---
title: 'MiSTr: *Mi*xed effects *S*core *T*est'
author: "Jianping Sun, Yingye Zheng and Li Hsu"
email: "mickael.canouil@cnrs.fr"
date: '`r format(Sys.time(), "%d %B %Y")`'
output:
  rmarkdown::html_vignette:
    number_sections: true
    toc: true
    toc_depth: 2
    fig_width: 6.3
    fig_height: 4.7
vignette: >
  %\VignetteIndexEntry{MiSTr}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r setup, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

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

```{r}
#| eval: false
# 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

```{r}
library(MiSTr)
data(mist_data)
attach(mist_data)
```

### Continuous Outcome

#### With Heterogeneity (τ)

```{r}
mist(
  y = phenotypes[, "y_tau"],
  X = phenotypes[, paste0("x_cov", 0:2)],
  G = genotypes,
  Z = variants_info[, 1, drop = FALSE],
  method = "liu",
  model = "continuous"
)
```

#### With Heterogeneity (τ) "Removed"

```{r}
variants_info[, "effect"] # simulated effect
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"
)
```

#### With "Average" Effect (π)

```{r}
mist(
  y = phenotypes[, "y_pi"],
  X = phenotypes[, paste0("x_cov", 0:2)],
  G = genotypes,
  Z = variants_info[, 1, drop = FALSE],
  method = "liu",
  model = "continuous"
)
```

#### With "Average" Effect (π) and Heterogeneity (τ)

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

#### With "Average" Effect (π) and With Heterogeneity (τ) "Removed"

```{r}
variants_info[, "effect"] # simulated effect
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"
)
```

### Continuous Outcome with Weights

#### With Heterogeneity (τ)

```{r}
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"]
)
```

#### With Heterogeneity (τ) "Removed"

```{r}
variants_info[, "effect"] # simulated effect
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"]
)
```

#### With "Average" Effect (π)

```{r}
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"]
)
```

#### With "Average" Effect (π) and Heterogeneity (τ)

```{r}
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"]
)
```

#### With "Average" Effect (π) and With Heterogeneity (τ) "Removed"

```{r}
variants_info[, "effect"] # simulated effect
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"]
)
```


### Binary Outcome

#### With Heterogeneity (τ)

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

#### With Heterogeneity (τ) "Removed"

```{r}
variants_info[, "effect"] # simulated effect
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"
)
```

### Binary Outcome with Weights

#### With Heterogeneity (τ)

```{r}
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"]
)
```

#### With Heterogeneity (τ) "Removed"

```{r}
variants_info[, "effect"] # simulated effect
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"]
)
```