MiSTr: *Mi*xed effects *S*core *T*est • MiSTr

Test for association between a set of SNPS/genes and continuous or binary outcomes by including variant characteristic information and using (weighted) score statistics.

Note:

From: https://cran.r-project.org/src/contrib/MiST_1.0.tar.gz
Reference: https://doi.org/10.1002/gepi.21717

Installation

# Install MiSTr from CRAN:
install.packages("MiSTr")

# Or the the development version from GitHub:
# install.packages("remotes")
remotes::install_github("mcanouil/MiSTr")

MiSTr in Action

library(MiSTr)
data(mist_data)
attach(mist_data)

Continuous Outcome

With Heterogeneity (τ)

mist(
  y = phenotypes[, "y_tau"],
  X = phenotypes[, paste0("x_cov", 0:2)],
  G = genotypes,
  Z = variants_info[, 1, drop = FALSE],
  method = "liu",
  model = "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          -0.467            0.284           -1.645
#>   p.value.pi.hat conf.low.pi.hat conf.high.pi.hat
#> 1          0.103          -1.031            0.097
#> 
#> - Statistics:
#> 
#>   + Overall effect: 
#>     * P-value = 3.4e-05
#>   + PI (mean effect):  
#>     * Score = 2.659
#>     * P-value = 0.103
#>   + TAU (heterogeneous effect):  
#>     * Score = 1464.924
#>     * P-value = 2.37e-05

With Heterogeneity (τ) “Removed”

variants_info[, "effect"] # simulated effect
#>  g_variant1  g_variant2  g_variant3  g_variant4  g_variant5  g_variant6 
#>  -0.1871895  -0.6536863  -0.6635657  -5.7526938  -0.7180167  -0.3196585 
#>  g_variant7  g_variant8  g_variant9 g_variant10 
#>   2.0680578  -0.7141979  -1.8494894   1.2992623
get_same_effect <- names(which(variants_info[, "effect"] > 0))
mist(
  y = phenotypes[, "y_tau"],
  X = phenotypes[, paste0("x_cov", 0:2)],
  G = genotypes[, get_same_effect],
  Z = variants_info[get_same_effect, 1, drop = FALSE],
  method = "liu",
  model = "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            0.77            0.627            1.228
#>   p.value.pi.hat conf.low.pi.hat conf.high.pi.hat
#> 1          0.223          -0.475            2.014
#> 
#> - Statistics:
#> 
#>   + Overall effect: 
#>     * P-value = 0.531
#>   + PI (mean effect):  
#>     * Score = 1.5
#>     * P-value = 0.221
#>   + TAU (heterogeneous effect):  
#>     * Score = 0.217
#>     * P-value = 0.932

With “Average” Effect (π)

mist(
  y = phenotypes[, "y_pi"],
  X = phenotypes[, paste0("x_cov", 0:2)],
  G = genotypes,
  Z = variants_info[, 1, drop = FALSE],
  method = "liu",
  model = "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           0.809            0.294             2.75
#>   p.value.pi.hat conf.low.pi.hat conf.high.pi.hat
#> 1          0.007           0.225            1.392
#> 
#> - Statistics:
#> 
#>   + Overall effect: 
#>     * P-value = 0.0376
#>   + PI (mean effect):  
#>     * Score = 7.083
#>     * P-value = 0.00778
#>   + TAU (heterogeneous effect):  
#>     * Score = 185.198
#>     * P-value = 0.793

With “Average” Effect (π) and Heterogeneity (τ)

mist(
  y = phenotypes[, "y_taupi"],
  X = phenotypes[, paste0("x_cov", 0:2)],
  G = genotypes,
  Z = variants_info[, 1, drop = FALSE],
  method = "liu",
  model = "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

With “Average” Effect (π) and With Heterogeneity (τ) “Removed”

variants_info[, "effect"] # simulated effect
#>  g_variant1  g_variant2  g_variant3  g_variant4  g_variant5  g_variant6 
#>  -0.1871895  -0.6536863  -0.6635657  -5.7526938  -0.7180167  -0.3196585 
#>  g_variant7  g_variant8  g_variant9 g_variant10 
#>   2.0680578  -0.7141979  -1.8494894   1.2992623
get_same_effect <- names(which(variants_info[, "effect"] > 0))
mist(
  y = phenotypes[, "y_taupi"],
  X = phenotypes[, paste0("x_cov", 0:2)],
  G = genotypes[, get_same_effect],
  Z = variants_info[get_same_effect, 1, drop = FALSE],
  method = "liu",
  model = "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           1.517            0.689            2.201
#>   p.value.pi.hat conf.low.pi.hat conf.high.pi.hat
#> 1           0.03           0.149            2.884
#> 
#> - Statistics:
#> 
#>   + Overall effect: 
#>     * P-value = 0.121
#>   + PI (mean effect):  
#>     * Score = 4.661
#>     * P-value = 0.0308
#>   + TAU (heterogeneous effect):  
#>     * Score = 1.427
#>     * P-value = 0.842

