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Variant track

TL;DR: a VariantTrack with a VcfTabixAdapter handles single- and multi-sample VCFs. Color variants with a jexl: expression on the display's color slot, and reach for the multi-sample or LD displays for cohort data.

Example config:

{
  "type": "VariantTrack",
  "trackId": "my_track",
  "name": "My Variants",
  "assemblyNames": ["hg19"],
  "adapter": {
    "type": "VcfTabixAdapter",
    "uri": "https://yourhost/file.vcf.gz"
  }
}
jbrowse add-track https://yourhost/file.vcf.gz \
  --trackId my_track \
  --name "My Variants" \
  --assemblyNames hg19

VcfTabixAdapter configuration options

The uri shorthand above resolves a sibling <uri>.tbi; adding "csi": true resolves <uri>.csi instead. See the uri shorthand for when CSI is required, and the VcfTabixAdapter config docs for the full slot form.

Coloring variants by type

Use a jexl expression on the display's color slot to color variants by their SVTYPE INFO field (or any other VCF field). The expression reads the INFO field via feature.INFO.SVTYPE and maps it to a color:

{
  "type": "VariantTrack",
  "trackId": "my_sv_track",
  "name": "SVs colored by type",
  "assemblyNames": ["hg38"],
  "adapter": {
    "type": "VcfTabixAdapter",
    "uri": "https://yourhost/svs.vcf.gz"
  },
  "displayDefaults": {
    "color": "jexl:{'DEL':'red','INS':'blue','DUP':'green','INV':'orange','BND':'purple','TRA':'purple'}[feature.INFO.SVTYPE[0]] || 'gray'"
  }
}
jbrowse add-track https://yourhost/svs.vcf.gz \
  --trackId my_sv_track \
  --name "SVs colored by type" \
  --assemblyNames hg38 \
  --displayDefaults "{\"color\":\"jexl:{'DEL':'red','INS':'blue','DUP':'green','INV':'orange','BND':'purple','TRA':'purple'}[feature.INFO.SVTYPE[0]] || 'gray'\"}"

The displayDefaults shorthand applies the color to the variant display for you.

The || 'gray' fallback colors any SVTYPE not in the map (or variants without an SVTYPE field) gray. INFO fields are parsed as arrays, so index the first value ([0]) as shown above. You can use the same pattern for SNP/INDEL VCFs by reading feature.INFO.CLNSIG[0] or any other INFO key. See customizing feature colors for more jexl color examples.

Helper functions for jexl color expressions

The variants plugin registers several helper functions for use in a jexl color expression:

jexl: maf(feature) // minor allele frequency over the called alleles
jexl: missingness(feature) // fraction of samples with no call
jexl: impact(feature) // HIGH, MODERATE, LOW or MODIFIER, from SnpEff ANN / VEP CSQ
jexl: consequence(feature) // e.g. missense_variant, from the same annotation — the MOST SEVERE one alone
jexl: 'missense_variant' in consequences(feature) // every consequence term on the record, across all transcripts (bcftools INFO/CSQ ~ "missense_variant")
jexl: impactColor(feature) // the color the "Color by consequence impact" menu item uses
jexl: svTypeColor(feature) // the color "Color by SV type" uses
jexl: alleleLength(feature) >= 50 // longest allele in bp, so an insertion is not measured by its reference span
jexl: svType(feature) == 'DEL' // SV class, read off a symbolic ALT before falling back to INFO/SVTYPE (bcftools INFO/SVTYPE)
jexl: nAlt(feature) == 1 // ALT alleles the record declares, i.e. biallelic-only (bcftools N_ALT)
jexl: genotypeCount(feature, 'het') > 0 // samples in a genotype class — ref, alt, hom, het or mis (bcftools N_PASS(GT="het"))

So a track can be colored by allele frequency without any preprocessing:

