{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Systems Immunogenetics Project\n",
    "\n",
    "## QTL Mapping Workflow\n",
    "\n",
    "### McWeeney Lab, Oregon Health & Science University\n",
    "\n",
    "#### Author: Michael Mooney (mooneymi@ohsu.edu)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Introduction\n",
    "\n",
    "This document will walk through the steps for mapping QTLs in the RIX lines.\n",
    "\n",
    "Required Files:\n",
    "\n",
    "1. This notebook** (`SIG_QTL_Mapping_Workflow.ipynb`): [[Download here]](https://raw.githubusercontent.com/biodev/SIG/master/SIG_QTL_Mapping_Workflow.ipynb)\n",
    "2. The R script `rix_qtl_mapping_functions.r`: [[Download here]](https://raw.githubusercontent.com/biodev/SIG/master/scripts/rix_qtl_mapping_functions.r)\n",
    "3. File for mapping between CC IDs and RIX IDs: \n",
    "    - File with Oas1b status for WNV mapping (`Oas1b_status_recoded.txt`)\n",
    "    - File with Mx1 status for Influenza mapping (`Mx1_status_recoded.txt`)\n",
    "\n",
    "** Note: this notebook can also be downloaded as an R script (only the code blocks seen below will be included): [[Download R script here]](https://raw.githubusercontent.com/biodev/SIG/master/SIG_QTL_Mapping_Workflow.r)\n",
    "\n",
    "Required R Libraries:\n",
    "\n",
    "1. `gdata`: [https://cran.r-project.org/web/packages/gdata/index.html](https://cran.r-project.org/web/packages/gdata/index.html)\n",
    "2. `DOQTL`: [https://www.bioconductor.org/packages/release/bioc/html/DOQTL.html](https://www.bioconductor.org/packages/release/bioc/html/DOQTL.html)\n",
    "3. `GenomicRanges`: [https://bioconductor.org/packages/release/bioc/html/GenomicRanges.html](https://bioconductor.org/packages/release/bioc/html/GenomicRanges.html)\n",
    "4. `VariantAnnotation`: [https://bioconductor.org/packages/release/bioc/html/VariantAnnotation.html](https://bioconductor.org/packages/release/bioc/html/VariantAnnotation.html)\n",
    "5. `foreach`: [https://cran.r-project.org/web/packages/foreach/index.html](https://cran.r-project.org/web/packages/foreach/index.html)\n",
    "6. `doParallel`: [https://cran.r-project.org/web/packages/doParallel/index.html](https://cran.r-project.org/web/packages/doParallel/index.html)\n",
    "7. `RColorBrewer`: [https://cran.r-project.org/web/packages/RColorBrewer/index.html](https://cran.r-project.org/web/packages/RColorBrewer/index.html)\n",
    "\n",
    "**All code is available on GitHub: [https://github.com/biodev/SIG](https://github.com/biodev/SIG)**"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 1. Load Necessary R Functions and Libraries"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "gdata: read.xls support for 'XLS' (Excel 97-2004) files ENABLED.\n",
      "\n",
      "gdata: read.xls support for 'XLSX' (Excel 2007+) files ENABLED.\n",
      "\n",
      "Attaching package: ‘gdata’\n",
      "\n",
      "The following object is masked from ‘package:stats’:\n",
      "\n",
      "    nobs\n",
      "\n",
      "The following object is masked from ‘package:utils’:\n",
      "\n",
      "    object.size\n",
      "\n",
      "The following object is masked from ‘package:base’:\n",
      "\n",
      "    startsWith\n",
      "\n",
      "Loading required package: BSgenome.Mmusculus.UCSC.mm10\n",
      "Loading required package: BSgenome\n",
      "Loading required package: BiocGenerics\n",
      "Loading required package: parallel\n",
      "\n",
      "Attaching package: ‘BiocGenerics’\n",
      "\n",
      "The following objects are masked from ‘package:parallel’:\n",
      "\n",
      "    clusterApply, clusterApplyLB, clusterCall, clusterEvalQ,\n",
      "    clusterExport, clusterMap, parApply, parCapply, parLapply,\n",
      "    parLapplyLB, parRapply, parSapply, parSapplyLB\n",
      "\n",
      "The following object is masked from ‘package:gdata’:\n",
      "\n",
      "    combine\n",
      "\n",
      "The following objects are masked from ‘package:stats’:\n",
      "\n",
      "    IQR, mad, xtabs\n",
      "\n",
      "The following objects are masked from ‘package:base’:\n",
      "\n",
      "    anyDuplicated, append, as.data.frame, as.vector, cbind, colnames,\n",
      "    do.call, duplicated, eval, evalq, Filter, Find, get, grep, grepl,\n",
      "    intersect, is.unsorted, lapply, lengths, Map, mapply, match, mget,\n",
      "    order, paste, pmax, pmax.int, pmin, pmin.int, Position, rank,\n",
      "    rbind, Reduce, rownames, sapply, setdiff, sort, table, tapply,\n",
      "    union, unique, unlist, unsplit\n",
      "\n",
      "Loading required package: S4Vectors\n",
      "Loading required package: stats4\n",
      "Loading required package: IRanges\n",
      "\n",
      "Attaching package: ‘IRanges’\n",
      "\n",
      "The following object is masked from ‘package:gdata’:\n",
      "\n",
      "    trim\n",
      "\n",
      "Loading required package: GenomeInfoDb\n",
      "Loading required package: GenomicRanges\n",
      "Loading required package: Biostrings\n",
      "Loading required package: XVector\n",
      "Loading required package: rtracklayer\n",
      "Loading required package: VariantAnnotation\n",
      "Loading required package: SummarizedExperiment\n",
      "Loading required package: Biobase\n",
      "Welcome to Bioconductor\n",
      "\n",
      "    Vignettes contain introductory material; view with\n",
      "    'browseVignettes()'. To cite Bioconductor, see\n",
      "    'citation(\"Biobase\")', and for packages 'citation(\"pkgname\")'.\n",
      "\n",
      "Loading required package: Rsamtools\n",
      "\n",
      "Attaching package: ‘VariantAnnotation’\n",
      "\n",
