0.05,
                              min.eq=1e-3,call.rate=0)


## End(Not run)

## Equivalent loading of data for example 2
## Imputation of the missing genotypes
load(system.file("extdata/dataScour.Rdata", package="GeneGeneInteR"))

data.imputed <- imputeSnpMatrix(data.scour$snpX, genes.info = data.scour$genes.info)
## Not run: 
ped <- system.file("extdata/example.ped", package="GeneGeneInteR")
info <- system.file("extdata/example.info", package="GeneGeneInteR")
posi <- system.file("extdata/example.txt", package="GeneGeneInteR")
data.imported <- importFile(file=ped, snps=info, pos=posi, pos.sep="\t")

## Example 1 without SNPs filtering
## In this example, 9 SNPs were removed due to remaining missing values.
imputed.snps.1 <- imputeSnpMatrix(data.imported$snpX, genes.info = data.imported$genes.info)


## Example 2 with SNPs filtering priori to the imputation
## Filtering of the data: SNPs with MAF < 0.05 or p.value for HWE < 1e-3 or SNPs with
## call.rate < 0.9 are removed. 
data.scour <- snpMatrixScour(snpX=dta$snpX,genes.info=dta$genes.info,min.maf=0.05,
                              min.eq=1e-3,call.rate=0)


## End(Not run)

## Equivalent loading of data for example 2
## Imputation of the missing genotypes
load(system.file("extdata/dataScour.Rdata", package="GeneGeneInteR"))

data.imputed <- imputeSnpMatrix(data.scour$snpX, genes.info = data.scour$genes.info)

KCCA (Kernel Canonical-Correlation Analysis) based GGI analysis.

Description

KCCA.test performs a Gene-Gene Interaction (GGI) analysis based on the difference of canonical correlations between cases and controls. The "kernel trick" is applied to the canonical correlation to allow the detection of non-linear co-association.

Usage

KCCA.test(Y, G1, G2,kernel=c("rbfdot","polydot","tanhdot","vanilladot","laplacedot",
"besseldot","anovadot","splinedot"),n.boot = 500,sigma=0.05,degree=1,scale=1,offset=1,
order=1)
KCCA.test(Y, G1, G2,kernel=c("rbfdot","polydot","tanhdot","vanilladot","laplacedot",
"besseldot","anovadot","splinedot"),n.boot = 500,sigma=0.05,degree=1,scale=1,offset=1,
order=1)

Arguments

`Y`	numeric or factor vector with exactly two different values. `Y` is the response variable and should be of length equal to the number of rows of `G1` and `G2` arguments (number of individuals).
`G1`	SnpMatrix object. Must have a number of rows equal to the length of `Y`.
`G2`	SnpMatrix object. Must have a number of rows equal to the length of `Y`.
`kernel`	A character string matching one of the kernel name in : "rbfdot","polydot","tanhdot","vanilladot","laplacedot","besseldot","anovadot","splinedot". For more details regarding kernel function see `dots` object. Default is a Gaussian RBF kernel
`n.boot`	positive integer. `n.boot` is the number of bootstrap replicates for estimating variances. By default, this is fixed to 500.
`sigma`	The inverse kernel width used by the Gaussian the Laplacian (`kernel="rbfdot"`), the Bessel (`kernel="besseldot"`) and the ANOVA (`kernel="anovadot"`) kernel. Default is `sigma=0.05`.
`degree`	The degree of the polynomial (`kernel="polydot"`), bessel (`kernel="besseldot"`) or ANOVA (`kernel="anovadot"`) kernel function. This has to be an positive integer. Default is `degree=1`.
`scale`	The scaling parameter of the polynomial (`kernel="polydot"`) and tangent (`kernel="tanhdot"`) kernel is a convenient way of normalizing patterns without the need to modify the data itself. Default is `scale=1`.
`offset`	The offset used in a polynomial (`kernel="polydot"`) or hyperbolic tangent (`kernel="tanhdot"`) kernel. Default is `offset=1`.
`order`	The order of the Bessel function to be used as a kernel (`kernel="besseldot"`). Default is `order=1`.

Details

The test statistic is based on the difference between a Fisher's transformation of the maximum of the kernelized canonical correlations in cases and controls. To calculate the test statistic for the interaction pvalue, KCCA.test estimates the variance of the Fisher's transformation of the maximum of the kernelized canonical correlations in cases and controls using a bootstrap method. The computation of kcca. can be very long.

Value

A list with class "htest" containing the following components:

`statistic`	The value of the statistic KCCU.
`p.value`	The p-value for the test.
`estimate`	A vector of the Fisher's transformed maximum kernel canonical correlation coefficient in cases and controls.
`parameter`	The number of boostrap samples used to estimate the p-value.
`null.value`	The value of KCCU under the null hypothesis.
`alternative`	a character string describing the alternative.
`method`	a character string indicating the method used.
`data.name`	a character string giving the names of the data.

References

Yuan, Z. et al. (2012): Detection for gene-gene co-association via kernel canonical correlation analysis, BMC Genetics, 13, 83.
Larson, N. B. et al. (2013): A kernel regression approach to gene-gene interaction detection for case-control studies, Genetic Epidemiology, 37, 695-703.

Examples

data(gene.pair)
## Not run: 
KCCA.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair$G2)

## End(Not run)
data(gene.pair)
## Not run: 
KCCA.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair$G2)

## End(Not run)

Gene-based Gene-Gene Interaction analysis by combining SNP-SNP interaction tests

Description

Minimum p-value in minP.test function
Gene Association Test with Extended Simes procedure in gates.test
Truncated Tail Strength in tTS.test function
Truncated p-value Product in tProd.test function

Usage

minP.test(Y, G1, G2)
minP.test(Y, G1, G2)

Arguments

`Y`	numeric or factor vector with exactly two different values. `Y` is the response variable and should be of length equal to the number of rows of `G1` and `G2` arguments (number of individuals).
`G1`	SnpMatrix object. Must have a number of rows equal to the length of `Y`.
`G2`	SnpMatrix object. Must have a number of rows equal to the length of `Y`.

