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Record W4394104236 · doi:10.6084/m9.figshare.20486767

Additional file 2 of Mendelian randomization of circulating proteome identifies actionable targets in heart failure

2022· dataset· en· W4394104236 on OpenAlexaff
Louis-Hippolyte Minvielle Moncla, Samuel Mathieu, Mame Sokhna Sylla, Yohan Bossé, Sébastien Thériault, Benoît J. Arsenault, Patrick Mathieu

Bibliographic record

VenueOpen MIND · 2022
Typedataset
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicMachine Learning in Bioinformatics
Canadian institutionsUniversité LavalInstitut universitaire de cardiologie et de pneumologie de Québec
Fundersnot available
KeywordsMendelian randomizationHeart failureComputational biologyProteomeBioinformaticsComputer scienceBiologyGeneticsMedicineInternal medicineGeneGenetic variants

Abstract

fetched live from OpenAlex

Additional file 2: Suppl. Table 1. Mendelian randomization INTERVAL (P < 1E-03). Results of MR analysis for the nineteen causal candidate proteins in INTERVAL. Table includes: the aptamer and corresponding id code along with the protein name; the number of SNPs used as IVs in MR for each protein; the median, minimum and maximum F-statistics value for IVs; the beta, standard error and p-values for Inverse Variance Weighted and Weighted Median methods; heterogeneity tests (Cochrane’s Q test and intercept Egger test). Suppl. Table 2. Mendelian randomization INTERVAL (P < 1E-03) (all results). Results of MR analysis for all the proteins with enough IVs in INTERVAL. Table includes: the aptamer and corresponding id code along with the protein name; the number of SNPs used as IVs in MR for each protein; the median, minimum and maximum F-statistics value for IVs; the beta, standard error and p-values for Inverse Variance Weighted and Weighted Median methods; heterogeneity tests (Cochrane’s Q test and intercept Egger test). Suppl. Table 3. Mendelian randomization INTERVAL (P < 1E-05). Results of MR analysis for the nineteen causal candidate proteins in INTERVAL. Table includes: the aptamer and corresponding id code along with the protein name; the number of SNPs used as IVs in MR for each protein; the median, minimum and maximum F-statistics value for IVs; the beta, standard error and p-values for Inverse Variance Weighted and Weighted Median methods; heterogeneity tests (Cochrane’s Q test and intercept Egger test). Suppl. Table 4. Mendelian randomization INTERVAL (Wald ratio; P < 1E-05). Results of mendelian randomization with Wald ratio, using INTERVAL as the exposure. Wald ratio was calculated using the most significant SNP for proteins not presenting enough instruments for previously described two-sample mendelian randomization. Table includes: the aptamer and corresponding id code along with the protein name; the corresponding lead SNP identification; the beta, standard error and p-values for the GWAS outcome, the GWAS pQTL and for the Wald ratio. Suppl. Table 5. Reverse Mendelian Randomization for the nineteen causal candidates. Results of reverse MR for the nineteen causal candidate proteins identified in the study. Table includes: the aptamer and corresponding id code along with the protein name; the number of SNPs used as IVs in MR for each protein; the beta, standard error and p-values for Inverse Variance Weighted and Weighted Median methods; heterogeneity tests (Cochrane’s Q test and intercept Egger test). Suppl. Table 6. Mendelian randomization deCODE (P < 1E-03). Results of mendelian randomization for all nineteen causal candidate proteins, using deCODE [21] as the exposure. Table includes: the aptamer and corresponding id code along with the protein name; the number of SNPs used as IVs in MR for each protein; the median, minimum and maximum F-statistics value for IVs; the beta, standard error and p-values for Inverse Variance Weighted and Weighted Median methods; heterogeneity tests (Cochrane’s Q test and intercept Egger test). Suppl. Table 7. Mendelian randomization deCODE (P < 1E-05). Results of mendelian randomization for all nineteen causal candidate proteins, using deCODE [21] as the exposure. Table includes: the aptamer and corresponding id code along with the protein name; the number of SNPs used as IVs in MR for each protein; the median, minimum and maximum F-statistics value for IVs; the beta, standard error and p-values for Inverse Variance Weighted