Genomic Imputation Pipeline

LifeMetrics operates a GRCh38 genomic imputation pipeline that transforms sparse genotype datasets, including genome-wide SNP-array data, into substantially denser genomic representations for downstream computation. The pipeline combines directly observed genotypes with high-coverage 1000 Genomes reference haplotypes, linkage disequilibrium structure, reference-based phasing and Beagle 5.5 statistical imputation.

The system is not designed to represent statistical inference as direct sequencing. It preserves the distinction between measured genotypes, phased observations and inferred genotypes through explicit provenance, engine and per-variant quality fields. An imputed call enters the standard downstream dataset only after reference harmonisation, conservative marker QC, engine completion checks, genotype validation and application of the production quality threshold.

Quality assurance is intentionally multi-layered. Reference resources are validated before execution; accepted and rejected markers are counted; ShapeIT4 and Beagle artifacts are verified; quality distributions are summarised; and observed source genotypes are retained. Empirical accuracy is treated as a separate question, addressed through masked-genotype experiments that compare inferred calls against withheld high-confidence truth genotypes.

Genomic imputation estimates genotypes at loci not directly assayed in an input sample by identifying haplotype patterns shared with a population reference panel. The resulting dataset can improve marker coverage for computational analyses while retaining uncertainty at the level of each inferred variant.

The LifeMetrics implementation is structured as an auditable analytical pipeline rather than a single inference command. It performs input parsing, coordinate and allele harmonisation, strict marker acceptance, chromosome-level VCF preparation, reference-based phasing, imputation, identifier annotation, post-imputation QC, provenance-aware recombination and production of complementary machine-readable artifacts.

2.1 Terminology

Sparse genotyping platforms assay a selected subset of common genomic positions. Many downstream models, association resources and haplotype analyses require variants that are not directly represented by that subset. Statistical imputation increases marker density by using correlations between nearby alleles in phased reference haplotypes.

The value of imputation is therefore conditional rather than absolute. It can increase coverage for polygenic score reconstruction, pharmacogenomic lookup, GWAS matching, research interpretation and population-genetic analyses, but performance varies by locus, allele frequency, input density and reference-panel representation.

observed genotypes + population reference haplotypes
                    + linkage disequilibrium
                              ↓
             statistically inferred genotypes

LifeMetrics accordingly evaluates the additional data in relation to its statistical support and provenance. The appropriate performance question is not how many variants were added, but whether the denser dataset preserves uncertainty, reproducibility and the distinction between measurement and inference.

The workflow begins with S3 ingestion and routing to a containerised LifeMetrics worker scheduled through AWS Batch. The worker processes each chromosome against mounted reference resources, retaining intermediate and final evidence required for audit.

Figure 1 — End-to-end processing from sparse input through harmonisation, phasing, imputation, annotation, filtering and provenance-aware merge.

The production configuration uses ShapeIT4 for reference-based phasing and Beagle 5.5 for genotype imputation. Architectural support for IMPUTE5 permits controlled alternative or comparative modes, but does not imply that every production genotype is dual-imputed.

Input observations are parsed into chromosome-specific collections containing chromosome, genomic position, genotype and rsID where available. Chromosome labels are normalised, variants are ordered by genomic position and compatible records are written to prepared GRCh38 VCFs.

5.1 Marker Matching

Markers are matched first by rsID and then, where necessary, by GRCh38 chromosome and position. Coordinate agreement alone is insufficient: the input genotype must be representable using the reference allele pair. This prevents positional coincidence from being mistaken for allele compatibility.

5.2 Conservative Strand Handling

The pipeline does not automatically reverse-complement every mismatch. A/T and C/G markers are strand-ambiguous because complementation does not uniquely establish orientation. When compatibility cannot be resolved, the marker is rejected and counted rather than silently transformed.

