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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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