`qtl2pleio`

is a software package for use with the R statistical computing environment. `qtl2pleio`

is freely available for download and use. I share it under the MIT license. The user will also want to download and install the `qtl2`

R package.

Click here to explore `qtl2pleio`

within a live Rstudio session in “the cloud”.

We eagerly welcome contributions to `qtl2pleio`

. All pull requests will be considered. Features requests and bug reports may be filed as Github issues. All contributors must abide by the code of conduct.

For technical support, please open a Github issue. If you’re just getting started with `qtl2pleio`

, please examine the vignettes. You can also email frederick.boehm@gmail.com for assistance.

The goal of `qtl2pleio`

is, for a pair of traits that show evidence for a QTL in a common region, to distinguish between pleiotropy (the null hypothesis, that they are affected by a common QTL) and the alternative that they are affected by separate QTL. It extends the likelihood ratio test of Jiang and Zeng (1995) for multiparental populations, such as Diversity Outbred mice, including the use of multivariate polygenic random effects to account for population structure. `qtl2pleio`

data structures are those used in the `rqtl/qtl2`

package.

To install qtl2pleio, use `install_github()`

from the devtools package.

You may also wish to install the R/qtl2. We will use it below.

Below, we walk through an example analysis with Diversity Outbred mouse data. We need a number of preliminary steps before we can perform our test of pleiotropy vs. separate QTL. Many procedures rely on the R package `qtl2`

. We first load the `qtl2`

and `qtl2pleio`

packages.

`qtl2data`

repository on githubWe’ll consider the `DOex`

data in the `qtl2data`

repository. We’ll download the DOex.zip file before calculating founder allele dosages.

```
file <- paste0("https://raw.githubusercontent.com/rqtl/",
"qtl2data/master/DOex/DOex.zip")
DOex <- read_cross2(file)
```

Let’s calculate the founder allele dosages from the 36-state genotype probabilities.

We now have an allele probabilities object stored in `pr`

.

We see that `pr`

is a list of 3 three-dimensional arrays - one array for each of 3 chromosomes.

For our statistical model, we need a kinship matrix. We get one with the `calc_kinship`

function in the `rqtl/qtl2`

package.

We use the multivariate linear mixed effects model:

[vec(Y) = X vec(B) + vec(G) + vec(E)]

where (Y) contains phenotypes, X contains founder allele probabilities and covariates, and B contains founder allele effects. G is the polygenic random effects, while E is the random errors. We provide more details in the vignette.

`qtl2pleio::sim1`

The function to simulate phenotypes in `qtl2pleio`

is `sim1`

.

```
# assemble B matrix of allele effects
B <- matrix(data = c(-1, -1, -1, -1, 1, 1, 1, 1, -1, -1, -1, -1, 1, 1, 1, 1), nrow = 8, ncol = 2, byrow = FALSE)
# set.seed to ensure reproducibility
set.seed(2018-01-30)
# call to sim1
Ypre <- sim1(X = X, B = B, Vg = diag(2), Ve = diag(2), kinship = kinship[[2]])
Y <- matrix(Ypre, nrow = 261, ncol = 2, byrow = FALSE)
rownames(Y) <- rownames(pp)
colnames(Y) <- c("tr1", "tr2")
```

Let’s perform univariate QTL mapping for each of the two traits in the Y matrix.

Here is a plot of the results.

And here are the observed QTL peaks with LOD > 8.

```
find_peaks(s1, map = DOex$pmap, threshold=8)
#> lodindex lodcolumn chr pos lod
#> 1 1 tr1 3 82.77806 13.39312
#> 2 1 tr1 X 97.07206 9.18941
#> 3 2 tr2 3 82.77806 23.57570
```

We now have the inputs that we need to do a pleiotropy vs. separate QTL test. We have the founder allele dosages for one chromosome, *i.e.*, Chr 3, in the R object `pp`

, the matrix of two trait measurements in `Y`

, and a LOCO-derived kinship matrix, `kinship[[2]]`

.

```
out <- suppressMessages(scan_pvl(probs = pp,
pheno = Y,
kinship = kinship[[2]], # 2nd entry in kinship list is Chr 3
start_snp = 38,
n_snp = 25, n_cores = 1
))
```

To visualize results from our two-dimensional scan, we calculate profile LOD for each trait. The code below makes use of the R package `ggplot2`

to plot profile LODs over the scan region.

```
library(dplyr)
out %>%
calc_profile_lods() %>%
add_pmap(pmap = DOex$pmap$`3`) %>%
ggplot() + geom_line(aes(x = marker_position, y = profile_lod, colour = trait))
```

We use the function `calc_lrt_tib`

to calculate the likelihood ratio test statistic value for the specified traits and specified genomic region.

Before we call `boot_pvl`

, we need to identify the index (on the chromosome under study) of the marker that maximizes the likelihood under the pleiotropy constraint. To do this, we use the `qtl2pleio`

function `find_pleio_peak_tib`

.

```
set.seed(2018-11-25) # set for reproducibility purposes.
b_out <- suppressMessages(boot_pvl(probs = pp,
pheno = Y,
pleio_peak_index = pleio_index,
kinship = kinship[[2]], # 2nd element in kinship list is Chr 3
nboot_per_job = 10,
start_snp = 38,
n_snp = 25
))
```

```
citation("qtl2pleio")
#>
#> To cite qtl2pleio in publications use:
#>
#> Boehm FJ, Chesler EJ, Yandell BS, Broman KW (2019) Testing
#> pleiotropy vs. separate QTL in multiparental populations G3
#> https://www.g3journal.org/content/9/7/2317
#>
#> A BibTeX entry for LaTeX users is
#>
#> @Article{Boehm2019testing,
#> title = {Testing pleiotropy vs. separate QTL in multiparental populations},
#> author = {Frederick J. Boehm and Elissa J. Chesler and Brian S. Yandell and Karl W. Broman},
#> journal = {G3},
#> year = {2019},
#> volume = {9},
#> issue = {7},
#> url = {https://www.g3journal.org/content/9/7/2317},
#> eprint = {https://www.g3journal.org/content/ggg/9/7/2317.full.pdf},
#> }
```