tTS.test: Gene-based Gene-Gene Interaction analysis by combining...

Description Usage Arguments Details Value References See Also Examples

View source: R/tTS.R


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:


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



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).


SnpMatrix object. Must have a number of rows equal to the length of Y.


SnpMatrix object. Must have a number of rows equal to the length of Y.


numeric in [0, 1]. See details section for its use.


positive integer. n.sim is the number of multivariate normal distribution simulations used to compute the significant level for tTS and tProd methods.


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 Σ. Σ is computed as described in the method developped in Emily (2016). The covariance Σ is used in each method as follows:


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


The value of the statistic tTS.


The p-value for the test.


Estimation of tTS.


The threshold value tau.


a character string describing the alternative.


a character string indicating the method used.

a character string giving the names of the data.


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.

See Also



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

MathieuEmily/GeneGeneInteR documentation built on May 7, 2019, 3:42 p.m.