A selected haplotype (Dog10K)

JBrowse v5 beta

The tutorials target the JBrowse v5 beta, and the v4.3.0 release on the download page lacks some of what they show. To install the beta of JBrowse Web, run npm install -g @jbrowse/cli@next, then jbrowse create jbrowse2 --branch v5.0.0-beta.20. Desktop beta builds for Linux and macOS are on the releases page; the Windows build waits on code signing.

Dogs differ in body size more than any other mammal, and a haplotype (a stretch of neighbouring variants inherited together) at the growth-factor gene IGF1 is a major reason. Breeds at the two ends of the size range differ in allele frequency there, which Fst measures on a scale from 0 to 1. In the Dog10K collection of sequenced dogs and wolves, from a genome-wide scan down to IGF1 on chr15, we:

load a windowed Fst scan between toy and giant breeds as a Manhattan track, with a reference line at a percentile cutoff
load the IGF1 peak, sliced out of the SNV callset, as a variant track whose rows a sample sheet colors by size class
cluster the rows with Clustering → Cluster rows by genotype... to find the haplotype each size class has and where the wolves fall

Prerequisites

the UU_Cfam_GSD_1.0 dog assembly (UCSC's canFam4) set up in JBrowse. The canFam4 hub on genomes.jbrowse.org is a config that loads it with its gene and repeat tracks, and the assemblies guide builds one by hand. This track reads its chrom.sizes alone.
bcftools built with libcurl
curl
python3
htslib (tabix)

# Debian/Ubuntu
sudo apt install bcftools tabix
# or conda, which has every tool above
conda install -c bioconda -c conda-forge bcftools htslib

The scripts write local files, which JBrowse Desktop opens by path and JBrowse Web takes through Add track.

The data

The Dog10K consortium's public share (Meadows et al. 2023), read directly over HTTP with no local copy of either callset.

The tracks below read local files that the build script writes from these, so run it first, or rerun the scan and the slice with groups and regions of your own, as Scanning for peaks in another trait lists. The script fetches everything itself.

What it is	File
The phased imputation panel, scored window by window for the Fst scan	AutoAndXPAR.Dog10K.phased.bcf
The SNV/indel callset the IGF1 window is sliced from	AutoAndXPAR.SNPs.vqsr99.vcf.gz
The sample table, which the scripts derive the breed groups and the wolf outgroup from	dog10K-alignment-sample-table.2022-02-23-v7.txt

Loading the UU_Cfam_GSD_1.0 dog assembly

The tracks name UU_Cfam_GSD_1.0, the Dog10K reference that UCSC calls canFam4. We load it from UCSC's 2bit, with the alias file that maps the chr names to GenBank accessions:

Config file

Goes in the assemblies array of config.json. See Assemblies.

{
  "name": "UU_Cfam_GSD_1.0",
  "aliases": ["canFam4"],
  "sequence": {
    "adapter": {
      "type": "TwoBitAdapter",
      "uri": "https://hgdownload.soe.ucsc.edu/goldenPath/canFam4/bigZips/canFam4.2bit"
    }
  },
  "refNameAliases": {
    "uri": "https://hgdownload.soe.ucsc.edu/goldenPath/canFam4/bigZips/canFam4.chromAlias.txt"
  }
}

CLI (add-assembly)

jbrowse add-assembly https://hgdownload.soe.ucsc.edu/goldenPath/canFam4/bigZips/canFam4.2bit \
  --name UU_Cfam_GSD_1.0 \
  --alias canFam4 \
  --refNameAliases https://hgdownload.soe.ucsc.edu/goldenPath/canFam4/bigZips/canFam4.chromAlias.txt

The figures also show NCBI's RefSeq genes as a track:

Config file

Goes in the tracks array of config.json. See Tracks.

