Find SNP proxies in R

get_snp_proxies.ipynb

{
 "cells": [
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "# Find SNP proxies in R\n",
    "\n",
    "2018-04-29\n",
    "\n",
    "Kamil Slowikowski\n",
    "\n",
    "## Summary\n",
    "\n",
    "We use R to find the proxies for one SNP in the public 1000 Genomes data.\n",
    "\n",
    "We'll take advantage of the public imputed genotypes hosted on a server at the\n",
    "University of Washington.\n",
    "\n",
    "This notebook is intentionally brief. For more details, check out the vignettes and\n",
    "reference manuals for these packages:\n",
    "\n",
    "- [snpStats]\n",
    "- [VariantAnnotation]\n",
    "- [Rsamtools]\n",
    "\n",
    "[snpStats]: https://www.bioconductor.org/packages/release/bioc/html/snpStats.html\n",
    "[VariantAnnotation]: https://www.bioconductor.org/packages/release/bioc/html/VariantAnnotation.html\n",
    "[Rsamtools]: https://www.bioconductor.org/packages/release/bioc/html/Rsamtools.html"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Install and load packages"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Bioconductor version 3.7 (BiocInstaller 1.29.6), ?biocLite for help\n"
     ]
    }
   ],
   "source": [
    "source(\"http://bioconductor.org/biocLite.R\")\n",
    "packages <- c(\"Rsamtools\", \"VariantAnnotation\", \"snpStats\")\n",
    "\n",
    "for (p in packages) {\n",
    "  if (!suppressMessages(require(p, character.only = TRUE, quietly = TRUE))) {\n",
    "    biocLite(p, suppressUpdates = TRUE, ask = FALSE)\n",
    "  }\n",
    "}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Loading required package: pacman\n"
     ]
    }
   ],
   "source": [
    "if (!require(\"pacman\")) { install.packages(\"pacman\") }\n",
    "pacman::p_load(\n",
    "  ggplot2,\n",
    "  glue,\n",
    "  Rsamtools,\n",
    "  snpStats,\n",
    "  VariantAnnotation\n",
    ")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Read a VCF file\n",
    "\n",
    "The VCF file can be located on a public HTTP server or on your local disk.\n",
    "\n",
    "The VCF should be compressed with **bgzip** (not with gzip) and accompanied by\n",
    "a **tabix** index file `filename.vcf.gz.tbi` to locate chunks of data quickly."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message in .local(x, ...):\n",
      "“variants with >1 ALT allele are set to NA”non-single nucleotide variations are set to NA\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>rs6489463</th><th scope=col>rs192120541</th><th scope=col>rs547723211</th><th scope=col>rs569164619</th><th scope=col>rs150886436</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>HG00096</th><td>01</td><td>01</td><td>00</td><td>01</td><td>01</td></tr>\n",
       "\t<tr><th scope=row>HG00097</th><td>03</td><td>01</td><td>00</td><td>01</td><td>01</td></tr>\n",
       "\t<tr><th scope=row>HG00099</th><td>03</td><td>01</td><td>00</td><td>01</td><td>01</td></tr>\n",
       "\t<tr><th scope=row>HG00100</th><td>02</td><td>01</td><td>00</td><td>01</td><td>01</td></tr>\n",
       "\t<tr><th scope=row>HG00101</th><td>02</td><td>01</td><td>00</td><td>01</td><td>01</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|lllll}\n",
       "  & rs6489463 & rs192120541 & rs547723211 & rs569164619 & rs150886436\\\\\n",
       "\\hline\n",
       "\tHG00096 & 01 & 01 & 00 & 01 & 01\\\\\n",
       "\tHG00097 & 03 & 01 & 00 & 01 & 01\\\\\n",
       "\tHG00099 & 03 & 01 & 00 & 01 & 01\\\\\n",
       "\tHG00100 & 02 & 01 & 00 & 01 & 01\\\\\n",
       "\tHG00101 & 02 & 01 & 00 & 01 & 01\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | rs6489463 | rs192120541 | rs547723211 | rs569164619 | rs150886436 | \n",
       "|---|---|---|---|---|\n",
       "| HG00096 | 01 | 01 | 00 | 01 | 01 | \n",
       "| HG00097 | 03 | 01 | 00 | 01 | 01 | \n",
       "| HG00099 | 03 | 01 | 00 | 01 | 01 | \n",
       "| HG00100 | 02 | 01 | 00 | 01 | 01 | \n",
       "| HG00101 | 02 | 01 | 00 | 01 | 01 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "        rs6489463 rs192120541 rs547723211 rs569164619 rs150886436\n",
