---
title: "Mapping QTLs in outbred mice using varbvs"
date: "`r Sys.Date()`"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{QTL mapping demo}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

In this vignette, we use **varbvs** to map QTLs for phenotypes
measured in CFW (Carworth Farms White) outbred mice. Phenotypes
include muscle weights---EDL and soleus muscle---and testis weight
measured at sacrifice. Running this script with `trait = "testis"`
reproduces the results and figures shown in the second example of a
forthcoming paper (Carbonetto *et al*, 2016).

```{r, echo = FALSE, message = FALSE}
knitr::opts_chunk$set(eval = FALSE,collapse = TRUE,comment = "#")
```

## Vignette parameters

Begin by loading packages into the R environment.

```{r, eval = TRUE, message = FALSE}
library(lattice)
library(varbvs)
```

These script parameters specify the candidate prior log-odds
settings, the prior variance of the coefficients, and which trait to
analyze. Set trait to "edl", "soleus" or "testis".

```{r, eval = TRUE}
trait   <- "testis"
logodds <- seq(-5,-3,0.25)
sa      <- 0.05
```

Set the random number generator seed.

```{r, eval = TRUE}
set.seed(1)
```

## Load the genotype and phenotype data

```{r}
load("cfw.RData")
if (trait == "edl" | trait == "soleus") {
  covariates <- c("batch16","tibia")
} else if (trait == "testis") {
  covariates <- "sacwt"
}
```

Only analyze samples for which the phenotype and all the covariates
are observed.

```{r}
rows <- which(apply(pheno[,c(trait,covariates)],1,
                    function (x) sum(is.na(x)) == 0))
pheno <- pheno[rows,]
geno  <- geno[rows,]
```

## Fit variational approximation to posterior

```{r}
runtime <- system.time(fit <-
  varbvs(geno,as.matrix(pheno[,covariates]),pheno[,trait],
         sa = sa,logodds = logodds,verbose = FALSE))
cat(sprintf("Model fitting took %0.2f minutes.\n",runtime["elapsed"]/60))
```

## Summarize the results of model fitting

```{r}
print(summary(fit))
```

Show three genome-wide scans: (1) one using the posterior inclusion
probabilities (PIPs) computed in the BVS analysis of all SNPs; (2)
one using the p-values computed using GEMMA; and (3) one using the
PIPs computed from the BVSR model in GEMMA.

```{r, fig.width = 7,fig.height = 5.5, fig.align = "center"}
trellis.par.set(axis.text     = list(cex = 0.7),
                par.ylab.text = list(cex = 0.7),
                par.main.text = list(cex = 0.7,font = 1))
w <- c(normalizelogweights(fit$logw))
j <- which(fit$alpha %*% w > 0.5)
r <- gwscan.gemma[[trait]]
r[is.na(r)] <- 0
chromosomes   <- levels(gwscan.bvsr$chr)
xticks        <- rep(0,length(chromosomes))
names(xticks) <- chromosomes
pos           <- 0
for (i in chromosomes) {
  n         <- sum(gwscan.bvsr$chr == i)
  xticks[i] <- pos + n/2
  pos       <- pos + n + 25
}
print(plot(fit,groups = map$chr,vars = j,gap = 1500,cex = 0.6,
           ylab = "probability",main = "a. multi-marker (varbvs)",
           scales = list(y = list(limits = c(-0.1,1.2),at = c(0,0.5,1))),
           vars.xyplot.args = list(cex = 0.6)),
      split = c(1,1,1,3),more = TRUE)
print(plot(fit,groups = map$chr,score = r,vars = j,cex = 0.6,gap = 1500,
           draw.threshold = 5.71,ylab = "-log10 p-value",
           main = "b. single-marker (GEMMA -lm 2)",
           scales = list(y = list(limits = c(-1,20),at = seq(0,20,5))),
           vars.xyplot.args = list(cex = 0.6)),
     split = c(1,2,1,3),more = TRUE)
print(xyplot(p1 ~ plot.x,gwscan.bvsr,pch = 20,col = "midnightblue",
             scales = list(x = list(at = xticks,labels = chromosomes),
                           y = list(limits = c(-0.1,1.2),at = c(0,0.5,1))),
             xlab = "",ylab = "probability",main = "c. multi-marker (BVSR)"),
      split = c(1,3,1,3),more = FALSE)
```