Details

minP test - minP test considered the significant of the observed minimum p-value. Significance is computed by integrating the multivariate normal distribution with covariance $\Sigma$ as proposed in Conneelly and Boehnke (2008).
GATES test - The p-value for GATES is the minimum p-value obtained after a multiple testing correction of the SNP-SNP interaction p-values. Correction for multiple testing is defined as

$me * p[i]/me[i]$

where $me$ is the effective number of independant tests, $p[i]$ is the i-th top p-values and $me[i]$ is the effective number of independant test among the top i p-values. Many methods exist to estimate $me$ and $me[i]$ terms:
- Cheverud-Nyholt method (Cheverud, 2001 and Nyholt, 2004)
- Keff method (Moskovina and Schmidt, 2008)
- Li & Ji method (Li and Ji, 2005)
- Galwey method (Galwey, 2009)
Details of each method can be found in the references.
tTS test - tTS test does not consider only the strongest signal but all signals that are inferior to a given threshold $\tau$ . For these p-values, the weighted sum of

$tTS=\sum (1-p[i]*(m1*m2+1)/i)$

is computed and represents the test statistic. The p-value is calculated using an empirical distribution of $tTS$ obtained by simulating multivariate normal statistics with a covariance $\Sigma$ as proposed by Jiang et al. (2011).
TProd test - The procedure is similar to $tTS$ with $tProd$ defined as

$tProd=\prod p[i].$

See Zaykin et al. (2002) for details.

Value

A list with class "htest" containing the following components:

`statistic`	The value of the statistic: the maximum of the absolute value of the observed pairwised statistics.
`p.value`	The p-value for the test.
`estimate`	The estimation of the maximum of the absolute value of the observed pairwised statistics.
`alternative`	a character string describing the alternative.
`method`	a character string indicating the method used.
`data.name`	a character string giving the names of the data.

References

M. Emily. AGGrEGATOr: A Gene-based GEne-Gene interActTiOn test for case-control association studies, Statistical Application in Genetics and Molecular Biology, 15(2): 151-171, 2016.
L. Ma, A.G. Clark and A. Keinan Gene-Based Testing Of Interactions in Association Studies of Quantitative Traits. PLoS Genetics 9(2):e1003321, 2013.
M.X. Li et al. GATES: A Rapid and Powerful Gene-Based Association Test Using Extended Simes Procedure, American Journal of Human Genetics, 88(3): 283-293, 2011.
V. Moskvina and K.M. Schmidt On multiple-testing correction in genome-wide association studies. Genetic Epidemiology, 32(6): 567-573, 2008.
J. Li and L. Ji. Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity 95: 221-227, 2005.
N.W. Galwey. A new measure of the effective number of tests, a practical tool for comparing families of non-independent significance tests. Genetic Epidemiology 33(7): 559-568, 2009.
J.M. Cheverud. A simple correction for multiple comparisons in interval mapping genome scans. Heredity. 87(1):52-58, 2001.
D.R. Nyholt. A Simple Correction for Multiple Testing for Single-Nucleotide Polymorphisms in Linkage Disequilibrium with Each Other. American journal of human genetics. 74(4): 765-769, 2004.
K.N. Conneely and M. Boehnke. So many correlated tests, so little time! rapid adjustment of p values for multiple correlated tests. The American Journal of Human Genetics, 81: 1158-1168, 2008
B. Jiang, X. Zhang, Y. Zuo and G. Kang. A powerful truncated tail strength method for testing multiple null hypotheses in one dataset. Journal of Theoretical Biology 277: 67-73, 2011.
D.V. Zaykin, L.A. Zhivotovsky, P.H. Westfall and B.S. Weir. Truncated product method for combining P-values. Genetic epidemiology 22: 170-185, 2002.

Examples

data(gene.pair)
minP.test(gene.pair$Y, gene.pair$G1, gene.pair$G2)

data(gene.pair)
minP.test(gene.pair$Y, gene.pair$G1, gene.pair$G2)

PCA-based Gene-Gene Interaction analysis.

Description

PCA.test performs a Gene-Gene Interaction (GGI) analysis by testing the interaction between the principal components of the two genes. With method="Std" PCA is standardized using standard deviation for each variable. With method="GenFreq", dataset is standardized using the standard deviation under Hardy-Weinberg equilibrium, as proposed in the snpStats Bioconductor package.

Usage

PCA.test(Y, G1, G2, threshold = 0.8,method="GenFreq")
PCA.test(Y, G1, G2, threshold = 0.8,method="GenFreq")

Arguments

`Y`	numeric or factor vector with exactly two different values. `Y` is the response variable and should be of length equal to the number of rows of `G1` and `G2` arguments (number of individuals).
`G1`	SnpMatrix object. Must have a number of rows equal to the length of `Y`.
`G2`	SnpMatrix object. Must have a number of rows equal to the length of `Y`.
`threshold`	(optional) numeric comprised in $[0, 1]$ interval. `threshold` is the minimum percentage of inertia kept for each PCA.
`method`	(optional) character string for PCA method. Must be one of the following: "GenFreq", "Std" (See details).

Details

In a first step, PCA.test performs a Principal Components Analysis on both G1 and G2 genes that are interpreted as matrices of allele counts. With the method="Std", the dataset is standardized using variables standard deviation, while dataset is standardized using standard deviation under Hardy-Weinberg equilibrium for method="GenFreq". Principal components are then retrieved to describe each dataset with user-defined inertia percentage (parameter threshold) and used in a logistic regression model. The consists is testing the significance of the interaction terms using a Likelihood Ratio Test (see Li et al. (2009)).

Value

A list with class "htest" containing the following components:

`statistic`	The value of the statistic: the deviance of the anova test.
`p.value`	The p-value for the test.
`estimate`	A vector of the residual deviances.
`parameter`	The degrees of freedom of the chi-squared distribution of the test statistic.
`null.value`	the value of the deviance under the null.
`alternative`	a character string describing the alternative.
`method`	a character string indicating the method used.
`data.name`	a character string giving the names of the data.