      "The following object is masked from ‘package:base’:\n",
      "\n",
      "    tabulate\n",
      "\n",
      "Loading required package: iterators\n"
     ]
    }
   ],
   "source": [
    "## Load R functions and libraries\n",
    "source('./scripts/rix_qtl_mapping_functions.r')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 2. Create Phenotype Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>15244</li>\n",
       "\t<li>21</li>\n",
       "</ol>\n"
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      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 15244\n",
       "\\item 21\n",
       "\\end{enumerate*}\n"
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      "text/markdown": [
       "1. 15244\n",
       "2. 21\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] 15244    21"
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     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Read phenotype data\n",
    "cleaned_data_dir = '/Users/mooneymi/Documents/SIG/WNV/Cleaned_Data_Releases/14-Jun-2016/'\n",
    "pheno_dir = '/Users/mooneymi/Documents/SIG/Mapping/phenotypes'\n",
    "mapping_dir = '/Users/mooneymi/Documents/SIG/Mapping'\n",
    "\n",
    "## For this example we're working with qPCR data\n",
    "pheno = read.xls(file.path(cleaned_data_dir, 'Gale_qPCR_byMouse_14-Jun-2016_final.xlsx'), header=T, as.is=T)\n",
    "dim(pheno)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>158</li>\n",
       "\t<li>21</li>\n",
       "</ol>\n"
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      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 158\n",
       "\\item 21\n",
       "\\end{enumerate*}\n"
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      "text/markdown": [
       "1. 158\n",
       "2. 21\n",
       "\n",
       "\n"
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      "text/plain": [
       "[1] 158  21"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## The phenotype will be D12 WNV (viral transcript) in the brain\n",
    "pheno = pheno[with(pheno, Virus=='WNV' & Timepoint==12 & Experiment=='WNV' & Tissue=='Brain'), ]\n",
    "dim(pheno)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Save the phenotype dataframe (optional)\n",
    "#save(pheno, file=file.path(pheno_dir, 'wnv_dCt_brain_d12_wnv_17-Jun-2016.rda'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
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7fuSlFHu+vN+G0xShQtZGTLS7GSLyhA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\n4x4UoAAFKEABClCAAhSggJcK/B8ooVHPv3JdSwAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/svg+xml": {
       "isolated": true
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## View the phenotype distribution accross the mapping population\n",
    "library(lattice)\n",
    "dotplot(reorder(pheno[,'UW_Line'], pheno[,'dCt'], mean, na.rm=T) ~ \n",
    "        pheno[,'dCt'] | pheno[,'Virus'], \n",
    "        panel = function(x,y,...) {panel.dotplot(x,y,...); panel.abline(v=0, col.line=\"red\")}, \n",
    "        pch=19, ylab='UW Line', xlab=\"D12 WNV dCt\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 3. Update Phenotypes and Create Covariate Dataframe"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## Sort pheno dataframe and set rownames\n",
    "pheno = pheno[with(pheno, order(Mating, RIX_ID)),]\n",
    "rownames(pheno) = pheno$ID\n",
    "\n",
    "## Add sex column\n",
    "pheno$sex = 'M'"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Create covariate dataframe (must include sex)\n",
    "covar = data.frame(sex = as.numeric(pheno$sex == 'M'))\n",
    "rownames(covar) = pheno$ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Get IDs and Matings for each sample\n",
    "samples = pheno$ID\n",
    "matings = unlist(lapply(strsplit(samples, '_'), function(x) {x[1]}))\n",
    "matings = unique(matings)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>Mating</th><th scope=col>CC_Mating</th><th scope=col>UW_Line</th><th scope=col>A</th><th scope=col>B</th><th scope=col>C</th><th scope=col>D</th><th scope=col>E</th><th scope=col>F</th><th scope=col>G</th><th scope=col>H</th><th scope=col>CW_prob</th><th scope=col>P_prob</th><th scope=col>Null_prob</th><th scope=col>Oas1b_status</th><th scope=col>Oas1b</th><th scope=col>Oas1b_High</th><th scope=col>Oas1b_Mod</th><th scope=col>Oas1b_Low</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>13067x16912</td><td>CC003xCC051</td><td>17         </td><td>1e-20      </td><td>1e-20      </td><td>1e-20      </td><td>1e-20      </td><td>0.5        </td><td>1e-20      </td><td>1e-20      </td><td>0.5        </td><td>0.5        </td><td>1e-20      </td><td>0.5        </td><td>High-Low   </td><td>High       </td><td>1          </td><td>0          </td><td>0          </td></tr>\n",
       "\t<tr><th scope=row>2</th><td>13067x5306  </td><td>CC003xCC062 </td><td>63          </td><td>1e-20       </td><td>1e-20       </td><td>1.995e-07   </td><td>0.4999998005</td><td>1e-20       </td><td>1e-20       </td><td>1e-20       </td><td>0.5         </td><td>0.5         </td><td>1e-20       </td><td>0.5         </td><td>High-Low    </td><td>High        </td><td>1           </td><td>0           </td><td>0           </td></tr>\n",
