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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：
9272151
负责人：
Alain T Laederach
金额：
$ 6.55万
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/9272151
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 Health 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 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 prediction algorithm 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.