Integrating functional data to prioritize causal variants in statistical fine-mapping studies.

Area of Science:

• Genetics and Genomics
• Statistical Genetics
• Bioinformatics

Background:

• Current fine-mapping methods rely on marginal association statistics or simplified posterior probabilities for variant prioritization.
• These standard approaches have limitations in accurately identifying causal variants, especially in complex genetic loci.
• Integrating functional genomic data offers a promising avenue to enhance the accuracy of variant selection for functional validation.

Purpose of the Study:

• To develop a probabilistic framework that integrates association strength with functional genomic annotations for improved causal variant prioritization.
• To empirically estimate the contribution of functional annotations to specific traits using summary association statistics.
• To allow for the possibility of multiple causal variants within a single risk locus.

Main Methods:

• Developed a probabilistic model integrating association statistics and functional genomic annotations.
• Employed efficient algorithms to estimate model parameters across multiple risk loci.
• Introduced a cost-benefit optimization framework for selecting variants for functional assays.
• Validated the approach using simulations and a large-scale meta-analysis of blood lipid traits.

Main Results:

• The proposed framework consistently outperformed state-of-the-art fine-mapping methods in simulations, reducing the number of variants needed to capture 90% of causal variants.
• In simulations, the number of variants to capture 90% of causal variants decreased from 13.3 to 10.4 SNPs per locus.
• Real data analysis showed increased causality probability for variants in exons and transcription start sites, and decreased probability in repressed regions.
• In the blood lipid meta-analysis, the 90% confidence set size was reduced from an average of 17.5 to 13.5 variants per locus.

Conclusions:

• The developed probabilistic framework significantly improves the accuracy of causal variant prioritization in fine-mapping studies by integrating functional genomics.
• The trait-specific functional annotations derived from the model enhance the identification of plausible causal variants.
• The cost-benefit optimization framework provides a practical tool for experimental design in functional validation studies.