Evidence map›Paper›PMID 34086846›Full record

ArticleBioinformatics (Oxford, England)2021

TIGA: target illumination GWAS analytics.

Jeremy J Yang, Dhouha Grissa, Christophe G Lambert, Cristian G Bologa, Stephen L Mathias, Anna Waller, David J Wild, Lars Juhl Jensen, Tudor I Oprea

Open access · greenAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Bioinformatics (Oxford, England), 2021. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 13 papers.

0numbers the graph read from it
0cells of the map it votes in
13citing papers in PubMed
0.8field-weighted citation impact, top 29% of its field
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

13 citing papers in PubMed, 17 citations in OpenAlex.

  1. A Framework for Benchmarking Pathway Reconstruction Algorithms.bioRxiv : the preprint server for biology · 2026
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

9 authors at 3 institutions in 2 countries.

Jeremy J YangDivision of Translational Informatics, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA.ORCID 0000-0002-1476-6192
Dhouha GrissaNovo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark.
Christophe G LambertDivision of Translational Informatics, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA.
Cristian G BologaDivision of Translational Informatics, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA.
Stephen L MathiasDivision of Translational Informatics, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA.
Anna WallerDepartment of Pathology, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA.
David J WildIntegrative Data Science Laboratory, School of Informatics, Computing and Engineering, Indiana University, Bloomington, IN 47408, USA.
Lars Juhl JensenNovo Nordisk Foundation Center for Protein Research, Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen 2200, Denmark.ORCID 0000-0001-7885-715X
Tudor I OpreaDivision of Translational Informatics, Department of Internal Medicine, University of New Mexico Health Sciences Center, Albuquerque, NM 87131, USA.ORCID 0000-0002-6195-6976
University of New Mexico · USUniversity of Copenhagen · DKIndiana University Bloomington · US

Funding

Machine Learning Model Validation for AD/ADRDU24CA224370 · NCI · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI EDWARDS, JEREMY S · 2018 to 2023
$6.4M
Illuminating the Druggable Genome Knowledge Management Center (IDG KMC)U54CA189205 · NCI · UNIVERSITY OF NEW MEXICO HEALTH SCIS CTR · PI OPREA, TUDOR I · 2014 to 2017
$5.7M
NCI NIH HHS U24 CA224370NCI NIH HHS U54 CA189205
6 · The paper itself

Abstract

motivationGenome-wide association studies can reveal important genotype-phenotype associations; however, data quality and interpretability issues must be addressed. For drug discovery scientists seeking to prioritize targets based on the available evidence, these issues go beyond the single study.

resultsHere, we describe rational ranking, filtering and interpretation of inferred gene-trait associations and data aggregation across studies by leveraging existing curation and harmonization efforts. Each gene-trait association is evaluated for confidence, with scores derived solely from aggregated statistics, linking a protein-coding gene and phenotype. We propose a method for assessing confidence in gene-trait associations from evidence aggregated across studies, including a bibliometric assessment of scientific consensus based on the iCite relative citation ratio, and meanRank scores, to aggregate multivariate evidence.This method, intended for drug target hypothesis generation, scoring and ranking, has been implemented as an analytical pipeline, available as open source, with public datasets of results, and a web application designed for usability by drug discovery scientists. AVAILABILITY AND IMPLEMENTATION: Web application, datasets and source code via https://unmtid-shinyapps.net/tiga/. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Indexed as

Genome-Wide Association StudyLightingGenotypePhenotypePolymorphism, Single Nucleotide

Identifiers

PMID34086846
PMCPMC11025677
OpenAlexW3164648392

What OpenQuestion holds

Textmetadata
Read underepoch 390

Registered trials

None linked

Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.