References

J. Li et al. (2009) Identification of gene-gene interaction using principal components. BMC Proceedings, 3 (Suppl. 7): S78

Examples

data(gene.pair)
PCA.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair$G2,threshold=0.7,method="Std")
PCA.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair$G2,threshold=0.7,method="GenFreq")
data(gene.pair)
PCA.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair$G2,threshold=0.7,method="Std")
PCA.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair$G2,threshold=0.7,method="GenFreq")

Plot a Gene-Gene Interaction Study with an heatmap-like visualization

Description

GGI.plot is a graphical function that allow a heatmap-like or a network-like visualization of a Gene-Gene Interaction study based on a set of genes.

Usage

## S3 method for class 'GGInetwork'
plot(x,method=c("heatmap","network"),threshold=NULL, 
col=c("#D6604D","#104E8B"),colbar.width=0.15, title=NULL,hclust.order=FALSE, 
use.log=FALSE,NA.col="#D3D3D3",draw.pvals=NULL, 
draw.names=NULL,interact=FALSE, 
method.adjust=c("none","holm","hochberg","hommel","Bonferroni","BH","BY","fdr"), 
genes=seq_len(ncol(x$p.value)), plot.nointer=TRUE, ...)
## S3 method for class 'GGInetwork'
plot(x,method=c("heatmap","network"),threshold=NULL, 
col=c("#D6604D","#104E8B"),colbar.width=0.15, title=NULL,hclust.order=FALSE, 
use.log=FALSE,NA.col="#D3D3D3",draw.pvals=NULL, 
draw.names=NULL,interact=FALSE, 
method.adjust=c("none","holm","hochberg","hommel","Bonferroni","BH","BY","fdr"), 
genes=seq_len(ncol(x$p.value)), plot.nointer=TRUE, ...)

Arguments

`x`	`GGInetwork`. Given as output of `GGI`.
`method`	Output graph ("heatmap" for heatmap-like, "network" for network-like). Default is `heatmap`.
`threshold`	A numeric between 0 and 1. All p-value strictly greater than that `threshold` are distinctly colored (See `NA.col`). Default is `NULL` corresponding to no threshold for applied for an heatmap graph and a 0.05 threshold applied for the network representation.
`method.adjust`	correction method for multiple testing as proposed in the `p.adjust` function. By default, no correction is applied to the p-values.
`genes`	Numeric vector allowing a selection of the genes that will be included in the relations. Default is set to all genes.
`col`	(Only for `method=heatmap`.) String vector. Marker colors to be used for the gradient. The first element of the vector is the value for 0 and the last is for 1. If only one value is parsed, that color is used for 0 and white is automatically used for 1. Any value compatible with `colorRampPalette` function.
`colbar.width`	(Only for `method=heatmap`.) A positive number describing the gradient bar width ratio. That number is used to keep the gradient bar's width steady as the size of `GGI` increases.
`title`	(Only for `method=heatmap`.) A string used as the plot title. If left as NULL, a generic name is generated.
`hclust.order`	(Only for `method=heatmap`.) A boolean. Should a hierachical clustering procedure be performed on `GGI` to order the matrix ?
`use.log`	(Only for `method=heatmap`.) A boolean. Should the p-values be reported in -log10? FALSE by default.
`NA.col`	(Only for `method=heatmap`.) A string. The color used when a p-value is strictly greater than `threshold`.
`draw.pvals`	(Only for `method=heatmap`.) Either NULL or a boolean. Should p-values be plotted ? Disabled when the size of `GGI` exceeds $15*15$ .
`draw.names`	(Only for `method=heatmap`.) Either NULL or a boolean. Should genes' names be plotted on matrix margins? Disabled when the size of `GGI` exceeds $25*25$ .
`interact`	(Only for `method=heatmap`.) A boolean. Should the plot be clickable ? (See Details for more information). Disabled when open R session is not interactive.
`plot.nointer`	(Only for `method=network`.) A boolean. Set TRUE if the genes with no interaction should be plotted.
`...`	further arguments passed to or from other methods.

Details

If method=heatmap, this function draw the upper half of a Gene-Gene Interaction results matrix without its diagonal. A gradient is created from 0 to 1 (by default from crimson to white) and the matrix cells are colored according to the corresponding p-value.

By default, when draw.pvals==NULL and draw.names==NULL, p-values and names are drawn to make matrix reading easier, but in case parameter GGI is large, p-values (and eventually gene names as GGI grows bigger) are not drawn. In that case, the default behavior of the function is to start an interactive process where user can click on a cell of interest to open a tooltip displaying which genes are involved in selected interaction and the p-value of the interaction test. Tooltips can be closed if user clicks anywhere else than on a cell. This process stops when the user presses the escape button (or terminates the locator procedure in general) or when the user clicks on any place other than a cell when no tooltip window is open.

To improve plot clarity, user may set a threshold above which cells are colored with a distinct color. By default, threshold is set to 1 and no cell is colored differently (as values must be strictly above the threshold).

If method=network, this function plots a graph representing the significative interactions between genes of a Gene-Gene Interaction study.

Value

The form of the value returned by plot depends on the class of its argument. See Details.