       "\t<tr><th scope=row>3</th><td>13140x16680</td><td>CC001xCC055</td><td>48         </td><td>1e-20      </td><td>0.5        </td><td>1e-20      </td><td>1e-20      </td><td>0.5        </td><td>1e-20      </td><td>1e-20      </td><td>1e-20      </td><td>2e-20      </td><td>1e-20      </td><td>1          </td><td>Low        </td><td>Low        </td><td>0          </td><td>0          </td><td>1          </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>13140x3015 </td><td>CC001xCC074</td><td>2          </td><td>1e-20      </td><td>0.5        </td><td>0.5        </td><td>1e-20      </td><td>1e-20      </td><td>1e-20      </td><td>1e-20      </td><td>1e-20      </td><td>2e-20      </td><td>1e-20      </td><td>1          </td><td>Low        </td><td>Low        </td><td>0          </td><td>0          </td><td>1          </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>13421x16034</td><td>CC007xCC070</td><td>78         </td><td>1e-20      </td><td>1e-20      </td><td>1.156e-06  </td><td>0.499998844</td><td>1e-20      </td><td>1e-20      </td><td>0.5        </td><td>1e-20      </td><td>2e-20      </td><td>0.5        </td><td>0.5        </td><td>Mod-Low    </td><td>Mod        </td><td>0          </td><td>1          </td><td>0          </td></tr>\n",
       "\t<tr><th scope=row>6</th><td>13421x16296 </td><td>CC007xCC049 </td><td>NA          </td><td>1e-20       </td><td>0.125       </td><td>1.2215e-06  </td><td>0.8749987785</td><td>1e-20       </td><td>1e-20       </td><td>1e-20       </td><td>1e-20       </td><td>2e-20       </td><td>1e-20       </td><td>1           </td><td>Low         </td><td>Low         </td><td>0           </td><td>0           </td><td>1           </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllllllllllllll}\n",
       "  & Mating & CC\\_Mating & UW\\_Line & A & B & C & D & E & F & G & H & CW\\_prob & P\\_prob & Null\\_prob & Oas1b\\_status & Oas1b & Oas1b\\_High & Oas1b\\_Mod & Oas1b\\_Low\\\\\n",
       "\\hline\n",
       "\t1 & 13067x16912 & CC003xCC051 & 17          & 1e-20       & 1e-20       & 1e-20       & 1e-20       & 0.5         & 1e-20       & 1e-20       & 0.5         & 0.5         & 1e-20       & 0.5         & High-Low    & High        & 1           & 0           & 0          \\\\\n",
       "\t2 & 13067x5306   & CC003xCC062  & 63           & 1e-20        & 1e-20        & 1.995e-07    & 0.4999998005 & 1e-20        & 1e-20        & 1e-20        & 0.5          & 0.5          & 1e-20        & 0.5          & High-Low     & High         & 1            & 0            & 0           \\\\\n",
       "\t3 & 13140x16680 & CC001xCC055 & 48          & 1e-20       & 0.5         & 1e-20       & 1e-20       & 0.5         & 1e-20       & 1e-20       & 1e-20       & 2e-20       & 1e-20       & 1           & Low         & Low         & 0           & 0           & 1          \\\\\n",
       "\t4 & 13140x3015  & CC001xCC074 & 2           & 1e-20       & 0.5         & 0.5         & 1e-20       & 1e-20       & 1e-20       & 1e-20       & 1e-20       & 2e-20       & 1e-20       & 1           & Low         & Low         & 0           & 0           & 1          \\\\\n",
       "\t5 & 13421x16034 & CC007xCC070 & 78          & 1e-20       & 1e-20       & 1.156e-06   & 0.499998844 & 1e-20       & 1e-20       & 0.5         & 1e-20       & 2e-20       & 0.5         & 0.5         & Mod-Low     & Mod         & 0           & 1           & 0          \\\\\n",
       "\t6 & 13421x16296  & CC007xCC049  & NA           & 1e-20        & 0.125        & 1.2215e-06   & 0.8749987785 & 1e-20        & 1e-20        & 1e-20        & 1e-20        & 2e-20        & 1e-20        & 1            & Low          & Low          & 0            & 0            & 1           \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "       Mating   CC_Mating UW_Line     A        B          C            D     E\n",
       "1 13067x16912 CC003xCC051      17 1e-20 1.00e-20 1.0000e-20 1.000000e-20 5e-01\n",
       "2  13067x5306 CC003xCC062      63 1e-20 1.00e-20 1.9950e-07 4.999998e-01 1e-20\n",
       "3 13140x16680 CC001xCC055      48 1e-20 5.00e-01 1.0000e-20 1.000000e-20 5e-01\n",
       "4  13140x3015 CC001xCC074       2 1e-20 5.00e-01 5.0000e-01 1.000000e-20 1e-20\n",
       "5 13421x16034 CC007xCC070      78 1e-20 1.00e-20 1.1560e-06 4.999988e-01 1e-20\n",
       "6 13421x16296 CC007xCC049      NA 1e-20 1.25e-01 1.2215e-06 8.749988e-01 1e-20\n",
       "      F     G     H CW_prob P_prob Null_prob Oas1b_status Oas1b Oas1b_High\n",
       "1 1e-20 1e-20 5e-01   5e-01  1e-20       0.5     High-Low  High          1\n",
       "2 1e-20 1e-20 5e-01   5e-01  1e-20       0.5     High-Low  High          1\n",
       "3 1e-20 1e-20 1e-20   2e-20  1e-20       1.0          Low   Low          0\n",
       "4 1e-20 1e-20 1e-20   2e-20  1e-20       1.0          Low   Low          0\n",
       "5 1e-20 5e-01 1e-20   2e-20  5e-01       0.5      Mod-Low   Mod          0\n",
       "6 1e-20 1e-20 1e-20   2e-20  1e-20       1.0          Low   Low          0\n",
       "  Oas1b_Mod Oas1b_Low\n",
       "1         0         0\n",
       "2         0         0\n",
       "3         0         1\n",
       "4         0         1\n",
       "5         1         0\n",
       "6         0         1"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Read strain ID mapping file (with Oas1b status for WNV mapping analyses)\n",
    "## Note: use the Mx1 file (linked at top) for Flu analyses\n",
    "strain_map = read.delim(file.path('./data', 'Oas1b_status_recoded.txt'), header=T, as.is=T, sep='\\t')\n",
    "head(strain_map)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [],
      "text/latex": [],
      "text/markdown": [],
      "text/plain": [
       "character(0)"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Check that matings match the strain mapping file\n",
    "setdiff(matings, strain_map$Mating)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Update covar with Oas1b status (only for WNV analyses)\n",
    "covar$Mating = pheno$Mating\n",
    "covar$Oas1b = sapply(covar$Mating, function(x) {if (x %in% strain_map$Mating) strain_map$Oas1b[strain_map$Mating == x] else NA})\n",
