Genome-wide fine-mapping improves identification of causal variants.

Area of Science:

• Genetics
• Genomics
• Statistical Genetics

Background:

• Fine-mapping identifies causal variants for complex traits but often focuses on limited genomic regions.
• Current methods lack consideration of global genetic architecture, potentially limiting accuracy and power.

Purpose of the Study:

• To demonstrate the advantages of genome-wide fine-mapping (GWFM) for complex traits.
• To develop and validate methods facilitating GWFM.
• To improve the identification and characterization of causal variants.

Main Methods:

• Development and application of genome-wide fine-mapping (GWFM) methods.
• Simulations and real-data analyses using UK Biobank data across 599 complex traits.
• Integration of functional annotations with genetic data.

Main Results:

• GWFM significantly outperforms existing methods in error control, mapping power, precision, and replication.
• Causal variants for 48 UK Biobank traits explain 17% of SNP-based heritability.
• Identified known (FTO for BMI) and novel causal variants for complex diseases like schizophrenia and Crohn's disease.

Conclusions:

• GWFM offers a powerful framework for dissecting the genetic architecture of complex traits.
• The approach enhances the discovery of causal variants and improves trans-ancestry phenotype prediction.
• Large sample sizes are necessary for comprehensive fine-mapping, with GWFM providing a robust strategy.