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Record W2912733745 · doi:10.1093/cvr/cvz030

Genome-wide identification of circulating-miRNA expression quantitative trait loci reveals the role of several miRNAs in the regulation of cardiometabolic phenotypes

2019· article· en· W2912733745 on OpenAlexaff
Majid Nikpay, Kaitlyn Beehler, Armand Valsesia, Jörg Hager, Mary‐Ellen Harper, Robert Dent, Ruth McPherson

Bibliographic record

VenueCardiovascular Research · 2019
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMicroRNA in disease regulation
Canadian institutionsOttawa HospitalUniversity of Ottawa
Fundersnot available
KeywordsBiologyExpression quantitative trait locimicroRNAGenome-wide association studySingle-nucleotide polymorphismQuantitative trait locusGeneticsMendelian randomizationPhenotypeGenetic variationGenetic associationAlleleGeneGenotypeGenetic variants

Abstract

fetched live from OpenAlex

AIMS: To identify genetic variants that have a regulatory impact on circulating microRNAs (miRNAs) and to connect genetic risk to blood traits/biomarkers through the circulating miRNAs. METHODS AND RESULTS: Leveraging miRNA-Seq data and the 1000 Genomes imputed genotypes, we carried out genome-wide association analysis for SNPs that regulate the expression of circulating miRNAs in a sample of 710 unrelated subjects of European ancestry. Wherever possible, we used data from the Framingham and the Geuvadis studies to replicate our findings. We found at least one genome-wide significant (P < 5e-8) miRNA-eQTL (mirQTL) for 143 circulating miRNAs. Overall each mirQTL explained a small portion (<1%) of variation in miRNA levels; however, we identified a few mirQTLs that explained 4% to 20% of variation in miRNA levels in plasma. Unlike trans-mirQTLs (P = 0.7), cis-mirQTLs tend to be also associated with their counterpart mature miRNAs (P < 0.0001), this suggests trans-mirQTLs exert their effect through processes that affect the stability of mature miRNAs; whereas, cis-mirQTLs mainly regulate the expression of primary-miRNAs. Next, we used the identified mirQTLs to investigate the links between circulating miRNAs with blood traits/biomarkers through Mendelian randomization analysis. We found miR-1908-5p plays an important role in regulating low-density lipoprotein (LDL), total cholesterol (TC), fasting glucose, HbA1c, and several lipid-metabolites in blood, whereas, miR-10b-5p mediates the trans-regulatory effect of the ABO locus on several blood proteins, coronary artery disease, and TC. Moreover, we demonstrated that a higher plasma level of miR-199a is causally associated with lower levels of LDL and TC. Finally, we found miR-143-3p and miR-145-5p are functionally related and mediate the effect of ZFPM2 on a number of its protein targets in blood including VEGFA, SERPINE1, and PDGFs. CONCLUSIONS: This study identifies SNPs that have a regulatory impact on circulating miRNAs, and underlines the role of several circulating miRNAs in mediating the effect of a number of GWAS loci on cardiometabolic phenotypes.

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: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.005

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

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 head0.025
GPT teacher head0.301
Teacher spread0.276 · 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

Citations81
Published2019
Admission routes1
Has abstractyes

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