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Updated: Jul 30, 2025

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Prioritization of genes associated with type 2 diabetes mellitus for functional studies

Wei Xuan Tan1,2, Xueling Sim3, Chin Meng Khoo2

  • 1Stem Cells and Diabetes Laboratory, Institute of Molecular and Cell Biology (IMCB), Agency for Science, Technology and Research (A*STAR), Singapore, Singapore.

Nature Reviews. Endocrinology
|May 11, 2023
PubMed

Insights

Identifying genes linked to type 2 diabetes mellitus (T2DM) is crucial for new treatments. This study presents tools to find and prioritize T2DM genes, aiming to advance precision medicine for better diabetes management.

Area of Science:

  • Genetics
  • Metabolic Diseases
  • Pharmacogenomics

Background:

  • Current type 2 diabetes mellitus (T2DM) treatments have limitations in efficacy and side effects.
  • Despite identifying numerous T2DM-associated genetic variants, translating these into viable drug targets remains a challenge.

Purpose of the Study:

  • To outline tools and platforms for identifying and prioritizing effector genes from T2DM-associated genetic variants.
  • To highlight QSER1 and SLC12A8 as examples of potential T2DM candidate genes identified through these methods.
  • To propose enhanced strategies for strengthening gene functionalization workflows in T2DM research.

Main Methods:

  • Utilizing bioinformatics tools and platforms to identify effector genes from coding and non-coding T2DM-associated variants.
  • Prioritizing candidate genes for subsequent functional studies.
  • Reviewing existing and proposing novel approaches for gene discovery and validation.

Main Results:

  • Demonstrated the application of specific tools and platforms in identifying candidate T2DM genes, exemplified by QSER1 and SLC12A8.
  • Provided a framework for prioritizing genetic variants for functional investigation in T2DM.

Conclusions:

  • Effective prioritization of T2DM-associated genes is essential for advancing our understanding of the disease's genetic underpinnings.
  • Enhanced gene functionalization workflows, incorporating larger and diverse datasets and advanced omics technologies, can accelerate the development of precision medicine for T2DM.

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