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Record W4387373828 · doi:10.1101/2023.10.04.560881

Ancestral genetic components are consistently associated with the complex trait landscape in European biobanks

2023· preprint· en· W4387373828 on OpenAlexaff
Vasili Pankratov, Massimo Mezzavilla, Serena Aneli, Daniela Fusco, James F. Wilson, Mait Metspalu, Paolo Provero, Luca Pagani, Davide Marnetto

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2023
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicForensic and Genetic Research
Canadian institutionsCentre for Global Health Research
FundersEuropean Regional Development FundUniversità degli Studi di TorinoUniversità Campus Bio-Medico di RomaEesti TeadusagentuurTartu ÜlikoolEuropean Commission
KeywordsBiobankEvolutionary biologyTraitBiologyGeographyConfoundingGenetic variantsDemographyGeneticsMedicine

Abstract

fetched live from OpenAlex

Abstract The genetic structure in Europe was mostly shaped by admixture between the Western Hunter-Gatherer, Anatolian Neolithic and Steppe’s Yamnaya ancestral components. Such structure is regarded as a confounder in GWAS and follow-up studies, and gold-standard methods exist to correct for it. However, it is still poorly understood to which extent these ancestral components contribute to complex trait variation in present-day Europe. In this work we harness the UK Biobank to address this question. By extensive demographic simulations and incorporating previous results obtained using the Estonian Biobank, we carefully evaluate the significance and scope of our findings. Heart rate, platelet count, monocyte percentage and many other traits show stratification similar to height and pigmentation traits, likely targets of selection and divergence across ancestral groups. The consistency of our results across biobanks indicates that these ancestry-specific genetic predispositions act as a source of variability and as potential confounders in Europe as a whole.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.776
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.041
GPT teacher head0.244
Teacher spread0.203 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations1
Published2023
Admission routes1
Has abstractyes

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