    "covar$Oas1b_High = sapply(covar$Mating, function(x) {if (x %in% strain_map$Mating) strain_map$Oas1b_High[strain_map$Mating == x] else NA})\n",
    "covar$Oas1b_Mod = sapply(covar$Mating, function(x) {if (x %in% strain_map$Mating) strain_map$Oas1b_Mod[strain_map$Mating == x] else NA})\n",
    "covar$Oas1b_Low = sapply(covar$Mating, function(x) {if (x %in% strain_map$Mating) strain_map$Oas1b_Low[strain_map$Mating == x] else NA})\n",
    "pheno$Oas1b = covar$Oas1b\n",
    "pheno$Oas1b_High = covar$Oas1b_High\n",
    "pheno$Oas1b_Mod = covar$Oas1b_Mod\n",
    "pheno$Oas1b_Low = covar$Oas1b_Low"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 4. Construct 3D Array of Probabilities"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Load universal model probabilities (loads a model.probs object containing all RIX lines)\n",
    "data_dir = './xdata'\n",
    "load(file.path(data_dir, 'rix_universal_model_prob_males_27-Jun-2016.rda'))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Create model.probs array specific to the mapping population\n",
    "model.probs = model.probs[pheno$Mating, , ]\n",
    "dimnames(model.probs)[[1]] = pheno$ID"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>158</li>\n",
       "\t<li>8</li>\n",
       "\t<li>76600</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 158\n",
       "\\item 8\n",
       "\\item 76600\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 158\n",
       "2. 8\n",
       "3. 76600\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1]   158     8 76600"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<ol class=list-inline>\n",
       "\t<li>'samples'</li>\n",
       "\t<li>'founders'</li>\n",
       "\t<li>'markers'</li>\n",
       "</ol>\n"
      ],
      "text/latex": [
       "\\begin{enumerate*}\n",
       "\\item 'samples'\n",
       "\\item 'founders'\n",
       "\\item 'markers'\n",
       "\\end{enumerate*}\n"
      ],
      "text/markdown": [
       "1. 'samples'\n",
       "2. 'founders'\n",
       "3. 'markers'\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "[1] \"samples\"  \"founders\" \"markers\" "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "TRUE"
      ],
      "text/latex": [
       "TRUE"
      ],
      "text/markdown": [
       "TRUE"
      ],
      "text/plain": [
       "[1] TRUE"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>UNC6</th><th scope=col>JAX00000010</th><th scope=col>JAX00240603</th><th scope=col>JAX00240610</th><th scope=col>JAX00240613</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>A</th><td>6.00e-06</td><td>5.25e-06</td><td>4.50e-06</td><td>3.75e-06</td><td>3.00e-06</td></tr>\n",
       "\t<tr><th scope=row>B</th><td>0.4999962</td><td>0.4999970</td><td>0.4999977</td><td>0.4999981</td><td>0.4999985</td></tr>\n",
       "\t<tr><th scope=row>C</th><td>4.2e-08</td><td>1.0e-20</td><td>1.0e-20</td><td>1.0e-20</td><td>1.0e-20</td></tr>\n",
       "\t<tr><th scope=row>D</th><td>4.2e-08</td><td>1.0e-20</td><td>1.0e-20</td><td>1.0e-20</td><td>1.0e-20</td></tr>\n",
       "\t<tr><th scope=row>E</th><td>0.4999961</td><td>0.4999970</td><td>0.4999977</td><td>0.4999981</td><td>0.4999985</td></tr>\n",
       "\t<tr><th scope=row>F</th><td>4.2e-08</td><td>1.0e-20</td><td>1.0e-20</td><td>1.0e-20</td><td>1.0e-20</td></tr>\n",
       "\t<tr><th scope=row>G</th><td>4.2e-08</td><td>1.0e-20</td><td>1.0e-20</td><td>1.0e-20</td><td>1.0e-20</td></tr>\n",
       "\t<tr><th scope=row>H</th><td>1.50075e-06</td><td>7.50750e-07</td><td>7.50000e-10</td><td>7.50000e-10</td><td>1.00000e-20</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       "  & UNC6 & JAX00000010 & JAX00240603 & JAX00240610 & JAX00240613\\\\\n",
       "\\hline\n",
       "\tA & 6.00e-06 & 5.25e-06 & 4.50e-06 & 3.75e-06 & 3.00e-06\\\\\n",
       "\tB & 0.4999962 & 0.4999970 & 0.4999977 & 0.4999981 & 0.4999985\\\\\n",
       "\tC & 4.2e-08 & 1.0e-20 & 1.0e-20 & 1.0e-20 & 1.0e-20\\\\\n",
       "\tD & 4.2e-08 & 1.0e-20 & 1.0e-20 & 1.0e-20 & 1.0e-20\\\\\n",
       "\tE & 0.4999961 & 0.4999970 & 0.4999977 & 0.4999981 & 0.4999985\\\\\n",
       "\tF & 4.2e-08 & 1.0e-20 & 1.0e-20 & 1.0e-20 & 1.0e-20\\\\\n",
       "\tG & 4.2e-08 & 1.0e-20 & 1.0e-20 & 1.0e-20 & 1.0e-20\\\\\n",
       "\tH & 1.50075e-06 & 7.50750e-07 & 7.50000e-10 & 7.50000e-10 & 1.00000e-20\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "1. 6e-06\n",
       "2. 0.4999962495\n",
       "3. 4.2e-08\n",
       "4. 4.2e-08\n",
       "5. 0.49999608175\n",
       "6. 4.2e-08\n",
       "7. 4.2e-08\n",
       "8. 1.50075e-06\n",
       "9. 5.25e-06\n",
       "10. 0.49999699925\n",
       "11. 1e-20\n",
       "12. 1e-20\n",
       "13. 0.499997\n",
       "14. 1e-20\n",
       "15. 1e-20\n",
       "16. 7.5075e-07\n",
       "17. 4.5e-06\n",
       "18. 0.49999774925\n",
       "19. 1e-20\n",
       "20. 1e-20\n",
       "21. 0.49999775\n",
       "22. 1e-20\n",
       "23. 1e-20\n",
       "24. 7.5e-10\n",
       "25. 3.75e-06\n",
       "26. 0.49999812425\n",
       "27. 1e-20\n",
       "28. 1e-20\n",
       "29. 0.499998125\n",
       "30. 1e-20\n",
       "31. 1e-20\n",
       "32. 7.5e-10\n",
       "33. 3e-06\n",
       "34. 0.4999985\n",
       "35. 1e-20\n",
       "36. 1e-20\n",
       "37. 0.4999985\n",
       "38. 1e-20\n",
       "39. 1e-20\n",
       "40. 1e-20\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "        markers\n",
       "founders         UNC6 JAX00000010  JAX00240603  JAX00240610  JAX00240613\n",
       "       A 6.000000e-06 5.25000e-06 4.500000e-06 3.750000e-06 3.000000e-06\n",
       "       B 4.999962e-01 4.99997e-01 4.999977e-01 4.999981e-01 4.999985e-01\n",