The principal reference resource is the high-coverage 1000 Genomes dataset represented in GRCh38 coordinate space. Chromosome-specific phased VCFs provide the population haplotypes from which ShapeIT4 and Beagle infer local haplotype structure and unobserved genotypes.

• Chromosome-specific phased reference VCFs
• Cleaned and normalised derivative VCFs
• GRCh38 chromosome-specific recombination maps
• dbSNP GRCh38 position-to-rsID resources
• Tabix and other random-access indexes

Mandatory resources are validated before imputation begins. Missing reference VCFs, maps or required contigs cause failure rather than partial execution against an incomplete panel. Recombination-map contig labels may be normalised between forms such as 1 and chr1; the underlying genetic map is not arbitrarily altered.

Reference-panel composition influences inferential performance. Its use provides broad population haplotype coverage, but does not guarantee uniform accuracy for every ancestry, region or allele-frequency class.

The prepared-VCF stage applies deliberately conservative QC before statistical processing. Per-chromosome counts make the usable evidence entering imputation explicit and auditable.

Prepared VCFs identify GRCh38, use standard genotype representation, and are sorted and indexed with established genomic tooling. This creates a controlled boundary between raw parsing and statistical inference. Post-imputation acceptance further requires a suitable biallelic SNP, a complete called genotype, successful conversion, non-duplicate identity and an acceptable quality metric.

Phasing estimates the arrangement of heterozygous alleles across homologous chromosome copies. An unphased genotype such as 0/1 records the presence of two alleles; 0|1 additionally records their inferred haplotype orientation. This information is central to linkage-based imputation.

For each chromosome, ShapeIT4 receives the prepared target VCF, the corresponding phased high-coverage reference chromosome, the GRCh38 recombination map and multithreaded execution parameters. Successful chromosome outputs are retained and can be concatenated into a phased target artifact.

8.1 Controlled Fallback

If ShapeIT4 fails, configuration determines whether Beagle may apply internal phasing or whether the run must terminate. A fallback result is recorded as such and is not represented as ShapeIT4 output. Where ShapeIT4 is mandatory, absence of its expected artifact is fatal.

Beagle 5.5 is the current production imputation engine. It receives target genotypes, high-coverage reference haplotypes, recombination information and configured computational parameters for each chromosome, producing phased chromosome-specific imputed VCF data.

These controls permit computational tuning without changing the surrounding ingestion, harmonisation, QC or output architecture. Beagle emission is not synonymous with LifeMetrics acceptance: every candidate record remains subject to post-imputation genotype, ambiguity, duplicate and quality checks.

Optional IMPUTE5 Mode

The architecture supports beagle, impute5 and both modes where configured. The production mode described here is Beagle and should not be interpreted as routine dual-engine inference.

Following imputation, variants are annotated with dbSNP rsIDs where an appropriate GRCh38 chromosome-position mapping is available. The pipeline maintains chromosome-specific position-to-rsID resources whose contig representation is compatible with the reference dataset.

Identifier restoration is important because many downstream resources are keyed by rsID rather than genomic coordinates. Representative uses include polygenic score calculation, pharmacogenomic matching, GWAS lookup, research interpretation and variant-centric reporting.

10.1 Annotation Boundaries

An rsID is an identifier, not evidence of analytical quality or clinical significance. Annotation occurs after statistical inference and does not elevate an imputed genotype to observed status. Coordinate and allele identity remain necessary to disambiguate identifiers that have changed, merged or acquired multiple mappings across resource releases.

The pipeline searches source VCF records for DR2, R2 and INFO, in that order. The numerical value and metric identity are retained independently so that semantically different scores are not collapsed into an unlabeled number.

In the supplied production configuration, an imputed variant requires a quality value of at least 0.60 to enter the normal flattened dataset. This is an operational QC policy, not a universal biological boundary between correct and incorrect calls.

Run summaries also retain the number of scored variants, mean quality and counts at or above 0.8 and 0.3. Distribution monitoring can identify an abnormal run even when many individual variants exceed the minimum threshold.

