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Annotation-Informed Causal Mixture Modeling (AI-MiXeR) reveals phenotype-specific differences in polygenicity and effect size distribution across functional annotation categories

By Alexey A. Shadrin, Oleksandr Frei, Olav B Smeland, Francesco Bettella, Kevin S. O’Connell, Osman Gani, Shahram Bahrami, Tea K. E. Uggen, Srdjan Djurovic, Dominic Holland, Ole A. Andreassen, Anders M. Dale

Posted 16 Sep 2019
bioRxiv DOI: 10.1101/772202

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.

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