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 by refining genotype-phenotype associations.
• Current fine-mapping methods focus on individual loci and neglect global genetic architecture.

Purpose of the Study:

• To demonstrate the advantages of genome-wide fine-mapping (GWFM) using functional annotations.
• To develop and validate methods facilitating GWFM.

Main Methods:

• Developed and applied genome-wide fine-mapping (GWFM) incorporating functional annotations.
• Evaluated GWFM performance through simulations and real data analyses across 48 complex traits.

Main Results:

• GWFM outperformed existing methods in error control, mapping power, resolution, precision, replication rate, and trans-ancestry phenotype prediction.
• Identified credible sets explaining 18% of SNP-based heritability ($h_{\mathrm{SNP}}^2$) on average, with 30% outside genome-wide significant loci.
• Highlighted a known causal variant (FTO for BMI) and identified novel missense causal variants for schizophrenia and Crohn's disease risk.

Conclusions:

• GWFM offers a superior approach for identifying causal variants underlying complex traits.
• Accurate estimation of genetic architecture via GWFM is crucial for future fine-mapping studies.
• Large sample sizes (approx. 2 million) may be needed to fine-map over 50% of SNP-based heritability.