Abstract

AbstractBackgroundWith the development of next‐generation sequencing technologies, it is possible to identify rare genetic variants that influence the risk of complex disorders. To date, whole exome sequencing (WES) strategies have shown that specific clusters of damaging rare variants in the TREM2, SORL1 and ABCA7 genes are associated with an increased risk of developing Alzheimer’s Disease (AD), reaching odds ratios comparable with the main AD genetic risk factor, the APOE‐ε4 allele. Here, we set out to identify additional AD‐associated genes by a genome‐wide investigation of the burden of rare damaging variants in the genomes of AD cases and cognitively healthy controls, the largest AD‐WES dataset available worldwide.MethodWe integrated the data from 25,982 cases and controls from the European ADES consortium and ADSP consortium from the USA on a single server. We developed a unique bioinformatic pipeline that applies new techniques to homogenize and analyze these data. Carriers of pathogenic variants in genes associated with Mendelian inheritance of AD were excluded. After quality control, we used 12,652 AD cases and 8,693 controls for analysis. Genes were analyzed using a burden analysis, using both non‐synonymous and loss‐of‐function rare variants. Variant impact was prioritized using REVEL.ResultIn our discovery phase, we confirmed and further substantiated that carriership of protein‐damaging genetic variants in TREM2, SORL1 or ABCA7 is a major AD‐risk factor. High‐impact variants in these genes are mostly extremely rare and they are enriched in AD patients with early ages at onset. Furthermore, we identified three additional genes that were significantly associated with AD, in which we identified a similar relationship between very rare, high‐impact variants and an enrichment in AD patients with early ages at onset. We are currently replicating these associations and several additional suggestive findings in independent datasets. The final results will be presented.ConclusionDevelopment of new homogenization methods has enabled the combined analysis of the largest AD‐WES dataset worldwide. With this, we were able to validate and expand on previous findings and to identify new genetic determinants of AD. Together, these genes pinpoint the most relevant pathways in the pathophysiological processes leading to AD.

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