Sequence data and association statistics from 12,940 type 2 diabetes cases and controls

Area of Science:

• Genetics
• Metabolic Diseases

Background:

• Type 2 diabetes (T2D) poses a significant global health challenge.
• Understanding the genetic underpinnings of T2D is crucial for developing effective prevention and treatment strategies.

Purpose of the Study:

• To create a high-resolution catalog of genetic variations linked to T2D.
• To identify low-frequency coding and non-coding variants associated with T2D risk.

Main Methods:

• Whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals from diverse ancestries.
• Identification of over 27 million single nucleotide polymorphisms (SNPs), indels, and structural variants.
• Association testing of identified variants with T2D in sequenced cohorts and larger validation cohorts using exome chips and genotype imputation.

Main Results:

• Catalogued 99% of low-frequency non-coding variants and 99.7% of low-frequency coding variants.
• Generated the largest reference dataset of human genetic information relevant to T2D to date.
• Provided comprehensive variant, genotype, and association statistics for T2D-related genetic research.

Conclusions:

• The generated genetic data serves as a valuable resource for T2D-focused genotype imputation.
• Facilitates functional characterization of T2D-associated variants and genes.
• Enables novel analyses to uncover associations between sequence variation and T2D susceptibility.