{
  "type": "VariantTrack",
  "trackId": "my_maf_track",
  "name": "Variants colored by allele frequency",
  "assemblyNames": ["hg38"],
  "adapter": {
    "type": "VcfTabixAdapter",
    "uri": "https://yourhost/file.vcf.gz"
  },
  "displayDefaults": {
    "color": "jexl:maf(feature)<0.01?'#ccc':maf(feature)<0.05?'#74a9cf':'#045a8d'"
  }
}
jbrowse add-track https://yourhost/file.vcf.gz \
  --trackId my_maf_track \
  --name "Variants colored by allele frequency" \
  --assemblyNames hg38 \
  --displayDefaults "{\"color\":\"jexl:maf(feature)<0.01?'#ccc':maf(feature)<0.05?'#74a9cf':'#045a8d'\"}"

maf and missingness also work in filter expressions, which is how the multi-sample displays' allele-frequency and missingness sliders are expressed.

If your config must run on older JBrowse releases, use the equivalent get(feature,'INFO').SVTYPE[0] function form instead of property access. See property access vs get().

MultiVariant display configuration

The MultiVariant displays (LinearMultiSampleVariantDisplay and LinearMultiSampleVariantMatrixDisplay) visualize multi-sample VCFs, showing genotypes across many samples as a heatmap.

Configuring default display settings

Preset these slots so the options are on when the track loads. The most commonly preset ones:

  • showReferenceAlleles - draw reference alleles in color. Off by default, where the row background is filled solid gray and only ALT alleles are painted on top, which makes overlapping variants easier to see
  • renderingMode - alleleCount (dosage, darker for homozygous) or phased (one row per haplotype)
  • minorAlleleFrequencyFilter - hide variants below a minor-allele-frequency threshold
  • maxMissingnessFilter - hide variants whose fraction of no-call genotypes rises above the threshold
  • featureColor - color each cell by the variant rather than by genotype (covered below)
  • showRowLabels - show the per-sample row labels in the sidebar
  • colorBy - auto-color samples by a sample-metadata attribute on load (covered below)

Both displays share these slots through SharedVariantDisplay. See the autogenerated SharedVariantDisplay, LinearMultiSampleVariantDisplay, and LinearMultiSampleVariantMatrixDisplay docs for every slot.

These displays are not a track's default, so name them in a displays array rather than using displayDefaults. Each display type has its own block, so to preset both the linear and matrix displays, set the slot on each:

{
  "type": "VariantTrack",
  "trackId": "diversity_panel",
  "name": "Diversity Panel",
  "assemblyNames": ["hg38"],
  "adapter": {
    "type": "VcfTabixAdapter",
    "uri": "https://yourhost/diversity.vcf.gz"
  },
  "displays": [
    {
      "type": "LinearMultiSampleVariantDisplay",
      "showReferenceAlleles": true,
      "minorAlleleFrequencyFilter": 0.05,
      "showRowLabels": true,
      "renderingMode": "alleleCount"
    },
    {
      "type": "LinearMultiSampleVariantMatrixDisplay",
      "showReferenceAlleles": true
    }
  ]
}

These are initial defaults. Users can change them at runtime, and their choice is stored for that session only.

Coloring cells by the variant instead of the genotype

featureColor overrides the per-genotype shading, painting every alt-carrying cell with a color derived from the variant itself. Two built-ins match the track menu's Color by... presets, described in coloring by consequence impact and coloring by SV type.

Consequence impact, via the impactColor helper:

{
  "type": "VariantTrack",
  "trackId": "diversity_panel",
  "name": "Diversity Panel",
  "assemblyNames": ["hg38"],
  "adapter": {
    "type": "VcfTabixAdapter",
    "uri": "https://yourhost/diversity.vcf.gz"
  },
  "displays": [
    {
      "type": "LinearMultiSampleVariantDisplay",
      "featureColor": "jexl:impactColor(feature)"
    }
  ]
}

SV type, via the literal value svType, on the same track:

{
  "type": "VariantTrack",
  "trackId": "diversity_panel",
  "name": "Diversity Panel",
  "assemblyNames": ["hg38"],
  "adapter": {
    "type": "VcfTabixAdapter",
    "uri": "https://yourhost/diversity.vcf.gz"
  },
  "displays": [
    {
      "type": "LinearMultiSampleVariantDisplay",
      "featureColor": "svType"
    }
  ]
}

featureColor accepts any per-feature jexl expression: a plain CSS color, or an expression over feature attributes and the helper functions above, the same as the single-sample color slot.