{
  "type": "FeatureTrack",
  "trackId": "canFam4_ncbi_refseq",
  "name": "NCBI RefSeq genes (canFam4)",
  "assemblyNames": ["UU_Cfam_GSD_1.0"],
  "adapter": {
    "type": "Gff3TabixAdapter",
    "gffGzLocation": {
      "uri": "https://jbrowse.org/ucsc/canFam4/ncbiRefSeq.gff.gz"
    },
    "index": {
      "indexType": "CSI",
      "location": {
        "uri": "https://jbrowse.org/ucsc/canFam4/ncbiRefSeq.gff.gz.csi"
      }
    }
  }
}

CLI (add-track-json)

jbrowse add-track-json '{
  "type": "FeatureTrack",
  "trackId": "canFam4_ncbi_refseq",
  "name": "NCBI RefSeq genes (canFam4)",
  "assemblyNames": ["UU_Cfam_GSD_1.0"],
  "adapter": {
    "type": "Gff3TabixAdapter",
    "gffGzLocation": {
      "uri": "https://jbrowse.org/ucsc/canFam4/ncbiRefSeq.gff.gz"
    },
    "index": {
      "indexType": "CSI",
      "location": {
        "uri": "https://jbrowse.org/ucsc/canFam4/ncbiRefSeq.gff.gz.csi"
      }
    }
  }
}'

In the app

In JBrowse Desktop, or in any running JBrowse Web session, open a view on this track’s assembly, then File → Open track..., choose Add track from pasted JSON, and paste:

{
  "type": "FeatureTrack",
  "trackId": "canFam4_ncbi_refseq",
  "name": "NCBI RefSeq genes (canFam4)",
  "assemblyNames": ["UU_Cfam_GSD_1.0"],
  "adapter": {
    "type": "Gff3TabixAdapter",
    "gffGzLocation": {
      "uri": "https://jbrowse.org/ucsc/canFam4/ncbiRefSeq.gff.gz"
    },
    "index": {
      "indexType": "CSI",
      "location": {
        "uri": "https://jbrowse.org/ucsc/canFam4/ncbiRefSeq.gff.gz.csi"
      }
    }
  }
}

Scanning the genome for toy-versus-giant Fst peaks

Body size is the trait, so the two groups are the breeds at its extremes: every animal of the toy and small breeds against every animal of the giant breeds. Hudson Fst (Hudson et al. 1992) scores how far apart the two groups' allele frequencies sit, computed per window over the Dog10K phased imputation panel as a ratio of averages (Bhatia et al. 2013). build_dog10k_size_fst.sh writes one BED line per window.

A Manhattan track expects a -log10(p) column, and this file has an Fst column. GWASAdapter takes the column to read as the score (scoreColumn) and the transform to apply to it (scoreTransform) as separate settings, so naming the column is enough: Fst is already on the scale the plot draws.

Config file

Goes in the tracks array of config.json. See Tracks.

{
  "type": "GWASTrack",
  "trackId": "dog10k_size_fst",
  "name": "Fst, toy/small vs giant breeds (200 kb windows)",
  "assemblyNames": ["UU_Cfam_GSD_1.0"],
  "adapter": {
    "type": "GWASAdapter",
    "uri": "dog10k_size_fst.bed.gz",
    "columnNames": ["chrom", "chromStart", "chromEnd", "name", "fst", "sites"],
    "scoreColumn": "fst"
  },
  "displayDefaults": {
    "encoding": {
      "y": {
        "axis": { "title": "Hudson Fst" },
        "rules": [
          {
            "value": 0.295,
            "color": "rgb(200,60,60)",
            "label": "99.9th percentile"
          }
        ]
      }
    }
  }
}

CLI (add-track-json)

jbrowse add-track-json '{
  "type": "GWASTrack",
  "trackId": "dog10k_size_fst",
  "name": "Fst, toy/small vs giant breeds (200 kb windows)",
  "assemblyNames": ["UU_Cfam_GSD_1.0"],
  "adapter": {
    "type": "GWASAdapter",
    "uri": "dog10k_size_fst.bed.gz",
    "columnNames": ["chrom", "chromStart", "chromEnd", "name", "fst", "sites"],
    "scoreColumn": "fst"
  },
  "displayDefaults": {
    "encoding": {
      "y": {
        "axis": { "title": "Hudson Fst" },
        "rules": [
          {
            "value": 0.295,
            "color": "rgb(200,60,60)",
            "label": "99.9th percentile"
          }
        ]
      }
    }
  }
}'

In the app

In JBrowse Desktop, or in any running JBrowse Web session, open a view on this track’s assembly, then File → Open track..., choose Add track from pasted JSON, and paste:

{
  "type": "GWASTrack",
  "trackId": "dog10k_size_fst",
  "name": "Fst, toy/small vs giant breeds (200 kb windows)",
  "assemblyNames": ["UU_Cfam_GSD_1.0"],
  "adapter": {
    "type": "GWASAdapter",
    "uri": "dog10k_size_fst.bed.gz",
    "columnNames": ["chrom", "chromStart", "chromEnd", "name", "fst", "sites"],
    "scoreColumn": "fst"
  },
  "displayDefaults": {
    "encoding": {
      "y": {
        "axis": { "title": "Hudson Fst" },
        "rules": [
          {
            "value": 0.295,
            "color": "rgb(200,60,60)",
            "label": "99.9th percentile"
          }
        ]
      }
    }
  }
}

dog10k_size_fst.bed.gz is relative to a config.json. Replace it with its URL or its path on this computer.

