Quantitative Methods for Genetic Epidemiology

遗传流行病学的定量方法

• 批准号: 8299665
• 负责人: Daniel J. Schaid
• 金额: $ 40.84万
• 依托单位: MAYO CLINIC ROCHESTER
• 依托单位国家: 美国
• 财政年份: 2002
• 资助国家: 美国
• 起止时间: 2002-04-01 至 2016-01-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8299665
• 关键词: Accounting; Adverse effects; Biological Assay; Chromosome Mapping; Complex; Computing Methodologies; DNA Sequence; Data; Data Set; Development; Diagnosis; Disease; Epidemiology; Etiology; Funding; Future; Gene Expression; Genes; Genetic; Genetic Predisposition to Disease; Genetic Variation; Genome; Genomics; Genotype; Haplotypes; Harvest; Human; Individual; Knowledge; Laboratories; Lasso; Measures; Methods; Modeling; Ontology; Overlapping Genes; Pharmacogenomics; Phenotype; Population Genetics; Public Health; Publishing; Quality Control; Research; Role; Sampling Biases; Scanning; Screening procedure; Single Nucleotide Polymorphism; Single Nucleotide Polymorphism Map; Statistical Methods; Statistical Models; Structure; Technology; Testing; Therapeutic; Time; Toxic effect; Variant; base; computer framework; design; expectation; flexibility; follow-up; genetic analysis; genetic epidemiology; genetic variant; genome wide association study; human disease; improved; insight; next generation; novel; outcome forecast; response; simulation; sound; statistics; trait

DESCRIPTION (provided by applicant): Modern genomic epidemiology has rapidly evolved beyond initial expectations, primarily because of cutting- edge genetic assays and next-generation sequencing technologies combined with large well-characterized studies. Yet, novel statistical analysis methods that combine genomic annotation with measured genotypes and phenotypes have lagged behind, with most published genome wide association studies (GWAS) focused on single-marker (single nucleotide polymorphisms, SNPs) analyses. Recognizing that the majority of common genetic variants have small effects on traits, and that there are many associated variants, the time is ripe to re-harvest the many existing GWAS data sets, and many expected in the near future, by joining genomic annotation with GWAS results. Hence, we propose to develop new statistical and computational methods in order to scan all possible gene-sets using GWAS SNP data and public gene annotation. We also plan to develop penalized regression models to simultaneously model the effects of individual SNPs on a trait, the effects of genes on a trait, and the effects o gene-sets on a trait. This will allow incorporation of annotation when available, but not lose SNPs or genes when annotation is incomplete. Rare variants are likely to have a prominent role in the etiology of complex traits, and next-generation sequencing technologies will soon be affordable for large studies. We propose new strategies to screen for the association of rare variants with traits based on both the first- and second-moments of generalized regression models (as well as censored survival models). Finally, including annotation information into statistical models is particularly important for analyzing rare variants because they are sparse, and has potential to improve analyses for common SNPs, or even combining both rare and common variants into models. For this, we propose novel statistical methods based on kernel matrices that provide information on how regression coefficients should be "fused" according to similarities of variants based on genomic annotation. PUBLIC HEALTH RELEVANCE: Our proposed plans to develop improved statistical analysis methods for genomic epidemiology are likely to have high impact on the many different past and ongoing studies of the genetic etiology of common human diseases and traits. By applying our new analytic methods to existing data sets, or to future studies, new insights are expected regarding the genetic etiology of disease causation or - in pharmacogenomic studies- the genetic etiology of response to treatments or toxicities. These insights should provide the basis for designing future follow-up studies, such as laboratory-based functional studies to further refine understanding of disease causation, or how best to tailor treatments for optimal therapeutic benefits with reduced side-effects. Hence, our research plans have broad public health implications, ranging from disease screening, to diagnosis, to prognosis and treatment.

描述（由申请人提供）： 现代基因组流行病学的发展已经超出了最初的预期，这主要是因为尖端的基因测定和下一代测序技术与大型的良好表征的研究相结合。然而，将基因组注释与测量的基因型和表型结合的联合收割机新的统计分析方法已经落后，大多数已发表的全基因组关联研究（GWAS）集中于单标记（单核苷酸多态性，SNP）分析。认识到大多数常见的遗传变异对性状的影响很小，并且有许多相关的变异，通过将基因组注释与GWAS结果相结合，重新收获许多现有的GWAS数据集的时机已经成熟，并且许多数据集预计在不久的将来会出现。因此，我们建议开发新的统计和计算方法，以便使用GWAS SNP数据和公共基因注释扫描所有可能的基因集。我们还计划开发惩罚回归模型，以同时模拟单个SNP对性状的影响，基因对性状的影响以及基因集对性状的影响。这将允许在可用时并入注释，但在注释不完整时不会丢失SNP或基因。罕见变异可能在复杂性状的病因学中发挥重要作用，下一代测序技术将很快用于大型研究。我们提出了新的策略来筛选基于广义回归模型（以及删失生存模型）的一阶矩和二阶矩的罕见变异与性状的关联。最后，将注释信息纳入统计模型对于分析罕见变异特别重要，因为它们是稀疏的，并且有可能改善对常见SNP的分析，甚至将罕见和常见变异结合到模型中。为此，我们提出了新的统计方法的基础上内核矩阵，提供信息的回归系数应如何“融合”根据相似性的变异基因组注释。 公共卫生相关性： 我们提出的计划，以开发改进的统计分析方法的基因组流行病学很可能有很大的影响，许多不同的过去和正在进行的研究遗传病因学的常见人类疾病和性状。通过将我们的新分析方法应用于现有数据集或未来的研究，预计将对疾病病因的遗传病因学或药物基因组学研究中对治疗或毒性反应的遗传病因学产生新的见解。这些见解应该为设计未来的后续研究提供基础，例如基于实验室的功能研究，以进一步完善对疾病原因的理解，或者如何最好地定制治疗方法，以获得最佳治疗效果并减少副作用。因此，我们的研究计划具有广泛的公共卫生影响，从疾病筛查到诊断，再到预后和治疗。