Phasing imputation

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

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.