Opening the assembly with no location shows all of its regions at once, so the display lays the autosomes out side by side. The red line is a rule on encoding.y, the y scale; Y axis... → Reference lines adds one to a track already open.

Rerunning the same script over one region rebins it, which is the lower half of the figure below: the same panel and the same estimator at 20 kb over two megabases, where the peak resolves into a sweep. WINDOW is the bin width, REGIONS any bcftools -r target, and OUTBED the output name:

curl -fO https://raw.githubusercontent.com/GMOD/jbrowse-components/main/scripts/build_dog10k_size_fst.sh
WINDOW=20000 REGIONS=chr15:40600000-42600000 \
  OUTBED=dog10k_size_fst_igf1_20kb.bed \
  bash build_dog10k_size_fst.sh

Top: Fst between toy/small and giant breeds across the autosomes. Bottom: the IGF1 region rebinned to 20 kb, the band marking the window from the top half.

Fst has no p-value, so the cutoff is a reference line at the 99.9th percentile of the scan's windows, which the build script prints beside the ranked windows.¹ The percentile depends on the window size, so a rebinned scan needs a new one. The tallest labelled peak, on chr10, is HMGA2, one of the six variants Rimbault et al. 2013 fit to about half the size variation across breeds.

Slicing the IGF1 window out of the SNV callset

Small breeds share a haplotype at IGF1 that large breeds largely lack (Sutter et al. 2007). The samples are the two groups the scan compared plus the Greek gray wolves, taken from the Dog10K sample table by breed name. They include whole breeds, because several breeds depart from the pattern one animal at a time and the clustering below has to show that variation. The SNV callset is a single large VCF over every sequenced canid with a tabix index beside it, and bcftools reads only the window:

SNVS=https://kiddlabshare.med.umich.edu/dog10K/SNP_and_indel_calls_2021-10-17/AutoAndXPAR.SNPs.vqsr99.vcf.gz
# --force-samples: proceed even if a name in igf1.samples isn't in the VCF's
# sample list, instead of exiting
# -f PASS: keep only sites that passed every quality filter
# -q 0.05:minor (second pass): drop sites where the minor allele is under 5%
# frequency in these samples, since most sites in a callset this size are rare
bcftools view -r chr15:41350000-41750000 -S igf1.samples --force-samples \
  -f PASS "$SNVS" \
  | bcftools view -q 0.05:minor -Oz -o dog10k_igf1.vcf.gz
tabix -p vcf dog10k_igf1.vcf.gz

A site that is reference in every animal draws an empty column, so the second bcftools view drops it.

Loading the IGF1 slice with a sample-metadata TSV

The display draws one row per sample. For this many samples, point the adapter at a TSV whose first column is the sample name and whose other columns are attributes; the display colors and orders rows by any of them:

name	breed	size
CHIH000005	Chihuahua	Toy/small
STBD000001	Saint Bernard	Giant
CLUPGR000001	Greek gray wolf	Gray wolf

rows.color is a color scale on each row's label bar, and here it names the column that paints the swatch:

Config file

Goes in the tracks array of config.json. See Tracks.