       "       C 4.200000e-08 1.00000e-20 1.000000e-20 1.000000e-20 1.000000e-20\n",
       "       D 4.200000e-08 1.00000e-20 1.000000e-20 1.000000e-20 1.000000e-20\n",
       "       E 4.999961e-01 4.99997e-01 4.999977e-01 4.999981e-01 4.999985e-01\n",
       "       F 4.200000e-08 1.00000e-20 1.000000e-20 1.000000e-20 1.000000e-20\n",
       "       G 4.200000e-08 1.00000e-20 1.000000e-20 1.000000e-20 1.000000e-20\n",
       "       H 1.500750e-06 7.50750e-07 7.500000e-10 7.500000e-10 1.000000e-20"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Check model.probs object\n",
    "dim(model.probs)\n",
    "names(dimnames(model.probs))\n",
    "dim(model.probs)[1] == dim(pheno)[1]\n",
    "model.probs[1,,1:5]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "## Fix very small genotype probabilities\n",
    "model.probs[model.probs < 0.005] = 1e-20"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>UNC6</th><th scope=col>JAX00000010</th><th scope=col>JAX00240603</th><th scope=col>JAX00240610</th><th scope=col>JAX00240613</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>A</th><td>1e-20</td><td>1e-20</td><td>1e-20</td><td>1e-20</td><td>1e-20</td></tr>\n",
       "\t<tr><th scope=row>B</th><td>0.4999962</td><td>0.4999970</td><td>0.4999977</td><td>0.4999981</td><td>0.4999985</td></tr>\n",
       "\t<tr><th scope=row>C</th><td>1e-20</td><td>1e-20</td><td>1e-20</td><td>1e-20</td><td>1e-20</td></tr>\n",
       "\t<tr><th scope=row>D</th><td>1e-20</td><td>1e-20</td><td>1e-20</td><td>1e-20</td><td>1e-20</td></tr>\n",
       "\t<tr><th scope=row>E</th><td>0.4999961</td><td>0.4999970</td><td>0.4999977</td><td>0.4999981</td><td>0.4999985</td></tr>\n",
       "\t<tr><th scope=row>F</th><td>1e-20</td><td>1e-20</td><td>1e-20</td><td>1e-20</td><td>1e-20</td></tr>\n",
       "\t<tr><th scope=row>G</th><td>1e-20</td><td>1e-20</td><td>1e-20</td><td>1e-20</td><td>1e-20</td></tr>\n",
       "\t<tr><th scope=row>H</th><td>1e-20</td><td>1e-20</td><td>1e-20</td><td>1e-20</td><td>1e-20</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       "  & UNC6 & JAX00000010 & JAX00240603 & JAX00240610 & JAX00240613\\\\\n",
       "\\hline\n",
       "\tA & 1e-20 & 1e-20 & 1e-20 & 1e-20 & 1e-20\\\\\n",
       "\tB & 0.4999962 & 0.4999970 & 0.4999977 & 0.4999981 & 0.4999985\\\\\n",
       "\tC & 1e-20 & 1e-20 & 1e-20 & 1e-20 & 1e-20\\\\\n",
       "\tD & 1e-20 & 1e-20 & 1e-20 & 1e-20 & 1e-20\\\\\n",
       "\tE & 0.4999961 & 0.4999970 & 0.4999977 & 0.4999981 & 0.4999985\\\\\n",
       "\tF & 1e-20 & 1e-20 & 1e-20 & 1e-20 & 1e-20\\\\\n",
       "\tG & 1e-20 & 1e-20 & 1e-20 & 1e-20 & 1e-20\\\\\n",
       "\tH & 1e-20 & 1e-20 & 1e-20 & 1e-20 & 1e-20\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "1. 1e-20\n",
       "2. 0.4999962495\n",
       "3. 1e-20\n",
       "4. 1e-20\n",
       "5. 0.49999608175\n",
       "6. 1e-20\n",
       "7. 1e-20\n",
       "8. 1e-20\n",
       "9. 1e-20\n",
       "10. 0.49999699925\n",
       "11. 1e-20\n",
       "12. 1e-20\n",
       "13. 0.499997\n",
       "14. 1e-20\n",
       "15. 1e-20\n",
       "16. 1e-20\n",
       "17. 1e-20\n",
       "18. 0.49999774925\n",
       "19. 1e-20\n",
       "20. 1e-20\n",
       "21. 0.49999775\n",
       "22. 1e-20\n",
       "23. 1e-20\n",
       "24. 1e-20\n",
       "25. 1e-20\n",
       "26. 0.49999812425\n",
       "27. 1e-20\n",
       "28. 1e-20\n",
       "29. 0.499998125\n",
       "30. 1e-20\n",
       "31. 1e-20\n",
       "32. 1e-20\n",
       "33. 1e-20\n",
       "34. 0.4999985\n",
       "35. 1e-20\n",
       "36. 1e-20\n",
       "37. 0.4999985\n",
       "38. 1e-20\n",
       "39. 1e-20\n",
       "40. 1e-20\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "        markers\n",
       "founders         UNC6 JAX00000010  JAX00240603  JAX00240610  JAX00240613\n",
       "       A 1.000000e-20 1.00000e-20 1.000000e-20 1.000000e-20 1.000000e-20\n",
       "       B 4.999962e-01 4.99997e-01 4.999977e-01 4.999981e-01 4.999985e-01\n",
       "       C 1.000000e-20 1.00000e-20 1.000000e-20 1.000000e-20 1.000000e-20\n",
       "       D 1.000000e-20 1.00000e-20 1.000000e-20 1.000000e-20 1.000000e-20\n",
       "       E 4.999961e-01 4.99997e-01 4.999977e-01 4.999981e-01 4.999985e-01\n",
       "       F 1.000000e-20 1.00000e-20 1.000000e-20 1.000000e-20 1.000000e-20\n",
       "       G 1.000000e-20 1.00000e-20 1.000000e-20 1.000000e-20 1.000000e-20\n",
       "       H 1.000000e-20 1.00000e-20 1.000000e-20 1.000000e-20 1.000000e-20"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Check model.probs object\n",
    "model.probs[1,,1:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 5. Calculate Kinship Matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "SNP 0\n",
      "SNP 1000\n",
      "SNP 2000\n",
      "SNP 3000\n",
      "SNP 4000\n",
      "SNP 5000\n",
      "SNP 6000\n",
      "SNP 7000\n",
      "SNP 8000\n",
      "SNP 9000\n",
      "SNP 10000\n",
      "SNP 11000\n",
      "SNP 12000\n",
      "SNP 13000\n",
      "SNP 14000\n",
      "SNP 15000\n",
      "SNP 16000\n",
      "SNP 17000\n",
      "SNP 18000\n",
      "SNP 19000\n",
      "SNP 20000\n",
      "SNP 21000\n",
      "SNP 22000\n",
      "SNP 23000\n",
      "SNP 24000\n",
      "SNP 25000\n",
      "SNP 26000\n",
      "SNP 27000\n",
      "SNP 28000\n",
      "SNP 29000\n",
      "SNP 30000\n",
      "SNP 31000\n",
      "SNP 32000\n",
      "SNP 33000\n",
      "SNP 34000\n",
      "SNP 35000\n",
      "SNP 36000\n",
      "SNP 37000\n",
      "SNP 38000\n",
      "SNP 39000\n",
      "SNP 40000\n",
      "SNP 41000\n",
      "SNP 42000\n",
      "SNP 43000\n",
      "SNP 44000\n",
      "SNP 45000\n",
      "SNP 46000\n",
      "SNP 47000\n",
      "SNP 48000\n",
