Local ancestry (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.13. Desktop beta builds are coming soon.

Two of the Dog10K breeds are wolf hybrids, so some stretches of their genome trace back to a gray wolf ancestor and the rest to domestic dogs. FLARE calls which of the two sources each stretch of DNA came from, against a wolf panel and 219 other breeds, and we paint the result as one colored row per haplotype (one copy of a chromosome).

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. These tracks read only its chrom.sizes.
Java 8+, for FLARE
bcftools built with libcurl
curl
python3
htslib (bgzip, tabix)
node, for the JBrowse CLI

On Debian/Ubuntu, apt install bcftools tabix curl python3 default-jre covers all of it, including a bcftools linked against libcurl. flare.jar is a single download from FLARE's releases page. The painted BED is a local file, so JBrowse Desktop opens it by path, while JBrowse Web needs it served.

Where the data comes from

Show full URLs

The Dog10K consortium's public phased reference panel (Meadows et al. 2023), plus a canFam4 genetic map published separately.

The build script fetches these files, so there is nothing to download by hand.

the phased reference panel of 1929 canids FLARE runs against: kiddlabshare.med.umich.edu/…/AutoAndXPAR.Dog10K.phased.bcfhttps://kiddlabshare.med.umich.edu/dog10K/phased-imputation-panel/AutoAndXPAR.Dog10K.phased.bcf
the sample table, breed and category labels the panels and targets are derived from: kiddlabshare.med.umich.edu/…/dog10K-alignment-sample-table.2022-02-23-v7.txthttps://kiddlabshare.med.umich.edu/dog10K/sample-information/dog10K-alignment-sample-table.2022-02-23-v7.txt
the Campbell pedigree map, transitioned onto the assembly of this panel (Wang et al. 2025): zenodo.org/…/campbell_sex_average_canFam4.tar.gzhttps://zenodo.org/records/17095604/files/campbell_sex_average_canFam4.tar.gz?download=1

Loading the UU_Cfam_GSD_1.0 dog assembly

UU_Cfam_GSD_1.0 is 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

Two wolfdog breeds and their wolf blocks

Local ancestry labels each stretch of a chromosome with the reference panel (a labelled set of animals) it most resembles. The Dog10K consortium's panel of 1929 canids includes both wolfdog breeds, 57 gray wolves and hundreds of breed dogs. As targets, FLARE paints one dog from each of the 219 breeds with four or more sequenced animals, to catch a wolf cross in any breed, plus:

The two wolfdog breeds: the Saarloos Wolfdog and the Czechoslovakian Wolfdog are 20th-century crosses between German Shepherd Dogs and captive gray wolves, bred back to dogs afterwards. Each should have wolf-derived haplotype blocks on a dog background, and a German Shepherd essentially none.
The Shiloh Shepherd, from the Dog10K paper's discussion of wolf-like dogs, shares more of its doubleton (F2) sites, those carried by only two chromosomes, with wolves than any other breed dog in the collection.
The Tamaskan, from the same discussion, is a wolf-lookalike bred from ordinary sled and herding dogs.
Eight European wolves, held out of the wolf panel.

The files FLARE reads and writes

Every file between the phased panel and the painted track is text, VCF or BED:

The Dog10K sample table and phased BCF at the top, the panel lists and per-chromosome VCFs FLARE takes as input in the middle, and the BED9 files the track reads at the bottom.

Choosing the wolf and dog reference panels

FLARE compares each target animal against a wolf panel and a dog panel. The build script derives both lists from the sample table's breed and category per animal: European gray wolves for the wolf panel, and one dog from every breed for the dog panel, minus the targets and both wolfdog breeds. The dog panel has to include the breed of each target, or the ordinary haplotypes of that target have no close match there.

No animal is painted against a panel that contains it:

the script removes the eight gray wolves from the wolf panel, since a target matched against itself paints solid
each target dog comes out of the dog panel while another dog of its breed stays in, so a Chow Chow is painted against a different Chow Chow

FLARE reads the two lists as one ref-panel file:

CLUPGR000001	Wolf
CLUPGR000002	Wolf
AFFN000001	Dog
AFGH000001	Dog

Slicing one chromosome out of the phased BCF

The panel is a single 6 GB BCF, which bcftools reads over HTTP by range request. The chromosome subset splits into chr1.ref.vcf.gz (the two panels) and chr1.gt.vcf.gz (the 243 targets).

