Predicting the causative SNPs in LD blocks by allele-specific structural analysis

通过等位基因特异性结构分析预测 LD 块中的致病 SNP

基本信息

批准号：
8792744
负责人：
Alain T Laederach
金额：
$ 75.13万
依托单位：
UNIV OF NORTH CAROLINA CHAPEL HILL
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-09-01 至 2018-06-30
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/8792744
关键词：
3&apos Untranslated Regions Acylation Adopted Affect Algorithms Alleles Automobile Driving Benchmarking Binding Proteins Cartilage Cataloging Catalogs Cataract Cells Chemical Structure Chemicals Code Communities Data Detection Development Disease Elements Entropy Free Energy Genetic Code Genetic Polymorphism Genome Genomics Genotype Hair Haplotypes Hepatitis C virus Higher Order Chromatin Structure Human Human Genome Hydroxyl Radical In Vitro Indium Individual Inherited Intercistronic Region Life Linkage Disequilibrium Maps Mediating Modeling Molecular Molecular Conformation Mutation Nature Phenotype Play Polymers Predictive Value Primer Extension Protein Binding Proteins RNA RNA Folding RNA-Binding Proteins Regulatory Pathway Research Design Retinoblastoma Ribosomes Risk Role Sampling Single Nucleotide Polymorphism Specificity Structural Models Structure Syndrome Transcript Translating Untranslated RNA Untranslated Regions Validation Variant Vision Work cooking design genetic information genome wide association study genotyping technology human disease improved in vivo public health relevance research study tool development

项目摘要

DESCRIPTION (provided by applicant): Genome wide association studies are powerful for correlating human genotype to phenotype. These studies are designed to identify the polymorphisms in the genetic code that are most predictive of a phenotype. Rapid advances in genotyping technologies enable comprehensive coverage of the genome, including a majority of intergenic polymorphisms. Interestingly, when included in the association analysis, non-coding polymorphisms are often the most highly predictive of the phenotype. Furthermore, Single Nucleotide Polymorphisms (SNPs) are inherited together in Linkage Disequilibrium (LD) blocks. As a result, identifying the causative SNP in an LD block mapping to non-coding regions of the genome remains a contemporary computational and experimental challenge in the field of genomics. Although non-coding regions of the genome are not translated into protein, they are in a majority of cases transcribed in RiboNucleic Acid (RNA). Since RNA is a single stranded polymer, it will fold and the higher-order structures it adopts are integral to numerous RNA-mediated post-transcriptional regulatory functions in the cell. In detailed and focused studies of individual transcripts, our team has discovered that disruption of RNA structural features in non-coding regions of transcribed RNAs are causative in at least three human disease states - hyperferritinemia cataract syndrome, retinoblastoma and cartilage hair hypoplasia - and that altered RNA structure determines hepatitis C virus clearance efficiency. The vision of this proposal is to improve our computational ability to predict RiboSNitches (structural features in RNA that are disrupted by a SNP) by improving the accuracy of ensemble suboptimal structure sampling and pseudoknot prediction, and by using chemical structure probing data to characterize allele-specific RNA conformations, both in vitro and in healthy living cells in vivo. Ultimately, this work will substantially improve our ability to predict the causative disease-associated SNP in an LD block mapping to non-coding, intergenic regions of the human genome.

描述（通过应用程序提供）：基因组广泛的关联研究对于将人类基因型与表型相关联非常有用。这些研究旨在确定最可预测表型的遗传密码中的多态性。基因分型技术的快速进步可实现基因组的全面覆盖，包括大多数基因间多态性。有趣的是，当包含在关联分析中时，非编码多态性通常是表型的最高度预测的。此外，单核苷酸多态性（SNP）在连锁不平衡（LD）块中共同遗传。结果，确定在基因组非编码区域的LD块映射中的严重SNP仍然是基因组领域的当代计算和实验挑战。尽管基因组的非编码区未转化为蛋白质，但在大多数情况下，它们在核糖核酸（RNA）中转录。由于RNA是单个链聚合物，因此它将折叠，并且其采用的高阶结构是细胞中许多RNA介导的转录后调节功能不可或缺的。详细研究和集中研究单个转录本，我们的团队发现，在至少三种人类疾病状态下，转录RNA的非编码区域中RNA结构特征的破坏是严重的 - 高铁蛋白血症综合症，视网膜细胞瘤，软骨瘤和软骨性毛发性下降毛发性下降毛发性毛发性毛发性毛Nra结构可改变RNA结构的rrNA结构，这改变了脑炎效应的效率。该建议的愿景是通过提高整体次级结构采样和伪研究预测的准确性，并使用化学结构探测数据来表征化学等位基因特异性RNA构象，从而在体内和健康的Living Living Living Living Living vivo中，通过改善集合次级结构采样和伪预测的准确性来提高我们的计算能力（RNA中的结构特征（RNA中的结构特征））。最终，这项工作将大大提高我们预测与人类基因组无编码的，非编码的基因间区域的LD块映射中与疾病相关的SNP的能力。