electricsheepafrica/ssa-structural-variation-catalog

SSA Multi-ancestry Structural Variation Catalog (Germline, Synthetic)

Dataset summary

This dataset provides a germline structural variation (SV) catalog for a multi-ancestry cohort of 20,000 synthetic individuals with a strong focus on sub-Saharan African (SSA) ancestry. It complements the genome-wide SNP array synthetic dataset by adding copy number variants (CNVs) and small indels with explicit population-specific structural variants.

The cohort includes:

• Four SSA regional groups (West, East, Central, Southern).
• An African American women (AAW) group as an admixed African diaspora reference.
• European (EUR) and East Asian (EAS) reference panels.

SVs are simulated on a synthetic genome scaffold (chromosomes 1–22, each 100 Mb) and are not aligned to a real reference genome. The dataset is therefore suitable for methods development and benchmarking (e.g., ancestry-aware SV detection, population genetics, burden analysis), not for clinical or individual-level inference.

All data are fully synthetic and were generated under the GENOMICS Synthetic Data Playbook used across the Electric Sheep Africa dataset family.

Cohort design

Sample size and populations

• Total N: 20,000 synthetic individuals.

• Populations and sample sizes:

  • SSA_West: 3,000
  • SSA_East: 3,000
  • SSA_Central: 2,000
  • SSA_Southern: 2,000
  • AAW (African American women, admixed): 3,000
  • EUR (European reference): 4,000
  • EAS (East Asian reference): 3,000

• Sex distribution:

  • Male: 50%
  • Female: 50%

The SSA subgroups are intended to be compatible with other SSA-focused synthetic datasets from Electric Sheep Africa (e.g., SNP array, colorectal genomic, ovarian somatic), enabling cross-dataset method development.

Structural variation model

SV classes

The catalog includes two broad classes of germline structural variants:

• Copy number variants (CNVs)
  • CNV_del – deletions.
  • CNV_dup – duplications.
• Small indels (1–50 bp)
  • indel_del – small deletions.
  • indel_ins – small insertions.

Each variant is represented as a region on a synthetic chromosome with:

• chrom – synthetic chromosome ("1"–"22").
• start, end – 0-based coordinates within the 100 Mb chromosome.
• length_bp – event length in base pairs.

CNV and indel burden per individual

Per-sample SV burdens were tuned using literature-informed expectations from:

• Redon et al., Nature 2006 (first global CNV map).
• Sudmant et al., Nature 2015 (1000 Genomes integrated SV map).
• Collins et al., Nature 2020 (gnomAD-SV reference).

Target mean counts per individual (approximated in the generator):

• CNVs
  • CNV_del: mean ~80 deletions per individual (std ~25).
  • CNV_dup: mean ~60 duplications per individual (std ~20).
• Small indels (1–50 bp)
  • indel_del: mean ~200 deletions per individual (std ~50).
  • indel_ins: mean ~200 insertions per individual (std ~50).

This yields roughly 140 CNVs and 400 small indels per genome on average, producing a diverse but computationally manageable SV catalog.

Length distributions

SV lengths follow type-specific distributions:

• CNVs (CNV_del, CNV_dup)

  • Log10-normal length distribution.
  • Approximate median length ~100 kb.
  • Length range: 1 kb – 5 Mb.

• Indels (indel_del, indel_ins)

  • Uniform integer length.
  • Length range: 1 – 50 bp.

These parameters are anchored qualitatively to the size spectra reported in large-scale SV resources, particularly 1000 Genomes SV and gnomAD-SV.

Population-specific structural variants

A key design feature is the inclusion of population-enriched structural variants, motivated by:

• Redon et al. 2006 – CNVs with marked population differentiation.
• Collins et al. 2020 – numerous African- and non-African-enriched SVs in gnomAD-SV.

In the synthetic model:

• A fixed fraction of events are designated population-specific:

  • CNV_del: 5% of deletions.
  • CNV_dup: 5% of duplications.
  • indel_del: 2% of small deletions.
  • indel_ins: 2% of small insertions.

• For each population-specific SV:

  • One target population is chosen (e.g., SSA_West, EUR, EAS, AAW).
  • In the target population, carrier frequencies are drawn to be moderately common (roughly 5–25%).
  • In non-target populations, carrier frequencies are constrained to be very low (≤0.5%).

This structure yields many SVs where target/non-target frequency ratios exceed 5x, giving a clear population-specific signal for benchmarking ancestry-aware SV methods and population genetics pipelines.

Files and schema

1. sv_samples.parquet

One row per synthetic individual.

Core columns:

• sample_id – unique synthetic sample identifier.
• population – one of SSA_West, SSA_East, SSA_Central, SSA_Southern, AAW, EUR, EAS.
• region – SSA subregion (for SSA populations) or Non_SSA for reference panels.
• is_SSA – boolean flag for SSA populations.
• is_reference_panel – boolean flag for AAW/EUR/EAS reference groups.
• sex – Male or Female.