Directly observed source genotypes are collected separately and retained in the final analytical representation. Imputed records carry distinct provenance and, where applicable, the responsible engine, quality value and quality-metric identity.

Figure 2 — Provenance-aware assembly prevents laboratory observations and statistical inferences from becoming indistinguishable.

Duplicate prevention uses genomic identity keys during assembly. This is especially important in comparative engine modes. Downstream systems can consequently apply application-specific evidence rules without losing the original distinction between measurement and inference.

The pipeline emits complementary representations rather than redundant copies. Flattened data support variant-centric workflows, while compressed and indexed VCF artifacts preserve genotype structure, phase and genomic queryability.

Figure 3 — Complementary analytical, phased, QC and operational artifacts generated for each imputation.

Compressed VCF files have Tabix indexes, enabling efficient region queries. An artifact is identified as ShapeIT4-phased only when ShapeIT4 generated the underlying chromosome data.

The workload is implemented as a high-compute AWS Batch process rather than a short-lived function. Lightweight ingestion and routing are separated from chromosome-scale genomic processing. AWS Batch schedules a reproducible container onto compatible managed EC2 capacity, while EFS provides mounted reference resources.

Runtime dependencies include Python 3.12-compatible tooling, boto3, pysam, bcftools, bgzip, tabix, Java, ShapeIT4, Beagle and IMPUTE5 tools where configured. Containerisation constrains the software environment; reproducibility additionally depends on explicit recording of container and reference versions.

S3 input → routing → AWS Batch job → EC2 container
                           ↕
                    EFS reference data
                           ↓
                  grouped S3 outputs

Quality assurance follows a conservative-failure design: uncertain information is rejected or explicitly flagged rather than converted into apparent certainty. Missing mandatory references, unresolved allele compatibility, absent expected engine outputs and unacceptable quality support prevent normal acceptance.

15.1 Run-Level Summary

• Input and output file identity, format and row counts
• Reference panel, phasing tool and imputation mode
• Minimum quality threshold, chromosome settings and thread count
• Beagle memory allocation and chromosomes processed
• Quality-value count, mean and threshold distributions
• Per-chromosome prepared-VCF acceptance and rejection counts

15.2 Structural Record Checks

Before inclusion, each imputed record must represent a suitable biallelic SNP, have a complete genotype, be convertible into the target representation, avoid conservative ambiguity exclusions and not duplicate an existing output identity. The statement “Beagle emitted a record” is therefore not equivalent to “LifeMetrics accepted the genotype.”

LifeMetrics separates computational QC from empirical analytical validation. QC establishes that the pipeline followed its specified controls. Empirical validation measures how accurately genuinely withheld genotypes are recovered against a high-confidence truth set.

Figure 4 — Eight independent layers spanning resources, marker preparation, phasing, inference, variant acceptance, run monitoring and provenance.

No single imputation score substitutes for this layered framework. Reference integrity, evidence entering the model, completion of expected stages, record-level support, aggregate behaviour and data provenance answer different analytical questions.

Masked-genotype validation begins with samples for which dense, high-confidence genotypes are known. Selected truth genotypes are removed from the imputation input, the normal production pipeline is executed, and inferred calls are compared with the retained truth.

retain truth → mask genotype → run production pipeline
             → recover imputed call → compare with truth

17.1 Required Measurements

• Total masked and successfully imputed variants
• Genotype and allele concordance
• Missing or imputation-failure rate
• Heterozygous, homozygous-reference and homozygous-alternate concordance
• Performance by chromosome, genotype class, density and variant category

17.2 Quality Calibration

Observed concordance should be measured within quality bands such as 0.60–0.69, 0.70–0.79, 0.80–0.89, 0.90–0.94 and 0.95–1.00. This tests whether reported statistical support predicts empirical accuracy and whether the operational 0.60 threshold is suitable for a stated downstream use.

