Phenotype-specific differences in polygenicity and effect size distribution across functional annotation categories revealed by AI-MiXeR | Zendy

Alexey Shadrin | Zendy; Oleksandr Frei | Zendy; Olav B. Smeland | Zendy; Francesco Bettella | Zendy; Kevin S. O’Connell | Zendy; Osman Gani | Zendy; Shahram Bahrami | Zendy; Tea K. E. Uggen | Zendy; Srdjan Djurovic | Zendy; Dominic Holland | Zendy; Ole A. Andreassen | Zendy; Anders M. Dale | Zendy

Open Access

Phenotype-specific differences in polygenicity and effect size distribution across functional annotation categories revealed by AI-MiXeR

Author(s) -

Alexey 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

Publication year - 2020

Publication title -

bioinformatics

Language(s) - English

Resource type - Journals

SCImago Journal Rank - 3.599

H-Index - 390

eISSN - 1367-4811

pISSN - 1367-4803

DOI - 10.1093/bioinformatics/btaa568

Subject(s) - phenotype , annotation , genome wide association study , biology , computational biology , genetic association , genome , gene , genetics , human genome , single nucleotide polymorphism , genotype

Determining the relative contributions 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 for estimating 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.

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