Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes.

Area of Science:

• Genetics
• Genomics
• Metabolic Diseases

Background:

• Coding variants offer direct biological insights into complex diseases.
• Identifying causal coding variants is crucial for understanding disease mechanisms and therapeutic targets.
• Previous studies have faced challenges in distinguishing true causal variants from associated ones.

Purpose of the Study:

• To identify novel coding variant association signals for type 2 diabetes.
• To assess the causality of identified coding variants using genome-wide association data.
• To evaluate the reliability of coding variant associations for mechanistic inference.

Main Methods:

• Aggregated coding variant data from 81,412 type 2 diabetes cases and 370,832 controls.
• Applied stringent statistical thresholds (P < 2.2 × 10⁻⁷) to identify association signals.
• Utilized large-scale genome-wide association data for fine-mapping and causality assessment of variants.

Main Results:

• Identified 40 coding variant association signals, with 16 mapping outside known loci.
• Only 5 signals were driven by low-frequency variants, exhibiting modest effect sizes (OR ≤ 1.29).
• Compelling evidence for causality was found for 16 signals, while 13 were identified as potential 'false leads'.

Conclusions:

• A significant portion of coding variant associations may not represent true causal effects.
• Rigorous validation is essential to avoid erroneous mechanistic inferences in complex disease genetics.
• Validated coding variants are valuable for understanding disease predisposition and identifying therapeutic targets.