CTC2019_tutorial.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Mapping QTL in BXD mice using R/qtl2\n",
    "\n",
    "[Karl Broman](https://kbroman.org)\n",
    "[<img style=\"display:inline-block;\" src=\"https://orcid.org/sites/default/files/images/orcid_16x16(1).gif\">](https://orcid.org/0000-0002-4914-6671),\n",
    "[Department of Biostatistics & Medical Informatics](https://www.biostat.wisc.edu), \n",
    "[University of Wisconsin&ndash;Madison](https://www.wisc.edu)\n",
    "\n",
    "Our aim in this tutorial is to demonstrate how to map quantitative trait loci (QTL) in the BXD mouse recombinant inbred lines using the [R/qtl2](https://kbroman.org/qtl2) software. We will first show how to download BXD phenotypes from [GeneNetwork2](http://gn2.genenetwork.org) using its API, via the R package [R/GNapi](https://github.com/rqtl/GNapi). At the end, we will use the [R/qtl2browse](https://github.com/rqtl/qtl2browse) package to display genome scan results using the [Genetics Genome Browser](https://github.com/chfi/purescript-genome-browser)."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Acquiring phenotypes with the GeneNetwork API\n",
    "\n",
    "We will first use the [GeneNetwork2](http://gn2.genenetwork.org) API to acquire BXD phenotypes to use for mapping. We will use the R package [R/GNapi](https://github.com/rqtl/GNapi). \n",
    "\n",
    "We first need to install the package, which is not available on [CRAN](https://cran.r-project.org), but is available via a private repository.\n",
    "\n",
    "```r\n",
    "install.packages(\"GNapi\", repos=\"http://rqtl.org/qtl2cran\")\n",
    "```\n",
    "\n",
    "We then load the package using `library()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(GNapi)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The [R/GNapi](https://github.com/kbroman/GNapi) has a variety of functions. For an overview, see [its vignette](http://kbroman.org/GNapi/GNapi.html). Here we will just do one thing: use the function `get_pheno()` to grab BXD phenotype data. You provide a data set and a phenotype. Phenotype 10038 concerns \"habituation\", measured as a difference in locomotor activity between day 1 and day 3 in a 5 minute test trial. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<caption>A data.frame: 6 × 5</caption>\n",
       "<thead>\n",
       "\t<tr><th></th><th scope=col>data_id</th><th scope=col>sample_name</th><th scope=col>sample_name_2</th><th scope=col>se</th><th scope=col>value</th></tr>\n",
       "\t<tr><th></th><th scope=col>&lt;int&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;chr&gt;</th><th scope=col>&lt;dbl&gt;</th><th scope=col>&lt;dbl&gt;</th></tr>\n",
       "</thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>BXD1</th><td>8967080</td><td>BXD1 </td><td>BXD1 </td><td>169.1</td><td> 398.0</td></tr>\n",
       "\t<tr><th scope=row>BXD11</th><td>8967080</td><td>BXD11</td><td>BXD11</td><td> 77.9</td><td> 355.0</td></tr>\n",
       "\t<tr><th scope=row>BXD12</th><td>8967080</td><td>BXD12</td><td>BXD12</td><td> 80.3</td><td> 388.0</td></tr>\n",
       "\t<tr><th scope=row>BXD14</th><td>8967080</td><td>BXD14</td><td>BXD14</td><td> 68.3</td><td> -74.7</td></tr>\n",
       "\t<tr><th scope=row>BXD16</th><td>8967080</td><td>BXD16</td><td>BXD16</td><td> 98.7</td><td> 293.0</td></tr>\n",
       "\t<tr><th scope=row>BXD18</th><td>8967080</td><td>BXD18</td><td>BXD18</td><td>110.0</td><td>-362.0</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "A data.frame: 6 × 5\n",
       "\\begin{tabular}{r|lllll}\n",
       "  & data\\_id & sample\\_name & sample\\_name\\_2 & se & value\\\\\n",
       "  & <int> & <chr> & <chr> & <dbl> & <dbl>\\\\\n",
       "\\hline\n",
       "\tBXD1 & 8967080 & BXD1  & BXD1  & 169.1 &  398.0\\\\\n",
       "\tBXD11 & 8967080 & BXD11 & BXD11 &  77.9 &  355.0\\\\\n",
       "\tBXD12 & 8967080 & BXD12 & BXD12 &  80.3 &  388.0\\\\\n",
       "\tBXD14 & 8967080 & BXD14 & BXD14 &  68.3 &  -74.7\\\\\n",
       "\tBXD16 & 8967080 & BXD16 & BXD16 &  98.7 &  293.0\\\\\n",
       "\tBXD18 & 8967080 & BXD18 & BXD18 & 110.0 & -362.0\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "A data.frame: 6 × 5\n",
       "\n",
       "| <!--/--> | data_id &lt;int&gt; | sample_name &lt;chr&gt; | sample_name_2 &lt;chr&gt; | se &lt;dbl&gt; | value &lt;dbl&gt; |\n",
       "|---|---|---|---|---|---|\n",
       "| BXD1 | 8967080 | BXD1  | BXD1  | 169.1 |  398.0 |\n",
       "| BXD11 | 8967080 | BXD11 | BXD11 |  77.9 |  355.0 |\n",
