Statistical Methods to Assess the role of rare Variants in Complex Traits.

描述(申请人提供)：常见疾病，如双相情感障碍、哮喘、心脏病、癌症等，是由多种遗传和环境风险因素之间复杂的相互作用引起的。常见和罕见的基因变异预计都会影响这些特征的风险。到目前为止，大多数关于疾病易感变异的研究出于必要，都集中在发现常见的易感变异(即群体频率至少为5%的变异)上。全基因组关联研究在寻找与许多复杂性状密切相关的常见变异方面非常成功。然而，综合来看，这些变异只解释了估计的性状遗传力的一小部分。测序技术的最新进展带来了成本的大幅降低和基因组吞吐量的三个数量级以上的增长。这些进展导致越来越多的测序研究正在进行，包括1000基因组计划，主要目标是识别罕见的遗传变异。因此，现在第一次有可能系统地评估稀有变异在各种复杂特征中可能发挥的作用。现有的检测常见易感变异的方法不适用于罕见变异的检测。我们认为，如果我们想要最大限度地利用目前产生的序列数据，就需要在分析稀有变异的统计方法方面取得新的进展。拟议的研究意在开发新的、有效的统计方法来满足这一需要。这里提出的方法利用有关病例和对照的罕见变异的完整频率分布的信息，实现了比现有方法更强大的能力，并且可以处理大的基因组区域，可能是整个基因组。我们计划在一套全面的疾病模型下模拟的数据上测试我们的方法，然后将它们应用于精神疾病的真实数据，对于这些疾病，常见的易感变异很难识别。这里提出的方法将被落实到一个软件包中，供更大的研究界使用。我们相信，这项提议有很强的潜力帮助目前的努力，将因果遗传变异的搜索扩大到迄今为止尚未探索的罕见变异领域。下一阶段是促进我们对复杂疾病的生物学基础的理解的关键，也是改善公共卫生的最终关键。 公共卫生相关性：测序技术的最新进展使人们能够在历史上第一次系统地评估罕见变异在各种复杂疾病中可能发挥的作用，如双相情感障碍和哮喘。我们建议为这一目标开发强大的统计方法，并将它们实施到一个软件包中，以供更大的研究社区使用。

DESCRIPTION (provided by applicant): Common diseases, such as bipolar disorder, asthma, heart disease, cancer, etc. are caused by a complex interplay among multiple genetic and environmental risk factors. Both common and rare genetic variants are expected to influence risk to these traits. Thus far, most research in nding disease susceptibility variants has focused, out of necessity, on the discovery of common susceptibility variants (i.e. variants with a population frequency of at least 5%). Genome-wide association studies have been very successful at nding common variants robustly associated with many complex traits. However, taken together, these variants only explain a small fraction of the estimated trait heritability. Recent advances in sequencing technologies have brought along substantial reductions in cost and in- creases in genomic throughput by more than three orders of magnitude. These developments have lead to an increasing number of sequencing studies being performed, including the 1000 Genomes Project, with the main goal to identify rare genetic variants. Therefore, for the first time, it is now possible to systematically assess the role rare variants may play in various complex traits. Existing methods for the detection of common susceptibility variants are not suitable for the detection of rare variants. We believe that there is a great need for new developments in statistical methodology for the analysis of rare variants, if we want to make the best use of the sequence data currently being generated. The proposed research intends to develop novel and efficient statistical approaches to address this need. The methods proposed here exploit information about the full frequency distributions of rare variants for cases and controls to achieve substantial increases in power over current methods, and can handle large genomic regions, possibly entire genomes. We plan to test our methods on data simulated under a comprehensive set of disease models, and then to apply them to real data on psychiatric diseases, for which common susceptibility variants are very hard to identify. The methods proposed here will be implemented into a software package, to be made available to the larger research community. We believe that this proposal has the strong potential to help in the current efforts to expand the search for causal genetic variants to the, until now, unexplored territory of rare variation. This next phase is key to advancing our understanding of the biological underpinnings of complex diseases, and ultimately essential to improving the public health. PUBLIC HEALTH RELEVANCE: Recent advances in sequencing technologies allow for the first time in history the systematic assessment of the potential role rare variants may play in various complex diseases, such as bipolar disorder and asthma. We propose to develop powerful statistical methods toward this goal, and implement them into a software package, to be made available to the larger research community.