BioinformaticsGeneticsPeer-reviewed journal article
Annotation-Informed Causal Mixture Modeling (AI-MiXeR) reveals phenotype-specific differences in polygenicity and effect size distribution across functional annotation categories
Alexey A. ShadriniD, Oleksandr FreiiD, Olav Bjerkehagen SmelandiD, Francesco BettellaiD, Kevin Sean O’ConnelliD, Osman Gani, Shahram BahramiiD, Tea K. E. Uggen, Srdjan DjuroviciD, Dominic HollandiD, Ole A. AndreasseniD, Anders Martin DaleiD
Abstract
Determining the contribution of functional genetic categories is fundamental to understanding the genetic etiology of complex human traits and diseases. Here we present Annotation Informed MiXeR: a likelihood-based method to estimate the number of variants influencing a phenotype and their effect sizes across different functional annotation categories of the genome using summary statistics from genome-wide association studies. Applying the model to 11 complex phenotypes suggests diverse patterns of functional category-specific genetic architectures across human diseases and traits.
- organism: human
- assay: GWAS summary statistics
- research focus: gene regulatory network inference
- research focus: variant effect prediction
- research focus: benchmarking
- approach: statistical model
- Citations
- 12
- FWCI
- 0.88
- Field percentile
- top 29%
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