| Title: | Gene Environment Wide Interaction Search Threshold |
|---|---|
| Description: | This 'GEWIST' package provides statistical tools to efficiently optimize SNP prioritization for gene-gene and gene-environment interactions. |
| Authors: | Wei Q. Deng, Guillaume Pare |
| Maintainer: | Wei Q. Deng <[email protected]> |
| License: | GPL-2 |
| Version: | 1.51.0 |
| Built: | 2024-11-29 05:55:57 UTC |
| Source: | https://github.com/bioc/GEWIST |
This 'GEWIST' package provides statistical tools to efficiently optimize SNP prioritization for gene-gene and gene-environment interactions.
| Package: | PathWei |
| Type: | Package |
| Version: | 0.99.z |
| License: | GPL-2 |
| LazyLoad: | yes |
Wei Q. Deng <[email protected]> Guillaume Pare <[email protected]>
Deng W.Q, Pare G. (2011) A fast algorithm to optimize SNP prioritization for gene-gene and gene-environment interactions. Genetic Epidemiology. 35: 729-738. doi: 10.1002/gepi.20624
Pare G, Cook NR, Ridker PM, Chasman DI (2010) On the Use of Variance per Genotype as a Tool to Identify Quantitative Trait Interaction Effects: A Report from the Women's Genome Health Study. PLoS Genet 6(6): e1000981. doi:10.1371/journal.pgen.1000981
Levene H. (1960) Robust tests for equality of variances. In Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling eds:I. Olkin, S.G. Ghurye, W. Hoeffding, W.G. Madow & H.B.Mann, pp.278-292. Stanford: Stanford University Press.
Compute the optimal Variance Prioritization power and corresponding Levene's test p-value threshold for prioritization given the interaction effect size distribution using GEWIST.
effectPDF(distribution = c("beta", "normal", "uniform", "weibull"), parameter1, parameter2 = NULL, parameter3 = NULL, p, N, theta_c, M, K = 20000, nb_incr = 50, range = NULL, verbose = FALSE)effectPDF(distribution = c("beta", "normal", "uniform", "weibull"), parameter1, parameter2 = NULL, parameter3 = NULL, p, N, theta_c, M, K = 20000, nb_incr = 50, range = NULL, verbose = FALSE)
distribution |
distribution of interaction effect size. Possible distributions are: "beta" for beta distribution "normal" for normal distribution "uniform" for uniform distribution "weibull" for weibull distribution |
parameter1 |
the first parameter used in the corresponding distribution |
parameter2 |
the second parameter used in the corresponding distribution, could set to be null |
parameter3 |
the third parameter used in the corresponding distribution, could set to be null |
p |
minor allele frequency of the SNP, a number between 0 and 0.5 |
N |
sample size |
theta_c |
proportion of quantitative trait variance explained by the covariate, should be a number between 0 and 1 |
M |
total number of SNPs to be tested |
K |
number of GEWIST procedures, by default, set to be 20,000 |
nb_incr |
number of effect size points in the range to be prioritized using GEWIST; by default set to be 50. |
range |
range of variance explained by interaction effect sizes, a vector of length 2 |
verbose |
logical; if TRUE, for each interaction effect size, function returns a data.frame class object listing the VP power at each p-value, from 0.001 to 1 with 0.001 incremental increase. |
A list with three components:
Optimal_VP_power |
VP power to detect interactions at the optimal VP p-value threshold |
Conventional_power |
power to detect interactions without prioritization, i.e, VP power at Levene' test p-value of 1 |
Optimal_pval_threshold |
levene'e test p-value at which optimal VP power is achieved |
Computational time is directly proportional to nb_incr.
Wei Q. Deng <[email protected]> Guillaume Pare <[email protected]>
Deng W.Q, Pare G. (2011) A fast algorithm to optimize SNP prioritization for gene-gene and gene-environment interactions. Genetic Epidemiology. 35: 729-738. doi: 10.1002/gepi.20624
Pare G, Cook NR, Ridker PM, Chasman DI (2010) On the Use of Variance per Genotype as a Tool to Identify Quantitative Trait Interaction Effects: A Report from the Women's Genome Health Study. PLoS Genet 6(6): e1000981. doi:10.1371/journal.pgen.1000981
Levene H. (1960) Robust tests for equality of variances. In Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling eds:I. Olkin, S.G. Ghurye, W. Hoeffding, W.G. Madow & H.B.Mann, pp.278-292. Stanford: Stanford University Press.
