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

Shadrin, A. A.; Frei, O.; Smeland, O. B.; Bettella, F.; O`Connell, K. S.; Gani, O.; Bahrami, S.; Uggen, T. K. E.; Djurovic, S.; Holland, D.; Andreassen, O. A.; Dale, A. M.

doi:10.1101/772202

bioRxiv · 10.1101/772202

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

Shadrin, A. A.·Frei, O.·Smeland, O. B.·Bettella, F.·O`Connell, K. S.·Gani, O.·Bahrami, S.·Uggen, T. K. E.·Djurovic, S.·Holland, D.·Andreassen, O. A.·Dale, A. M.

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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.

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Shadrin, A. A., Frei, O., Smeland, O. B., Bettella, F., O`Connell, K. S., Gani, O., Bahrami, S., Uggen, T. K. E., Djurovic, S., Holland, D., Andreassen, O. A., Dale, A. M.. 2019-09-16. Annotation-Informed Causal Mixture Modeling (AI-MiXeR) reveals phenotype-specific differences in polygenicity and effect size distribution across functional annotation categories. https://doi.org/10.1101/772202

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