Continuous Outcome with Weights

With Heterogeneity (τ)

mist(
  y = phenotypes[, "y_tau"],
  X = phenotypes[, paste0("x_cov", 0:2)],
  G = genotypes,
  Z = variants_info[, 1, drop = FALSE],
  method = "liu",
  model = "continuous",
  weight.beta = c(1, 25),
  maf = variants_info[, "maf"]
)
#> [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          -0.467            0.284           -1.645
#>   p.value.pi.hat conf.low.pi.hat conf.high.pi.hat
#> 1          0.103          -1.031            0.097
#> 
#> - Statistics:
#> 
#>   + Overall effect: 
#>     * P-value = 0.000115
#>   + PI (mean effect):  
#>     * Score = 2.659
#>     * P-value = 0.103
#>   + TAU (heterogeneous effect):  
#>     * Score = 171103.8
#>     * P-value = 8.87e-05

With Heterogeneity (τ) “Removed”

variants_info[, "effect"] # simulated effect
#>  g_variant1  g_variant2  g_variant3  g_variant4  g_variant5  g_variant6 
#>  -0.1871895  -0.6536863  -0.6635657  -5.7526938  -0.7180167  -0.3196585 
#>  g_variant7  g_variant8  g_variant9 g_variant10 
#>   2.0680578  -0.7141979  -1.8494894   1.2992623
get_same_effect <- names(which(variants_info[, "effect"] > 0))
mist(
  y = phenotypes[, "y_tau"],
  X = phenotypes[, paste0("x_cov", 0:2)],
  G = genotypes[, get_same_effect],
  Z = variants_info[get_same_effect, 1, drop = FALSE],
  method = "liu",
  model = "continuous",
  weight.beta = c(1, 25),
  maf = variants_info[get_same_effect, "maf"]
)
#> [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            0.77            0.627            1.228
#>   p.value.pi.hat conf.low.pi.hat conf.high.pi.hat
#> 1          0.223          -0.475            2.014
#> 
#> - Statistics:
#> 
#>   + Overall effect: 
#>     * P-value = 0.531
#>   + PI (mean effect):  
#>     * Score = 1.5
#>     * P-value = 0.221
#>   + TAU (heterogeneous effect):  
#>     * Score = 25.306
#>     * P-value = 0.932

With “Average” Effect (π)

mist(
  y = phenotypes[, "y_pi"],
  X = phenotypes[, paste0("x_cov", 0:2)],
  G = genotypes,
  Z = variants_info[, 1, drop = FALSE],
  method = "liu",
  model = "continuous",
  weight.beta = c(1, 25),
  maf = variants_info[, "maf"]
)
#> [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           0.809            0.294             2.75
#>   p.value.pi.hat conf.low.pi.hat conf.high.pi.hat
#> 1          0.007           0.225            1.392
#> 
#> - Statistics:
#> 
#>   + Overall effect: 
#>     * P-value = 0.0383
#>   + PI (mean effect):  
#>     * Score = 7.083
#>     * P-value = 0.00778
#>   + TAU (heterogeneous effect):  
#>     * Score = 23859.6
#>     * P-value = 0.81

With “Average” Effect (π) and Heterogeneity (τ)

mist(
  y = phenotypes[, "y_taupi"],
  X = phenotypes[, paste0("x_cov", 0:2)],
  G = genotypes,
  Z = variants_info[, 1, drop = FALSE],
  method = "liu",
  model = "continuous",
  weight.beta = c(1, 25),
  maf = variants_info[, "maf"]
)
#> [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 = 0.0527
#>   + PI (mean effect):  
#>     * Score = 0.601
#>     * P-value = 0.438
#>   + TAU (heterogeneous effect):  
#>     * Score = 118494.8
#>     * P-value = 0.0212

With “Average” Effect (π) and With Heterogeneity (τ) “Removed”

variants_info[, "effect"] # simulated effect
#>  g_variant1  g_variant2  g_variant3  g_variant4  g_variant5  g_variant6 
#>  -0.1871895  -0.6536863  -0.6635657  -5.7526938  -0.7180167  -0.3196585 
#>  g_variant7  g_variant8  g_variant9 g_variant10 
#>   2.0680578  -0.7141979  -1.8494894   1.2992623
get_same_effect <- names(which(variants_info[, "effect"] > 0))
mist(
  y = phenotypes[, "y_taupi"],
  X = phenotypes[, paste0("x_cov", 0:2)],
  G = genotypes[, get_same_effect],
  Z = variants_info[get_same_effect, 1, drop = FALSE],
  method = "liu",
  model = "continuous",
  weight.beta = c(1, 25),
  maf = variants_info[get_same_effect, "maf"]
)
#> [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           1.517            0.689            2.201
#>   p.value.pi.hat conf.low.pi.hat conf.high.pi.hat
#> 1           0.03           0.149            2.884
#> 
#> - Statistics:
#> 
#>   + Overall effect: 
#>     * P-value = 0.121
#>   + PI (mean effect):  
#>     * Score = 4.661
#>     * P-value = 0.0308
#>   + TAU (heterogeneous effect):  
#>     * Score = 166.235
#>     * P-value = 0.842

