Human whole genome genotype and transcriptome data for Alzheimer’s and other neurodegenerative diseases

Mariet Allen(Jacksonville College), Minerva M. Carrasquillo(Jacksonville College), Cory C. Funk(Institute for Systems Biology), Ben Heavner(Institute for Systems Biology), Fanggeng Zou(Jacksonville College), Curtis Younkin(Jacksonville College), Jeremy D. Burgess(Jacksonville College), High-Seng Chai(Mayo Clinic), Julia E. Crook(Institute for Systems Biology), James A. Eddy(Institute for Systems Biology), Hong‐Dong Li(Institute for Systems Biology), Ben Logsdon(Sage Bionetworks), Mette A. Peters(Sage Bionetworks), Kristen K. Dang(Sage Bionetworks), Xue Wang(Jacksonville College), Daniel Serie(Jacksonville College), Chen Wang(Mayo Clinic), Thuy Nguyen(Jacksonville College), Sarah Lincoln(Jacksonville College), Kimberly G. Malphrus(Jacksonville College), Gina Bisceglio(Jacksonville College), Li Ma(Jacksonville College), Todd E. Golde(University of Florida), Lara M. Mangravite(Sage Bionetworks), Yan W. Asmann(Institute for Systems Biology), Nathan D. Price(Institute for Systems Biology), Ronald Petersen(Mayo Clinic), Neill R. Graff‐Radford(Jacksonville College), Dennis W. Dickson(Jacksonville College), Steven G. Younkin(Jacksonville College), Nilüfer Ertekin‐Taner(Jacksonville College)
Scientific Data
October 10, 2016
Cited by 530Open Access
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Abstract

Previous genome-wide association studies (GWAS), conducted by our group and others, have identified loci that harbor risk variants for neurodegenerative diseases, including Alzheimer's disease (AD). Human disease variants are enriched for polymorphisms that affect gene expression, including some that are known to associate with expression changes in the brain. Postulating that many variants confer risk to neurodegenerative disease via transcriptional regulatory mechanisms, we have analyzed gene expression levels in the brain tissue of subjects with AD and related diseases. Herein, we describe our collective datasets comprised of GWAS data from 2,099 subjects; microarray gene expression data from 773 brain samples, 186 of which also have RNAseq; and an independent cohort of 556 brain samples with RNAseq. We expect that these datasets, which are available to all qualified researchers, will enable investigators to explore and identify transcriptional mechanisms contributing to neurodegenerative diseases.


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