ukb-research

name

ukb-research

description

Design and evaluate UK Biobank research topics from user-provided keywords, exposures, outcomes, omics layers, or clinical domains. Use this skill to perform literature-informed topic scouting, summarize existing work and its limitations, identify research gaps, propose gap-resolving epidemiological or omics study designs, verify PMID/DOI references, identify required software and UKBAnalytica functions, and return a structured academic Markdown research brief. Triggers: UK Biobank research idea, UKB topic selection, feasibility analysis, PubMed scoping review, literature gap analysis, study design, research proposal, UKB选题, 研究设计, 文献调研, gap分析, 可行性分析, /UKB-research.

UKB Research — topic scouting and study-design brief

0. Scope and privacy boundary

Use this skill to help users turn a broad UK Biobank idea into a feasible, literature-informed study plan. The output should be a polished Markdown research brief written in academic English.

Privacy boundary:

• Do not request, read, inspect, summarize, or process real UK Biobank participant-level data, screenshots, row-level logs, exact dates, eid values, or per-row model outputs.
• It is acceptable to use public literature, UKB Showcase metadata, public codelists, package documentation, and user-provided variable names or field IDs.
• Generate scripts or workflow plans for execution inside the official UKB RAP environment. Interpret only aggregate tables, figures, and summary metrics.

1. When to use

Use this skill when the user asks to:

• identify a promising UK Biobank research topic;
• evaluate feasibility for a keyword, exposure, phenotype, omics layer, or disease area;
• summarize PubMed or related literature for study design;
• compare existing studies, identify their limitations, and define the research gap the new study should address;
• propose a complete UKB analysis workflow;
• map the workflow to R packages, Python libraries, external tools, and UKBAnalytica functions;
• generate a Markdown research proposal or scoping report.

Do not use this skill for direct code execution on real participant-level data. Route implementation details to the relevant UKBAnalytica workflow skill after the study design is agreed.

2. Required inputs

Ask for the minimum missing information only. A good prompt can contain:

• disease or phenotype keyword, e.g. "COPD", "atrial fibrillation";
• exposure or feature domain, e.g. "air pollution", "diet", "proteomics";
• preferred study type, e.g. prospective cohort, cross-sectional, prediction, mediation, MR, omics profiling;
• target output, e.g. methods section, case-study plan, grant idea, figure plan.

If the user provides only broad keywords, proceed with a scoping design and state assumptions explicitly.

3. Literature search workflow

When the user asks for evidence or feasibility, perform a focused web/literature search unless they explicitly prohibit browsing.

Prioritize sources in this order:

1. PubMed-indexed papers and PMC full text when available.
2. Journal pages or supplementary material for phenotype/codelist details.
3. UK Biobank Showcase, RAP documentation, and public UKB codelist resources.
4. GitHub repositories only when they are linked by a publication or are clearly maintained as public phenotype-code resources.

For each search:

• use 2-4 targeted queries combining the keyword, "UK Biobank", exposure, outcome, and study type;
• record the publication year, cohort, exposure, outcome, main method, main finding, and whether codes/variables are reusable;
• distinguish established findings from unresolved gaps or speculative opportunities;
• cite sources with PMID/DOI/URL when available.

For reusable search patterns, read references/search-strategies.md. Use it when the user gives only broad keywords or when the topic spans omics, prediction, mediation, GWAS, or MR.

4. Existing-work and gap analysis

Every report must move beyond a generic literature summary. For the user's research idea, explicitly answer:

1. What has already been done? Identify closely related UKB and non-UKB studies, their design, exposure/outcome definitions, data modality, and main conclusions.
2. What are the limitations? Note gaps in temporality, phenotype ascertainment, exposure measurement, confounding control, omics coverage, external validation, interpretability, causal inference, and reproducibility.
3. What is the actionable gap? Define a gap that can plausibly be addressed using UK Biobank and available software.
4. How can the new study address it? Propose concrete design choices, methods, sensitivity analyses, and expected outputs.

Citation rule: any claim about prior work, limitation of prior work, or gap must be supported by a verified PMID, DOI, or source URL. If a reference cannot be verified, mark it as "unverified" and do not use it as primary support.

