Oct 31, 2014

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

PLoS Genetics
Gleb KichaevBogdan Pasaniuc

Abstract

Standard statistical approaches for prioritization of variants for functional testing in fine-mapping studies either use marginal association statistics or estimate posterior probabilities for variants to be causal under simplifying assumptions. Here, we present a probabilistic framework that integrates association strength with functional genomic annotation data to improve accuracy in selecting plausible causal variants for functional validation. A key feature of our approach is that it empirically estimates the contribution of each functional annotation to the trait of interest directly from summary association statistics while allowing for multiple causal variants at any risk locus. We devise efficient algorithms that estimate the parameters of our model across all risk loci to further increase performance. Using simulations starting from the 1000 Genomes data, we find that our framework consistently outperforms the current state-of-the-art fine-mapping methods, reducing the number of variants that need to be selected to capture 90% of the causal variants from an average of 13.3 to 10.4 SNPs per locus (as compared to the next-best performing strategy). Furthermore, we introduce a cost-to-benefit optimization framework for de...Continue Reading

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References

Mentioned in this Paper

Establishment and Maintenance of Localization
Genome-Wide Association Study
Meta-Analysis (Publications)
Deoxyribonuclease I
Low Density Lipoprotein Cholesterol Measurement
Exons
Genome
Transcription Initiation Site
Aggregation
Total cholesterol

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