{
  "type": "VariantTrack",
  "trackId": "dog10k_igf1_haplotype",
  "name": "Dog10K SNVs across IGF1 (toy, giant, wolf)",
  "assemblyNames": ["UU_Cfam_GSD_1.0"],
  "adapter": {
    "type": "VcfTabixAdapter",
    "uri": "dog10k_igf1.vcf.gz",
    "samplesTsvLocation": { "uri": "dog10k_igf1_samples.tsv" }
  },
  "displays": [
    {
      "type": "LinearVariantDisplay",
      "rows": { "color": "size" },
      "height": 760
    }
  ]
}

CLI (add-track-json)

jbrowse add-track-json '{
  "type": "VariantTrack",
  "trackId": "dog10k_igf1_haplotype",
  "name": "Dog10K SNVs across IGF1 (toy, giant, wolf)",
  "assemblyNames": ["UU_Cfam_GSD_1.0"],
  "adapter": {
    "type": "VcfTabixAdapter",
    "uri": "dog10k_igf1.vcf.gz",
    "samplesTsvLocation": { "uri": "dog10k_igf1_samples.tsv" }
  },
  "displays": [
    {
      "type": "LinearVariantDisplay",
      "rows": { "color": "size" },
      "height": 760
    }
  ]
}'

In the app

In JBrowse Desktop, or in any running JBrowse Web session, open a view on this track’s assembly, then File → Open track..., choose Add track from pasted JSON, and paste:

{
  "type": "VariantTrack",
  "trackId": "dog10k_igf1_haplotype",
  "name": "Dog10K SNVs across IGF1 (toy, giant, wolf)",
  "assemblyNames": ["UU_Cfam_GSD_1.0"],
  "adapter": {
    "type": "VcfTabixAdapter",
    "uri": "dog10k_igf1.vcf.gz",
    "samplesTsvLocation": { "uri": "dog10k_igf1_samples.tsv" }
  },
  "displays": [
    {
      "type": "LinearVariantDisplay",
      "rows": { "color": "size" },
      "height": 760
    }
  ]
}

dog10k_igf1.vcf.gz, dog10k_igf1_samples.tsv are relative to a config.json. Replace each with its URL or its path on this computer.

Clustering the IGF1 rows by genotype

The build script prints which sites separate the two size classes. Frame the view on that span with a margin of undifferentiated sequence on each side, as chr15:41,348,000-41,752,000 does, so Fst drops back to near zero at both edges.

Rows arrive in the VCF's order, which is the order the sample list was built in, so they start out grouped by breed. Clustering reads the region on screen, and over the whole window the undifferentiated sites dilute the separating columns, so we cluster the core first:

Zoom to the core, chr15:41,440,000-41,580,000.
Choose Clustering → Cluster rows by genotype... in the track menu, then Run clustering. The rows reorder by genotype similarity and a dendrogram appears in the sidebar.
Widen back out to see how far the block runs.

The display applies the size-class color from the sample table after clustering, so the order comes from genotypes alone. A session can set the region directly with clusterRegion beside runClustering, as the figure below does:

Config file

Goes at the top level of config.json, replacing any defaultSession there. See Default session.

{
  "defaultSession": {
    "name": "IGF1 haplotypes",
    "views": [
      {
        "id": "igf1_lgv",
        "type": "LinearGenomeView",
        "assembly": "UU_Cfam_GSD_1.0",
        "loc": "chr15:41,348,000-41,752,000",
        "tracks": [
          {
            "type": "VariantTrack",
            "configuration": "dog10k_igf1_haplotype",
            "displays": [
              {
                "type": "LinearVariantDisplay",
                "configuration": "dog10k_igf1_haplotype-LinearVariantDisplay",
                "runClustering": true,
                "clusterRegion": "chr15:41,440,000-41,580,000"
              }
            ]
          }
        ]
      }
    ]
  }
}

CLI (set-default-session)

jbrowse set-default-session --session - << 'EOF'
{
  "name": "IGF1 haplotypes",
  "views": [
    {
      "id": "igf1_lgv",
      "type": "LinearGenomeView",
      "assembly": "UU_Cfam_GSD_1.0",
      "loc": "chr15:41,348,000-41,752,000",
      "tracks": [
        {
          "type": "VariantTrack",
          "configuration": "dog10k_igf1_haplotype",
          "displays": [
            {
              "type": "LinearVariantDisplay",
              "configuration": "dog10k_igf1_haplotype-LinearVariantDisplay",
              "runClustering": true,
              "clusterRegion": "chr15:41,440,000-41,580,000"
            }
          ]
        }
      ]
    }
  ]
}
EOF

Clustering the rows from the track menu over the peak's core.

