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The Mount Sinai cohort of large-scale genomic, transcriptomic and proteomic data in Alzheimer's disease

2018; Nature Portfolio; Volume: 5; Issue: 1 Linguagem: Inglês

10.1038/sdata.2018.185

2052-4463

Minghui Wang, Noam D. Beckmann, Panos Roussos, Erming Wang, Xianxiao Zhou, Qian Wang, Ming Chen, Ryan Neff, Weiping Ma, John F. Fullard, Mads E. Hauberg, Jaroslav Bendl, Mette A. Peters, Ben Logsdon, Pei Wang, Milind Mahajan, Lara M. Mangravite, Eric B. Dammer, Duc M. Duong, James J. Lah, Nicholas T. Seyfried, Allan I. Levey, Joseph D. Buxbaum, Michelle E. Ehrlich, Sam Gandy, Pavel Katsel, Vahram Haroutunian, Eric E. Schadt, Bin Zhang,

Genetic Associations and Epidemiology

Abstract Alzheimer’s disease (AD) affects half the US population over the age of 85 and is universally fatal following an average course of 10 years of progressive cognitive disability. Genetic and genome-wide association studies (GWAS) have identified about 33 risk factor genes for common, late-onset AD (LOAD), but these risk loci fail to account for the majority of affected cases and can neither provide clinically meaningful prediction of development of AD nor offer actionable mechanisms. This cohort study generated large-scale matched multi-Omics data in AD and control brains for exploring novel molecular underpinnings of AD. Specifically, we generated whole genome sequencing, whole exome sequencing, transcriptome sequencing and proteome profiling data from multiple regions of 364 postmortem control, mild cognitive impaired (MCI) and AD brains with rich clinical and pathophysiological data. All the data went through rigorous quality control. Both the raw and processed data are publicly available through the Synapse software platform.