PANEL=https://kiddlabshare.med.umich.edu/dog10K/phased-imputation-panel/AutoAndXPAR.Dog10K.phased.bcf
# -r fetches one chromosome of the 6 GB file by range request
# the two later views re-slice that local subset
bcftools view -r chr1 -S all.txt --force-samples -Oz -o chr1.subset.vcf.gz "$PANEL"
bcftools view -S <(cat wolves.txt dogs.txt) --force-samples \
  -Oz -o chr1.ref.vcf.gz chr1.subset.vcf.gz
bcftools view -S targets.txt --force-samples -Oz -o chr1.gt.vcf.gz chr1.subset.vcf.gz

all.txt, wolves.txt, dogs.txt and targets.txt list one sample per line.

Reshaping the Campbell genetic map for FLARE

FLARE requires a genetic map in four PLINK columns: chromosome, marker ID, genetic position in cM and base-pair position. The build script reshapes the Campbell pedigree map's POS/rate/Map(cM) columns into them, and the chromosome names must match the VCF's. With no map for your organism, write one at a constant rate per megabase from the assembly's chrom.sizes; segment edges are then approximate.

Running FLARE

FLARE takes the two panel VCFs, the ref-panel file and the map, and writes wolfdog_chr1.anc.vcf.gz plus a summary:

# FLARE draws random samples while it infers, so two runs of the same input
# give slightly different block boundaries unless the seed is pinned.
#   seed=42    any fixed number, so a re-run reproduces this painting exactly
#   -Xmx12g    the Java memory ceiling; raise it for more targets or a longer
#              chromosome, since FLARE exits with an OutOfMemoryError past it
#   out=       a prefix; FLARE appends .anc.vcf.gz and .global.anc.gz
java -Xmx12g -jar flare.jar ref=chr1.ref.vcf.gz ref-panel=refpanel.txt \
  gt=chr1.gt.vcf.gz map=chr1.map out=wolfdog_chr1 seed=42

wolfdog_chr1.global.anc.gz is the per-sample summary, and chr1 alone already ranks the targets:

SAMPLE          Wolf    Dog
CLUPRU000001    0.996   0.004     held-out gray wolf
SAAR000001      0.446   0.554     Saarloos Wolfdog
CZEC000003      0.281   0.719     Czechoslovakian Wolfdog
SHIL000001      0.225   0.775     Shiloh Shepherd
THAI000009      0.08    0.92      top of the 219-breed sweep
TMSK000001      0.033   0.967     Tamaskan
GRSD000002      0       1         German Shepherd Dog

Nearly all swept breeds show a trace of wolf, and seven of the eight wolfdogs (four Saarloos, four Czechoslovakian) sit far above them. The eighth, Czechoslovakian 2 (CZEC000002), lands inside the range of the sweep with no long block anywhere.

Collapsing FLARE's calls into BED blocks

FLARE writes per-marker calls into the AN1/AN2 FORMAT fields of wolfdog_chr1.anc.vcf.gz. flare_anc_to_bed.py collapses each haplotype's run of identical calls into one BED9 line, taking row labels from a two-column labels.tsv and coloring by ancestry via itemRgb:

curl -fO https://raw.githubusercontent.com/GMOD/jbrowse-components/main/scripts/flare_anc_to_bed.py
python3 flare_anc_to_bed.py wolfdog_chr1.anc.vcf.gz labels.tsv ancestry.chr1.bed

The BED sorts and indexes like any other BED track. The build script sorts inline to run without node:

jbrowse sort-bed ancestry.chr1.bed | bgzip > ancestry.chr1.bed.gz
tabix -p bed ancestry.chr1.bed.gz

Each output line looks like this:

#chrom	chromStart	chromEnd	name	score	strand	thickStart	thickEnd	itemRgb	sample	ancestry
chr1	49135137	57939751	Wolf	0	.	49135137	57939751	230,159,0	Czechoslovakian 1 hap1	Wolf

The last two columns hold the block's row and its called ancestry, and the # header names them, so the track config needs no columnNames. The build script runs the converter twice:

labels.tsv over every target, into dog10k_wolfdog_ancestry.chr1.bed.gz
named.tsv over the animals the figure names, into dog10k_wolfdog_named.chr1.bed.gz, the file the track below loads

Loading the blocks as a multi-row track

The multi-row feature display (LinearMultiRowFeatureDisplay) draws one row per sample. In grammar-of-graphics terms, rows assigns the rows and rows.domain orders them, and color is a color scale whose scale: "identity" names the BED's itemRgb colors in the key.