Auto-coloring samples by metadata

Point the adapter's samplesTsvLocation at a TSV whose first column is name (matching the VCF sample names), with any further columns as metadata:

name	population	region	phenotype
SAMPLE001	EUR	Western	case
SAMPLE002	AFR	Eastern	control
SAMPLE003	EUR	Western	control
SAMPLE004	EAS	Pacific	case

Then set colorBy on the display to one of those column names. Each distinct value gets its own color from the palette:

{
  "type": "VariantTrack",
  "trackId": "population_vcf",
  "name": "Population VCF",
  "assemblyNames": ["hg38"],
  "adapter": {
    "type": "VcfTabixAdapter",
    "uri": "https://yourhost/samples.vcf.gz",
    "samplesTsvLocation": {
      "uri": "https://yourhost/sample_metadata.tsv"
    }
  },
  "displays": [
    {
      "type": "LinearMultiSampleVariantDisplay",
      "colorBy": "population"
    }
  ]
}

If the named attribute isn't in the TSV, JBrowse logs a console warning and skips the coloring.

The first column has to match the VCF's sample names exactly. A file that matches only some of them draws the samples it matched and notifies you about the ones it dropped; a file that matches none of them is a configuration error and the track says so, rather than drawing an empty band. The usual cause is a prefixed or suffixed ID — 1000GP_HG00096 against a header naming HG00096.

Linkage disequilibrium (LD) display

JBrowse renders a triangular heatmap of pairwise r² (or D') between variants, from either of two sources. The figure below is computed live from phased 1000 Genomes genotypes.

Point an LD track at a single population panel: r² is a correlation across the samples in the file, so a pooled multi-population callset averages the block away.

An LD triangle of pairwise r² at the human lactase locus (LCT/MCM6), over the haplotype matrix it summarises. Red is a pair of SNPs almost always inherited together, and the block over the highlighted gene is one long haplotype. Open this view in JBrowse ↗ (large dataset — this session takes a while to load)

The steps behind the figure above, run against your own data.

  1. Open your genome: on the JBrowse Desktop start screen click Open new genome (or Show all available genomes to pick a hosted one).This figure uses hg38.
  2. Add your own track: File → Open track..., then paste a URL or choose a local file.This figure uses the “hg38-ncbiRefSeqCurated” track.
  3. Drag the bar at the bottom edge of the track to resize it.“hg38-ncbiRefSeqCurated”.
  4. Track menu → Show only genes (checked)“hg38-ncbiRefSeqCurated”.
  5. Add your own track: File → Open track..., then paste a URL or choose a local file.This figure uses the “hg38-clinvarMain” track.
  6. Drag the bar at the bottom edge of the track to resize it.“hg38-clinvarMain”.
  7. Track menu → Filter by... → add the expressionThis figure filters on `get(feature,'phenotypeList')=='LACTASE PERSISTENCE'`.“hg38-clinvarMain”.
  8. Add your own track: File → Open track..., then paste a URL or choose a local file. This one needs a bgzip-compressed, tabix-indexed VCF (.vcf.gz + .tbi).This figure uses “LCT lactase-persistence LD, 1000G European panel (r²)”.
  9. Add your own track: File → Open track..., then paste a URL or choose a local file. This one needs a bgzip-compressed, tabix-indexed VCF (.vcf.gz + .tbi).This figure uses “1000 Genomes haplotypes across LCT (one row per haplotype)”.
  10. Drag the bar at the bottom edge of the track to resize it.“1000 Genomes haplotypes across LCT (one row per haplotype)”.
  11. Click Force load in the track's "Zoom in to see features or force load" message.Loads the region even past the byte-size limit, which can be slow.“1000 Genomes haplotypes across LCT (one row per haplotype)”.
  12. Drag the bottom edge of the band of connecting lines above the rows to resize it.“1000 Genomes haplotypes across LCT (one row per haplotype)”.
  13. Track menu → Rendering mode → PhasedSplits each sample into one row per haplotype. The item stays disabled until phased variants are found in the file.“1000 Genomes haplotypes across LCT (one row per haplotype)”.
  14. Track menu → Clustering → Cluster rows by similarityOrders the rows by how alike they are across the region in view, and draws the tree it built beside them. It clusters over what is displayed, so the same menu item somewhere else gives a different order.“1000 Genomes haplotypes across LCT (one row per haplotype)”.
  15. Navigate to the region you want to cluster on, then Track menu → Cluster rows by genotype...Clustering is scoped to the region it was run over — this figure clustered on chr2:135,000,000-136,150,000, which is what the row order reflects even after navigating elsewhere.“1000 Genomes haplotypes across LCT (one row per haplotype)”.
  16. Track menu → Color by... → Samples → PopulationThe Samples section lists whichever metadata columns your samples carry, so "population" appears only if yours have it.“1000 Genomes haplotypes across LCT (one row per haplotype)”.
  17. Track menu → Filter by... → Minor allele frequency → 0.35Hides variants whose minor allele frequency is below this.“1000 Genomes haplotypes across LCT (one row per haplotype)”.
  18. Type your region of interest into the location box and press Enter.This figure is at chr2:134,400,000-136,900,000.
  19. View menu → Zoom to region / use the location box, then add a highlightThis figure highlights 1 region; highlights can also be set with the &highlight= URL parameter.

