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
)

## Arguments

y

[numeric] A numeric vector of the continuous outcome variables. Missing values are not allowed.

X

[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

G

[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.

Z

[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.

method

[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.

model

[character] A character vector specifying the model. Default is to "guess". Possible choices are "guess", "continuous" (linear regression) or "binary" (logistic regression).

weight.beta

[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).

maf

[numeric] A numeric vector of MAF (minor allele frequency) for each SNP.

## Value

• 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.

## Examples


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 Pi_hat    SE CI_2.5 CI_97.5
#> 1    cluster1  0.248 0.321 -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 Pi_hat    SE CI_2.5 CI_97.5    OR
#> 1    cluster1  1.274 0.344   0.66   2.019 3.576
#>
#> - 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
#>