      "SNP 49000\n",
      "SNP 50000\n",
      "SNP 51000\n",
      "SNP 52000\n",
      "SNP 53000\n",
      "SNP 54000\n",
      "SNP 55000\n",
      "SNP 56000\n",
      "SNP 57000\n",
      "SNP 58000\n",
      "SNP 59000\n",
      "SNP 60000\n",
      "SNP 61000\n",
      "SNP 62000\n",
      "SNP 63000\n",
      "SNP 64000\n",
      "SNP 65000\n",
      "SNP 66000\n",
      "SNP 67000\n",
      "SNP 68000\n",
      "SNP 69000\n",
      "SNP 70000\n",
      "SNP 71000\n",
      "SNP 72000\n",
      "SNP 73000\n",
      "SNP 74000\n",
      "SNP 75000\n",
      "SNP 76000\n"
     ]
    }
   ],
   "source": [
    "## Create kinship probability matrix\n",
    "K = kinship.probs(model.probs)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>used</th><th scope=col>(Mb)</th><th scope=col>gc trigger</th><th scope=col>(Mb)</th><th scope=col>max used</th><th scope=col>(Mb)</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>Ncells</th><td>4024269.0</td><td>    215.0</td><td>6861544.0</td><td>    366.5</td><td>5167425.0</td><td>    276.0</td></tr>\n",
       "\t<tr><th scope=row>Vcells</th><td> 99667044.0</td><td>      760.4</td><td>345600613.0</td><td>     2636.8</td><td>390134154.0</td><td>     2976.5</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllll}\n",
       "  & used & (Mb) & gc trigger & (Mb) & max used & (Mb)\\\\\n",
       "\\hline\n",
       "\tNcells & 4024269.0 &     215.0 & 6861544.0 &     366.5 & 5167425.0 &     276.0\\\\\n",
       "\tVcells &  99667044.0 &       760.4 & 345600613.0 &      2636.8 & 390134154.0 &      2976.5\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "1. 4024269\n",
       "2. 99667044\n",
       "3. 215\n",
       "4. 760.4\n",
       "5. 6861544\n",
       "6. 345600613\n",
       "7. 366.5\n",
       "8. 2636.8\n",
       "9. 5167425\n",
       "10. 390134154\n",
       "11. 276\n",
       "12. 2976.5\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "           used  (Mb) gc trigger   (Mb)  max used   (Mb)\n",
       "Ncells  4024269 215.0    6861544  366.5   5167425  276.0\n",
       "Vcells 99667044 760.4  345600613 2636.8 390134154 2976.5"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Garbage collection might help free memory\n",
    "gc()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>13067x16912_133</th><th scope=col>13067x16912_134</th><th scope=col>13067x16912_135</th><th scope=col>13067x5306_47</th><th scope=col>13067x5306_58</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>13067x16912_133</th><td>1.0000000</td><td>1.0000000</td><td>1.0000000</td><td>0.5946385</td><td>0.5946385</td></tr>\n",
       "\t<tr><th scope=row>13067x16912_134</th><td>1.0000000</td><td>1.0000000</td><td>1.0000000</td><td>0.5946385</td><td>0.5946385</td></tr>\n",
       "\t<tr><th scope=row>13067x16912_135</th><td>1.0000000</td><td>1.0000000</td><td>1.0000000</td><td>0.5946385</td><td>0.5946385</td></tr>\n",
       "\t<tr><th scope=row>13067x5306_47</th><td>0.5946385</td><td>0.5946385</td><td>0.5946385</td><td>1.0000000</td><td>1.0000000</td></tr>\n",
       "\t<tr><th scope=row>13067x5306_58</th><td>0.5946385</td><td>0.5946385</td><td>0.5946385</td><td>1.0000000</td><td>1.0000000</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       "  & 13067x16912\\_133 & 13067x16912\\_134 & 13067x16912\\_135 & 13067x5306\\_47 & 13067x5306\\_58\\\\\n",
       "\\hline\n",
       "\t13067x16912\\_133 & 1.0000000 & 1.0000000 & 1.0000000 & 0.5946385 & 0.5946385\\\\\n",
       "\t13067x16912\\_134 & 1.0000000 & 1.0000000 & 1.0000000 & 0.5946385 & 0.5946385\\\\\n",
       "\t13067x16912\\_135 & 1.0000000 & 1.0000000 & 1.0000000 & 0.5946385 & 0.5946385\\\\\n",
       "\t13067x5306\\_47 & 0.5946385 & 0.5946385 & 0.5946385 & 1.0000000 & 1.0000000\\\\\n",
       "\t13067x5306\\_58 & 0.5946385 & 0.5946385 & 0.5946385 & 1.0000000 & 1.0000000\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "1. 1\n",
       "2. 1\n",
       "3. 1\n",
       "4. 0.594638489825643\n",
       "5. 0.594638489825643\n",
       "6. 1\n",
       "7. 1\n",
       "8. 1\n",
       "9. 0.594638489825643\n",
       "10. 0.594638489825643\n",
       "11. 1\n",
       "12. 1\n",
       "13. 1\n",
       "14. 0.594638489825643\n",
       "15. 0.594638489825643\n",
       "16. 0.594638489825643\n",
       "17. 0.594638489825643\n",
       "18. 0.594638489825643\n",
       "19. 1\n",
       "20. 1\n",
       "21. 0.594638489825643\n",
       "22. 0.594638489825643\n",
       "23. 0.594638489825643\n",
       "24. 1\n",
       "25. 1\n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "                13067x16912_133 13067x16912_134 13067x16912_135 13067x5306_47\n",
       "13067x16912_133       1.0000000       1.0000000       1.0000000     0.5946385\n",
       "13067x16912_134       1.0000000       1.0000000       1.0000000     0.5946385\n",
       "13067x16912_135       1.0000000       1.0000000       1.0000000     0.5946385\n",
       "13067x5306_47         0.5946385       0.5946385       0.5946385     1.0000000\n",
       "13067x5306_58         0.5946385       0.5946385       0.5946385     1.0000000\n",
       "                13067x5306_58\n",
       "13067x16912_133     0.5946385\n",
       "13067x16912_134     0.5946385\n",
       "13067x16912_135     0.5946385\n",
       "13067x5306_47       1.0000000\n",
       "13067x5306_58       1.0000000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Check kinship matrix\n",
    "K[1:5, 1:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 6. Perform QTL Scan"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>marker</th><th scope=col>chromosome</th><th scope=col>position.B38.</th><th scope=col>position_cM</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>1</th><td>UNC6   </td><td>1      </td><td>3010274</td><td>NA     </td></tr>\n",
