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

Description Usage Arguments Details Value References See Also Examples

View source: R/tTS.R

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:

Usage

1
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

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:

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.

See Also

GGI

Examples

1
2
data(gene.pair)
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.