Examples

## Not run: 
## Dataset is included in the package
ped <- system.file("extdata/example.ped", package="GeneGeneInteR")
info <- system.file("extdata/example.info", package="GeneGeneInteR")
posi <- system.file("extdata/example.txt", package="GeneGeneInteR")

## Importation of the genotypes
data.imported <- importFile(file=ped, snps=info, pos=posi, pos.sep="\t")
## Filtering of the data: SNPs with MAF < 0.05 or p.value for HWE < 1e-3 or 
## call rate < 0.9 are removed. 
data.scour <- snpMatrixScour(snpX=data.imported$snpX,genes.info=data.imported$genes.info,
                              min.maf=0.05,min.eq=1e-3,call.rate=0.9)
## Imputation of the missing genotypes
data.imputed <- imputeSnpMatrix(data.scour$snpX, genes.info = data.scour$genes.info)

## Importation of the phenotype
resp <- system.file("extdata/response.txt", package="GeneGeneInteR")
Y  <- read.csv(resp, header=FALSE)

## plot of the interaction between the 17 genes with the CLD method -- can take a few minutes
GGI.res <- GGI(Y=Y, snpX=dta$snpX, genes.info=dta$genes.info,method="CLD")
plot(GGI.res,threshold=0.05)

## Selection of 12 genes among 17
data.select <- selectSnps(data.imputed$snpX, data.imputed$genes.info, c("bub3","CDSN","Gc","GLRX",
                          "PADI1","PADI2","PADI4","PADI6","PRKD3","PSORS1C1","SERPINA1","SORBS1"))
GGI.res <- GGI(Y=Y, snpX=data.select$snpX, genes.info=data.select$genes.info,method="PCA")

## End(Not run)
## Equivalent importation of the GGI.res object
load(system.file("extdata/GGIRes.Rdata", package="GeneGeneInteR"))


## Plot of the results with default values
plot(GGI.res)

## Plot of the results with a threshold and an ordering of the genes. 
## Default method is an heatmap-like representation
plot(GGI.res,threshold=0.1,hclust.order=TRUE)

## Example of network with default threshold 0.05
plot(GGI.res,method="network")

## Example of network with threshold 0.01 where genes with no interaction are not plotted 
# (plot.nointer=FALSE)
plot(GGI.res,method="network",threshold=0.1,plot.nointer=FALSE)

## Not run: 
## Dataset is included in the package
ped <- system.file("extdata/example.ped", package="GeneGeneInteR")
info <- system.file("extdata/example.info", package="GeneGeneInteR")
posi <- system.file("extdata/example.txt", package="GeneGeneInteR")

## Importation of the genotypes
data.imported <- importFile(file=ped, snps=info, pos=posi, pos.sep="\t")
## Filtering of the data: SNPs with MAF < 0.05 or p.value for HWE < 1e-3 or 
## call rate < 0.9 are removed. 
data.scour <- snpMatrixScour(snpX=data.imported$snpX,genes.info=data.imported$genes.info,
                              min.maf=0.05,min.eq=1e-3,call.rate=0.9)
## Imputation of the missing genotypes
data.imputed <- imputeSnpMatrix(data.scour$snpX, genes.info = data.scour$genes.info)

## Importation of the phenotype
resp <- system.file("extdata/response.txt", package="GeneGeneInteR")
Y  <- read.csv(resp, header=FALSE)

## plot of the interaction between the 17 genes with the CLD method -- can take a few minutes
GGI.res <- GGI(Y=Y, snpX=dta$snpX, genes.info=dta$genes.info,method="CLD")
plot(GGI.res,threshold=0.05)

## Selection of 12 genes among 17
data.select <- selectSnps(data.imputed$snpX, data.imputed$genes.info, c("bub3","CDSN","Gc","GLRX",
                          "PADI1","PADI2","PADI4","PADI6","PRKD3","PSORS1C1","SERPINA1","SORBS1"))
GGI.res <- GGI(Y=Y, snpX=data.select$snpX, genes.info=data.select$genes.info,method="PCA")

## End(Not run)
## Equivalent importation of the GGI.res object
load(system.file("extdata/GGIRes.Rdata", package="GeneGeneInteR"))


## Plot of the results with default values
plot(GGI.res)

## Plot of the results with a threshold and an ordering of the genes. 
## Default method is an heatmap-like representation
plot(GGI.res,threshold=0.1,hclust.order=TRUE)

## Example of network with default threshold 0.05
plot(GGI.res,method="network")

## Example of network with threshold 0.01 where genes with no interaction are not plotted 
# (plot.nointer=FALSE)
plot(GGI.res,method="network",threshold=0.1,plot.nointer=FALSE)

PLSPM (Partial Least Squares Path Modelling Analysis) based Gene-Gene Interaction analysis.

Description

PLSPM.test performs a Gene-Gene Interaction (GGI) analysis based on the modelisation of a netwrok of statistical relations. The aim is to quantify the connections between the latent and the manifest variables.

Usage

PLSPM.test(Y, G1, G2,n.perm=500)
PLSPM.test(Y, G1, G2,n.perm=500)

Arguments

`Y`	numeric or factor vector with exactly two different values. `Y` is the response variable and should be of length equal to the number of rows of `G1` and `G2` arguments (number of individuals).
`G1`	SnpMatrix object. Must have a number of rows equal to the length of `Y`.
`G2`	SnpMatrix object. Must have a number of rows equal to the length of `Y`.
`n.perm`	positive integer. `n.perm` is the number of permutations performed to compute the pvalue. By default, this is fixed to 500.

Details

The PLSPM-based method, as described in Zhang et al. (2013), aims at comparing the path coefficients $\beta_D$ and $\beta_C$ , where $\beta_D$ is calculated among cases and $\beta_C$ among controls. The co-association between genes G1 and G2 is defined by:

$U=\frac{\beta_D-\beta_C}{\sqrt{Var(\beta_D-\beta_C)}}.$

The plspm R package is used to estimate $U$ . The significance pvalue is obtained by using a permutation method on the difference between the path coefficients.

Value

A list with class "htest" containing the following components:

`statistic`	The value of the statistic U.
`p.value`	The p-value for the test.
`estimate`	A vector of the path coefficients in cases and controls.
`parameter`	The number of boostrap samples used to estimate the p-value.
`null.value`	The value of U under the null hypothesis.
`alternative`	a character string describing the alternative.
`method`	a character string indicating the method used.
`data.name`	a character string giving the names of the data.

References

X. Zhang et al. (2013) A PLSPM-based test statistic for detecting gene-gene co-association in genome-wide association study with case-control design. PLoS ONE, 8(4) :e62129.

