Integrating Functional Data to Prioritize Causal Variants in Statistical Fine-Mapping Studies

By Gleb Kichaev, Wen-Yun Yang, S. Lindstrom, Farhad Hormozdiari, Eleazar Eskin, Alkes L Price, Peter Kraft, Bogdan Pasaniuc. 2014

492 citations recorded by the source corpus.

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 determining the number of variants to be followed up in functional assays and assess its performance using real and simulation data. We validate our findings using a large scale meta-analysis of four blood lipids traits and find that the relative probability for causality is increased for variants in exons and transcription start sites and decreased in repressed genomic regions at the risk loci of these traits. Using these highly predictive, trait-specific functional annotations, we estimate causality probabilities across all traits and variants, reducing the size of the 90% confidence set from an average of 17.5 to 13.5 variants per locus in this data.

Subfields: Genetics

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Citations in the corpus

CADD: predicting the deleteriousness of variants throughout the human genome (2018, neutral): “…prioritize variants based on functional or evolutionary properties such as sequence conservation, genic effects and regulatory element annotations can serve to improve power and ultimately the success of disease studies, for both Mendelian phenotypes (3) as well as common traits and diseases (4).”
Identification of 64 Novel Genetic Loci Provides an Expanded View on the Genetic Architecture of Coronary Artery Disease (2017, methodology): “Probabilistic Annotation Integrator was used to fine-map loci by integrating genetic association signal strength with genomic functional annotation data.(15) We explored the potential target genes of these candidate causal variants by determining their direct effects on protein function (missense va”
Fine-mapping from summary data with the “Sum of Single Effects” model (2021, methodology): “FINEMAP has been shown to be faster and at least as accurate as PAINTOR and CAVIARBF [12].”
Polygenic scores via penalized regression on summary statistics (2016, background): “Likewise additional information gained in the consideration of functional annotations of the genome (Schork et al., 2013; Pickrell, 2014; Kichaev et al., 2014) can be incorporated similarly.”
Partitioning heritability by functional annotation using genome-wide association summary statistics (2015, methodology): “[8] introduce a new method (PAINTOR) that leverages functional data for improved fine-mapping.”