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Mitonuclear interactions influence Alzheimer's disease risk

2019· article· en· W2976280485 on OpenAlexaff
Shea J. Andrews, Brian Fulton‐Howard, Christopher Patterson, G. Peggy McFall, Alden L. Gross, Elias K. Michaelis, Alison Goate, Russell H. Swerdlow, Judy Pa

Bibliographic record

VenueNeurobiology of Aging · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMitochondrial Function and Pathology
Canadian institutionsUniversity of AlbertaWomen and Children’s Health Research Institute
FundersNational Institute on AgingNational Institutes of HealthJPB FoundationU.S. Department of Defense
KeywordsOdds ratioDementiaHaplotypeHazard ratioMitochondrial DNAInternal medicineSingle-nucleotide polymorphismHaplogroupBiologyGeneticsMedicineDiseaseBioinformaticsConfidence intervalAlleleGeneGenotype

Abstract

fetched live from OpenAlex

We examined the associations between mitochondrial DNA haplogroups (MT-hgs; mitochondrial haplotype groups defined by a specific combination of single nucleotide polymorphisms labeled as letters running from A to Z) and their interactions with a polygenic risk score composed of nuclear-encoded mitochondrial genes (nMT-PRS) with risk of dementia and age of onset (AOO) of dementia. MT-hg K (Odds ratio [OR]: 2.03 [95% CI: 1.04, 3.97]) and a 1 SD larger nMT-PRS (OR: 2.2 [95% CI: 1.68, 2.86]) were associated with elevated odds of dementia. Significant antagonistic interactions between the nMT-PRS and MT-hg K (OR: 0.45 [95% CI: 0.22, 0.9]) and MT-hg T (OR: 0.22 [95% CI: 0.1, 0.49]) were observed. Individual MT-hgs were not associated with AOO; however, a significant antagonistic interactions was observed between the nMT-PRS and MT-hg T (Hazard ratio: 0.62 [95% CI: 0.42, 0.91]) and a synergistic interaction between the nMT-PRS and MT-hg V (Hazard ratio: 2.28 [95% CI: 1.19, 4.35]). These results suggest that MT-hgs influence dementia risk and that variants in the nuclear and mitochondrial genome interact to influence the AOO of dementia.

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)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.445
Threshold uncertainty score0.401

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
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 science0.0000.000
Research integrity0.0000.000
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 head0.008
GPT teacher head0.245
Teacher spread0.236 · 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.

The models applied no category: nothing in the taxonomy fit this work.
Study designBench or experimental
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

Citations35
Published2019
Admission routes1
Has abstractyes

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