# Given a SNP with minor allele frequency of 10% and a sample # of 10,000 individuals, we are interested in testing interactions # between this SNP and a covariate of effect size 10%. The # total number of SNP is 500,000. Assume the unknown interaction # effect size has a Weibull distribution in the range of 0.05% # and 0.3% variance explained with 50 increments.Repeat GEWIST # for each of the 50 interaction effect sizes. library(GEWIST) effectPDF(distribution = "weibull", parameter1 = 0.8, parameter2 = 0.3, parameter3 = NULL, p = 0.1 ,N = 10000, theta_c = 0.1, M = 350000, K = 20000, nb_incr = 50, range = c(0.05/100,0.3/100), verbose = FALSE) ## End of script# Given a SNP with minor allele frequency of 10% and a sample # of 10,000 individuals, we are interested in testing interactions # between this SNP and a covariate of effect size 10%. The # total number of SNP is 500,000. Assume the unknown interaction # effect size has a Weibull distribution in the range of 0.05% # and 0.3% variance explained with 50 increments.Repeat GEWIST # for each of the 50 interaction effect sizes. library(GEWIST) effectPDF(distribution = "weibull", parameter1 = 0.8, parameter2 = 0.3, parameter3 = NULL, p = 0.1 ,N = 10000, theta_c = 0.1, M = 350000, K = 20000, nb_incr = 50, range = c(0.05/100,0.3/100), verbose = FALSE) ## End of script
Compute the optimal Variance Prioritization power and the corresponding Levene's test prioritization p-value threshold for a given interaction effect size
gewistLevene(p, N, theta_gc, theta_c, M, K = 20000, verbose = FALSE)gewistLevene(p, N, theta_gc, theta_c, M, K = 20000, verbose = FALSE)
p |
minor allele frequency of the SNP, a number between 0 and 0.5 |
N |
sample size |
theta_gc |
proportion of quantitative trait variance explained by the interaction, should be a number between 0 and 1 |
theta_c |
proportion of quantitative trait variance explained by the covariate, should be a number between 0 and 1 |
M |
total number of SNPs to be tested |
K |
number of procedures, by default, set to be 20,000 |
verbose |
logical; if TRUE, function returns a data.frame class object listing the VP power at each p-value, from 0.001 to 1 with 0.001 incremental increase. |
A list with three components:
Optimal_VP_power |
VP power to detect interactions at the optimal Levene's test p-value threshold |
Conventional_power |
power to detect interactions without prioritization, i.e, VP power at Levene's test p-value of 1 |
Optimal_pval_threshold |
levene'e test p-value at which optimal VP power is achieved |
Wei Q. Deng <[email protected]> Guillaume Pare <[email protected]>
Deng W.Q, Pare G. (2011) A fast algorithm to optimize SNP prioritization for gene-gene and gene-environment interactions. Genetic Epidemiology 35: 729-738. doi: 10.1002/gepi.20624
Pare G, Cook NR, Ridker PM, Chasman DI (2010) On the Use of Variance per Genotype as a Tool to Identify Quantitative Trait Interaction Effects: A Report from the Women's Genome Health Study. PLoS Genet 6(6): e1000981. doi:10.1371/journal.pgen.1000981
Levene H. (1960) Robust tests for equality of variances. In Contributions to Probability and Statistics: Essays in Honor of Harold Hotelling eds:I. Olkin, S.G. Ghurye, W. Hoeffding, W.G. Madow & H.B.Mann, pp.278-292. Stanford: Stanford University Press.
# Given a SNP with minor allele frequency of 10% and a sample # of 15,000 individuals, we are interested in testing interactions # between this SNP and a covariate of effect size #10%. The # total number of SNP is 500,000. Assume the interaction # explains 0.1% of the quantitative trait variance. library(GEWIST) gewistLevene(p = 0.1, N = 15000, theta_gc =0.1/100, theta_c = 0.1 , M = 500000, K = 20000, verbose=FALSE)# Given a SNP with minor allele frequency of 10% and a sample # of 15,000 individuals, we are interested in testing interactions # between this SNP and a covariate of effect size #10%. The # total number of SNP is 500,000. Assume the interaction # explains 0.1% of the quantitative trait variance. library(GEWIST) gewistLevene(p = 0.1, N = 15000, theta_gc =0.1/100, theta_c = 0.1 , M = 500000, K = 20000, verbose=FALSE)