Diabetes in the post-GWAS era.

Constantin Polychronakos, Maha Alriyami

Abstract readComment

In one paragraph

Article in Nature genetics, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.

0numbers the graph read from it

0cells of the map it votes in

6citing papers in PubMed

–field-weighted citation impact

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

6 citing papers in PubMed.

Single-cell eQTL mapping reveals convergent glial-neuronal risk architecture in Parkinson's disease.bioRxiv : the preprint server for biology · 2026
Article
Polymorphisms inBiomedicines · 2025
Review
Molecular Mechanisms of the Melatonin Receptor Pathway Linking Circadian Rhythm to Type 2 Diabetes Mellitus.Nutrients · 2023
Review
Diversity and inclusion in genomic research: why the uneven progress?Journal of community genetics · 2017
Article
Association of CpG-SNP and 3'UTR-SNP of WFS1 with the Risk of Type 2 Diabetes Mellitus in an Iranian Population.International journal of molecular and cellular medicine · 2017
Article
HASE: Framework for efficient high-dimensional association analyses.Scientific reports · 2016
Article

4 · The record

Corrections and comments

Comment on
Genetic fine mapping and genomic annotation defines causal mechanisms at type 2 diabetes susceptibility loci.2015

5 · Who and what money

Authors and funding

2 authors.

Constantin PolychronakosThe Endocrine Genetics Laboratory, Child Health and Human Development Program and Department of Pediatrics, McGill University Health Centre Research Institute, Montreal, Quebec, Canada.

Maha AlriyamiThe Endocrine Genetics Laboratory, Child Health and Human Development Program and Department of Pediatrics, McGill University Health Centre Research Institute, Montreal, Quebec, Canada.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Large sample sizes, high-resolution arrays and comprehensive imputation are pushing genetic fine-mapping of complex trait loci to its limits without, in most cases, pinpointing a unique variant-gene combination. Superimposing these results on sophisticated maps of functional chromatin elements promises to break this logjam, as a new study of type 2 diabetes compellingly demonstrates.

Indexed as

Chromosome MappingGenetic LociGenetic Predisposition to DiseaseDiabetes Mellitus, Type 2Hepatocyte Nuclear Factor 3-betaHumansPolymorphism, Single NucleotideReceptor, Melatonin, MT2Hepatocyte Nuclear Factor 3-betaReceptor, Melatonin, MT2

Identifiers

PMID26620109

DOI10.1038/ng.3453

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.