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- W2895916477 abstract "Set-based analysis offers an attractive alternative to single SNP analyses, since the combined effect of SNPs within the set may be captured. Single SNP analyses are often underpowered due to the small effect sizes of individual SNPs, set-based analysis considers the combined effect of all SNPs within the set, which may have a larger combined effect size and hence higher power to detect association than any individual SNP. Polygenic risk scores (PRSs) are a method to summarise the additive trait variance captured by a set of SNPs, and can increase the power of set-based analyses by leveraging public GWAS datasets. We propose the application of PRSs as a set-based method with an additional component of adjustment for linkage disequilibrium (LD), to analyse biologically meaningful SNP sets. We call this method POLARIS: POlygenic Ld-Adjusted RIsk Score. POLARIS identifies the LD structure of SNPs using spectral decomposition of the SNP correlation matrix and replaces the individuals’ SNP allele counts with LD-adjusted dosages. This POLARIS methodology was applied to the new HRC imputation of the GERAD data, using the imputed IGAP summary statistics (excluding GERAD subjects). SNPs were assigned to genes using GENCODE (v19) gene models (Harrow et al. [2012]). Only genes with known gene status and those marked as protein coding were used. SNPs which belong to multiple genes were assigned to all those genes. A total of 14,620 distinct genes were assessed. Results will be presented. A discussion of gene-wide significant genes and their potential biological implications will be presented." @default.
- W2895916477 created "2018-10-26" @default.
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- W2895916477 date "2018-07-01" @default.
- W2895916477 modified "2023-10-16" @default.
- W2895916477 title "P1‐152: GENE‐BASED ANALYSIS IN HRC IMPUTED GERAD GWAS" @default.
- W2895916477 doi "https://doi.org/10.1016/j.jalz.2018.06.156" @default.
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