Video

Open the session the video starts in ↗, then take the steps it shows:

Rows start in breed order; the left stripe is each dog's size class
Cluster the rows by genotype from the track menu
On genotypes alone, the size classes gather into blocks
Widen the window to see how far the block runs
Small breeds, orange, share one haplotype across IGF1; most giants lack it

Steps

Open this view in JBrowse Web ↗ ·

Open in JBrowse Desktop ↗

Needs JBrowse Desktop 5.0 or newer. If nothing opens, copy the link and paste it into Desktop's Open JBrowse Web link..., on the start screen or under File → Session.

https://jbrowse.org/code/jb2/main/?config=test_data%2Fdog10k%2Fconfig.json&session=spec-%7B%22views%22%3A%5B%7B%22type%22%3A%22LinearGenomeView%22%2C%22assembly%22%3A%22UU_Cfam_GSD_1.0%22%2C%22loc%22%3A%22chr15%3A41%2C440%2C000-41%2C580%2C000%22%2C%22tracks%22%3A%5B%7B%22trackId%22%3A%22canFam4_ncbi_refseq%22%2C%22geneGlyphMode%22%3A%22representative%22%2C%22height%22%3A80%7D%2C%7B%22trackId%22%3A%22dog10k_igf1_haplotype%22%2C%22variantLayout%22%3A%22columns%22%2C%22height%22%3A620%2C%22connectors%22%3A%7B%22height%22%3A34%7D%2C%22rows%22%3A%7B%22color%22%3A%22size%22%7D%7D%5D%7D%5D%7D&sessionName=JBrowse+docs%3A+dog10k%2Figf1_cluster_route

Quickest: open the view, then File → Open track... for your files.

From scratch:

Open your genome: Open new genome in JBrowse Desktop, or jbrowse add-assembly for JBrowse Web.This figure uses UU_Cfam_GSD_1.0, loaded from a .2bit genome.
Add your track: File → Open track..., or check it in the track selector. Needs a bgzip-compressed, tabix-indexed GFF3 (.gff.gz + .tbi).This figure uses “NCBI RefSeq genes (canFam4)”.
1. Track menu → Gene glyph → Representative transcript
Add the next track the same way. Needs a bgzip-compressed, tabix-indexed VCF (.vcf.gz + .tbi).This figure uses “Dog10K SNVs across IGF1 (toy, giant, wolf)”.
1. Track menu → Layout → Genotype matrixChecked: one equal-width column per variant.
2. Drag the bottom edge of the band of connecting lines above the rows to resize it.
3. Track menu → Color by → Samples → SizeThe Samples section lists whichever metadata columns your samples carry, so "size" appears only if yours have it.
Go to your region in the location box.This figure is at chr15:41,440,000-41,580,000.

Agent

Paste into a coding agent, with your goal on the first line.

My goal: [write yours, e.g. this view of my own files, or these track settings in my config.json]

Start from this JBrowse view.
- Config it loads: https://jbrowse.org/code/jb2/main/test_data/dog10k/config.json
- Tracks:
  - NCBI RefSeq genes (canFam4) (canFam4_ncbi_refseq), FeatureTrack, a bgzip-compressed, tabix-indexed GFF3 (.gff.gz + .tbi), drawn as LinearBasicDisplay
  - Dog10K SNVs across IGF1 (toy, giant, wolf) (dog10k_igf1_haplotype), VariantTrack, a bgzip-compressed, tabix-indexed VCF (.vcf.gz + .tbi), drawn as LinearVariantDisplay

Docs: https://jbrowse.org/jb2/docs/agents_authoring/ (writing a config), https://jbrowse.org/jb2/docs/agents/ (driving a running JBrowse). Use the jbrowse-authoring, jbrowse-hosted-data and jbrowse-capture skills if you have them. Check any config with `npx @jbrowse/cli validate`, then render it and look: a config that loads is not yet a track that drew.

The session spec that draws the view:
{
  "views": [
    {
      "type": "LinearGenomeView",
      "assembly": "UU_Cfam_GSD_1.0",
      "loc": "chr15:41,440,000-41,580,000",
      "tracks": [
        {
          "trackId": "canFam4_ncbi_refseq",
          "geneGlyphMode": "representative",
          "height": 80
        },
        {
          "trackId": "dog10k_igf1_haplotype",
          "variantLayout": "columns",
          "height": 620,
          "connectors": {
            "height": 34
          },
          "rows": {
            "color": "size"
          }
        }
      ]
    }
  ]
}

