ArticleGenomics data2016
Transcriptomics in type 2 diabetes: Bridging the gap between genotype and phenotype.
Article in Genomics data, 2016. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 17 papers.
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Who cites it
17 citing papers in PubMed.
- Advanced Mass Spectrometry-Based Biomarker Identification for Metabolomics of Diabetes Mellitus and Its Complications.Molecules (Basel, Switzerland) · 2024Review
- Novel diabetes gene discovery through comprehensive characterization and integrative analysis of longitudinal gene expression changes.Human molecular genetics · 2022Article
- Review
- AGEomics Biomarkers and Machine Learning-Realizing the Potential of Protein Glycation in Clinical Diagnostics.International journal of molecular sciences · 2022Review
- A human-based multi-gene signature enables quantitative drug repurposing for metabolic disease.eLife · 2022Article
- Protein glycation - biomarkers of metabolic dysfunction and early-stage decline in health in the era of precision medicine.Redox biology · 2021Article
- Further evidence supporting a potential role for ADH1B in obesity.Scientific reports · 2021Article
- Multi-Omics Comparison of the Spontaneous Diabetes Mellitus and Diet-Induced Prediabetic Macaque Models.Frontiers in pharmacology · 2021Article
- Cancer Signaling Transcriptome Is Upregulated in Type 2 Diabetes Mellitus.Journal of clinical medicine · 2020Article
- Comparative effectiveness of bariatric surgeries in patients with obesity and type 2 diabetes mellitus: A network meta-analysis of randomized controlled trials.Obesity reviews : an official journal of the International Association for the Study of Obesity · 2020Review
- The Construction and Analysis of the Aberrant lncRNA-miRNA-mRNA Network in Adipose Tissue from Type 2 Diabetes Individuals with Obesity.Journal of diabetes research · 2020Article
- Recent advances and perspectives in next generation sequencing application to the genetic research of type 2 diabetes.World journal of diabetes · 2019Review
- Article
- Overview of genomics and post-genomics research on type 2 diabetes mellitus: Future perspectives and a framework for further studies.Journal of biosciences · 2019Review
- Therapeutic Silencing of Centromere Protein X Ameliorates Hyperglycemia in Zebrafish and Mouse Models of Type 2 Diabetes Mellitus.Frontiers in genetics · 2019Article
- A coding and non-coding transcriptomic perspective on the genomics of human metabolic disease.Nucleic acids research · 2018Article
- Shared Genetic Control of Brain Activity During Sleep and Insulin Secretion: A Laboratory-Based Family Study.Diabetes · 2018Article
Corrections and comments
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Authors and funding
6 authors.
Funding
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Abstract
Type 2 diabetes (T2D) is a common, multifactorial disease that is influenced by genetic and environmental factors and their interactions. However, common variants identified by genome wide association studies (GWAS) explain only about 10% of the total trait variance for T2D and less than 5% of the variance for obesity, indicating that a large proportion of heritability is still unexplained. The transcriptomic approach described here uses quantitative gene expression and disease-related physiological data (deep phenotyping) to measure the direct correlation between the expression of specific genes and physiological traits. Transcriptomic analysis bridges the gulf between GWAS and physiological studies. Recent GWAS studies have utilized very large population samples, numbering in the tens of thousands (or even hundreds of thousands) of individuals, yet establishing causal functional relationships between strongly associated genetic variants and disease remains elusive. In light of the findings described below, it is appropriate to consider how and why transcriptomic approaches in small samples might be capable of identifying complex disease-related genes which are not apparent using GWAS in large samples.
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