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TopMed‐imputed genome‐wide association study of Alzheimer’s disease in more than 100,000 European samples from the EADB project
Author(s) -
Bellenguez Céline
Publication year - 2020
Publication title -
alzheimer's and dementia
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 6.713
H-Index - 118
eISSN - 1552-5279
pISSN - 1552-5260
DOI - 10.1002/alz.037971
Subject(s) - genome wide association study , imputation (statistics) , genotyping , genetic association , 1000 genomes project , minor allele frequency , genetics , biology , biobank , allele frequency , single nucleotide polymorphism , allele , genotype , missing data , gene , statistics , mathematics
Background Genome‐wide association studies (GWAS), and more recently the next‐generation sequencing strategy, improved greatly our knowledge of the genetics of Alzheimer’s disease (AD), with more than 40 risk genes/loci now identified. As part of the genetic component of AD remains unknown, we conducted a complementary GWAS study with increased sample size and improved imputation quality of low frequency variants thanks to the new TopMed imputation panel. Method The GWAS was performed in the European Alzheimer DNA Biobank (EADB) dataset. It combines genome‐wide genotyping data from several studies and from more than 18,000 AD cases and 21,000 controls never included in previous AD discovery GWAS, for a total of more than 35,000 AD cases and 65,000 controls of European ancestry. Imputation was performed with the TopMed reference panel for the majority of the samples or with the Haplotype Reference Consortium panel. Variants with imputation quality below 0.3 were excluded. Logistic regressions were performed in each study with adjustment on principal components and results were combined across studies with a fixed‐effect inverse variance meta‐analysis. Result Preliminary association results were available for 27,752,571 variants following quality control, including 8,633,349 common variants (minor allele frequency (MAF) above 1%), and 1,324,211 low frequency variants (MAF range between 0.5% and 1%). We identified 57 loci with at least suggestive evidence (P < 1 × 10 −6 ) of association, including 31 known AD loci. Among the 26 new loci, 10 loci reached genome‐wide significance (P < 5 × 10 −8 ) and 9 loci had a rare lead variant (MAF below 1%). Conclusion Preliminary results of the EADB project allowed to identify several new associated loci for AD, including several good candidate genes linked to the APP metabolism. Additional insights on the genetics of AD are expected from ongoing analyses, including gene‐based tests of rare variants, fine‐mapping, functional annotations and pathway analyses.