       "| BXD12 | 8967080 | BXD12 | BXD12 |  80.3 |  388.0 |\n",
       "| BXD14 | 8967080 | BXD14 | BXD14 |  68.3 |  -74.7 |\n",
       "| BXD16 | 8967080 | BXD16 | BXD16 |  98.7 |  293.0 |\n",
       "| BXD18 | 8967080 | BXD18 | BXD18 | 110.0 | -362.0 |\n",
       "\n"
      ],
      "text/plain": [
       "      data_id sample_name sample_name_2 se    value \n",
       "BXD1  8967080 BXD1        BXD1          169.1  398.0\n",
       "BXD11 8967080 BXD11       BXD11          77.9  355.0\n",
       "BXD12 8967080 BXD12       BXD12          80.3  388.0\n",
       "BXD14 8967080 BXD14       BXD14          68.3  -74.7\n",
       "BXD16 8967080 BXD16       BXD16          98.7  293.0\n",
       "BXD18 8967080 BXD18       BXD18         110.0 -362.0"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "phe <- get_pheno(\"BXD\", \"10038\")\n",
    "head(phe)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will use just the column \"value\", but we need to include the strain names so that R/qtl2 can line up these phenotypes with the genotypes."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<dl class=dl-horizontal>\n",
       "\t<dt>BXD1</dt>\n",
       "\t\t<dd>398</dd>\n",
       "\t<dt>BXD11</dt>\n",
       "\t\t<dd>355</dd>\n",
       "\t<dt>BXD12</dt>\n",
       "\t\t<dd>388</dd>\n",
       "\t<dt>BXD14</dt>\n",
       "\t\t<dd>-74.699997</dd>\n",
       "\t<dt>BXD16</dt>\n",
       "\t\t<dd>293</dd>\n",
       "\t<dt>BXD18</dt>\n",
       "\t\t<dd>-362</dd>\n",
       "</dl>\n"
      ],
      "text/latex": [
       "\\begin{description*}\n",
       "\\item[BXD1] 398\n",
       "\\item[BXD11] 355\n",
       "\\item[BXD12] 388\n",
       "\\item[BXD14] -74.699997\n",
       "\\item[BXD16] 293\n",
       "\\item[BXD18] -362\n",
       "\\end{description*}\n"
      ],
      "text/markdown": [
       "BXD1\n",
       ":   398BXD11\n",
       ":   355BXD12\n",
       ":   388BXD14\n",
       ":   -74.699997BXD16\n",
       ":   293BXD18\n",
       ":   -362\n",
       "\n"
      ],
      "text/plain": [
       "  BXD1  BXD11  BXD12  BXD14  BXD16  BXD18 \n",
       " 398.0  355.0  388.0  -74.7  293.0 -362.0 "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "pheno <- setNames(phe$value, phe$sample_name)\n",
    "head(pheno)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Acquiring genotype data with R/qtl2\n",
    "\n",
    "We now want to get genotype data for the BXD panel. We first need to install the [R/qtl2](https://kbroman.org/qtl2) package. As with R/GNapi, it is not available on CRAN, but rather is distributed via a private repository.\n",
    "\n",
    "```r\n",
    "install.packages(\"qtl2\", repos=\"http://rqtl.org/qtl2cran\")\n",
    "```\n",
    "\n",
    "We then load the package with `library()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(qtl2)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "R/qtl2 uses a special file format for QTL data ([described here](https://kbroman.org/qtl2/assets/vignettes/input_files.html)). There are a variety of sample datasets [on Github](https://github.com/rqtl/qtl2data), including genotypes for the [mouse BXD lines](https://github.com/rqtl/qtl2data/tree/master/BXD), taken from [GeneNetwork2](http://gn2.genenetwork.org). We'll load those data directly into R using the function `read_cross2()`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in recode_geno(sheet, genotypes):\n",
      "“117497 genotypes treated as missing: \"H\"”"
     ]
    }
   ],
   "source": [
    "bxd_file <- \"https://raw.githubusercontent.com/rqtl/qtl2data/master/BXD/bxd.zip\"\n",
    "bxd <- read_cross2(bxd_file)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We get a warning message about heterozygous genotypes being omitted. A number of the newer BXD lines have considerable heterozygosity. But these lines weren't among those phenotyped in the data we downloaded above, and so we don't need to worry about it here.\n",
    "\n",
    "The data are read into the object `bxd`, which has class `\"cross2\"`. It contains the genotypes and well as genetic and physical marker maps. There are also phenotype data (which we will ignore).\n",
    "\n",
    "We can get a quick summary of the dataset with `summary()`. For reasons that I don't understand, it gets printed as a big mess within this Jupyter notebook, and so here we need to surround it with `print()` to get the intended output."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Object of class cross2 (crosstype \"risib\")\n",