Open this view in JBrowse Desktop ↗

Opens JBrowse Desktop (JBrowse Desktop 5.0+) at this view and saves it as a reopenable session — swap in your own files afterwards.

Nothing happens? Paste this link into Desktop's Open JBrowse Web link... (start screen, or File → Session):

https://jbrowse.org/code/jb2/main/?config=https%3A%2F%2Fjbrowse.org%2Fucsc%2Fhg38%2Fconfig.json&session=spec-%7B%22sessionTracks%22%3A%5B%7B%22type%22%3A%22VariantTrack%22%2C%22trackId%22%3A%22kgp_lct_ld%22%2C%22name%22%3A%22LCT+lactase-persistence+LD%2C+1000G+European+panel+%28r%C2%B2%29%22%2C%22assemblyNames%22%3A%5B%22hg38%22%5D%2C%22adapter%22%3A%7B%22type%22%3A%22VcfTabixAdapter%22%2C%22uri%22%3A%22https%3A%2F%2Fjbrowse.org%2Fdemos%2Fpopgen%2Flct_1kg38_chr2_eur_wide.vcf.gz%22%7D%2C%22displays%22%3A%5B%7B%22type%22%3A%22LDDisplay%22%2C%22showLDTriangle%22%3Atrue%2C%22showLegend%22%3Atrue%2C%22forceLoad%22%3Atrue%2C%22minorAlleleFrequencyFilter%22%3A0.35%2C%22useGenomicPositions%22%3Atrue%2C%22height%22%3A260%7D%5D%7D%2C%7B%22type%22%3A%22VariantTrack%22%2C%22trackId%22%3A%22kgp_lct_haplotypes%22%2C%22name%22%3A%221000+Genomes+haplotypes+across+LCT+%28one+row+per+haplotype%29%22%2C%22assemblyNames%22%3A%5B%22hg38%22%5D%2C%22adapter%22%3A%7B%22type%22%3A%22VcfTabixAdapter%22%2C%22uri%22%3A%22https%3A%2F%2Fjbrowse.org%2Fdemos%2Fpopgen%2Flct_1kg38_chr2_6pop.vcf.gz%22%2C%22samplesTsvLocation%22%3A%7B%22uri%22%3A%22https%3A%2F%2Fjbrowse.org%2Fgenomes%2Fhg19%2F1000g.sorted.csv.gz%22%7D%7D%7D%5D%2C%22views%22%3A%5B%7B%22type%22%3A%22LinearGenomeView%22%2C%22assembly%22%3A%22hg38%22%2C%22loc%22%3A%22chr2%3A134%2C400%2C000-136%2C900%2C000%22%2C%22highlight%22%3A%5B%7B%22refName%22%3A%22chr2%22%2C%22start%22%3A135787850%2C%22end%22%3A135876467%2C%22assemblyName%22%3A%22hg38%22%7D%5D%2C%22tracks%22%3A%5B%7B%22trackId%22%3A%22hg38-ncbiRefSeqCurated%22%2C%22type%22%3A%22LinearBasicDisplay%22%2C%22height%22%3A60%2C%22showOnlyGenes%22%3Atrue%7D%2C%7B%22trackId%22%3A%22hg38-clinvarMain%22%2C%22type%22%3A%22LinearBasicDisplay%22%2C%22height%22%3A70%2C%22jexlFiltersSetting%22%3A%5B%22jexl%3Aget%28feature%2C%27phenotypeList%27%29%3D%3D%27LACTASE+PERSISTENCE%27%22%5D%7D%2C%7B%22trackId%22%3A%22kgp_lct_ld%22%2C%22type%22%3A%22LDDisplay%22%7D%2C%7B%22trackId%22%3A%22kgp_lct_haplotypes%22%2C%22type%22%3A%22LinearMultiSampleVariantMatrixDisplay%22%2C%22height%22%3A520%2C%22forceLoad%22%3Atrue%2C%22lineZoneHeight%22%3A34%2C%22renderingMode%22%3A%22phased%22%2C%22runClustering%22%3Atrue%2C%22clusterRegion%22%3A%22chr2%3A135%2C000%2C000-136%2C150%2C000%22%2C%22colorBy%22%3A%22population%22%2C%22minorAlleleFrequencyFilter%22%3A0.35%7D%5D%7D%5D%7D&sessionName=JBrowse+docs%3A+ld%2Flct_haploblock