Config file

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

{
  "type": "FeatureTrack",
  "trackId": "dog10k_wolfdog_named",
  "name": "Local ancestry, named animals (FLARE, chr1)",
  "assemblyNames": ["UU_Cfam_GSD_1.0"],
  "adapter": {
    "type": "BedTabixAdapter",
    "disableGeneHeuristic": true,
    "uri": "dog10k_wolfdog_named.chr1.bed.gz"
  },
  "displays": [
    {
      "type": "LinearMultiRowFeatureDisplay",
      "rows": {
        "field": "sample",
        "domain": [
          "Gray wolf 7 hap1",
          "Gray wolf 7 hap2",
          "Saarloos 1 hap1",
          "Saarloos 1 hap2",
          "Czechoslovakian 3 hap1",
          "Czechoslovakian 3 hap2",
          "German Shepherd hap1",
          "German Shepherd hap2"
        ]
      },
      "color": {
        "scale": "identity",
        "domain": ["rgb(0,114,178)", "rgb(230,159,0)"],
        "labels": ["Breed dog", "Gray wolf"],
        "title": "Ancestry (FLARE)"
      }
    }
  ]
}

CLI (add-track-json)

jbrowse add-track-json '{
  "type": "FeatureTrack",
  "trackId": "dog10k_wolfdog_named",
  "name": "Local ancestry, named animals (FLARE, chr1)",
  "assemblyNames": ["UU_Cfam_GSD_1.0"],
  "adapter": {
    "type": "BedTabixAdapter",
    "disableGeneHeuristic": true,
    "uri": "dog10k_wolfdog_named.chr1.bed.gz"
  },
  "displays": [
    {
      "type": "LinearMultiRowFeatureDisplay",
      "rows": {
        "field": "sample",
        "domain": [
          "Gray wolf 7 hap1",
          "Gray wolf 7 hap2",
          "Saarloos 1 hap1",
          "Saarloos 1 hap2",
          "Czechoslovakian 3 hap1",
          "Czechoslovakian 3 hap2",
          "German Shepherd hap1",
          "German Shepherd hap2"
        ]
      },
      "color": {
        "scale": "identity",
        "domain": ["rgb(0,114,178)", "rgb(230,159,0)"],
        "labels": ["Breed dog", "Gray wolf"],
        "title": "Ancestry (FLARE)"
      }
    }
  ]
}'

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": "dog10k_wolfdog_named",
  "name": "Local ancestry, named animals (FLARE, chr1)",
  "assemblyNames": ["UU_Cfam_GSD_1.0"],
  "adapter": {
    "type": "BedTabixAdapter",
    "disableGeneHeuristic": true,
    "uri": "dog10k_wolfdog_named.chr1.bed.gz"
  },
  "displays": [
    {
      "type": "LinearMultiRowFeatureDisplay",
      "rows": {
        "field": "sample",
        "domain": [
          "Gray wolf 7 hap1",
          "Gray wolf 7 hap2",
          "Saarloos 1 hap1",
          "Saarloos 1 hap2",
          "Czechoslovakian 3 hap1",
          "Czechoslovakian 3 hap2",
          "German Shepherd hap1",
          "German Shepherd hap2"
        ]
      },
      "color": {
        "scale": "identity",
        "domain": ["rgb(0,114,178)", "rgb(230,159,0)"],
        "labels": ["Breed dog", "Gray wolf"],
        "title": "Ancestry (FLARE)"
      }
    }
  ]
}

dog10k_wolfdog_named.chr1.bed.gz is relative to a config.json. Replace it with its URL or its path on this computer.

