Test for association between a set of SNPS/genes and continuous outcomes by including variant characteristic information and using score statistics.
mist(
y,
X,
G,
Z,
method = "liu",
model = c("guess", "continuous", "binary"),
weight.beta = NULL,
maf = NULL
)[numeric] A numeric vector of the continuous outcome variables. Missing values are not allowed.
[numeric] A numeric matrix of covariates with rows for individuals and columns for covariates.
If there is no covariate, it does not need to be specified
[numeric] A numeric genotype matrix with rows for individuals and columns for SNPs.
Each SNP should be coded as 0, 1, and 2 for AA, Aa, aa, where A is a major allele and a is a minor allele.
Missing genotypes are not allowed.
[numeric] a numeric matrix of second level covariates for variant characteristics.
Each row corresponds to a variant and each column corresponds to a variant characteristic.
If there is no second level covariates, a vector of 1 should be used.
[character] A method to compute the p-value and the default value is "liu". Method "davies" represents an exact method that computes the p-value by inverting the characteristic function of the mixture chisq. Method "liu" represents an approximation method that matches the first 3 moments.
[character] A character vector specifying the model. Default is to "guess".
Possible choices are "guess", "continuous" (linear regression) or "binary" (logistic regression).
[numeric] A numeric vector of parameters of beta function
which is the weight for scorestatistics.
The default value is NULL, i.e. no weight.
Default weight value could be c(1, 25).
[numeric] A numeric vector of MAF (minor allele frequency) for each SNP.
S.tau score Statistic for the variant heterogeneous effect.
S.pi score Statistic for the variant mean effect.
p.value.S.tau P-value for testing the variant heterogeneous effect.
p.value.S.pi P-value for testing the variant mean effect.
p.value.overall Overall p-value for testing the association between the set of SNPS/genes and outcomes. It combines p.value.S.pi and p.value.S.tau by using Fisher's procedure.
library(MiSTr)
data(mist_data)
attach(mist_data)
mist(
y = phenotypes[, "y_taupi"],
X = phenotypes[, paste0("x_cov", 0:2)],
G = genotypes,
Z = variants_info[, 1, drop = FALSE]
)
#> [MiSTr] "y" seems to be "continuous", model is set to "continuous"!
#> [MiSTr] Linear regression is ongoing ...
#>
#> MiSTr: Mixed effects Score Test
#> -------------------------------
#>
#> - (Raw) Estimates:
#>
#> SubClusters term.pi.hat estimate.pi.hat std.error.pi.hat statistic.pi.hat
#> 1 1 Mcluster1 0.248 0.321 0.774
#> p.value.pi.hat conf.low.pi.hat conf.high.pi.hat
#> 1 0.441 -0.389 0.885
#>
#> - Statistics:
#>
#> + Overall effect:
#> * P-value = 0.0307
#> + PI (mean effect):
#> * Score = 0.601
#> * P-value = 0.438
#> + TAU (heterogeneous effect):
#> * Score = 1006.125
#> * P-value = 0.0111
#>
mist(
y = phenotypes[, "y_binary"],
X = phenotypes[, paste0("x_cov", 0:2)],
G = genotypes,
Z = variants_info[, 1, drop = FALSE]
)
#> [MiSTr] "y" seems to be "binary", model is set to "binary"!
#> [MiSTr] Logistic regression is ongoing ...
#>
#> MiSTr: Mixed effects Score Test
#> -------------------------------
#>
#> - (Raw) Estimates:
#>
#> SubClusters term.pi.hat estimate.pi.hat std.error.pi.hat statistic.pi.hat
#> 1 1 GZ 3.576 0.344 3.7
#> p.value.pi.hat conf.low.pi.hat conf.high.pi.hat
#> 1 0 1.935 7.528
#>
#> - Statistics:
#>
#> + Overall effect:
#> * P-value = 6.54e-05
#> + PI (mean effect):
#> * Score = 17.527
#> * P-value = 2.83e-05
#> + TAU (heterogeneous effect):
#> * Score = 5.4
#> * P-value = 0.175
#>