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

In GeneGeneInteR: Tools for Testing Gene-Gene Interaction at the Gene Level

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

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

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:

• minP test - minP test considered the significant of the observed minimum p-value. Significance is computed by integrating the multivariate normal distribution with covariance Σ 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 τ. For these p-values, the weighted sum of

  tTS=∑ (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 Σ as proposed by Jiang et al. (2011).

• TProd test - The procedure is similar to tTS with tProd defined as

  tProd=∏ 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.

See Also

GGI

Examples

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

GeneGeneInteR documentation built on Nov. 8, 2020, 6:28 p.m.