RESUMO
Our objective was to design a genotyping platform that would allow rapid genetic characterization of samples in the context of genetic mutations and risk factors associated with common neurodegenerative diseases. The platform needed to be relatively affordable, rapid to deploy, and use a common and accessible technology. Central to this project, we wanted to make the content of the platform open to any investigator without restriction. In designing this array we prioritized a number of types of genetic variability for inclusion, such as known risk alleles, disease-causing mutations, putative risk alleles, and other functionally important variants. The array was primarily designed to allow rapid screening of samples for disease-causing mutations and large population studies of risk factors. Notably, an explicit aim was to make this array widely available to facilitate data sharing across and within diseases. The resulting array, NeuroX, is a remarkably cost and time effective solution for high-quality genotyping. NeuroX comprises a backbone of standard Illumina exome content of approximately 240,000 variants, and over 24,000 custom content variants focusing on neurologic diseases. Data are generated at approximately $50-$60 per sample using a 12-sample format chip and regular Infinium infrastructure; thus, genotyping is rapid and accessible to many investigators. Here, we describe the design of NeuroX, discuss the utility of NeuroX in the analyses of rare and common risk variants, and present quality control metrics and a brief primer for the analysis of NeuroX derived data.
Assuntos
Estudos de Associação Genética/métodos , Predisposição Genética para Doença/genética , Técnicas de Genotipagem/métodos , Doenças Neurodegenerativas/genética , Análise de Sequência com Séries de Oligonucleotídeos/métodos , Alelos , Custos e Análise de Custo , Variação Genética , Técnicas de Genotipagem/economiaRESUMO
Recent large-scale association studies have identified over 100 MS risk loci. One of these MS risk variants is single-nucleotide polymorphism (SNP) rs17066096, located ~14 kb downstream of IL22RA2. IL22RA2 represents a compelling MS candidate gene due to the role of IL-22 in autoimmunity; however, rs17066096 does not map into any known functional element. We assessed whether rs17066096 or a nearby proxy SNP may exert pathogenic effects by affecting microRNA-to-mRNA binding and thus IL22RA2 expression using comprehensive in silico predictions, in vitro reporter assays, and genotyping experiments in 6,722 individuals. In silico screening identified two predicted microRNA binding sites in the 3'UTR of IL22RA2 (for hsa-miR-2278 and hsa-miR-411-5p) encompassing a SNP (rs28366) in moderate linkage disequilibrium with rs17066096 (r (2) = 0.4). The binding of both microRNAs to the IL22RA2 3'UTR was confirmed in vitro, but their binding affinities were not significantly affected by rs28366. Association analyses revealed significant association of rs17066096 and MS risk in our independent German dataset (odds ratio = 1.15, P = 3.48 × 10(-4)), but did not indicate rs28366 to be the cause of this signal. While our study provides independent validation of the association between rs17066096 and MS risk, this signal does not appear to be caused by sequence variants affecting microRNA function.
Assuntos
Regiões 3' não Traduzidas , Regulação da Expressão Gênica , MicroRNAs/metabolismo , Esclerose Múltipla/genética , Polimorfismo de Nucleotídeo Único , Receptores de Interleucina/genética , Sítios de Ligação , Feminino , Estudos de Associação Genética , Predisposição Genética para Doença , Células HEK293 , Humanos , Masculino , RNA Mensageiro/metabolismo , Fatores de RiscoRESUMO
PURPOSE: The aim of this study was to demonstrate that modern data mining tools can be used as one step in reducing the labor necessary to produce and maintain systematic reviews. METHODS: We used four continuously updated, manually curated resources that summarize MEDLINE-indexed articles in entire fields using systematic review methods (PDGene, AlzGene, and SzGene for genetic determinants of Parkinson disease, Alzheimer disease, and schizophrenia, respectively; and the Tufts Cost-Effectiveness Analysis (CEA) Registry for cost-effectiveness analyses). In each data set, we trained a classification model on citations screened up until 2009. We then evaluated the ability of the model to classify citations published in 2010 as "relevant" or "irrelevant" using human screening as the gold standard. RESULTS: Classification models did not miss any of the 104, 65, and 179 eligible citations in PDGene, AlzGene, and SzGene, respectively, and missed only 1 of 79 in the CEA Registry (100% sensitivity for the first three and 99% for the fourth). The respective specificities were 90, 93, 90, and 73%. Had the semiautomated system been used in 2010, a human would have needed to read only 605/5,616 citations to update the PDGene registry (11%) and 555/7,298 (8%), 717/5,381 (13%), and 334/1,015 (33%) for the other three databases. CONCLUSION: Data mining methodologies can reduce the burden of updating systematic reviews, without missing more papers than humans.