A figure adds its tracks to one session. The jbrowse CLI writes the same tracks into a config.json instead, where every session that opens it has them: add-track where flags cover the whole track, add-track-json where they do not.

jbrowse add-track-json '{
  "type": "VariantTrack",
  "trackId": "kgp_lct_ld",
  "name": "LCT lactase-persistence LD, 1000G European panel (r²)",
  "assemblyNames": [
    "hg38"
  ],
  "adapter": {
    "type": "VcfTabixAdapter",
    "uri": "https://jbrowse.org/demos/popgen/lct_1kg38_chr2_eur_wide.vcf.gz"
  },
  "displays": [
    {
      "type": "LDDisplay",
      "showLDTriangle": true,
      "showLegend": true,
      "forceLoad": true,
      "minorAlleleFrequencyFilter": 0.35,
      "useGenomicPositions": true,
      "height": 260
    }
  ]
}'

jbrowse add-track-json '{
  "type": "VariantTrack",
  "trackId": "kgp_lct_haplotypes",
  "name": "1000 Genomes haplotypes across LCT (one row per haplotype)",
  "assemblyNames": [
    "hg38"
  ],
  "adapter": {
    "type": "VcfTabixAdapter",
    "uri": "https://jbrowse.org/demos/popgen/lct_1kg38_chr2_6pop.vcf.gz",
    "samplesTsvLocation": {
      "uri": "https://jbrowse.org/genomes/hg19/1000g.sorted.csv.gz"
    }
  }
}'

Run these where the config.json is, or add --out <dir>, and point each uri at your own file. The config needs hg38 already — jbrowse add-assembly adds yours. The location and the settings the steps carry are session state rather than track config — that half is the Session spec tab, or jbrowse set-default-session.

This session spec pastes after &session=spec- on any JBrowse Web instance.