5. Reference verification

Before finalizing a Markdown report, run the bundled verifier when a file is available:

python3 inst/skills/ukb-research/scripts/verify_references.py report.md \
  --output reference_audit.json

If network access is unavailable, run:

python3 inst/skills/ukb-research/scripts/verify_references.py report.md \
  --offline --output reference_audit.json

Use the audit to catch malformed or invented PMIDs/DOIs. Do not fabricate references. Prefer PubMed IDs and DOIs over vague author-year citations. If the user needs reference-manager files, export BibTeX or RIS:

python3 inst/skills/ukb-research/scripts/verify_references.py report.md \
  --output reference_audit.json \
  --bibtex references.bib \
  --ris references.ris

6. Feasibility assessment

Score feasibility qualitatively as High, Moderate, or Low across:

• phenotype ascertainment in UKB;
• exposure availability and measurement quality;
• sample size and expected event count;
• temporality and censoring suitability;
• covariate availability;
• omics or imaging availability, if relevant;
• reproducibility and public-code support;
• novelty relative to prior UKB literature.

Flag common blockers:

• rare outcomes with too few incident cases;
• exposure measured after outcome onset;
• missing or ambiguous GP-only phenotypes;
• unavailable biomarker/omics platform for the intended population;
• high risk of reverse causation or collider bias;
• analysis requiring external GWAS/MR pipelines not yet implemented in UKBAnalytica.

7. Study design rules

Propose one primary design and, where useful, one or two alternatives.

For each design, specify:

• population and exclusions;
• exposure definition;
• outcome definition and data sources;
• time zero, follow-up, event, and censoring rules;
• covariates and confounder rationale;
• primary model;
• secondary, sensitivity, and subgroup analyses;
• how the design addresses the literature gap;
• expected direction of results or plausible null scenarios;
• key figures and tables.

Prefer designs that can be implemented with UKBAnalytica on RAP:

• rap_plan_extract() / ukb_create_extraction_manifest() for field planning;
• get_variable_info() / get_variable_sets() for variable discovery;
• preprocess_baseline() for baseline covariate harmonization;
• get_predefined_diseases() / get_disease_catalog() for endpoint codes;
• build_survival_dataset() for prevalent/incident separation and follow-up;
• create_baseline_table() for baseline summaries;
• runmulti_cox(), runmulti_logit(), runmulti_lm(), runmulti_glm(), runmulti_negbin(), runmulti_gam(), run_rcs() for modeling;
• ukb_ml_flow() / ukb_ml_compare() for machine-learning workflows;
• proteomics, metabolomics, enrichment, PPI, forest, volcano, ROC, SHAP, KM, calibration, and DCA plotting helpers when relevant.

8. Software mapping

Always include an implementation table with:

• UKBAnalytica functions for RAP extraction planning, preprocessing, phenotyping, survival data construction, regression, ML, omics, and plotting;
• R packages such as data.table, survival, tableone, pROC, ggplot2, rms, clusterProfiler, STRINGdb, xgboost, or shapviz when relevant;
• Python libraries such as pandas, scikit-learn, lifelines, statsmodels, or shap only if the analysis is better suited to Python;
• external tools such as PLINK, SAIGE, BOLT-LMM, METAL, LDSC, TwoSampleMR resources, or GWAS Catalog when the proposal requires large-scale genotype, GWAS, or MR analysis.

State which parts can be handled directly by UKBAnalytica and which require external software.

9. Required output

Return a complete Markdown document. If the user asks for a file, write a .md file to the requested path or a sensible project path.

Use the report structure in references/report-template.md. Keep the main text concise, with evidence and workflow tables for detail.

Quality bar:

• academic English;
• explicit assumptions;
• citations or source links for literature claims;
• explicit "existing work → limitations → gap → proposed solution" logic;
• reference audit completed when possible;
• no unsupported claims of novelty;
• no participant-level data exposure;
• actionable next steps.

Before modifying the verifier script, run its offline smoke test:

python3 inst/skills/ukb-research/scripts/smoke_test_reference_verifier.py

10. Handoff to other skills

After the research brief is approved:

• use ukbsci-rap-extract for extraction manifests;
• use ukbsci-preprocess and ukbsci-cohort for baseline variables and phenotype definition;
• use ukbsci-regression, ukbsci-survival, ukbsci-ml, ukbsci-proteomics, or ukbsci-metabolomics for implementation;
• use ukbsci-plot for manuscript-ready visualization.