      "\n",
      "Total individuals             198\n",
      "No. genotyped individuals     198\n",
      "No. phenotyped individuals    198\n",
      "No. with both geno & pheno    198\n",
      "\n",
      "No. phenotypes               5806\n",
      "No. covariates                  0\n",
      "No. phenotype covariates        1\n",
      "\n",
      "No. chromosomes                20\n",
      "Total markers                7320\n",
      "\n",
      "No. markers by chr:\n",
      "  1   2   3   4   5   6   7   8   9  10  11  12  13  14  15  16  17  18  19   X \n",
      "636 583 431 460 470 449 437 319 447 317 375 308 244 281 247 272 291 250 310 193 \n"
     ]
    }
   ],
   "source": [
    "print( summary(bxd) )"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## QTL mapping in R/qtl2\n",
    "\n",
    "The first step in QTL analysis is to calculate genotype probabilities at putative QTL positions across the genome, conditional on the observed marker data. This allows us that consider positions between the genotyped markers and to allow for the presence of genotyping errors.\n",
    "\n",
    "First, we need to define the positions that we will consider. We will take the observed marker positions and insert a set of \"pseudomarkers\" (marker-like positions that are not actually markers). We do this with the function `insert_pseudomarkers()`. We pull the genetic map (`gmap`) out of the `bxd` data as our basic map; `step=0.2` and `stepwidth=\"max\"` mean to insert pseudomarkers so that no two adjacent markers or pseudomarkers are more than 0.2 cM apart. That is, in any marker interval that is greater than 0.2 cM, we will insert one or more evenly spaced pseudomarkers, so that the intervals between markers and pseudomarkers are no more than 0.2 cM."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {},
   "outputs": [],
   "source": [
    "gmap <- insert_pseudomarkers(bxd$gmap, step=0.2, stepwidth=\"max\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We will be interested in results with respect to the physical map (in Mbp), and so we need to create a corresponding map that includes the pseudomarker positions. We do this with the function `interp_map()`, which uses linear interpolation to get estimated positions for the inserted pseudomarkers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [],
   "source": [
    "pmap <- interp_map(gmap, bxd$gmap, bxd$pmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now proceed with calculating genotype probabilities for all BXD strains at all markers and pseudomarkers, conditional on the observed marker genotypes and assuming a 0.2% genotyping error rate. We use the [Carter-Falconer](https://doi.org/10.1007/BF02996226) map function to convert between cM and recombination fractions; it assumes a high degree of crossover interference, appropriate for the mouse."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [],
   "source": [
    "pr <- calc_genoprob(bxd, gmap, error_prob=0.002, map_function=\"c-f\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "In the QTL analysis, we will fit a linear mixed model to account for polygenic background effects. We will use the \"leave one chromosome out\" (LOCO) method for this. When we scan a chromosome for a QTL, we include a polygenic term with a kinship matrix derived from all other chromosomes. \n",
    "\n",
    "We first need to calculate this set of kinship matrices, which we do with the function `calc_kinship()`. The second argument, `\"loco\"`, indicates that we want to calculate a vector of kinship matrices, each derived from the genotype probabilities but leaving one chromosome out."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [],
   "source": [
    "k <- calc_kinship(pr, \"loco\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now, finally, we're ready to perform the genome scan, which we do with the function `scan1()`. It takes the genotype probabilities and a set of phenotypes (here, just one phenotype). If kinship matrices are provided (here, as `k`), the scan is performed using a linear mixed model. To make the calculations faster, the residual polygenic variance is first estimated without including any QTL effect and is then taking to be fixed and known during the scan."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {},
   "outputs": [],
   "source": [