Imputation performance is not uniform across the allele-frequency spectrum. Common variants are generally represented by more reference haplotypes and are typically easier to infer than rare variants. Aggregate concordance can therefore conceal material differences between frequency bands.

Validation should report performance by allele frequency or minor allele frequency as determined from the applicable reference resource. It should also stratify results by input marker density because reduced observed evidence can alter local haplotype assignment and downstream inference.

18.1 Ancestry-Stratified Evaluation

Accuracy can depend on the degree to which a sample’s ancestry is represented by the reference panel. A globally deployed platform should include multiple ancestry backgrounds and report performance separately where sample numbers support interpretable estimates. Relatedness between validation samples and reference individuals must also be considered to avoid inflated results.

The imputation pipeline is an enabling analytical layer. It does not independently validate every interpretation built from its outputs. Each downstream application must define acceptable evidence, quality thresholds, coverage requirements and confirmation policies.

A clinically consequential genotype may require direct or orthogonal laboratory confirmation even when its imputation quality is high. Conversely, a research model may accept statistically supported imputed SNPs under a predefined threshold policy. Preservation of SOURCE, ENGINE, QUALITY and QUALITY_METRIC permits those decisions to remain application-specific.

For polygenic analysis, imputation can improve the availability of score variants that were not directly assayed. A valid scoring workflow must nevertheless evaluate variant coverage, effect-allele harmonisation, imputation support, score reconstruction and population calibration independently.

The retained <sample>-imputed-phased.vcf.gz artifact provides an interface to advanced ancestry methods. Unlike a flattened genotype table, it retains haplotype orientation and linkage information that can support future chromosome painting, local ancestry inference, regional decomposition and haplotype-based population similarity.

These capabilities constitute a technical foundation, not a validation claim. Fine-scale ancestry and other haplotype-aware analyses require independent truth definitions, population sampling, calibration and performance evaluation. Completion of phasing and imputation does not itself establish ancestry accuracy.

Imputation is statistical inference rather than sequencing, and phasing is itself inferred. Quality varies across genomic regions, allele-frequency classes and populations. Rare variants are generally more difficult to impute than common variants, and performance depends on reference-panel representation and input marker density.

A high quality score expresses stronger model support but is not direct laboratory confirmation. The operational threshold of 0.60 is a LifeMetrics pipeline policy whose suitability must be empirically calibrated for each intended use. Aggregate concordance does not establish identical performance for every locus or genotype class.

Clinically consequential use may require orthogonal or direct confirmation under the requirements of the specific application. Likewise, polygenic scores, pharmacogenomics, disease-risk analysis, ancestry, carrier status and trait reporting require validation beyond successful imputation.

Reference releases, software versions and population composition can change results. These factors should be treated as controlled analytical inputs and re-evaluated when materially revised.

22.1 Reproducibility

Each analysis should be reproducible from the input file, container and pipeline versions, reference resource versions, runtime configuration, quality threshold, phasing and imputation settings, run summary and detailed execution log. Recommended explicit version fields include LifeMetrics, Beagle, ShapeIT4, the reference panel, dbSNP, recombination maps and the container image.

Where every version is not yet persisted in a single output manifest, consolidated manifest generation is a recommended enhancement rather than an assumed current capability. Grouping analytical outputs, indexes, summaries and logs in one sample-specific S3 hierarchy already provides the structural basis for this evidence package.

22.2 Conclusion

The LifeMetrics pipeline does not treat imputation as the indiscriminate filling of missing SNPs. It implements an auditable sequence of GRCh38 harmonisation, conservative allele QC, reference-based phasing, high-coverage population-reference imputation, per-variant quality assessment, provenance preservation and structured validation.

Its appropriate measure of quality is the ability to produce a denser genomic dataset while preserving uncertainty, reproducibility and the distinction between direct measurement and statistical inference. Empirical performance remains demonstrable through masking experiments stratified by quality, genotype, frequency, density and population context.