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Record W2885979645 · doi:10.1161/circ.133.suppl_1.p185

Abstract P185: Novel Loci for Blood Pressure Using Gene-alcohol Interactions and 1000g Imputed Data

2016· article· en· W2885979645 on OpenAlexaff
Mary F. Feitosa, Thomas W Winkler, Amy R Bentley, Michael R Brown, Traci M Bartz, Daniel I Chasman, Rajkumar Dorajoo, Myriam Fornage, Nora Franceschini, Xiuqing Guo, Stella Aslibekyan, Caroline Hayward, Sharon L Kardia, Kurt Lohman, Ruth J Loos, Jonathan Marten, Bamidele Tayo, Dina Vojinović, Wang Xu, Alanna Morrison, Dabeeru C Rao, Ingrid B Borecki, Michael A Province, Aldi T Kraja, L. Adrienne Cupples

Bibliographic record

VenueCirculation · 2016
Typearticle
Languageen
FieldMedicine
TopicAlcohol Consumption and Health Effects
Canadian institutionsBentley (Canada)
Fundersnot available
KeywordsMedicineBlood pressureAlcohol consumptionGenome-wide association studySNPPulse pressureGeneticsSingle-nucleotide polymorphismInternal medicineDiastoleGeneOncologyAlcoholGenotypeBiology

Abstract

fetched live from OpenAlex

Alcohol consumption has a complex relationship with blood pressure (BP). Whereas heavy alcohol consumption is associated with high BP, moderate alcohol consumption is associated with unchanged or modestly lower BP. We performed genome-wide meta-analysis of single SNP x alcohol drinking status interaction on systolic (SBP), diastolic (DBP), mean arterial (MAP), and pulse (PP) blood pressure using 1000G imputed data. Our preliminary results comprise 23 European ancestry studies, including 87,098 individuals with known drinking status. Interaction associations were preformed using linear regression with main and interaction effects and accounting for family structure as needed. Meta-analyses were performed with METAL, for a 2 degrees of freedom joint test, after applying study- and meta-based genomic control adjustments. Results were further filtered if the meta-analysis N < 5,000 individuals or if the number of cohorts contributing to meta-analysis were < 3 for each SNP. For DBP, six novel loci (TRAF3IP1, ANKRD36, NLGN1, UBE3D, OFCC1 and C1orf167) were identified (p < 5E-08). Known BP loci were found to be significant using the same interaction model, including MTHFR, CLCN6, NPPB, MECOM, NOS3, CACNB2, SH2B3, ATXN2, ALDH2, ATP2B1, CSK, FURIN and ZNF831. The significant DBP genes are enriched for hypertension (p-FDR=8.5E-08) and cardiac arrhythmias (p-FDR=5.3E-07) disease loci. MAP analyses identified similar loci as for DBP, while for SBP, ANKRD36 SNPs yielded the most significant associations. For PP, MYPN variants were most significant. In general for the joint test, known BP gene variants showed non-significant levels of meta-heterogeneity and common allele frequency (≥5%), whereas the novel loci often presented meta-heterogeneity and low allele frequency (≈1-5%). TRAF3IP1 interacts among others with TRAF3, reported in mice involved in a new pathway TRAF3-TBK1-AKT for cardiac hypertrophy and heart failure. ANKRD36 interacts with PHLPP2, involved in rats in FKBP51-PHLPP2-AKT signaling during cerebral ischemia/reperfusion injury. NLGN1 has been reported to associate with BP in ASCOT study (N=3,802), while in our meta-analysis out of 6 contributing studies, the joint test significance originates only from the GS_SFHS (N=6,504). Another significant result was C1orf167- rs12561919, a missense SNP considered cis-eQTL for neighboring BP known genes CLCN6 and MTHFR. Employing SNP x drinking status interaction for BP traits, we identified both novel and previously known BP genes. Our findings may provide insights into mechanisms underlying BP and inform lifestyle and therapeutic BP interventions.

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)0.016
metaresearch head score (Gemma)0.029
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 score0.016
Threshold uncertainty score0.085

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0160.029
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0040.020
Bibliometrics0.0060.012
Science and technology studies0.0010.001
Scholarly communication0.0030.001
Open science0.0030.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0140.001

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 head0.256
GPT teacher head0.425
Teacher spread0.170 · 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

Citations0
Published2016
Admission routes1
Has abstractyes

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