    "out <- scan1(pr, pheno, k)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The output of `scan1()` is a matrix of LOD scores; the rows are marker/pseudomarker positions and the columns are phenotypes. We can plot the results using `plot.scan1()`, and we can just use `plot()` because it uses the class of its input to determine what plot to make.\n",
    "\n",
    "Here I'm using the package [repr](https://cran.r-project.org/package=repr) to control the height and width of the plot that's created. I installed it with `install.packages(\"repr\")`. You can ignore that part, if you want."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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VWsY0TDCBE/AyUIoAEAACDcCKEyI6pj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\n3K8+OyL8GRPYQYRepqARPwOEkeLCRvGfXgfQarUqPqGzz7S/DxL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ozJ720ReumWW+Tdtm+Oqzo+1txutvmTBilGe35ggheTn1wuPCgogb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TvZuK9nmHABYa7Ojqqdyghk1fPsDARkVP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\nAxJoAAAAAAA//D+qYIBT97bcsAAAAABJRU5ErkJggg==",
      "text/plain": [
       "Plot with title “”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "library(repr)\n",
    "options(repr.plot.height=4, repr.plot.width=8)\n",
    "par(mar=c(5.1, 4.1, 0.6, 0.6))\n",
    "plot(out, pmap)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "There's a clear QTL on chromosome 8. We can make a plot of just that chromosome with the argument `chr=15`."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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5hkdBGF7IEgARqdJ89NhEe/8u7476f9+8KxXz8/6/QjR/XskdtYsWv1/E/f+2F9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\neLJ2cG3FhsUAAAAASUVORK5CYII=",
      "text/plain": [
       "Plot with title “”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mar=c(5.1, 4.1, 0.6, 0.6))\n",
    "plot(out, pmap, chr=15)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let's create a plot of the phenotype vs the genotype at the inferred QTL. We first need to identify the QTL location, which we can do using `max()`. We then use `maxmarg()` to get inferred genotypes at the inferred QTL."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {},
   "outputs": [],
   "source": [
    "mx <- max(out, pmap)\n",
    "g_imp <- maxmarg(pr, pmap, chr=mx$chr, pos=mx$pos, return_char=TRUE)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can use `plot_pxg()` to plot the phenotype as a function of QTL genotype. We use `swap_axes=TRUE` to have the phenotype on the x-axis and the genotype on the y-axis, rather than the other way around. Here we see that the BB and DD genotypes are completely separated, phenotypically. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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R4ebm1tXb169YoVK+ZZwQCQZwjQAID/UqhQoY8/NL1UqVKlSpXK1XoAQHY8RAgA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      "text/plain": [
       "Plot with title “”"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "par(mar=c(5.1, 4.1, 0.6, 0.6))\n",
    "plot_pxg(g_imp, pheno, swap_axes=TRUE, xlab=\"Habituation phenotype\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Browsing genome scan results with the Genetics Genome Browser\n",
    "\n",
    "The [Genetics Genome Browser](https://github.com/chfi/purescript-genome-browser) is a fast, lightweight, [purescript]-based genome browser developed for browsing GWAS or QTL analysis results. We'll use the R package [R/qtl2browse](https://github.com/rqtl/qtl2browse) to view our QTL mapping results in the GGB.\n",
    "\n",
    "We first need to install the R/qtl2browse package, again from a private [CRAN](https://cran.r-project.org)-like repository.\n",
    "\n",
    "```r\n",
    "install.packages(\"qtl2browse\", repos=\"http://rqtl.org/qtl2cran\")\n",
    "```\n",
    "\n",
    "We then load the package and use its one function, `browse()`, which takes the `scan1()` output and corresponding physical map (in Mbp). This will open the Genetics Genome Browser in a separate tab in your web browser."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {},
   "outputs": [],
   "source": [
    "library(qtl2browse)\n",
    "browse(out, pmap)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
   "language": "R",
   "name": "ir"
  },
  "language_info": {
   "codemirror_mode": "r",
   "file_extension": ".r",
   "mimetype": "text/x-r-source",
   "name": "R",
   "pygments_lexer": "r",
   "version": "3.6.0"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}