Concordance and dissonance: A genome-wide analysis of self-declared versus inferred ancestry in 10,250 participants from the HostSeq cohort
Bibliographic record
Abstract
Abstract Accurate characterization of human diversity is foundational to equitable genomics. In this study, we analyzed self-declared and genome-derived ancestry in 10,250 participants from the pan-Canadian HostSeq cohort. Using the alignment-free ntRoot algorithm on whole genome sequencing data, we inferred global and local ancestry at the continental super-population level and compared these with self-reported sociocultural identity categories. We observed high concordance among individuals self-identifying as White (98.8%), Black (97.2%), East Asian (96.1%), and South Asian (89.9%). Concordance was lower among those self-identifying as Hispanic (74.6%), Middle Eastern / Central Asian (67.9%), or Indigenous (40.7%), reflecting greater admixture complexity. Agreement between expected and inferred ancestry labels was modest (Cohen’s kappa κ = −0.01 unweighted; 0.35 weighted), and ancestry discordance was strongly associated with higher Shannon entropy of ancestry fractions. Principal component analysis of ntRoot-derived ancestry composition revealed tightly clustered profiles in some groups and broader, overlapping distributions in others, illustrating how sociocultural identities and genomic data capture distinct but intersecting dimensions of human diversity. These findings support the complementary use of genome-derived continental ancestry fractions alongside self-identification, particularly in settings where sociocultural labels may be incomplete, heterogenous, or poorly aligned with genetic background. This approach can improve scientific rigor and enhance inclusion in population-scale genomics while respecting the social meaning of identity. We emphasize that genetic ancestry estimates are not proxies for race, which is a social construct with no biological basis.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".