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Abstract Motivation 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 Shadrin options, 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.
Results Extensive simulations demonstrate that the model is valid for a broad range of genetic architectures. The model suggests that complex human phenotypes substantially differ in the number of causal variants, their localization in the genome and their effect sizes.
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Supplementary information Supplementary data are available at Bioinformatics online. A key effort has been to estimate single nucleotide polymorphism SNP -based heritability, either using individual-level genotype data Yang et al.
However, heritability estimates provide a limited picture of the Shadrin options architecture underlying complex phenotypes. For example, they are agnostic about the number of genetic variants influencing a phenotype and their effect sizes Timpson et al.
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Importantly, the proportion of variants influencing a phenotype polygenicity and the Shadrin options their effect sizes discoverability substantially affects the power of GWAS and may inform the design of future genetic studies to maximize discovery Schork et Shadrin options.
Recently, we developed a model which Shadrin options the breakdown of SNP-heritability into the number of variants influencing a given phenotype non-null variants and the distribution of their effect sizes using summary statistics from GWAS and detailed population-specific linkage disequilibrium LD structure Frei et al.
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The model assumes that the Shadrin options variants are distributed uniformly throughout the genome and that their effect sizes are drawn from a Gaussian distribution.
However, prior genetic studies suggest that non-null variants are differentially enriched across functional genomic categories and complex phenotypes Schaub et al.
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Here, we present a model, Annotation Informed AI -MiXeR, which extends our previous work by allowing different non-overlapping predefined functional annotation categories of the genome to have various densities of non-null variants with different effect size distributions.
Several conceptually related methods that aim to characterize the genetic architecture of phenotypes using GWAS summary statistics have recently been developed. The RSS-E method Zhu and Stephens, only estimates the abundance of non-null variants in different annotation categories, while the distribution of effect sizes of non-null variants is assumed to be the same for all Shadrin options categories.
In contrast to these methods, AI-MiXeR allows simultaneous modeling of abundance and effect size magnitudes of non-null variants in arbitrary predefined Shadrin options annotation categories. Here, we extensively tested AI-MiXeR on synthetic GWAS data generated under various setups to establish scenarios where it reconstructs the underlying parameters correctly. Our analysis suggests that both densities and effect sizes of non-null variants vary considerably across different genomic annotation categories and reveals diverse patterns Shadrin options genetic architecture in different phenotypes.
Variant effect sizes are modeled with point-normal mixture priors, where both proportion of non-null variants and distribution of their effect sizes can vary between different predefined functional genomic categories. The pure i.
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