---
title: "rNeighborGWAS"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{rNeighborGWAS}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

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

## Overview
<p>This R package provides a set of functions to test neighbor effects in marker-based regressions. In this vignette, we first estimate an effective range of neighbor effects, and then perform an association mapping of neighbor effects. See Sato et al. (2021) for model description.</p>

## Input files
<p>First, let us see the data structure of input files necessary for the neighbor GWAS. Here is an example using a phenotype simulated from "TTN" genotype in the "gaston" package (Perdry & Dandine-Roulland 2020). Genotype data are a matrix including individuals (rows) x markers (columns) with -1 or +1 digit for bialleles. A spatial map indicates a individual distribution along x- and y-axes in a 2D space.</p>
```{r input}
set.seed(1234)
library(rNeighborGWAS)

# convert "TTN" genotype data into a rNeighborGWAS format
data("TTN", package="gaston")
x <- gaston::as.bed.matrix(TTN.gen, TTN.fam, TTN.bim)
g <- gaston2neiGWAS(x)

# simulate "fake_nei" dataset using nei_simu()
geno <- g$geno
gmap <- g$gmap
x <- runif(nrow(geno),1,100)
y <- runif(nrow(geno),1,100)
smap <- cbind(x,y)
grouping <- c(rep(1,nrow(geno)/2), rep(2,nrow(geno)/2), 2)
pheno <- nei_simu(geno=geno, smap=smap, scale=43,
                  grouping=grouping, n_causal=50,
                  pveB=0.3, pve=0.6
                  )

fake_nei <- list()
fake_nei[[1]] <- geno
fake_nei[[2]] <- gmap
fake_nei[[3]] <- smap
fake_nei[[4]] <- data.frame(pheno,grouping)
names(fake_nei) <- c("geno","gmap","smap","pheno")

fake_nei$geno[1:5,1:10] # Note: 0 indicates heterozygotes
head(fake_nei$smap)
```

## Variation partitioning across a space
<p>To estimate an effective range of neighbor effects, we calculate a heritability-like metric, that is the proportion of phenotypic variation explained by neighbor effects (PVE_nei). The optimal scale of neighbor effects is analyzed by how PVE_nei approaches to a plateau.</p>
```{r PVE}
scale_seq <- quantile(dist(fake_nei$smap),c(0.2*rep(1:5)))

pve_out <- calc_PVEnei(geno=fake_nei$geno, pheno=fake_nei$pheno[,1],
                       smap=fake_nei$smap, scale_seq=scale_seq,
                       addcovar=as.matrix(fake_nei$pheno$grouping),
                       grouping=fake_nei$pheno$grouping
                       )
delta_PVE(pve_out)
```

## Association mapping
<p>Based on the estimated scale of neighbor effects, we then perform genome-wide association mapping of neighbor effects as follows.</p>
```{r GWAS}
scale <- 43.9
gwas_out <- neiGWAS(geno=fake_nei$geno, pheno=fake_nei$pheno[,1],
                    gmap=fake_nei$gmap, smap=fake_nei$smap,
                    scale=scale, addcovar=as.matrix(fake_nei$pheno$grouping),
                    grouping=fake_nei$pheno$grouping
                    )

gaston::manhattan(gwas_out)
gaston::qqplot.pvalues(gwas_out$p)
```

## Linear mixed models
<p>To separate linear mixed models from "neiGWAS()", we can rewrite the code for association mapping. The "nei_coval()" calculates neighbor genotypic identity. The "nei_lmm()" takes them as an input and performs association tests for self and neighbor effects.</p>
```{r LMM, eval=FALSE}
scale <- 43.9
g_nei <- nei_coval(geno=fake_nei$geno, smap=fake_nei$smap,
                   scale=scale, grouping=fake_nei$pheno$grouping
                   )

gwas_out <- nei_lmm(geno=fake_nei$geno, g_nei=g_nei,
                    pheno=fake_nei$pheno[,1],
                    addcovar=as.matrix(fake_nei$pheno$grouping)
                    )
```

## Binary phenotype
<p>The line of analyses can work with logistic mixed models that allow a binary phenotype. Convert the phenotype values into 0 or 1, and choose "binary" in the "response" argument.</p>
```{r bin, eval=FALSE}
fake_nei$pheno[,1][fake_nei$pheno[,1]>mean(fake_nei$pheno[,1])] <- 1
fake_nei$pheno[,1][fake_nei$pheno[,1]!=1] <- 0

pve_out <- calc_PVEnei(geno=fake_nei$geno, pheno=fake_nei$pheno[,1],
                       smap=fake_nei$smap, scale_seq=scale_seq,
                       addcovar=as.matrix(fake_nei$pheno$grouping),
                       grouping=fake_nei$pheno$grouping,
                       response="binary"
                       )

gwas_out <- neiGWAS(geno=fake_nei$geno, pheno=fake_nei$pheno[,1],
                    gmap=fake_nei$gmap, smap=fake_nei$smap,
                    scale=scale, addcovar=as.matrix(fake_nei$pheno$grouping),
                    grouping=fake_nei$pheno$grouping,
                    response="binary"
                    )
gaston::manhattan(gwas_out)
gaston::qqplot.pvalues(gwas_out$p)

gwas_out <- nei_lmm(geno=fake_nei$geno, g_nei=g_nei,
                    pheno=fake_nei$pheno[,1],
                    addcovar=as.matrix(fake_nei$pheno$grouping),
                    response="binary"
                    )
```

## Asymmetric neighbor effects
<p>If neighbor effects are asymmetric from one to another allele (see Sato et al. 2021 for the model), we can test it using the option "asym". If asym==TRUE, an additional coefficient and $p$-value of such asymmetric neighbor effects are added to the original results.</p>
```{r asymmetry, eval=FALSE}
scale <- 43.9
g_nei <- nei_coval(geno=fake_nei$geno, smap=fake_nei$smap,
                   scale=scale, grouping=fake_nei$pheno$grouping
                   )

gwas_out <- nei_lmm(geno=fake_nei$geno, g_nei=g_nei,
                    pheno=fake_nei$pheno[,1],
                    addcovar=as.matrix(fake_nei$pheno$grouping),
                    asym=TRUE)
```


## References
- Perdry H, Dandine-Roulland C. (2020) gaston: Genetic Data Handling (QC, GRM, LD, PCA) & Linear Mixed Models. R package version 1.5.6. https://CRAN.R-project.org/package=gaston  
- Sato Y, Yamamoto E, Shimizu KK, Nagano AJ (2021) Neighbor GWAS: incorporating neighbor genotypic identity into genome-wide association studies of field herbivory. Heredity 126(4):597-614. https://doi.org/10.1038/s41437-020-00401-w

