ReviewCurrent diabetes reports2012
What will diabetes genomes tell us?
Review in Current diabetes reports, 2012. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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.
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.
Who cites it
5 citing papers in PubMed.
- Trial
- Epigenetic modification and therapeutic targets of diabetes mellitus.Bioscience reports · 2020Review
- Transcriptomics in type 2 diabetes: Bridging the gap between genotype and phenotype.Genomics data · 2016Article
- Pedigree-based random effect tests to screen gene pathways.BMC proceedings · 2014Article
- Bringing genome-wide association findings into clinical use.Nature reviews. Genetics · 2013Review
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
2 authors.
Funding
Abstract
A new generation of genetic studies of diabetes is underway. Following from initial genome-wide association (GWA) studies, more recent approaches have used genotyping arrays of more densely spaced markers, imputation of ungenotyped variants based on improved reference haplotype panels, and sequencing of protein-coding exomes and whole genomes. Experimental and statistical advances make possible the identification of novel variants and loci contributing to trait variation and disease risk. Integration of sequence variants with functional analysis is critical to interpreting the consequences of identified variants. We briefly review these methods and technologies and describe how they will continue to expand our understanding of the genetic risk factors and underlying biology of diabetes.
Indexed as
Identifiers
What OpenQuestion holds
Registered trials
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.