       "\t<tr><th scope=row>2</th><td>JAX00000010</td><td>1          </td><td>3135418    </td><td>NA         </td></tr>\n",
       "\t<tr><th scope=row>3</th><td>JAX00240603</td><td>1          </td><td>3252796    </td><td>NA         </td></tr>\n",
       "\t<tr><th scope=row>4</th><td>JAX00240610</td><td>1          </td><td>3266608    </td><td>NA         </td></tr>\n",
       "\t<tr><th scope=row>5</th><td>JAX00240613</td><td>1          </td><td>3323400    </td><td>NA         </td></tr>\n",
       "\t<tr><th scope=row>6</th><td>JAX00240636</td><td>1          </td><td>3389563    </td><td>NA         </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llll}\n",
       "  & marker & chromosome & position.B38. & position\\_cM\\\\\n",
       "\\hline\n",
       "\t1 & UNC6    & 1       & 3010274 & NA     \\\\\n",
       "\t2 & JAX00000010 & 1           & 3135418     & NA         \\\\\n",
       "\t3 & JAX00240603 & 1           & 3252796     & NA         \\\\\n",
       "\t4 & JAX00240610 & 1           & 3266608     & NA         \\\\\n",
       "\t5 & JAX00240613 & 1           & 3323400     & NA         \\\\\n",
       "\t6 & JAX00240636 & 1           & 3389563     & NA         \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "       marker chromosome position.B38. position_cM\n",
       "1        UNC6          1       3010274          NA\n",
       "2 JAX00000010          1       3135418          NA\n",
       "3 JAX00240603          1       3252796          NA\n",
       "4 JAX00240610          1       3266608          NA\n",
       "5 JAX00240613          1       3323400          NA\n",
       "6 JAX00240636          1       3389563          NA"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## First get marker positions\n",
    "marker_pos = read.csv(file.path(data_dir, 'CC001-Uncb38V01.csv'), as.is=T)\n",
    "marker_pos = marker_pos[,1:3]\n",
    "marker_pos$position_cM = NA\n",
    "head(marker_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"Mapping with 158 samples.\"\n",
      "[1] \"Mapping with 76600 markers.\"\n",
      "[1] \"dCt\"\n"
     ]
    }
   ],
   "source": [
    "## Run QTL scan\n",
    "qtl = scanone(pheno=pheno, pheno.col='dCt', probs=model.probs, K=K, \n",
    "              addcovar=covar[, c('sex'), drop=F], snps=marker_pos)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "[1] \"dCt\"\n",
      "[1] \"1 of 10\"\n",
      "[1] \"2 of 10\"\n",
      "[1] \"3 of 10\"\n",
      "[1] \"4 of 10\"\n",
      "[1] \"5 of 10\"\n",
      "[1] \"6 of 10\"\n",
      "[1] \"7 of 10\"\n",
      "[1] \"8 of 10\"\n",
      "[1] \"9 of 10\"\n",
      "[1] \"10 of 10\"\n",
      "[1] \"1 of 10\"\n",
      "[1] \"2 of 10\"\n",
      "[1] \"3 of 10\"\n",
      "[1] \"4 of 10\"\n",
      "[1] \"5 of 10\"\n",
      "[1] \"6 of 10\"\n",
      "[1] \"7 of 10\"\n",
      "[1] \"8 of 10\"\n",
      "[1] \"9 of 10\"\n",
      "[1] \"10 of 10\"\n"
     ]
    }
   ],
   "source": [
    "## Run permutations to calculate significance threshold\n",
    "## Note: nperm=10 is for demonstration purposes only. Normally, at least 1000 permutations should be done.\n",
    "perms = scanone.perm(pheno=pheno, pheno.col='dCt', probs=model.probs, \n",
    "                     addcovar=covar[, c('sex'), drop=F], snps=marker_pos, nperm = 10)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
    "collapsed": false
   },
   "outputs": [],
   "source": [
    "## Get the significance thresholds\n",
    "sig.thr = get.sig.thr(perms[,,'dCt'], alpha=0.05)\n",
    "sugg.thr = get.sig.thr(perms[,,'dCt'], alpha=0.1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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iGAAAIIIIAAAggggEDGAiRIGYNzOAQQQAABBBBAAAEEECiuAAlScceGliGAAAII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\n0FtVXRzO2lvq7PNZ4xR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3/ZR5D2I1AQARKkggxEGZvhXpnn/8yUcfTq0+a5c+fW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KZa5fqCCy4oDjzwwOJtb3ub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\nT+Ve457Pdfet03jnD/kYGhevLvB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OrJ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\nF3hzdY2NGipHaYQ2SQ8LhGMsMeJPxanpioiVkUbELfpfV2U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nwbwIZm4mhYsA9rjzcOihh/ZUsrHQtu8kcUHE+Nno2DBBn+BuGXcVEE1T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\nnhv7tDbWUD7aCDfxxt7vKc1/g26OAQ1sS8FaNlYAY/8Y0sR+A+qXB04b2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FmOXF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",
      "text/plain": [
       "Plot with title “Day 12 Brain WNV dCt”"
      ]
     },
     "metadata": {
      "image/svg+xml": {
       "isolated": true
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Plot QTL results\n",
    "plot(qtl, sig.thr = sig.thr, main = 'Day 12 Brain WNV dCt')\n",
    "\n",
    "## Add lines for suggestive peaks (alpha = 0.1)\n",
    "lines(x = c(0, 2462), y = rep(sugg.thr[1,\"A\"], 2), lwd = 2, lty=2, col = 'red')\n",
    "lines(x = c(2462, 2633), y = rep(sugg.thr[1,\"X\"], 2), lwd = 2, lty=2, col = 'red')"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 7. Identify QTL Intervals"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>marker</th><th scope=col>chromosome</th><th scope=col>position.B38.</th><th scope=col>position_cM</th><th scope=col>perc.var</th><th scope=col>lrs</th><th scope=col>lod</th><th scope=col>p</th><th scope=col>neg.log10.p</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>23173</th><td>UNC10027338         </td><td>5                   </td><td>119.898431          </td><td>NA                  </td><td>19.290635           </td><td>33.861861           </td><td>7.35300965868645    </td><td>1.82806638316241e-05</td><td>4.73800803764066    </td></tr>\n",