{
  "sessionTracks": [
    {
      "type": "VariantTrack",
      "trackId": "kgp_lct_ld",
      "name": "LCT lactase-persistence LD, 1000G European panel (r²)",
      "assemblyNames": [
        "hg38"
      ],
      "adapter": {
        "type": "VcfTabixAdapter",
        "uri": "https://jbrowse.org/demos/popgen/lct_1kg38_chr2_eur_wide.vcf.gz"
      },
      "displays": [
        {
          "type": "LDDisplay",
          "showLDTriangle": true,
          "showLegend": true,
          "forceLoad": true,
          "minorAlleleFrequencyFilter": 0.35,
          "useGenomicPositions": true,
          "height": 260
        }
      ]
    },
    {
      "type": "VariantTrack",
      "trackId": "kgp_lct_haplotypes",
      "name": "1000 Genomes haplotypes across LCT (one row per haplotype)",
      "assemblyNames": [
        "hg38"
      ],
      "adapter": {
        "type": "VcfTabixAdapter",
        "uri": "https://jbrowse.org/demos/popgen/lct_1kg38_chr2_6pop.vcf.gz",
        "samplesTsvLocation": {
          "uri": "https://jbrowse.org/genomes/hg19/1000g.sorted.csv.gz"
        }
      }
    }
  ],
  "views": [
    {
      "type": "LinearGenomeView",
      "assembly": "hg38",
      "loc": "chr2:134,400,000-136,900,000",
      "highlight": [
        {
          "refName": "chr2",
          "start": 135787850,
          "end": 135876467,
          "assemblyName": "hg38"
        }
      ],
      "tracks": [
        {
          "trackId": "hg38-ncbiRefSeqCurated",
          "type": "LinearBasicDisplay",
          "height": 60,
          "showOnlyGenes": true
        },
        {
          "trackId": "hg38-clinvarMain",
          "type": "LinearBasicDisplay",
          "height": 70,
          "jexlFiltersSetting": [
            "jexl:get(feature,'phenotypeList')=='LACTASE PERSISTENCE'"
          ]
        },
        {
          "trackId": "kgp_lct_ld",
          "type": "LDDisplay"
        },
        {
          "trackId": "kgp_lct_haplotypes",
          "type": "LinearMultiSampleVariantMatrixDisplay",
          "height": 520,
          "forceLoad": true,
          "lineZoneHeight": 34,
          "renderingMode": "phased",
          "runClustering": true,
          "clusterRegion": "chr2:135,000,000-136,150,000",
          "colorBy": "population",
          "minorAlleleFrequencyFilter": 0.35
        }
      ]
    }
  ]
}

Loaded against config: https://jbrowse.org/ucsc/hg38/config.json

The same view with jbrowse-anywidget. Point the adapter at your own file.

from jbrowse_anywidget import LinearGenomeView, fetch_hub

view = LinearGenomeView(
    assembly=fetch_hub("hg38"),
    location="chr2:134,400,000-136,900,000",
)

view.add_track({
    "type": "FeatureTrack",
    "trackId": "my_track_1",
    "name": "My track 1",
    "assemblyNames": ["hg38"],
    "adapter": {"type": "BamAdapter", "uri": "https://your-server/your-file"},
})
view.add_track({
    "type": "FeatureTrack",
    "trackId": "my_track_2",
    "name": "My track 2",
    "assemblyNames": ["hg38"],
    "adapter": {"type": "BamAdapter", "uri": "https://your-server/your-file"},
})
view.add_track({
    "type": "VariantTrack",
    "trackId": "my_track_3",
    "name": "My track 3",
    "assemblyNames": ["hg38"],
    "adapter": {"type": "VcfTabixAdapter", "uri": "https://your-server/your-file"},
})
view.add_track({
    "type": "VariantTrack",
    "trackId": "my_track_4",
    "name": "My track 4",
    "assemblyNames": ["hg38"],
    "adapter": {"type": "VcfTabixAdapter", "uri": "https://your-server/your-file"},
})

view  # display the widget

Hand this to a coding agent. It rebuilds the figure above headlessly, and the session file is then the thing to edit — swap an adapter uri for your own file and rerun.

