ANNORE: genetic fine-mapping with functional annotation.

Area of Science:

• Genetics and Genomics
• Statistical Genetics
• Bioinformatics

Background:

• Genome-wide association studies (GWASs) identify genomic loci associated with complex traits but struggle to pinpoint causal variants.
• Current fine-mapping methods have limitations in identifying specific causal variants and biological mechanisms.
• Distinguishing causal variants from linked variants in high linkage disequilibrium (LD) remains a challenge.

Purpose of the Study:

• To propose and validate AnnoRE, a novel computational method for prioritizing causal single-nucleotide polymorphisms (SNPs) in GWAS-identified loci.
• To improve genetic fine-mapping by integrating GWAS summary statistics with functional annotation data.
• To provide a robust framework for identifying potential causal variants for complex traits.

Main Methods:

• AnnoRE utilizes GWAS summary statistics and local genotype correlation structure.
• It incorporates functional annotation data from external databases to prioritize causal SNPs.
• A multiple regression model with differential shrinkage via random effects is employed, avoiding assumptions on the number of causal SNPs per locus.

Main Results:

• Application to a body mass index (BMI) GWAS meta-analysis identified six loci with strong evidence for causal variants.
• In 24 additional loci, AnnoRE prioritized one or two variants over others in the region.
• The method effectively uses functional annotation to differentiate variants in high LD, aiding in the identification of promising targets.

Conclusions:

• AnnoRE offers a powerful approach for fine-mapping complex trait loci by prioritizing plausible causal SNPs.
• Integrating functional annotation significantly enhances the ability to distinguish causal variants from associated signals.
• The method facilitates the identification of promising genetic targets for further biological investigation and validation.