       "HG00096 01        01          00          01          01         \n",
       "HG00097 03        01          00          01          01         \n",
       "HG00099 03        01          00          01          01         \n",
       "HG00100 02        01          00          01          01         \n",
       "HG00101 02        01          00          01          01         "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "region <- list(\n",
    "  genome = \"hg19\",\n",
    "  chrom  = \"12\",\n",
    "  start  = 483090,\n",
    "  end    = 683090\n",
    ")\n",
    "\n",
    "# A local file on your disk.\n",
    "# vcf_file <- glue(\"chr{chrom}.1kg.phase3.v5a.vcf.gz\", chrom = region$chrom)\n",
    "\n",
    "# A remote file on an HTTP server.\n",
    "vcf_file <- glue(file.path(\n",
    "  \"http://bochet.gcc.biostat.washington.edu\",\n",
    "  \"beagle/1000_Genomes_phase3_v5a/vcf.b37\",\n",
    "  \"chr{chrom}.1kg.phase3.v5a.vcf.gz\"\n",
    "), chrom = region$chrom)\n",
    "\n",
    "# Set parameters to retrieve one chunk of data.\n",
    "param <- ScanVcfParam(\n",
    "  which = GRanges(\n",
    "    seqnames = region$chrom, \n",
    "    ranges = IRanges(region$start, region$end)\n",
    "  )\n",
    ")\n",
    "\n",
    "# Get VCF data from a local file or remote URL.\n",
    "vcf <- readVcf(file = TabixFile(vcf_file), genome = region$genome, param = param)\n",
    "gt <- genotypeToSnpMatrix(vcf)\n",
    "\n",
    "# Glance at the upper left corner of the genotype matrix.\n",
    "gt$genotypes[1:5,1:5]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Get r squared values for a specific SNP\n",
    "\n",
    "Let's define a convenience function that makes a dataframe with $D'$ and $r^2$ values\n",
    "for each SNP in the region with our chosen SNP."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [],
   "source": [
    "get_snp_proxies <- function(this_snp, vcf, gt) {\n",
    "  # Ensure the SNP is present.\n",
    "  stopifnot(this_snp %in% colnames(gt$genotypes))\n",
    "  # Compute linkage disequilibrium statistics.\n",
    "  gt_ld <- ld(\n",
    "    x = gt$genotypes,\n",
    "    y = gt$genotypes[,this_snp,drop=FALSE],\n",
    "    stats = c(\"D.prime\", \"R.squared\")\n",
    "  )\n",
    "  # Bind the stats into a dataframe.\n",
    "  cols <- names(gt_ld)\n",
    "  gt_ld <- do.call(cbind, gt_ld)\n",
    "  colnames(gt_ld) <- cols\n",
    "  # Get the position, width, strand, ..., for each SNP\n",
    "  gt_meta <- rowRanges(vcf)\n",
    "  # Bind the metadata to the LD statistics.\n",
    "  stopifnot(all(names(gt_meta) == rownames(gt_ld)))\n",
    "  gt_ld <- cbind(as.data.frame(gt_meta), gt_ld)\n",
    "  return(gt_ld)\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "You can run `?snpStats::ld` to learn more about the statistics\n",
    "implemented in the `snpStats` package."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<table>\n",
       "<thead><tr><th></th><th scope=col>seqnames</th><th scope=col>start</th><th scope=col>end</th><th scope=col>width</th><th scope=col>strand</th><th scope=col>paramRangeID</th><th scope=col>REF</th><th scope=col>ALT</th><th scope=col>QUAL</th><th scope=col>FILTER</th><th scope=col>D.prime</th><th scope=col>R.squared</th></tr></thead>\n",
       "<tbody>\n",
       "\t<tr><th scope=row>rs6489463</th><td>12          </td><td>483130      </td><td>483130      </td><td>1           </td><td>*           </td><td>NA          </td><td>G           </td><td> T          </td><td>NA          </td><td>PASS        </td><td>0.01531193  </td><td>2.271030e-05</td></tr>\n",
       "\t<tr><th scope=row>rs192120541</th><td>12          </td><td>483173      </td><td>483173      </td><td>1           </td><td>*           </td><td>NA          </td><td>G           </td><td> A          </td><td>NA          </td><td>PASS        </td><td>1.00000000  </td><td>6.329190e-05</td></tr>\n",
       "\t<tr><th scope=row>rs547723211</th><td>12          </td><td>483350      </td><td>483350      </td><td>1           </td><td>*           </td><td>NA          </td><td>T           </td><td>TA          </td><td>NA          </td><td>PASS        </td><td>        NA  </td><td>          NA</td></tr>\n",