Advanced → Edit plot... in the track menu shows rows and color as text and applies edits live.

rows.domain is abbreviated here; the build script writes all sixty-four rows in descending order of chr1 wolf fraction from FLARE's summary. For your own animals:

omit domain and cluster the rows, or list the animals in the order you want
load a second BED of all 243 animals the same way, with no domain: at two rows per animal there is no room for labels, so the named-animals track shows the labels and the all-animals track the extent

Wolf blocks along chr1 in wolfdogs and controls

Local ancestry along chr1 called by FLARE against gray wolf and breed-dog panels: wolf-derived in orange, dog in blue, two rows per animal in descending order of wolf fraction.

Each pair of rows is the two chromosome copies of one animal. Orange marks a stretch resembling a present-day gray wolf more than a breed dog. Wolf on one row and dog on the other is heterozygous, and both orange is homozygous wolf-derived.

Blocks break up towards the end of chr1, tracking the genetic map: the build script tiles the chromosome and prints block-edge count and recombination per tile, and the tile with the most block edges has recombination far above the median.

Held-out wolves where FLARE and allele counts disagree

Most held-out wolves come out essentially all wolf. The two Swedish museum specimens come out partly dog in FLARE's calls, yet score highest of the held-out wolves on a second measure the build script prints, the fraction of near-fixed differing sites with the wolf allele. That measure scores one site at a time, where FLARE matches whole haplotypes against a panel.

How long the Tamaskan's and Shiloh Shepherd's wolf blocks run

The build script prints a count of wolf blocks with their median and longest, one line per animal:

The Tamaskan's wolf blocks are many and short, no longer than ordinary breeds' (the Kars, the Eurasier, the Spanish Mastiff).
The Shiloh Shepherd has the longest wolf block of any dog outside the two wolfdog breeds, and many more blocks than the runner-up, a Great Anglo-French Tricolor Hound with three. A later genome-wide run over the same collection puts it among the three dogs with the longest, most recent wolf tracts (Lin et al. 2025).

Clustering → Cluster rows by similarity... in the track menu orders the rows of the full 243-animal track by their blocks. It puts the held-out wolves on a small branch with the wolfdog haplotypes with the most wolf, apart from the breed dogs. Clustering runs over the region in view, and a chip in the corner of the tree shows the locus.

Reproduce it end to end

build_dog10k_wolfdog_ancestry.sh runs every step above. It:

derives the panel and target lists and slices the chromosome
generates the map and runs FLARE
writes both painted BEDs (flare_anc_to_bed.py) plus indexes
prints every measurement read above and, for each painted block edge, how many ancestry-informative markers each haplotype has on either side. The long wolfdog blocks have marker support at their edges, and the short blocks in ordinary breeds lack it.

curl -fO https://raw.githubusercontent.com/GMOD/jbrowse-components/main/scripts/build_dog10k_wolfdog_ancestry.sh
bash build_dog10k_wolfdog_ancestry.sh       # chr1, into ./dog10k_wolfdog_build
bash build_dog10k_wolfdog_ancestry.sh chr38 # any other autosome

Citations

Meadows et al. (2023). Genome sequencing of 2000 canids by the Dog10K consortium advances the understanding of demography, genome function and architecture
Browning et al. (2023). Fast, accurate local ancestry inference with FLARE
Lin et al. (2025). A legacy of genetic entanglement with wolves shapes modern dogs, local ancestry over the same collection
Campbell et al. (2016). A pedigree-based map of recombination in the domestic dog genome, the genetic map used here, in the canFam4 transition published with Wang et al. (2025), Fine-scale recombination rates inferred using the canFam4 assembly

Feedback on this tutorial is welcome: contact us.

Local ancestry (Dog10K)

Prerequisites#

Where the data comes from#

Loading the UU_Cfam_GSD_1.0 dog assembly#

Two wolfdog breeds and their wolf blocks#

The files FLARE reads and writes#

Choosing the wolf and dog reference panels#

Slicing one chromosome out of the phased BCF#

Reshaping the Campbell genetic map for FLARE#

Running FLARE#

Collapsing FLARE's calls into BED blocks#

Loading the blocks as a multi-row track#

Wolf blocks along chr1 in wolfdogs and controls#

Held-out wolves where FLARE and allele counts disagree#

How long the Tamaskan's and Shiloh Shepherd's wolf blocks run#

Reproduce it end to end#

See also#

Citations#