Examples

data(gene.pair)
PLSPM.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair$G2,n.perm=50)
data(gene.pair)
PLSPM.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair$G2,n.perm=50)

Printing objects of class `"GGItest"`

Description

Printing objects of class "GGItest"

Usage

## S3 method for class 'GGItest'
print(x, ...)
## S3 method for class 'GGItest'
print(x, ...)

Arguments

`x`	`GGItest`. Given as output of `CCA.test` for example.
`...`	further arguments passed to or from other methods

Details

print.GGItest provides a customized output of a GGItest object.

Value

the argument x, invisibly, as for all print methods.

Examples


data(gene.pair)
print(PCA.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair$G2,threshold=0.7,method="Std"))

data(gene.pair)
print(PCA.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair$G2,threshold=0.7,method="Std"))

Selects SNPs in snpMatrix and associated info data.frame, outputs of importFile.

Description

selectSnps allows the user to select snps from an object output of importFile. It generates the same object, with the columns of the snpMatrix and the rows of the data.frame corresponding to the selected snps.

Usage

selectSnps(snpX, genes.info, select)
selectSnps(snpX, genes.info, select)

Arguments

`snpX`	snpMatrix object. Given as output of `importFile`.
`genes.info`	Data.frame containing informations about snps. For more details, refer to `importFile` help file.
`select`	Numeric or character vector for selecting snps in `snpX` and `genes.info`. See details for more information.

Details

The column names of the genes.info data.frame should correspond to the output genes.info object returned by importFile function.

The select argument should one of the following:

a numeric vector with only the column number in the snpMatrix (or row number for genes.info) of each snp selected.
a character vector with the names of each snp selected or each gene selected.
a character vector which elements are position bounds of genes. Each element of the vector is either of the form "begin:end", or "chr:begin:end" if you have to precise the chromosome of the gene.

Value

A list of two objects :

`snpX`	a snpMatrix
`genes.info`	a data frame with 4 columns, and one row per SNP selected with `select`. The columns are Chromosome, Genenames, SNPnames and Position.

An error message is displayed if the genes of snps selected are not found in the either snpX or genes.info.

Examples

## Importation of the dataset
## Not run: 
ped <- system.file("extdata/example.ped", package="GeneGeneInteR")
info <- system.file("extdata/example.info", package="GeneGeneInteR")
posi <- system.file("extdata/example.txt", package="GeneGeneInteR")
data.imported <- importFile(file=ped, snps=info, pos=posi, pos.sep="\t")

## End(Not run)
### Equivalent loading of the imported data
load(system.file("extdata/dataImported.Rdata", package="GeneGeneInteR"))

## Selection of the genes DNAH9 and TXNDC5
selec <- selectSnps(data.imported$snpX, data.imported$genes.info, c("DNAH9","TXNDC5"))

## Selection of the snps from position 101342000 to 101490000 on chromosome 15
selec <- selectSnps(data.imported$snpX, data.imported$genes.info, c("15:101342000:101490000"))
## Importation of the dataset
## Not run: 
ped <- system.file("extdata/example.ped", package="GeneGeneInteR")
info <- system.file("extdata/example.info", package="GeneGeneInteR")
posi <- system.file("extdata/example.txt", package="GeneGeneInteR")
data.imported <- importFile(file=ped, snps=info, pos=posi, pos.sep="\t")

## End(Not run)
### Equivalent loading of the imported data
load(system.file("extdata/dataImported.Rdata", package="GeneGeneInteR"))

## Selection of the genes DNAH9 and TXNDC5
selec <- selectSnps(data.imported$snpX, data.imported$genes.info, c("DNAH9","TXNDC5"))

## Selection of the snps from position 101342000 to 101490000 on chromosome 15
selec <- selectSnps(data.imported$snpX, data.imported$genes.info, c("15:101342000:101490000"))

SNP filtering based on Minor Allele Frequency, Hardy-Weinberg Equilibrium and call rate

Description

snpMatrixScour aims at filtering out SNPs of a snpMatrix object based on Minor Allele Frequency criterion, deviation to Hardy-Weinberg Equilibrium and SNPs call rate.

Usage

snpMatrixScour(snpX, genes.length = NULL, genes.info = NULL,
  min.maf = 0.01, min.eq = 0.01, call.rate = 0.9)
snpMatrixScour(snpX, genes.length = NULL, genes.info = NULL,
  min.maf = 0.01, min.eq = 0.01, call.rate = 0.9)

Arguments

`snpX`	SnpMatrix object from which SNPs are to be removed
`genes.length`	(optional) numeric vector. It is the length (in columns/SNPs) of each gene. Each gene declared is considered contiguous with the one before and after it. `genes.lengths` can be named (names will be kept).
`genes.info`	(optional) a `data.frame` with four columns named `Genenames`, `SNPnames`, `Position` and `Chromosome`. Each row describes a SNP and missing values are not allowed.
`min.maf`	a single value between 0 and 0.5 that gives the threshold for the MAF (Minor Allele Frequency) of a SNP. SNP with MAF < `min.maf` are removed. Default is 0.01.
`min.eq`	a single value between 0 and 1 that gives the maximum acceptable p-value for the $\chi ^2$ verifying HWE deviation. SNP that does not meet that criterion are removed. Default is 0.01.
`call.rate`	a single value between 0 and 1 that gives the minimum acceptable call rate for a SNP. Default is 0.9. Low values for SNPs call rate can make imputation harder (residual missing values).

Details

This function removes SNPs that does not meet all following criteria:

$MAF <$ min.maf,
$HWE <$ min.eq, where HWE is the p-value of the $\chi ^2$ test of deviation to Hardy-Weinberg Equilibrium,
Call rate $>$ call.rate.

If genes.length and genes.info are provided by the user, an updated version is returned by snpMatrixScour. The returned object can be directly used as inputs of the GGI function.