       "\t<tr><th scope=row>rs569164619</th><td>12          </td><td>483416      </td><td>483416      </td><td>1           </td><td>*           </td><td>NA          </td><td>G           </td><td> T          </td><td>NA          </td><td>PASS        </td><td>1.00000000  </td><td>8.442296e-05</td></tr>\n",
       "\t<tr><th scope=row>rs150886436</th><td>12          </td><td>483479      </td><td>483479      </td><td>1           </td><td>*           </td><td>NA          </td><td>T           </td><td> C          </td><td>NA          </td><td>PASS        </td><td>0.25794641  </td><td>1.503325e-02</td></tr>\n",
       "\t<tr><th scope=row>rs376349655</th><td>12          </td><td>483768      </td><td>483768      </td><td>1           </td><td>*           </td><td>NA          </td><td>T           </td><td> C          </td><td>NA          </td><td>PASS        </td><td>1.00000000  </td><td>5.273271e-05</td></tr>\n",
       "</tbody>\n",
       "</table>\n"
      ],
      "text/latex": [
       "\\begin{tabular}{r|llllllllllll}\n",
       "  & seqnames & start & end & width & strand & paramRangeID & REF & ALT & QUAL & FILTER & D.prime & R.squared\\\\\n",
       "\\hline\n",
       "\trs6489463 & 12           & 483130       & 483130       & 1            & *            & NA           & G            &  T           & NA           & PASS         & 0.01531193   & 2.271030e-05\\\\\n",
       "\trs192120541 & 12           & 483173       & 483173       & 1            & *            & NA           & G            &  A           & NA           & PASS         & 1.00000000   & 6.329190e-05\\\\\n",
       "\trs547723211 & 12           & 483350       & 483350       & 1            & *            & NA           & T            & TA           & NA           & PASS         &         NA   &           NA\\\\\n",
       "\trs569164619 & 12           & 483416       & 483416       & 1            & *            & NA           & G            &  T           & NA           & PASS         & 1.00000000   & 8.442296e-05\\\\\n",
       "\trs150886436 & 12           & 483479       & 483479       & 1            & *            & NA           & T            &  C           & NA           & PASS         & 0.25794641   & 1.503325e-02\\\\\n",
       "\trs376349655 & 12           & 483768       & 483768       & 1            & *            & NA           & T            &  C           & NA           & PASS         & 1.00000000   & 5.273271e-05\\\\\n",
       "\\end{tabular}\n"
      ],
      "text/markdown": [
       "\n",
       "| <!--/--> | seqnames | start | end | width | strand | paramRangeID | REF | ALT | QUAL | FILTER | D.prime | R.squared | \n",
       "|---|---|---|---|---|---|\n",
       "| rs6489463 | 12           | 483130       | 483130       | 1            | *            | NA           | G            |  T           | NA           | PASS         | 0.01531193   | 2.271030e-05 | \n",
       "| rs192120541 | 12           | 483173       | 483173       | 1            | *            | NA           | G            |  A           | NA           | PASS         | 1.00000000   | 6.329190e-05 | \n",
       "| rs547723211 | 12           | 483350       | 483350       | 1            | *            | NA           | T            | TA           | NA           | PASS         |         NA   |           NA | \n",
       "| rs569164619 | 12           | 483416       | 483416       | 1            | *            | NA           | G            |  T           | NA           | PASS         | 1.00000000   | 8.442296e-05 | \n",
       "| rs150886436 | 12           | 483479       | 483479       | 1            | *            | NA           | T            |  C           | NA           | PASS         | 0.25794641   | 1.503325e-02 | \n",
       "| rs376349655 | 12           | 483768       | 483768       | 1            | *            | NA           | T            |  C           | NA           | PASS         | 1.00000000   | 5.273271e-05 | \n",
       "\n",
       "\n"
      ],
      "text/plain": [
       "            seqnames start  end    width strand paramRangeID REF ALT QUAL\n",
