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---
name: phasing-imputation
description: Workflow for haplotype phasing, genotype imputation, reference-panel matching, and imputation QC.
tool_type: python
primary_tool: phasing
---
# Phasing And Imputation
## Version Compatibility
Reference examples assume recent stable releases of the preferred tools, especially `phasing` and the other tools listed below.
Before using code or command patterns, verify installed versions match the environment:
- Python: `python -c "import <module>; print(<module>.__version__)"`
- CLI: `<tool> --version`
- If signatures differ, inspect the installed help or API and adapt the pattern instead of retrying unchanged.
## Overview
Workflow for haplotype phasing, genotype imputation, reference-panel matching, and imputation QC.
## When To Use This Skill
- use when the task is genotype phasing or imputation from array or sequence-derived variant data
- use when the study requires haplotypes, imputed markers, or downstream association-ready genotypes
- use when reference panel choice and QC are central to the analysis
## Quick Route
- If the input is raw or minimally processed data, start with validation and QC before any modeling.
- If the input is already processed, skip directly to the first workflow step that matches the user goal.
- If the user asks for a biological conclusion, always produce at least one QC or confidence artifact alongside the final result.
## Progressive Disclosure
- Read `references/technical_reference.md` when you need deeper tool-selection rules, environment adaptation notes, or extra validation guidance.
- Keep `SKILL.md` as the main execution path and load the reference file only when the task or failure mode needs the extra detail.
## Default Rules
- Prefer Python-first workflows unless the task explicitly requires something else.
- Keep intermediate and final outputs separated.
- Record software versions, reference builds, and key parameters when they affect interpretation.
- Favor reproducible tables and figures over one-off interactive-only outputs.
## Expected Inputs
- VCF genotype data
- sample metadata
- reference panel
## Expected Outputs
- phased genotypes
- imputed genotype set
- imputation QC metrics
## Preferred Tools
- phasing tools
- imputation tools
- bcftools
- pandas
## Starter Pattern
```text
Preferred starting point: phasing
Inputs: VCF genotype data, sample metadata, reference panel
Outputs: phased genotypes, imputed genotype set, imputation QC metrics
```
## Workflow
### 1. Validate cohort and reference compatibility
Choose a reference panel matched to ancestry and build.
### 2. Phase genotypes
Produce haplotype-aware inputs appropriate for the imputation engine.
### 3. Impute variants
Run imputation and retain quality metrics such as INFO or dosage confidence.
### 4. Filter post-imputation
Apply frequency and quality thresholds aligned with the downstream use case.
### 5. Export association-ready outputs
Save phased or imputed VCFs and QC summaries.
## Output Artifacts
- Recommended output layout:
- `results/` for final tables and serialized objects
- `figures/` for plots and static visual exports
- `qc/` for checks that justify downstream interpretation
- Minimum expected outputs for this skill:
- `phased genotypes`
- `imputed genotype set`
- `imputation QC metrics`
## Quality Review
- Confirm identifiers and metadata join correctly before modeling or summarizing.
- Generate at least one QC artifact before final biological interpretation.
- Keep raw or minimally processed inputs separate from transformed outputs.
- Record reference build, caller assumptions, and filtering rules in the final outputs.
- Separate raw calls from filtered or interpreted results.
## Anti-Patterns
- using a poorly matched reference panel without documenting the limitation
- keeping low-confidence imputed sites as if they were observed genotypes
- forgetting genome build harmonization
## Related Skills
- `Variant Calling`
- `Copy Number`
- `Long-Read Genomics`
- `Genome Assembly`
## Optional Supplements
- None required for the first pass.