cat > session.json <<'JSON'
{
  "sessionTracks": [
    {
      "type": "VariantTrack",
      "trackId": "kgp_lct_ld",
      "name": "LCT lactase-persistence LD, 1000G European panel (r²)",
      "assemblyNames": [
        "hg38"
      ],
      "adapter": {
        "type": "VcfTabixAdapter",
        "uri": "https://jbrowse.org/demos/popgen/lct_1kg38_chr2_eur_wide.vcf.gz"
      },
      "displays": [
        {
          "type": "LDDisplay",
          "showLDTriangle": true,
          "showLegend": true,
          "forceLoad": true,
          "minorAlleleFrequencyFilter": 0.35,
          "useGenomicPositions": true,
          "height": 260
        }
      ]
    },
    {
      "type": "VariantTrack",
      "trackId": "kgp_lct_haplotypes",
      "name": "1000 Genomes haplotypes across LCT (one row per haplotype)",
      "assemblyNames": [
        "hg38"
      ],
      "adapter": {
        "type": "VcfTabixAdapter",
        "uri": "https://jbrowse.org/demos/popgen/lct_1kg38_chr2_6pop.vcf.gz",
        "samplesTsvLocation": {
          "uri": "https://jbrowse.org/genomes/hg19/1000g.sorted.csv.gz"
        }
      }
    }
  ],
  "views": [
    {
      "type": "LinearGenomeView",
      "assembly": "hg38",
      "loc": "chr2:134,400,000-136,900,000",
      "highlight": [
        {
          "refName": "chr2",
          "start": 135787850,
          "end": 135876467,
          "assemblyName": "hg38"
        }
      ],
      "tracks": [
        {
          "trackId": "hg38-ncbiRefSeqCurated",
          "type": "LinearBasicDisplay",
          "height": 60,
          "showOnlyGenes": true
        },
        {
          "trackId": "hg38-clinvarMain",
          "type": "LinearBasicDisplay",
          "height": 70,
          "jexlFiltersSetting": [
            "jexl:get(feature,'phenotypeList')=='LACTASE PERSISTENCE'"
          ]
        },
        {
          "trackId": "kgp_lct_ld",
          "type": "LDDisplay"
        },
        {
          "trackId": "kgp_lct_haplotypes",
          "type": "LinearMultiSampleVariantMatrixDisplay",
          "height": 520,
          "forceLoad": true,
          "lineZoneHeight": 34,
          "renderingMode": "phased",
          "runClustering": true,
          "clusterRegion": "chr2:135,000,000-136,150,000",
          "colorBy": "population",
          "minorAlleleFrequencyFilter": 0.35
        }
      ]
    }
  ]
}
JSON

npx @jbrowse/capture --instance https://jbrowse.org/code/jb2/main/ \
  --config https://jbrowse.org/ucsc/hg38/config.json \
  --session session.json -o figure.png

Using JBrowse with AI agents covers the rest: hosted genomes to point it at, and why waiting for the render is the part that goes wrong.

Computed from a VCF. Add an LDDisplay to a normal VariantTrack. No extra files are needed, and because the raw genotypes are present, the filtering (minor allele frequency, HWE, call rate, jexl) and signed-LD options are available:

{
  "type": "VariantTrack",
  "trackId": "variants_ld",
  "name": "Variants with LD",
  "assemblyNames": ["hg38"],
  "adapter": {
    "type": "VcfTabixAdapter",
    "uri": "https://yourhost/variants.vcf.gz"
  },
  "displays": [{ "type": "LDDisplay" }]
}

Pre-computed with PLINK. Use a standalone LDTrack when the cohort is too large to compute in the browser, or when you want to publish a fixed matrix. Its adapter is PlinkLDAdapter for a plain .ld or PlinkLDTabixAdapter for a bgzipped, tabix-indexed .ld.gz (which fetches only the visible region):

{
  "type": "LDTrack",
  "trackId": "ld_plink",
  "name": "Linkage disequilibrium",
  "assemblyNames": ["hg38"],
  "adapter": {
    "type": "PlinkLDTabixAdapter",
    "uri": "https://yourhost/study.ld.gz"
  }
}

The .ld file is PLINK's --r2 report. The GWAS track guide has the plink/bgzip/tabix commands; the same file also drives LD coloring on a GWAS track.

Both displays share the same track-menu controls (LD metric, legend, fit-to-height). Only the VCF-computed one offers filtering and signed LD, since PLINK data carries only the final r²/D' values.

See also