       "\t<tr><th scope=row>23255</th><td>UNC10043432         </td><td>5                   </td><td>121.263782          </td><td>NA                  </td><td>21.918455           </td><td>39.0918             </td><td>8.48867647727879    </td><td>1.87722070165332e-06</td><td>5.72648466511146    </td></tr>\n",
       "\t<tr><th scope=row>23291</th><td>UNC10062151         </td><td>5                   </td><td>122.900748          </td><td>NA                  </td><td>17.674955           </td><td>30.73018            </td><td>6.67297371217539    </td><td>6.97164944526511e-05</td><td>4.15666445859798    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllll}\n",
       "  & marker & chromosome & position.B38. & position\\_cM & perc.var & lrs & lod & p & neg.log10.p\\\\\n",
       "\\hline\n",
       "\t23173 & UNC10027338          & 5                    & 119.898431           & NA                   & 19.290635            & 33.861861            & 7.35300965868645     & 1.82806638316241e-05 & 4.73800803764066    \\\\\n",
       "\t23255 & UNC10043432          & 5                    & 121.263782           & NA                   & 21.918455            & 39.0918              & 8.48867647727879     & 1.87722070165332e-06 & 5.72648466511146    \\\\\n",
       "\t23291 & UNC10062151          & 5                    & 122.900748           & NA                   & 17.674955            & 30.73018             & 6.67297371217539     & 6.97164944526511e-05 & 4.15666445859798    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "           marker chromosome position.B38. position_cM perc.var      lrs\n",
       "23173 UNC10027338          5      119.8984          NA 19.29064 33.86186\n",
       "23255 UNC10043432          5      121.2638          NA 21.91846 39.09180\n",
       "23291 UNC10062151          5      122.9007          NA 17.67496 30.73018\n",
       "           lod            p neg.log10.p\n",
       "23173 7.353010 1.828066e-05    4.738008\n",
       "23255 8.488676 1.877221e-06    5.726485\n",
       "23291 6.672974 6.971649e-05    4.156664"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Identify the Bayes Credible Interval for the peak on chromosome 5\n",
    "interval = bayesint_v2(qtl, chr = 5)\n",
    "interval"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Step 8. Create a Coefficient Plot and Probability Plot for Significant Peaks"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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NJPt0m1seZzn8wlxk68rhtsZetbYcpjNtJdxXvu6pYaVaXOZEAyls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7Ppq3C+VdbX63zrL7OlfSW2Y1VHS53Hzl163tJynaNC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\n5B9X+D+6nta5NF/VSQ0I+OZSn1PspfBvU8XkD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BBAAIEVAo9YkSMzaAI7qEM7Ku5W/KBF59ZS+faKTRQ3KS5T/F1RlmaocDvF1oqrFFco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\n",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/svg+xml": {
       "isolated": true
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Create founder effects (coefficient) plot for peak interval\n",
    "coefplot_v2(qtl, chr=5, start=interval[1,3], end=interval[3,3])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>marker</th><th scope=col>chromosome</th><th scope=col>position.B38.</th><th scope=col>position_cM</th><th scope=col>perc.var</th><th scope=col>lrs</th><th scope=col>lod</th><th scope=col>p</th><th scope=col>neg.log10.p</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>23255</th><td>UNC10043432         </td><td>5                   </td><td>121.263782          </td><td>NA                  </td><td>21.9184548398778    </td><td>39.0917998316627    </td><td>8.48867647727879    </td><td>1.87722070165332e-06</td><td>5.72648466511146    </td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllllllll}\n",
       "  & marker & chromosome & position.B38. & position\\_cM & perc.var & lrs & lod & p & neg.log10.p\\\\\n",
       "\\hline\n",
       "\t23255 & UNC10043432          & 5                    & 121.263782           & NA                   & 21.9184548398778     & 39.0917998316627     & 8.48867647727879     & 1.87722070165332e-06 & 5.72648466511146    \\\\\n",
       "\\end{tabular}\n"
      ],
      "text/plain": [
       "           marker chromosome position.B38. position_cM perc.var     lrs\n",
       "23255 UNC10043432          5      121.2638          NA 21.91845 39.0918\n",
       "           lod            p neg.log10.p\n",
       "23255 8.488676 1.877221e-06    5.726485"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Get marker at peak\n",
    "get_min_marker(qtl, chr=5)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "metadata": {
    "collapsed": false
   },
   "outputs": [
    {
     "data": {
      "image/png": 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G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\nEIAABCAAAQhAAAIQgEB9BP4fdJrPLDltS7cAAAAASUVORK5CYII=",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {
      "image/svg+xml": {
       "isolated": true
      }
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "## Create probability plot\n",
    "prob.plot(pheno=pheno, pheno.col='dCt', probs=model.probs, marker='UNC10043432', qtl=qtl)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Last Updated: 31-Aug-2016"
   ]
  }
 ],
 "metadata": {
  "anaconda-cloud": {},
  "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": "3.3.1"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 0
}