and Weighted Median methods; heterogeneity tests (Cochrane’s Q test and intercept Egger test). Suppl. Table 8. Mendelian randomization deCODE (Wald ratio; P < 1E-05). Results of mendelian randomization with Wald ratio, using deCODE [21] as the exposure. Wald ratio was calculated using the most significant SNP for protein not presenting enough instruments for previously described two-sample mendelian randomization. Table includes: the aptamer and corresponding id code along with the protein name; the corresponding lead SNP identification; the beta, standard error and p-values for the GWAS outcome, the GWAS pQTL and for the Wald ratio. Suppl. Table 9. Ligand-receptor pairs generated from causal blood candidate proteins. Ligand-receptor interactions for causal candidate blood proteins by using the comprehensive repository reported by Shao et al. [24]. Bold are causal candidate blood proteins. Suppl. Table 10. Gene Ontology (molecular function) for ligand-receptor pairs generated from the causal candidate proteins. Enrichment of all ligand-receptor pairs in Gene Ontology (GO Molecular Function). Suppl. Table 11. Network nodes (proteins) and degree. Degree for each protein in the network. Suppl. Table 12. Pathway enrichment (KEGG) for the network. Summary results of enrichment for all proteins in the network by using the Kyoto Encyclopedia of Gene and Genomes (KEGG) database. Suppl. Table 13. Cross-phenotype association analysis of HF by using iCPAG. Summary of iCPAG results. Trait 1 is the GWAS data from the HERMES study; Trait2 is the trait-disorder, which is compared to trait1 for sharing a similar genetic architecture. Reported in the table: p-value, FDR and Bonferroni adjusted p-value for the Fisher’s exact test; the Chao-Sorensen similarity index between trait 1 and trait 2; the list of SNPs in common between trait 1 and 2; links to corresponding experimental factor ontology (EFO) in EMBL-EBI database. Suppl. Table 14. Multi-trait MR. Table summarizing the 31 traits and diseases used in the multi-trait MR. Suppl. Table 15. Results of multivariable MR corrected for cardiovascular traits. Summary of multivariable MR and univariate MR for BAG3, MIF and APOA5. Univariate exposure is HF, whereas multivariate exposure is HF corrected for the selected trait in parenthesis. Reported are: estimate (beta), se (standard error) and p-value. DBP: diastolic blood pressure; SBP: systolic blood pressure; CAD: coronary artery disease. Suppl. Table 16. Therapeutic Target Database (TTD) for the causal candidate proteins. Summary results of druggable genome in the Therapeutic Target Database (TTD) for the causal candidate proteins. Reported are: the gene symbol and name; target type; disease for which there is an indication; drugs associated to the target. NA: not available. Suppl. Table 17. Drug Gene interaction Database (DGIdb) for the causal candidate proteins. Summary of druggable genome in the Drug Interaction database (DGIdb) for the causal candidate proteins. Reported are: the gene symbol; drug associated with the target; sources and pmids from the National Library of Medicine. Suppl. Table 18. Open Targets for the causal candidate proteins. Summary of druggable genome in the Open Targets for the causal candidate proteins. Reported are: gene symbol; drugs associated with the target; drug type (sm: small molecule, ab: antibody); category ab is the prediction confidence that the target is tractable for the development of an antibody.

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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)0.001
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Dataset · Consensus signal: Dataset
Teacher disagreement score0.906
Threshold uncertainty score0.578

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.001
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)0.9060.000

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 head0.010
GPT teacher head0.270
Teacher spread0.260 · 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 designNot applicable
Domainnot available
GenreDataset

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".

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Citations0
Published2022
Admission routes1
Has abstractyes

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Same venueOpen MINDSame topicMachine Learning in BioinformaticsFrench-language works237,207