Publication: A High-Density Genome-Wide Association Screen of Sporadic ALS in US Veterans
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Date
2012
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Public Library of Science
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Kwee, Lydia Coulter, Yutao Liu, Carol Haynes, Jason R. Gibson, Annjanette Stone, Steven A. Schichman, Freya Kamel, et al. 2012. A high-density genome-wide association screen of sporadic ALS in US veterans. PLoS ONE 7(3): e32768.
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Abstract
Following reports of an increased incidence of amyotrophic lateral sclerosis (ALS) in U.S. veterans, we have conducted a high-density genome-wide association study (GWAS) of ALS outcome and survival time in a sample of U.S. veterans. We tested \(\sim\)1.3 million single nucleotide polymorphisms (SNPs) for association with ALS outcome in 442 incident Caucasian veteran cases diagnosed with definite or probable ALS and 348 Caucasian veteran controls. To increase power, we also included genotypes from 5909 publicly-available non-veteran controls in the analysis. In the survival analysis, we tested for association between SNPs and post-diagnosis survival time in 639 Caucasian veteran cases with definite or probable ALS. After this discovery phase, we performed follow-up genotyping of 299 SNPs in an independent replication sample of Caucasian veterans and non-veterans (ALS outcome: 183 cases and 961 controls; survival: 118 cases). Although no SNPs reached genome-wide significance in the discovery phase for either phenotype, three SNPs were statistically significant in the replication analysis of ALS outcome: rs6080539 (177 kb from PCSK2), rs7000234 (4 kb from ZNF704), and rs3113494 (13 kb from LOC100506746). Two SNPs located in genes that were implicated by previous GWA studies of ALS were marginally significant in the pooled analysis of discovery and replication samples: rs17174381 in DPP6 (p = 4.4×10\(^{−4}\)) and rs6985069 near ELP3 (p = 4.8×10\(^{−4}\)). Our results underscore the difficulty of identifying and convincingly replicating genetic associations with a rare and genetically heterogeneous disorder such as ALS, and suggest that common SNPs are unlikely to account for a substantial proportion of patients affected by this devastating disorder.
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Keywords
Biology, Computational Biology, Population Genetics, Genetics, Human Genetics, Genomics, Genome Analysis Tools, Mathematics, Statistics, Medicine, Epidemiology, Neurology, Motor Neuron Diseases
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