Value

A list with two objects:

snpX: the SnpMatrix object where non-conform SNPs are removed.
genes.info: the object that contains the updated gene lengths information. Can be a numeric vector (possibly named) or a data frame. If genes.length and genes.info are not provided by the user as input of the snpMatrixScour function, the genes.info object is NULL.

Examples

## Not run: 
ped <- system.file("extdata/example.ped", package="GeneGeneInteR")
info <- system.file("extdata/example.info", package="GeneGeneInteR")
posi <- system.file("extdata/example.txt", package="GeneGeneInteR")
data.imported <- importFile(file=ped, snps=info, pos=posi, pos.sep="\t")

## End(Not run)
### Equivalent loading of the imported data
load(system.file("extdata/dataImported.Rdata", package="GeneGeneInteR"))

## In this example, SNPs are with MAF lower than 0.2 or p-value for HWE testing lower than 0.05 or
# a proportion of missing value higher than 0.2 are removed
data.scour1 <- snpMatrixScour(data.imported$snpX, genes.info = data.imported$genes.info,
                               min.maf = 0.2, min.eq=0.05, call.rate = 0.8)
## Two genes have been completely removed from the resulting dataset.
## Not run: 
ped <- system.file("extdata/example.ped", package="GeneGeneInteR")
info <- system.file("extdata/example.info", package="GeneGeneInteR")
posi <- system.file("extdata/example.txt", package="GeneGeneInteR")
data.imported <- importFile(file=ped, snps=info, pos=posi, pos.sep="\t")

## End(Not run)
### Equivalent loading of the imported data
load(system.file("extdata/dataImported.Rdata", package="GeneGeneInteR"))

## In this example, SNPs are with MAF lower than 0.2 or p-value for HWE testing lower than 0.05 or
# a proportion of missing value higher than 0.2 are removed
data.scour1 <- snpMatrixScour(data.imported$snpX, genes.info = data.imported$genes.info,
                               min.maf = 0.2, min.eq=0.05, call.rate = 0.8)
## Two genes have been completely removed from the resulting dataset.

Printing summaries class `"GGInetwork"` objects

Description

Printing summaries for objects of class "GGInetwork"

Usage

## S3 method for class 'GGInetwork'
summary(object, ...)
## S3 method for class 'GGInetwork'
summary(object, ...)

Arguments

`object`	`GGInetwork`. Given as output of `GGI`.
`...`	further arguments passed to or from other methods

Details

summary.GGItest provides a customized summary of a GGInetwork object.

Value

The form of the value returned by summary depends on the class of its argument.

Examples

## Not run: 
## Dataset is included in the package
ped <- system.file("extdata/example.ped", package="GeneGeneInteR")
info <- system.file("extdata/example.info", package="GeneGeneInteR")
posi <- system.file("extdata/example.txt", package="GeneGeneInteR")

## Importation of the genotypes
data.imported <- importFile(file=ped, snps=info, pos=posi, pos.sep="\t")
## Filtering of the data: SNPs with MAF < 0.05 or p.value for HWE < 1e-3 or 
## call rate < 0.9 are removed. 
data.scour <- snpMatrixScour(snpX=data.imported$snpX,genes.info=data.imported$genes.info,
                             min.maf=0.05,min.eq=1e-3,call.rate=0.9)
## Imputation of the missing genotypes
data.imputed <- imputeSnpMatrix(data.scour$snpX, genes.info = data.scour$genes.info)

## Importation of the phenotype
resp <- system.file("extdata/response.txt", package="GeneGeneInteR")
Y  <- read.csv(resp, header=FALSE)

## plot of the interaction between the 17 genes with the CLD method -- can take a few minutes
GGI.res <- GGI(Y=Y, snpX=dta$snpX, genes.info=dta$genes.info,method="CLD")
plot(GGI.res,threshold=0.05)

## Selection of 12 genes among 17
data.select <- selectSnps(data.imputed$snpX, data.imputed$genes.info, c("bub3","CDSN","Gc","GLRX",
                          "PADI1","PADI2","PADI4","PADI6","PRKD3","PSORS1C1","SERPINA1","SORBS1"))
GGI.res <- GGI(Y=Y, snpX=data.select$snpX, genes.info=data.select$genes.info,method="PCA")

## End(Not run)
## Equivalent importation of the GGI.res object
load(system.file("extdata/GGIRes.Rdata", package="GeneGeneInteR"))

summary(GGI.res)

## Not run: 
## Dataset is included in the package
ped <- system.file("extdata/example.ped", package="GeneGeneInteR")
info <- system.file("extdata/example.info", package="GeneGeneInteR")
posi <- system.file("extdata/example.txt", package="GeneGeneInteR")

## Importation of the genotypes
data.imported <- importFile(file=ped, snps=info, pos=posi, pos.sep="\t")
## Filtering of the data: SNPs with MAF < 0.05 or p.value for HWE < 1e-3 or 
## call rate < 0.9 are removed. 
data.scour <- snpMatrixScour(snpX=data.imported$snpX,genes.info=data.imported$genes.info,
                             min.maf=0.05,min.eq=1e-3,call.rate=0.9)
## Imputation of the missing genotypes
data.imputed <- imputeSnpMatrix(data.scour$snpX, genes.info = data.scour$genes.info)

## Importation of the phenotype
resp <- system.file("extdata/response.txt", package="GeneGeneInteR")
Y  <- read.csv(resp, header=FALSE)

## plot of the interaction between the 17 genes with the CLD method -- can take a few minutes
GGI.res <- GGI(Y=Y, snpX=dta$snpX, genes.info=dta$genes.info,method="CLD")
plot(GGI.res,threshold=0.05)

## Selection of 12 genes among 17
data.select <- selectSnps(data.imputed$snpX, data.imputed$genes.info, c("bub3","CDSN","Gc","GLRX",
                          "PADI1","PADI2","PADI4","PADI6","PRKD3","PSORS1C1","SERPINA1","SORBS1"))
GGI.res <- GGI(Y=Y, snpX=data.select$snpX, genes.info=data.select$genes.info,method="PCA")

## End(Not run)
## Equivalent importation of the GGI.res object
load(system.file("extdata/GGIRes.Rdata", package="GeneGeneInteR"))

summary(GGI.res)

Printing summaries class `"GGItest"` objects

Description

Printing summaries for objects of class "GGItest"

Usage

## S3 method for class 'GGItest'
summary(object, ...)
## S3 method for class 'GGItest'
summary(object, ...)

Arguments

`object`	`GGItest`. Given as output of `CCA.test` for example.
`...`	further arguments passed to or from other methods

Details

summary.GGItest provides a customized summary of a GGItest object.

