Combining functional annotation and multi-trait fine-mapping methods improves fine-mapping resolution at glycaemic...

Area of Science:

• Genetics
• Genomics
• Metabolic Diseases

Background:

• The Meta-Analysis of Glucose and Insulin-related traits Consortium (MAGIC) identified 242 genetic loci linked to glycaemic traits, but causal variants remain largely unknown.
• Improving fine-mapping resolution is crucial for understanding the genetic architecture of glycaemic traits.

Purpose of the Study:

• To evaluate whether combining multi-trait analysis and functional annotation integration enhances genetic fine-mapping resolution for glycaemic traits.
• To refine the identification of causal variants within the 242 loci identified by MAGIC.

Main Methods:

• Employed multi-trait fine-mapping using flashfm on loci associated with multiple glycaemic traits.
• Integrated functional annotations (cell-type specific and static) using fGWAS to inform prior probabilities.
• Performed annotation-informed fine-mapping using both single-trait (FINEMAP) and multi-trait (flashfm) approaches.

Main Results:

• Multi-trait fine-mapping significantly reduced credible set size by 64.5% compared to single-trait fine-mapping.
• Annotation-informed single-trait fine-mapping reduced credible set size by 27.8% compared to agnostic single-trait fine-mapping.
• Combined annotation-informed multi-trait fine-mapping achieved the greatest reduction, decreasing median credible set size by 71.1% compared to single-trait agnostic fine-mapping.

Conclusions:

• The integration of multi-trait analysis and functional annotations provides a powerful strategy for improving genetic fine-mapping resolution.
• This combined approach significantly refines the identification of potential causal variants for glycaemic traits, advancing our understanding of their genetic basis.