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

作者: Alexey A. Shadrin , Oleksandr Frei , Olav B. Smeland , Francesco Bettella , Kevin S. O’Connell

DOI: 10.1101/772202

关键词: Computational biologyGenomeDistribution (economics)Mixture modelingGenetic associationAnnotationBiologyPhenotypeFunctional annotationGenetic etiology

摘要: Abstract Determining the contribution of functional genetic categories is fundamental to understanding etiology complex human traits and diseases. Here we present Annotation Informed MiXeR: a likelihood-based method estimate number variants influencing phenotype their effect sizes across different annotation genome using summary statistics from genome-wide association studies. Applying model 11 phenotypes suggests diverse patterns category-specific architectures diseases traits.

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