Value

The form of the value returned by summary depends on the class of its argument.

Examples


data(gene.pair)
summary(PCA.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair$G2,threshold=0.7,method="Std"))


data(gene.pair)
summary(PCA.test(Y=gene.pair$Y, G1=gene.pair$G1,G2=gene.pair$G2,threshold=0.7,method="Std"))

Gene-based Gene-Gene Interaction analysis by combining SNP-SNP interaction tests

Description

Minimum p-value in minP.test function
Gene Association Test with Extended Simes procedure in gates.test
Truncated Tail Strength in tTS.test function
Truncated p-value Product in tProd.test function

Usage

tProd.test(Y, G1, G2, tau = 0.05, n.sim = 1000)
tProd.test(Y, G1, G2, tau = 0.05, n.sim = 1000)

Arguments

`Y`	numeric or factor vector with exactly two different values. `Y` is the response variable and should be of length equal to the number of rows of `G1` and `G2` arguments (number of individuals).
`G1`	SnpMatrix object. Must have a number of rows equal to the length of `Y`.
`G2`	SnpMatrix object. Must have a number of rows equal to the length of `Y`.
`tau`	numeric in [0, 1]. See details section for its use.
`n.sim`	positive integer. `n.sim` is the number of multivariate normal distribution simulations used to compute the significant level for tTS and tProd methods.

Details

minP test - minP test considered the significant of the observed minimum p-value. Significance is computed by integrating the multivariate normal distribution with covariance $\Sigma$ as proposed in Conneelly and Boehnke (2008).
GATES test - The p-value for GATES is the minimum p-value obtained after a multiple testing correction of the SNP-SNP interaction p-values. Correction for multiple testing is defined as

$me * p[i]/me[i]$

where $me$ is the effective number of independant tests, $p[i]$ is the i-th top p-values and $me[i]$ is the effective number of independant test among the top i p-values. Many methods exist to estimate $me$ and $me[i]$ terms:
- Cheverud-Nyholt method (Cheverud, 2001 and Nyholt, 2004)
- Keff method (Moskovina and Schmidt, 2008)
- Li & Ji method (Li and Ji, 2005)
- Galwey method (Galwey, 2009)
Details of each method can be found in the references.
tTS test - tTS test does not consider only the strongest signal but all signals that are inferior to a given threshold $\tau$ . For these p-values, the weighted sum of

$tTS=\sum (1-p[i]*(m1*m2+1)/i)$

is computed and represents the test statistic. The p-value is calculated using an empirical distribution of $tTS$ obtained by simulating multivariate normal statistics with a covariance $\Sigma$ as proposed by Jiang et al. (2011).
TProd test - The procedure is similar to $tTS$ with $tProd$ defined as

$tProd=\prod p[i].$

See Zaykin et al. (2002) for details.

Value

A list with class "GGItest" containing the following components:

`statistic`	The value of the statistic tProd.
`p.value`	The p-value for the test
`estimate`	Estimation of tProd.
`parameter`	The threshold value tau.
`alternative`	a character string describing the alternative.
`method`	a character string indicating the method used.
`data.name`	a character string giving the names of the data.

References

M. Emily AGGrEGATOr: A Gene-based GEne-Gene interActTiOn test for case-control association studies, Statistical Application in Genetics and Molecular Biology, 15(2): 151-171, 2016.
L. Ma, A.G. Clark and A. Keinan Gene-Based Testing Of Interactions in Association Studies of Quantitative Traits. PLoS Genetics 9(2):e1003321, 2013.
V. Moskvina and K.M. Schmidt On multiple-testing correction in genome-wide association studies. Genetic Epidemiology, 32(6): 567-573, 2008.
J. Li and L. Ji. Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity 95: 221-227, 2005.
N.W. Galwey. A new measure of the effective number of tests, a practical tool for comparing families of non-independent significance tests. Genetic Epidemiology 33(7): 559-568, 2009.
J.M. Cheverud. A simple correction for multiple comparisons in interval mapping genome scans. Heredity. 87(1):52-58, 2001.
D.R. Nyholt. A Simple Correction for Multiple Testing for Single-Nucleotide Polymorphisms in Linkage Disequilibrium with Each Other. American journal of human genetics. 74(4): 765-769, 2004.
K.N. Conneely and M. Boehnke. So many correlated tests, so little time! rapid adjustment of p values for multiple correlated tests. The American Journal of Human Genetics, 81: 1158-1168, 2008.
B. Jiang, X. Zhang, Y. Zuo and G. Kang. A powerful truncated tail strength method for testing multiple null hypotheses in one dataset. Journal of Theoretical Biology 277: 67-73, 2011.
D.V. Zaykin, L.A. Zhivotovsky, P.H. Westfall and B.S. Weir. Truncated product method for combining P-values. Genetic epidemiology 22: 170-185, 2002.

