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Ancestry methods & validation

This page explains how Yeliztli infers ancestry, and — just as importantly — the limitations of those methods. It's the deeper companion to the user-facing Ancestry module.

Ancestry is an estimate, not an identity

Everything here produces statistical estimates with genuine uncertainty. Genetic ancestry is a continuous, model- and reference-dependent quantity — not a fixed label, and not a statement about culture, ethnicity, or identity.

Yeliztli uses a two-tier system:

  • Tier 1 (always available, instant): genome-wide global ancestry via PCA, admixture proportions, and haplogroups.
  • Tier 2 (optional, slower): local ancestry inference — "painting" each chromosome by ancestral origin.

Tier 1 — Global ancestry by PCA

Principal Component Analysis (PCA) is the standard way to summarise genome-wide genetic structure: a handful of principal components capture the major axes of variation that separate populations 1. Yeliztli does not re-run PCA on your data; instead it projects you onto a pre-computed reference space.

  • Reference panel & markers. A panel of 3,419 reference individuals spanning 7 super-populations — African (AFR), American (AMR), Central/South Asian (CSA), East Asian (EAS), European (EUR), Middle Eastern (MID), and Oceanian (OCE) — with 8 principal components (their significance assessed by Tracy–Widom statistics).
  • Ancestry-informative markers (AIMs). Projection uses 5,000 AIMs — markers chosen because their allele frequencies differ most between populations. Carefully selected AIMs can assign continental ancestry with high accuracy using only a small fraction of the genome 2.
  • Projection onto reference PCs. Reusing pre-computed PC loadings from a reference panel (rather than re-deriving them) is an established, accurate approach to ancestry inference 3.

Projection shrinkage on sparse data

Markers your file doesn't cover are mean-imputed before projection. Projected PCs are known to suffer a shrinkage bias toward the origin, which grows as coverage drops 4. On sparse genotypes this can pull you toward the centre of the PCA plot and soften the signal — which is one reason Yeliztli reports a coverage fraction and can return Uncertain.

Admixture proportions

PCA places you in a space; admixture estimation turns that position into fractions:

  • Primary estimate — NNLS. Your coordinates are decomposed into per-population proportions by non-negative least squares (proportions can't be negative and sum sensibly). Combining PCA with NNLS is a fast, validated way to estimate ancestry proportions, even with substantial missing data 5. A 95% confidence interval is produced by bootstrapping (100 iterations, resampling AIMs).
  • Secondary estimate — kNN. A k-nearest-neighbours estimate (k = 15) is computed independently; the cosine similarity between the NNLS and kNN vectors becomes a confidence signal. When the two disagree, confidence is lowered.

How a call is decided

  • A confident single-population call requires AIM coverage above ~55%; below that, Yeliztli returns Uncertain rather than guess.
  • If your second-nearest population centroid is not much farther than the nearest (within ~3×), you're reported as Admixed rather than forced into one label.
  • Quality flags (low coverage, narrow centroid margin) travel with the result.

Haplogroups

Yeliztli also assigns deep-lineage haplogroups from lineage-defining variants:

  • Mitochondrial (maternal) haplogroups via the PhyloTree reference phylogeny, the widely accepted reference tree for human mtDNA variation 6.
  • Y-chromosome (paternal) haplogroups via the ISOGG tree — only for samples inferred as XY (skipped otherwise).

Tier 2 — Local ancestry inference (optional)

Where Tier 1 gives one genome-wide summary, local ancestry inference (LAI) estimates the ancestral origin of each segment of each chromosome — "chromosome painting." This is the optional LAI bundle (large download, Java 8+, ~15–30 minutes per sample; see reference data).

The pipeline first statistically phases your genotypes against a reference panel (Beagle), then assigns ancestry to short windows using machine-learning models, and smooths the result. This is the same family of methods as RFMix, a discriminative (random-forest) approach that is fast and robust for continental-scale local ancestry 7, and as the large-panel deconvolution pipelines used by consumer-genomics services 10.

LAI accuracy depends on phasing and reference panels

Two dependencies materially affect LAI quality:

  • Phase quality. Phase-based LAI degrades when statistical phasing is imperfect; switch errors directly reduce accuracy 9.
  • Reference representation. Accuracy is ancestry-specific and depends on having a well-matched reference panel. In benchmarks, true-positive rates were ~96–99% for European and African tracts but notably lower (~88–94%) for Indigenous-American tracts, and mis-calls tended to default toward European ancestry 8.

Treat painted segments — especially for under-represented ancestries — as estimates, not ground truth.

Limitations & honest caveats

  • Reference panels bound what can be seen. Populations that are sparsely represented in the reference panel (e.g. MID, OCE, many Indigenous groups) are inferred less reliably 8.
  • PCA itself has pitfalls. PCA results can be sensitive to data processing and ascertainment and can be over-interpreted; they should be read as a summary of structure, not a literal map of populations 11.
  • Array coverage matters. Sparse or low-coverage files shrink toward the centre of the PCA space 4; Yeliztli surfaces coverage and downgrades confidence accordingly.
  • It is not an identity test. Admixture percentages and haplogroups describe genetic similarity to reference groups; they don't define who you are.

  1. Principal components analysis corrects for stratification in genome-wide association studies (Price et al., 2006, Nature Genetics). ↩

  2. Ancestry informative markers for fine-scale individual assignment to worldwide populations (Paschou et al., 2010, J. Med. Genet.). ↩

  3. Improved ancestry inference using weights from external reference panels (Chen et al., 2013, Bioinformatics). ↩

  4. Efficient toolkit implementing best practices for principal component analysis of population genetic data (Privé et al., 2019, Bioinformatics). ↩↩

  5. PANE: fast and reliable ancestral reconstruction on ancient genotype data with non-negative least square and principal component analysis (de Gennaro et al., 2025, Genome Biology). ↩

  6. Updated comprehensive phylogenetic tree of global human mitochondrial DNA variation (PhyloTree) (van Oven & Kayser, 2009, Human Mutation). ↩

  7. RFMix: a discriminative modeling approach for rapid and robust local-ancestry inference (Maples et al., 2013, Am. J. Hum. Genet.). ↩

  8. Characterizing features affecting local ancestry inference performance in admixed populations (Honorato-Mauer et al., 2024, Am. J. Hum. Genet.). ↩↩

  9. Phase-free local ancestry inference mitigates the impact of switch errors on phase-based methods (Avadhanam et al., 2025, G3). ↩

  10. A scalable pipeline for local ancestry inference using tens of thousands of reference haplotypes (Durand et al., 2021). ↩

  11. Principal Component Analyses (PCA)-based findings in population genetic studies are highly biased and must be reevaluated (Elhaik, 2022, Scientific Reports). ↩