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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