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

 描述（由申请人提供）：全基因组关联研究对于将人类基因型与表型相关联是有效的。这些研究旨在鉴定遗传密码中最能预测表型的多态性。基因分型技术的快速发展使基因组能够全面覆盖，包括大多数基因间多态性。有趣的是，当包括在关联分析中时，非编码多态性通常是表型的最高预测性。此外，单核苷酸多态性（SNP）在连锁不平衡（LD）区块中一起遗传。因此，在LD块映射到基因组的非编码区域中识别致病SNP仍然是基因组学领域中的当代计算和实验挑战。虽然基因组的非编码区不翻译成蛋白质，但它们在大多数情况下转录为核糖核酸（RNA）。由于RNA是单链聚合物，它会折叠，并且它所采用的高阶结构是细胞中许多RNA介导的转录后调节功能的组成部分。在对单个转录物的详细和集中的研究中，我们的团队发现转录RNA的非编码区中的RNA结构特征的破坏是至少三种人类疾病状态的原因-高铁蛋白血症白内障综合征，视网膜母细胞瘤和软骨毛发发育不全-并且改变的RNA结构决定了丙型肝炎病毒的清除效率。该提案的愿景是通过提高整体次优结构采样和假结预测的准确性，以及通过使用化学结构探测数据来表征体外和体内健康活细胞中等位基因特异性RNA构象，来提高我们预测RiboSNitch（被SNP破坏的RNA结构特征）的计算能力。最终，这项工作将大大提高我们预测致病性疾病相关SNP的能力，在LD块映射到人类基因组的非编码，基因间区域。

 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.