Redraw the figure headlessly

cat > session.json <<'JSON'
{
  "views": [
    {
      "type": "LinearGenomeView",
      "assembly": "UU_Cfam_GSD_1.0",
      "loc": "chr15:41,440,000-41,580,000",
      "tracks": [
        {
          "trackId": "canFam4_ncbi_refseq",
          "geneGlyphMode": "representative",
          "height": 80
        },
        {
          "trackId": "dog10k_igf1_haplotype",
          "variantLayout": "columns",
          "height": 620,
          "connectors": {
            "height": 34
          },
          "rows": {
            "color": "size"
          }
        }
      ]
    }
  ]
}
JSON

npx @jbrowse/capture --instance https://jbrowse.org/code/jb2/main/ \
  --config https://jbrowse.org/code/jb2/main/test_data/dog10k/config.json \
  --width 1920 --height 1070 \
  --spec session.json -o figure.png

More: Using JBrowse with AI agents.

CLI

Adds these tracks to your config.json, styled as in the figure. Point each uri at your file (replace YOUR_FILE).

jbrowse add-track-json '{
  "type": "FeatureTrack",
  "trackId": "canFam4_ncbi_refseq",
  "name": "NCBI RefSeq genes (canFam4)",
  "assemblyNames": [
    "UU_Cfam_GSD_1.0"
  ],
  "adapter": {
    "type": "Gff3TabixAdapter",
    "uri": "YOUR_FILE"
  },
  "displayDefaults": {
    "geneGlyphMode": "representative",
    "height": 80
  }
}'

jbrowse add-track-json '{
  "type": "VariantTrack",
  "trackId": "dog10k_igf1_haplotype",
  "name": "Dog10K SNVs across IGF1 (toy, giant, wolf)",
  "assemblyNames": [
    "UU_Cfam_GSD_1.0"
  ],
  "adapter": {
    "type": "VcfTabixAdapter",
    "uri": "YOUR_FILE"
  },
  "displayDefaults": {
    "variantLayout": "columns",
    "height": 620,
    "connectors": {
      "height": 34
    },
    "rows": {
      "color": "size"
    }
  }
}'

Run beside config.json, or add --out <dir>. Needs UU_Cfam_GSD_1.0 in the config (jbrowse add-assembly).

Notebook

This view in a notebook, with jbrowse-anywidget. Swap a track's uri for your file.

from jbrowse_anywidget import JBrowseApp, fetch_hub

config = fetch_hub("https://jbrowse.org/code/jb2/main/test_data/dog10k/config.json")
app = JBrowseApp(
    assemblies=config["assemblies"],
    tracks=config.get("tracks", []),
    views=[
        {
            "type": "LinearGenomeView",
            "assembly": "UU_Cfam_GSD_1.0",
            "loc": "chr15:41,440,000-41,580,000",
            "tracks": [
                {
                    "trackId": "canFam4_ncbi_refseq",
                    "geneGlyphMode": "representative",
                    "height": 80
                },
                {
                    "trackId": "dog10k_igf1_haplotype",
                    "variantLayout": "columns",
                    "height": 620,
                    "connectors": {
                        "height": 34
                    },
                    "rows": {
                        "color": "size"
                    }
                }
            ]
        }
    ],
)
app  # display the widget

Image

jbrowse-img draws this view as an SVG, no browser needed. Swap --config or a track for your own.

npx @jbrowse/img --config https://jbrowse.org/code/jb2/main/test_data/dog10k/config.json \
  --assembly UU_Cfam_GSD_1.0 --loc chr15:41,440,000-41,580,000 \
  --track canFam4_ncbi_refseq '{"geneGlyphMode":"representative","height":80}' \
  --track dog10k_igf1_haplotype '{"variantLayout":"columns","height":620,"connectors":{"height":34},"rows":{"color":"size"}}' \
  --width 1920 --out figure.svg

For a PNG, --out figure.png (needs rsvg-convert: apt install librsvg2-bin, brew install librsvg).

Session

The session spec behind the figure, over this config.

{
  "views": [
    {
      "type": "LinearGenomeView",
      "assembly": "UU_Cfam_GSD_1.0",
      "loc": "chr15:41,440,000-41,580,000",
      "tracks": [
        {
          "trackId": "canFam4_ncbi_refseq",
          "geneGlyphMode": "representative",
          "height": 80
        },
        {
          "trackId": "dog10k_igf1_haplotype",
          "variantLayout": "columns",
          "height": 620,
          "connectors": {
            "height": 34
          },
          "rows": {
            "color": "size"
          }
        }
      ]
    }
  ]
}

The IGF1 haplotype block in toy, giant and wolf genotypes

The build script also scores Fst one site at a time over the slice, with the scan's estimator and size classes, and writes it as dog10k_igf1_fst.bed.gz. It loads like the genome scan, as a GWASTrack whose GWASAdapter takes scoreColumn fst and columnNames chrom, chromStart, chromEnd, name, fst, freqToySmall, freqGiant.

