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  "slug": "ancestry-stops-being-a-label",
  "edition": "2026-11-14T00",
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  "newsroom_title": "Health & Biotech",
  "angle": "Genomics is dropping categorical ancestry labels for continuous profiles, and the change alters risk prediction, not just terminology.",
  "headline": "Ancestry in genomics stops being a label and becomes a profile",
  "dek": "Across four preprints posted on 13 November, continuous ancestry beats categories at placing people, explains gene regulation, and sharpens disease timing.",
  "published_at": "2026-11-14T01:18:08Z",
  "labels": {
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    "trust_text": "Reported · 4 Manifests",
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    {
      "text": "In a preprint, continuous ancestry profiles correctly placed 78% of documented ancestry in the correct region, compared with 32% for categorical labels.",
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      "text": "In a preprint, a genome-wide scan identified 52 non-redundant loci where ancestry influences allelic regulation, and higher West African ancestry was associated with more allele-specific-expression-positive genes per person.",
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      "text": "In a preprint, Yq12 heterochromatin length ranged from about 1 to 52 Mb with a mean of 26.5 Mb, and Yq12-derived transcripts were found in haploid spermatids.",
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      "text": "In a preprint, the Mendelnoulli longitudinal model improved 1-year disease discrimination to a median AUC of 0.95 from 0.92 and flagged 8.6 more new cases per 100 people in the top 5% at risk with no prior diagnoses.",
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  "sections": [
    {
      "heading": "Categories lose",
      "paragraphs": [
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          "markdown": "In a preprint, researchers argue that genomics should stop sorting people into ancestry categories and start describing each person as a continuous profile. Applying haplotype painting and ordination, they show that continuous profiles capture substantially more ancestral information than traditional classification."
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          "markdown": "The gap is not subtle. Continuous profiles correctly placed 78% of documented ancestry in the correct region, compared with 32% for categorical labels. That is a measurement difference, not a matter of taste: a label that puts a person in the wrong region feeds every downstream analysis the wrong starting point, from population stratification to polygenic risk scores."
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          "markdown": "The practical point is that ancestry is a mixture, and a method that treats it as one keeps information a category throws away. For teams building risk models, the question is no longer whether to use continuous profiles but whether their existing pipelines can accept them."
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    {
      "heading": "Ancestry changes what genes do",
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          "markdown": "A second preprint shows ancestry is not only a labelling problem; it changes gene regulation itself. Using the MAGENTA cohort, the authors found that a higher proportion of West African ancestry is associated with an increased number of allele-specific-expression-positive genes per person, and a genome-wide scan identified 52 non-redundant loci across four population groups where ancestry influences allelic regulation."
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          "markdown": "One locus illustrates the point concretely: CSMD1, on chromosome 8, showed consistent directional allelic imbalance across multiple population groups. The finding challenges the European-centric bias in current studies of genomic regulation, since a model trained on one ancestry's regulatory patterns will misread others."
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          "markdown": "A third preprint looks at a part of the genome that categories ignore altogether. Using an assembly-independent method on male genomes, researchers measured the Yq12 heterochromatic array and found its length ranges from about 1 to 52 Mb, with a mean of 26.5 Mb, while the proportions of DYZ1 and DYZ2 repeats stayed near parity. Yq12-derived transcripts were expressed in haploid spermatids, suggesting these repetitive regions play a functional role in male germ-cell biology rather than sitting idle."
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    {
      "heading": "Timing matters too",
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          "markdown": "A fourth preprint adds the dimension the other three leave out: time. Mendelnoulli, a longitudinal model, integrates polygenic and monogenic genetic data to predict age-dependent disease risk, accounting for the fact that genetic effects rise and fall over a lifetime rather than staying constant."
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          "markdown": "Against static polygenic risk scores, the model improved held-out one-year discrimination for 28 tested diseases, with a median AUC of 0.95 compared with 0.92. Among people with no prior diagnoses, flagging the top 5% at highest risk identified 8.6 more new cases per 100 individuals than static models did. That is the difference between a score that describes risk in general and one that says when to look."
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          "markdown": "The model also recovered known differences in disease timing, such as earlier peak breast cancer effects for BRCA1, at 48 years, compared with BRCA2, at 66 years. A risk model that knows when a variant acts is more useful than one that only knows that it acts."
        }
      ]
    },
    {
      "heading": "What to weigh",
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          "markdown": "Taken together, the four preprints point in one direction: genomics tools should carry ancestry as a continuous, time-aware signal, not as a category stamped on a record. Continuous profiles place people better, ancestry-aware features capture regulation that categories miss, and longitudinal models sharpen when risk becomes real."
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          "markdown": "All four are preprints, not peer-reviewed papers, and the Yq12 transcript finding suggests a functional role rather than proving one. The ancestry results come from specific cohorts, and Mendelnoulli's gains are measured on held-out data. None of that weakens the direction of travel, but it does mean the clinical payoff is still to be demonstrated, not assumed."
        }
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  "unresolved": [
    "Whether continuous ancestry profiles improve polygenic risk scores in clinical use, not just in cohort analysis.",
    "What functional role, if any, Yq12-derived transcripts play in male germ-cell biology.",
    "Whether Mendelnoulli's gains hold outside the data it was validated on."
  ],
  "decision_card": {
    "not_for": [
      "Anyone needing peer-reviewed results",
      "Clinical decisions for individual patients"
    ],
    "useful_for": [
      "Genomics teams building risk models",
      "Developers of ancestry-aware precision medicine tools"
    ],
    "compare_against": [
      "Categorical ancestry labels in polygenic scores",
      "Static polygenic risk scores"
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