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Record W2981677813 · doi:10.1101/809244

Increased ultra-rare variant load in an isolated Scottish population impacts exonic and regulatory regions

2019· preprint· en· W2981677813 on OpenAlexaff
Mihail Halachev, Alison Meynert, Martin S. Taylor, Véronique Vitart, Shona M. Kerr, Lucija Klarić, Timothy J. Aitman, Chris Haley, James Prendergast, Carys Pugh, David Hume, Sarah E. Harris, David C. Liewald, Ian J. Deary, Colin A. Semple, James F. Wilson

Bibliographic record

VenuebioRxiv (Cold Spring Harbor Laboratory) · 2019
Typepreprint
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicGenomics and Rare Diseases
Canadian institutionsCentre for Global Health Research
FundersMedical Research CouncilChief Scientist Office, Scottish Government Health and Social Care DirectorateScottish Government
KeywordsBiologyShetlandFounder effectPopulationGeneticsEvolutionary biologyGenetic variationGenetic driftHuman geneticsGenetic heterogeneityBalancing selectionPhenotypeGeneGenotypeDemographyHaplotypeGeography

Abstract

fetched live from OpenAlex

Abstract Human population isolates provide a snapshot of the impact of historical demographic processes on population genetics. Such data facilitate studies of the functional impact of rare sequence variants on biomedical phenotypes, as strong genetic drift can result in higher frequencies of variants that are otherwise rare. We present the first whole genome sequencing (WGS) study of the VIKING cohort, a representative collection of samples from the isolated Shetland population in northern Scotland, and explore how its genetic characteristics compare to a mainland Scottish population. Our analyses reveal the strong contributions played by the founder effect and genetic drift in shaping genomic variation in the VIKING cohort. About one tenth of all high-quality variants discovered are unique to the VIKING cohort or are seen at frequencies at least ten fold higher than in more cosmopolitan control populations. Multiple lines of evidence also suggest relaxation of purifying selection during the evolutionary history of the Shetland isolate. We demonstrate enrichment of ultra-rare VIKING variants in exonic regions and for the first time we also show that ultra-rare variants are enriched within regulatory regions, particularly promoters, suggesting that gene expression patterns may diverge relatively rapidly in human isolates. Author Summary Population isolates provide a valuable window on the roles of rare genetic variation in human phenotypes, as a result of their unusual evolutionary histories, that often lead to relatively high frequencies of variants that are exceptionally rare elsewhere. Such populations show increased levels of background relatedness among individuals and are often subject to stronger genetic drift, leading to a higher frequency of deleterious variants. Here, for the first time, we present whole genome sequencing data from the Shetland population in Northern Scotland, encompassing 500 individuals, and compare these genomes to the mainland Scottish population. As expected we find the imprint of Shetland population history in the Shetland genome, with strong evidence for founder effects and genetic drift, but we also discover a relaxation of selective constraint across the genome. These influences have combined to endow the Shetland genome with thousands of ultra-rare genetic variants, not observed previously in other populations. Surprisingly these variants are significantly enriched in functional regions including protein coding regions of genes and regulatory elements. Among regulatory regions, promoters are particularly enriched for ultra-rare variants, suggesting the potential for rapid divergence of gene expression in isolates.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.009
Threshold uncertainty score0.018

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
Science and technology studies0.0010.001
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0030.000

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 head0.008
GPT teacher head0.212
Teacher spread0.204 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations2
Published2019
Admission routes1
Has abstractyes

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