       "rs6489463   12       483130 483130 1     *      NA           G    T  NA  \n",
       "rs192120541 12       483173 483173 1     *      NA           G    A  NA  \n",
       "rs547723211 12       483350 483350 1     *      NA           T   TA  NA  \n",
       "rs569164619 12       483416 483416 1     *      NA           G    T  NA  \n",
       "rs150886436 12       483479 483479 1     *      NA           T    C  NA  \n",
       "rs376349655 12       483768 483768 1     *      NA           T    C  NA  \n",
       "            FILTER D.prime    R.squared   \n",
       "rs6489463   PASS   0.01531193 2.271030e-05\n",
       "rs192120541 PASS   1.00000000 6.329190e-05\n",
       "rs547723211 PASS           NA           NA\n",
       "rs569164619 PASS   1.00000000 8.442296e-05\n",
       "rs150886436 PASS   0.25794641 1.503325e-02\n",
       "rs376349655 PASS   1.00000000 5.273271e-05"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "this_snp <- \"rs11063140\"\n",
    "this_ld <- get_snp_proxies(this_snp, vcf, gt)\n",
    "\n",
    "head(this_ld)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plot the r squared values in a region"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "Warning message:\n",
      "“Removed 291 rows containing missing values (geom_point).”"
     ]
    },
    {
     "data": {},
     "metadata": {},
     "output_type": "display_data"
    },
    {
     "data": {
      "image/png": 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Y03oZkm3kqc+SUBEiABEiABEiABEpgQASrQE8LHk0mABEiABEiABEiABOKNABXo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\nXETYtQPjSA3ONLR0gMlJoaQpD+bkc5O3r7tFgh7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pOfwpQykLkjAZ5BfvEUpSIrZKUofxJrwS+1igQlJeKjCcOFYFu3dVZbc8WVyr0Qjz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\nsgAAAABJRU5ErkJggg==",
      "text/plain": [
       "plot without title"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "options(repr.plot.width = 6, repr.plot.height = 3)\n",
    "\n",
    "ggplot(this_ld, aes(x = end / 1e6, y = R.squared, color = R.squared)) +\n",
    "geom_point(size = 0.5) +\n",
    "scale_color_gradient2(mid = \"grey80\", high = \"red\") +\n",
    "theme_minimal(base_size = 14) +\n",
    "theme(legend.position = \"none\") +\n",
    "labs(x = \"Position (Mb)\", y = bquote(\"r\"^2), title = this_snp)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "R",
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Hi,
I am trying to find SNP proxies across the whole genome (chr1-chrY; hg19) and for the CEU population from the 1k genome project. Is there a way to customise the "region" variable for this?
I am quite new to coding and R so I do apologise if this is an obvious question!
Thank you.
Randa

You might consider tomahawk.

Hi Slowkow,

I don't want to compute LD, I have a list of SNP rs numbers, and I would like to find proxy SNPs (SNPs in LD r2>0.8) for each rs number using information from the CEU population. I tried to use rsnap package, however, it stoped working as it links to SNAP proxy website but the website isn't working at the moment.

Do you know of another R package I could use?

Let's consider a SNP x. What would it take to find all genome-wide proxies with r2 > 0.8?

We would need to compute the r2 between our chosen SNP x and every other SNP in the genome. If there are 20 million SNPs in the genome, then we need to compute 20 million r2 values and filter for the subset of SNPs that have r2 > 0.8.

The fastest way to compute these values is with the tomahawk software.

Since you do not want to compute these values, you might consider searching for a table that someone else already created and shared publicly. I am not aware of a publicly available LD table with genome-wide values for each SNP, but I'd like to make one when I have some time.

If you're interested to find additional R packages for genetics, this is a good reference:

https://CRAN.R-project.org/view=Genetics

Also check out Bioconductor.

Hi Slowkow,

Thank you for explaning this to me, I didn't think of it that way! I really appreciate your help.

Regards,

Randa