Open chr15:41,348,000-41,752,000 with that Fst track above the clustered variant track:

SNVs across IGF1 as a matrix, one row per canid with its size class as the sidebar swatch, under per-site Fst.

Reading the matrix against the Fst track above it:

Clustering on genotypes alone recovers the size split. Both ends of the block fall inside the window, and the block is a run of columns where one class is enriched, since the classes differ by a shift in allele frequency.
The Fst track has one point per matrix column. The matrix gives each record equal width and the Fst track keeps genomic spacing, so sloped lines tie each column to its coordinate.
Rows depart from their size-class color in both directions: single orange rows sit within the giant cluster and single blue rows within the small one.
The wolves form a contiguous band directly below the toy and small block, and have part of its haplotype.

Scanning for peaks in another trait

The Fst scan and the IGF1 slice take the same two inputs, a pair of groups and a region, so any trait the sample table records can substitute. To scan another trait:

Edit the SMALL and GIANT breed lists in the script's panel step and rerun it to find new peaks.
Change REGIONS to a peak and slice that window with the bcftools command above.
Add a column to the samples TSV to color by.

Schall and Kidd (2025) list structural variants whose frequencies track breed clades, and those slice from their SV callsets the same way.

Reproduce it end to end

Two scripts, in order:

build_dog10k_size_fst.sh runs the scan. It corrects each class's allele frequency for that class's sample size, so the unequal classes do not tilt the score, skips windows with too few sites to score, and prints the percentiles behind the reference line.
build_dog10k_igf1.sh slices the IGF1 window for those samples and scores each site with the scan's estimator.

The two scripts take their breed groups from the Dog10K sample table by breed name, so a sample's class never depends on its genotype.

BASE=https://raw.githubusercontent.com/GMOD/jbrowse-components/main/scripts
curl -fO $BASE/build_dog10k_size_fst.sh
curl -fO $BASE/build_dog10k_igf1.sh
bash build_dog10k_size_fst.sh   # writes ./dog10k_size_fst_build/
bash build_dog10k_igf1.sh       # writes ./dog10k_igf1_build/

Citations

Bhatia et al. (2013). Estimating and interpreting FST: the impact of rare variants
Hudson et al. (1992). Estimation of levels of gene flow from DNA sequence data
Rimbault et al. (2013). Derived variants at six genes explain nearly half of size reduction in dog breeds
Schall & Kidd (2025). Integrative genotyping and analysis of canine structural variation using long-read and short-read data
Sutter et al. (2007). A single IGF1 allele is a major determinant of small size in dogs
Meadows et al. (2023). Genome sequencing of 2000 canids by the Dog10K consortium advances the understanding of demography, genome function and architecture

Notes

Breeds are closed populations, so drift (random change in allele frequency) inside one large breed raises Fst just as differentiation between the groups does. Pooling many breeds on each side dilutes drift in any one breed. ↩

Feedback on this tutorial is welcome: contact us.

A selected haplotype (Dog10K)

Prerequisites#

The data#

Loading the UU_Cfam_GSD_1.0 dog assembly#

Scanning the genome for toy-versus-giant Fst peaks#

Slicing the IGF1 window out of the SNV callset#

Loading the IGF1 slice with a sample-metadata TSV#

Clustering the IGF1 rows by genotype#

The IGF1 haplotype block in toy, giant and wolf genotypes#

Scanning for peaks in another trait#

Reproduce it end to end#

See also#

Citations#

Notes

Prerequisites

The data

Loading the UU_Cfam_GSD_1.0 dog assembly

Scanning the genome for toy-versus-giant Fst peaks

Slicing the IGF1 window out of the SNV callset

Loading the IGF1 slice with a sample-metadata TSV

Clustering the IGF1 rows by genotype

The IGF1 haplotype block in toy, giant and wolf genotypes

Scanning for peaks in another trait

Reproduce it end to end

See also

Citations