Annotation-Informed Causal Mixture Modeling (AI-MiXeR) reveals phenotype-specific differences in polygenicity and effect size distribution across functional annotation categories
Alexey A. Shadrin, Oleksandr Frei, Olav Bjerkehagen Smeland, Francesco Bettella, Kevin Sean O’Connell, Osman Gani, Shahram Bahrami, Tea K. E. Uggen, Srdjan Djurovic, Dominic Holland, Ole A. Andreassen, Anders Martin Dale
The paper introduces AI-MiXeR, a method to estimate variant counts and effect sizes across functional annotations using GWAS summary statistics, revealing phenotype-specific polygenicity and effect size differences.
Bioinformatics · Jun 9, 2020Peer-reviewedGenetics