tayloranthonyanderson/haploqtl

Candidate-Gene Interpretation for Tomato QTL

Turn a fine-mapped QTL interval into a ranked, mechanistically-reasoned set of candidate genes plus marker-assisted-selection (MAS) markers — the manual triage a breeder/geneticist does with a genome browser, an annotation database, and the literature, done as a repeatable workflow.

Target users: plant breeders and geneticists who have localized a trait (e.g. via haploqtl local-ancestry clustering or traditional QTL mapping) and need to know which genes are in the interval, what they do, and which markers to use for selection.

When to use this skill

Use when the user:

• Has a genomic interval on the SL4.0 tomato assembly (e.g. ch09:62,452,852-63,002,852) and wants the genes in it.
• Asks for candidate or causal genes, candidate-gene hypotheses, or "what's in this QTL".
• Wants MAS / KASP markers diagnostic of a resistant haplotype.
• Has haploqtl cluster output and wants to interpret a locus biologically.

Inputs

• Required: an interval — chromosome + start + end (SL4.0 bp).
• Optional (for MAS markers): a VCF of the relevant accessions plus the names of resistant donors and susceptible controls.

Workflow

[ ] Step 1  Genes in the interval        (offline, ITAG4.1)
[ ] Step 2  Protein function             (live UniProt)        -- needs network
[ ] Step 3  Literature context           (PubMed connector)    -- optional
[ ] Step 4  Diagnostic MAS markers       (offline, from VCF)   -- if accessions given
[ ] Step 5  Synthesize candidate report  (reasoning)

Step 1 — Genes in the interval

List the protein-coding genes overlapping the interval, with ITAG4.1 functional descriptions:

python scripts/genes_in_interval.py --chrom ch09 --start 62452852 --end 63002852

Returns JSON (gene_id, seqid, start, end, strand, description). The bundled source is an ITAG4.1 slice (SL4.0), so IDs and coordinates match the published annotation. For an interval outside the bundled EB-5/EB-9 regions, pass a full ITAG4.1 GFF3 with --gff (see references/data-sources.md).

Step 2 — Protein function (UniProt)

Enrich the most promising genes with protein-level function from UniProtKB (live):

python scripts/gene_function.py Solyc09g074790 Solyc09g074510

DECISION POINT: this requires network access. If offline, skip and rely on the ITAG4.1 descriptions from Step 1. Not every Solyc gene has a UniProt entry; null fields are expected and fine.

Step 3 — Literature context (optional)

If the PubMed connector is available, search for evidence linking the candidate gene families (Step 1–2) to disease resistance — e.g. "tomato F-box protein defense Alternaria", "potassium transporter stomatal immunity". Cite PMIDs in the report.

Step 4 — Diagnostic MAS markers (if accessions provided)

Find variants whose allele is present in all resistant donors and absent from all susceptible controls — diagnostic markers for marker-assisted selection:

python scripts/diagnostic_variants.py --vcf <accessions.vcf.gz> \
    --chrom ch09 --start 62452852 --end 63002852

Defaults reproduce the EB-9 contrast from Anderson et al. (2024) (resistant = Devon Surprise pathway; susceptible = NC EBR 1, NC 84173, Brandywine). Override with --resistant/--susceptible (comma-separated accession names) and relax with --max-exceptions.

Step 5 — Synthesize the candidate-gene report

Using references/interpretation-guide.md, reason over the assembled evidence and produce a report:

1. Ranked candidate genes — weight gene families implicated in (quantitative) disease resistance (R-genes/NBS-LRR, receptor-like kinases, F-box/ubiquitin signaling, transporters tied to stomatal/ion defense, defense TFs) above housekeeping genes; justify each with its function + literature.
2. Mechanistic hypothesis per top candidate (how it could plausibly affect the trait).
3. MAS markers — the diagnostic marker table from Step 4, ready for assay design.
4. Caveats — overlapping loci, annotation version, that interval co-location is not causation.

Output

A markdown report: ranked candidates with evidence, mechanistic hypotheses, a MAS marker table, and explicit caveats. See EXAMPLE.md for a worked EB-9 run.

References

• references/data-sources.md — data provenance (ITAG4.1, UniProt), coordinates, regenerating the gene slice, why Ensembl Plants REST is not used.
• references/interpretation-guide.md — how to rank candidate genes for quantitative disease resistance, and MAS-marker conventions.