Burden summary columns:

• n_CNV_del – count of CNV deletions in this sample.
• n_CNV_dup – count of CNV duplications in this sample.
• n_indel_del – count of small deletions in this sample.
• n_indel_ins – count of small insertions in this sample.
• n_cnvs – total CNV count (n_CNV_del + n_CNV_dup).
• n_indels – total indel count (n_indel_del + n_indel_ins).
• n_sv_total – total SV count per sample.

These columns allow simple burden analyses by ancestry, region, and sex without loading the full event table.

2. sv_events.parquet

One row per SV carrier (i.e., per event per sample).

Core columns:

• sv_id – structural variant identifier (shared across carriers of the same event).
• sample_id – ID of the carrier.
• sv_type – CNV_del, CNV_dup, indel_del, or indel_ins.
• population – population label of the carrier sample.
• chrom – synthetic chromosome ("1"–"22").
• start – 0-based start coordinate (inclusive).
• end – end coordinate (exclusive).
• length_bp – event length in base pairs.
• is_population_specific – boolean flag; True for population-enriched events.
• target_population – population in which the event is enriched (if is_population_specific=True).

This table is the main event-level catalog for SV-based analyses.

3. sv_frequencies.parquet

One row per SV–population combination, summarizing carrier frequencies.

Core columns:

• sv_id – structural variant identifier.
• sv_type – SV type.
• population – population label.
• carrier_count – number of carriers in that population.
• carrier_frequency – carrier_count / N_population.
• is_population_specific – matches the flag in sv_events.parquet.
• target_population – target population for enriched SVs.

This table is designed for population genetics use cases (e.g., allele frequency spectra, Fst-like metrics, enrichment analyses) without needing to aggregate the full event table.

Generation and validation

Generation

The dataset was generated using the Python script:

• structural_variation/scripts/generate_structural_variation.py

Key steps:

1. Sample generation
   • Creates 20,000 individuals partitioned across the seven populations with the configured sex distribution.
2. SV event definition
   • For each SV type, defines a set of synthetic events with positions and lengths on the 22 synthetic chromosomes.
   • Distinguishes a subset of population-specific events with a target population.
3. Frequency and carrier assignment
   • For each event and population, draws carrier frequencies from Beta distributions (with different behavior for common vs low-frequency variants), modified for population-specific events.
   • Samples carrier individuals accordingly, generating the event-level and frequency tables.
4. Burden summarization
   • Aggregates per-sample SV counts by type and totals.

The configuration driving this process is stored in:

• structural_variation/configs/structural_variation_config.yaml
• Literature links are documented in:
• structural_variation/docs/LITERATURE_INVENTORY.csv

Validation

Validation follows the GENOMICS Synthetic Data Playbook and was performed using:

• structural_variation/scripts/validate_structural_variation.py

The validator reads the three Parquet tables and computes multiple checks, including:

• C01 – Sample size matches config
  • Confirms N = 20,000.
• C02 – Population sample sizes vs config
  • Per-population counts within an acceptable relative deviation (10%).
• C03 – Required columns present
  • Ensures essential schema columns in samples, events, and frequencies.
• C04 – SV burden per sample vs config
  • Compares observed mean counts by SV type to configured targets.
• C05 – SV length spectrum by type
  • Checks that min/median/max lengths are consistent with configured ranges.
• C06 – Population-specific enrichment
  • Quantifies target vs non-target carrier frequency ratios for population-specific SVs and confirms strong enrichment.
• C07 – Missingness in key variables
  • Ensures negligible missingness in key columns.

The validation outputs a Markdown report:

• structural_variation/output/validation_report.md

For the released version of this dataset, all defined checks completed with an overall status of PASS.

Intended use

This dataset is intended for:

• Methods development for SV detection, genotyping, and frequency estimation in multi-ancestry cohorts.
• Population genetics and ancestry-aware modeling of CNVs and indels, including SSA-focused questions.
• Benchmarking of burden tests and association pipelines that incorporate structural variation.
• Teaching and demonstration of SV analysis workflows without access to sensitive human data.

It is not suitable for:

• Clinical decision-making.
• Individual-level risk prediction.
• Inference about real individuals or specific real-world populations.

All samples and variants are fully synthetic and do not correspond to real persons.

Ethical and privacy considerations

• The dataset is entirely synthetic and contains no real patient data.
• Cohort labels (e.g., SSA regions, AAW, EUR, EAS) are intended for methodological realism only.
• Users should avoid framing analyses as statements about real-world groups and should instead treat this resource as a simulation tool.

License

• License: CC BY-NC 4.0.
• Non-commercial use is encouraged for research, teaching, and methods development.

Citation

If you use this dataset in your work, please cite:

Electric Sheep Africa. "SSA Multi-ancestry Structural Variation Catalog (Germline, Synthetic)." Hugging Face Datasets.

and, where appropriate, cite the SV resources that inspired the design:

• Redon R, et al. Global variation in copy number in the human genome. Nature. 2006.
• Sudmant PH, et al. An integrated map of structural variation in 2,504 human genomes. Nature. 2015.
• Collins RL, et al. A structural variation reference for medical and population genetics. Nature. 2020.