Examples

data(gene.pair)
tProd.test(gene.pair$Y, gene.pair$G1, gene.pair$G2, tau = 0.5, n.sim = 500)
data(gene.pair)
tProd.test(gene.pair$Y, gene.pair$G1, gene.pair$G2, tau = 0.5, n.sim = 500)

Gene-based Gene-Gene Interaction analysis by combining SNP-SNP interaction tests

Description

Minimum p-value in minP.test function
Gene Association Test with Extended Simes procedure in gates.test
Truncated Tail Strength in tTS.test function
Truncated p-value Product in tProd.test function

Usage

tTS.test(Y, G1, G2, tau = 0.05, n.sim = 1000)
tTS.test(Y, G1, G2, tau = 0.05, n.sim = 1000)

Arguments

`Y`	numeric or factor vector with exactly two different values. `Y` is the response variable and should be of length equal to the number of rows of `G1` and `G2` arguments (number of individuals).
`G1`	SnpMatrix object. Must have a number of rows equal to the length of `Y`.
`G2`	SnpMatrix object. Must have a number of rows equal to the length of `Y`.
`tau`	numeric in [0, 1]. See details section for its use.
`n.sim`	positive integer. `n.sim` is the number of multivariate normal distribution simulations used to compute the significant level for tTS and tProd methods.

Details

minP test - minP test considered the significant of the observed minimum p-value. Significance is computed by integrating the multivariate normal distribution with covariance $\Sigma$ as proposed in Conneelly and Boehnke (2008).
GATES test - The p-value for GATES is the minimum p-value obtained after a multiple testing correction of the SNP-SNP interaction p-values. Correction for multiple testing is defined as

$me * p[i]/me[i]$

where $me$ is the effective number of independant tests, $p[i]$ is the i-th top p-values and $me[i]$ is the effective number of independant test among the top i p-values. Many methods exist to estimate $me$ and $me[i]$ terms:
- Cheverud-Nyholt method (Cheverud, 2001 and Nyholt, 2004)
- Keff method (Moskovina and Schmidt, 2008)
- Li & Ji method (Li and Ji, 2005)
- Galwey method (Galwey, 2009)
Details of each method can be found in the references.
tTS test - tTS test does not consider only the strongest signal but all signals that are inferior to a given threshold $\tau$ . For these p-values, the weighted sum of

$tTS=\sum (1-p[i]*(m1*m2+1)/i)$

is computed and represents the test statistic. The p-value is calculated using an empirical distribution of $tTS$ obtained by simulating multivariate normal statistics with a covariance $\Sigma$ as proposed by Jiang et al. (2011).
TProd test - The procedure is similar to $tTS$ with $tProd$ defined as

$tProd=\prod p[i].$

See Zaykin et al. (2002) for details.

Value

A list with class "GGItest" containing the following components:

`statistic`	The value of the statistic tTS.
`p.value`	The p-value for the test.
`estimate`	Estimation of tTS.
`parameter`	The threshold value tau.
`alternative`	a character string describing the alternative.
`method`	a character string indicating the method used.
`data.name`	a character string giving the names of the data.

References

M. Emily AGGrEGATOr: A Gene-based GEne-Gene interActTiOn test for case-control association studies, Statistical Application in Genetics and Molecular Biology, 15(2): 151-171, 2016.
L. Ma, A.G. Clark and A. Keinan Gene-Based Testing Of Interactions in Association Studies of Quantitative Traits. PLoS Genetics 9(2):e1003321, 2013.
V. Moskvina and K.M. Schmidt On multiple-testing correction in genome-wide association studies. Genetic Epidemiology, 32(6): 567-573, 2008.
J. Li and L. Ji. Adjusting multiple testing in multilocus analyses using the eigenvalues of a correlation matrix. Heredity 95: 221-227, 2005.
N.W. Galwey. A new measure of the effective number of tests, a practical tool for comparing families of non-independent significance tests. Genetic Epidemiology 33(7): 559-568, 2009.
J.M. Cheverud. A simple correction for multiple comparisons in interval mapping genome scans. Heredity. 87(1):52-58, 2001.
D.R. Nyholt. A Simple Correction for Multiple Testing for Single-Nucleotide Polymorphisms in Linkage Disequilibrium with Each Other. American journal of human genetics. 74(4): 765-769, 2004.
K.N. Conneely and M. Boehnke. So many correlated tests, so little time! rapid adjustment of p values for multiple correlated tests. The American Journal of Human Genetics, 81: 1158-1168, 2008.
B. Jiang, X. Zhang, Y. Zuo and G. Kang. A powerful truncated tail strength method for testing multiple null hypotheses in one dataset. Journal of Theoretical Biology 277: 67-73, 2011.
D.V. Zaykin, L.A. Zhivotovsky, P.H. Westfall and B.S. Weir. Truncated product method for combining P-values. Genetic epidemiology 22: 170-185, 2002.

Examples

data(gene.pair)
tTS.test(gene.pair$Y, gene.pair$G1, gene.pair$G2, tau = 0.5, n.sim = 500)

data(gene.pair)
tTS.test(gene.pair$Y, gene.pair$G1, gene.pair$G2, tau = 0.5, n.sim = 500)

Package 'GeneGeneInteR'

Help Index

CCA (Canonical-Correlation Analysis) based GGI analysis.

Description

Usage

Arguments

Details

Value

References

See Also

Examples

CLD (Composite Linkage Desequilibrium) based GGI analysis.

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Multiple genes dataset

Description

Usage

Format

Value

Source

Gene-based Gene-Gene Interaction analysis by combining SNP-SNP interaction tests

Description

Usage

Arguments

Details

Value

References

See Also

Examples

GBIGM (Gene-based information gain method) for GGI analysis.

Description

Usage

Arguments

Details

Value

References

See Also

Examples

A case-control dataset with two genes

Description

Usage

Format

Value

Source

Gene-Gene Interaction Analysis of a set of genes

Description

Usage

Arguments

Details

Value

References

See Also

Examples

Imports SNPs information from pedfile, PLINK, VCF (4.0) file, or genotypes imputed by IMPUTE2.

Description

Usage

Arguments

Details

Value

Examples

Missing values handling for SnpMatrix object

Description

Usage

Arguments

Details

Value

See Also

Examples

KCCA (Kernel Canonical-Correlation Analysis) based GGI analysis.

Description

Usage

Arguments

Details

Value

Printing objects of class `"GGItest"`

Printing summaries class `"GGInetwork"` objects

Printing summaries class `"GGItest"` objects