Advanced Haplotype Analyses in Coronary Artery Disease

冠状动脉疾病的高级单倍型分析

• 批准号: 6934516
• 负责人: ANDREW S ALLEN
• 金额: $ 14.21万
• 依托单位: DUKE UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2004
• 资助国家: 美国
• 起止时间: 2004-08-02 至 2009-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/6934516
• 关键词: cardiovascular disorder risk; clinical research; computer program /software; coronary disorder; disease /disorder onset; family genetics; gene environment interaction; gene interaction; genetic mapping; genetic models; genetic susceptibility; human data; method development; model design /development; single nucleotide polymorphism; statistics /biometry

DESCRIPTION (provided by applicant): Modeling human genetic variation is critical to understanding the genetic basis of complex disease. The Human Genome Project has discovered millions of DNA sequence variants (single nucleotide polymorphisms or SNPs), and millions more may exist. As the coding for proteins takes place along chromosomes, SNP organization along each chromosome, the haplotype structure, will be most useful for discovering genetic variants associated with disease. Haplotype-based association studies are powerful procedures for detecting genetic influences on complex diseases. However, association tests of haplotype effects with unphased genotype data can be sensitive to estimates of haplotype frequencies even with family-based study designs and complete genotype information. The broad objectives of this proposal focus on enhancing the arsenal of statistical methods researchers use to dissect genetic factors in complex diseases. Specifically, we propose to apply results from coarsened-data semi-parametric efficient model theory to derive optimal tests and estimates of haplotype and haplotype interaction effects that are robust to haplotype frequencies using unphased, and possibly missing, genotype data. The data structures we will consider are motivated by those found in the Genetics of Early Onset Cardiovascular Disease (GENECARD) study and the GENECARD Offspring Study. In addition, we propose to apply these newly developed techniques to the GENECARD samples in fine mapping and candidate gene studies. This research will form the core of a 5-year career development plan for Dr. Andrew Allen under the mentorship of three exceptional researchers, each with expertise that complements one another and represent the three areas addressed in this proposal: cardiology, genetics, and statistics. They propose a career development plan that combines didactic and practical training in genetics, cardiology, and genetic epidemiology with an ongoing research program within the unique research environment of Duke University. This career development plan will foster Dr. Allen's development into an established independent quantitative research scientist with expertise in both methodology for dissecting genetic factors in complex disease and cardiovascular genetics.

描述（由申请人提供）： 模拟人类遗传变异对于理解复杂疾病的遗传基础至关重要。人类基因组计划已经发现了数百万种DNA序列变异（单核苷酸多态性或SNP），并且可能存在数百万种。由于蛋白质的编码发生在沿着染色体上，因此沿着每条染色体的SNP组织，即单倍型结构，对于发现与疾病相关的遗传变异将是最有用的。基于单体型的关联研究是检测遗传对复杂疾病影响的有力程序。然而，单倍型效应与非定相基因型数据的关联检验对单倍型频率的估计敏感，即使是基于家族的研究设计和完整的基因型信息。 这项提案的广泛目标集中在增强研究人员用于剖析复杂疾病中遗传因素的统计方法库。具体而言，我们建议应用粗化数据半参数有效模型理论的结果，以获得最佳的测试和估计的单倍型和单倍型的相互作用的影响，是强大的单倍型频率使用非定相，并可能丢失，基因型数据。我们将考虑的数据结构是由早发性心血管疾病遗传学（GENECARD）研究和GENECARD后代研究中发现的数据结构驱动的。此外，我们建议将这些新开发的技术应用于基因卡样品的精细定位和候选基因的研究。 这项研究将成为安德鲁艾伦博士5年职业发展计划的核心，由三位杰出的研究人员指导，每一位研究人员都具有相互补充的专业知识，并代表本提案中提到的三个领域：心脏病学，遗传学和统计学。他们提出了一个职业发展计划，将遗传学，心脏病学和遗传流行病学的教学和实践培训与杜克大学独特的研究环境中正在进行的研究计划相结合。这个职业发展计划将促进艾伦博士发展成为一个成熟的独立定量研究科学家，在复杂疾病和心血管遗传学中解剖遗传因素的方法学方面具有专业知识。