Binary Outcome

With Heterogeneity (τ)

mist(
  y = phenotypes[, "y_binary"],
  X = phenotypes[, paste0("x_cov", 0:2)],
  G = genotypes,
  Z = variants_info[, 1, drop = FALSE],
  method = "liu",
  model = "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

With Heterogeneity (τ) “Removed”

variants_info[, "effect"] # simulated effect
#>  g_variant1  g_variant2  g_variant3  g_variant4  g_variant5  g_variant6 
#>  -0.1871895  -0.6536863  -0.6635657  -5.7526938  -0.7180167  -0.3196585 
#>  g_variant7  g_variant8  g_variant9 g_variant10 
#>   2.0680578  -0.7141979  -1.8494894   1.2992623
get_same_effect <- names(which(variants_info[, "effect"] > 0))
mist(
  y = phenotypes[, "y_binary"],
  X = phenotypes[, paste0("x_cov", 0:2)],
  G = genotypes[, get_same_effect],
  Z = variants_info[get_same_effect, 1, drop = FALSE],
  method = "liu",
  model = "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           3.279            0.645            1.841
#>   p.value.pi.hat conf.low.pi.hat conf.high.pi.hat
#> 1          0.066           1.064           14.571
#> 
#> - Statistics:
#> 
#>   + Overall effect: 
#>     * P-value = 0.154
#>   + PI (mean effect):  
#>     * Score = 3.969
#>     * P-value = 0.0463
#>   + TAU (heterogeneous effect):  
#>     * Score = 0.04
#>     * P-value = 0.766

Binary Outcome with Weights

With Heterogeneity (τ)

mist(
  y = phenotypes[, "y_binary"],
  X = phenotypes[, paste0("x_cov", 0:2)],
  G = genotypes,
  Z = variants_info[, 1, drop = FALSE],
  method = "liu",
  model = "binary",
  weight.beta = c(1, 25),
  maf = variants_info[, "maf"]
)
#> [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 = 9.29e-05
#>   + PI (mean effect):  
#>     * Score = 17.527
#>     * P-value = 2.83e-05
#>   + TAU (heterogeneous effect):  
#>     * Score = 616.283
#>     * P-value = 0.256

With Heterogeneity (τ) “Removed”

variants_info[, "effect"] # simulated effect
#>  g_variant1  g_variant2  g_variant3  g_variant4  g_variant5  g_variant6 
#>  -0.1871895  -0.6536863  -0.6635657  -5.7526938  -0.7180167  -0.3196585 
#>  g_variant7  g_variant8  g_variant9 g_variant10 
#>   2.0680578  -0.7141979  -1.8494894   1.2992623
get_same_effect <- names(which(variants_info[, "effect"] > 0))
mist(
  y = phenotypes[, "y_binary"],
  X = phenotypes[, paste0("x_cov", 0:2)],
  G = genotypes[, get_same_effect],
  Z = variants_info[get_same_effect, 1, drop = FALSE],
  method = "liu",
  model = "binary",
  weight.beta = c(1, 25),
  maf = variants_info[get_same_effect, "maf"]
)
#> [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           3.279            0.645            1.841
#>   p.value.pi.hat conf.low.pi.hat conf.high.pi.hat
#> 1          0.066           1.064           14.571
#> 
#> - Statistics:
#> 
#>   + Overall effect: 
#>     * P-value = 0.154
#>   + PI (mean effect):  
#>     * Score = 3.969
#>     * P-value = 0.0463
#>   + TAU (heterogeneous effect):  
#>     * Score = 4.691
#>     * P-value = 0.766

MiSTr: Mixed effects Score Test

Jianping Sun, Yingye Zheng and Li Hsu

05 December 2022

Installation

MiSTr in Action

Continuous Outcome

With Heterogeneity (τ)

With Heterogeneity (τ) “Removed”

With “Average” Effect (π)

With “Average” Effect (π) and Heterogeneity (τ)

With “Average” Effect (π) and With Heterogeneity (τ) “Removed”

Continuous Outcome with Weights

With Heterogeneity (τ)

With Heterogeneity (τ) “Removed”

With “Average” Effect (π)

With “Average” Effect (π) and Heterogeneity (τ)

With “Average” Effect (π) and With Heterogeneity (τ) “Removed”

Binary Outcome

With Heterogeneity (τ)

With Heterogeneity (τ) “Removed”

Binary Outcome with Weights

With Heterogeneity (τ)

With Heterogeneity (τ) “Removed”