Rare and common variants in complex disease

对常见和罕见遗传变异的分析为许多人提供了关键的生物学见解。 复杂的疾病。然而，对于大多数疾病，包括精神疾病， 遗传性仍然无法解释。遗传学界越来越关注罕见的 变体，其动机是技术的改进， 全基因组和全外显子组测序（WGS和WES）数据集。越来越多的 已经发表了关于罕见和常见变异分析的备受瞩目的研究， 由本更新申请的PI发布，并由R 01 MH 101244资助。尽管如此， 关于复杂疾病的遗传结构有许多未解之谜。 在这里，我们提出了一个研究计划，将调查复杂的疾病结构， 开发方法，以最佳方式利用罕见和常见的变异贡献， 生物学发现 我们将评估整个等位基因频谱对疾病遗传力的贡献; 针对疾病遗传性而富集的基因集和功能注释;以及 这些发现增加了罕见和常见变异研究的统计能力， 控制混淆。我们的合作有多种优势：我们的统计和 计算专业知识;我们在上一个资助周期的广泛出版记录;我们的 我们在制作广泛应用的实用软件方面的往绩;以及我们的数据- 驱动的方法，确保我们开发的方法将广泛应用于 精神病和其他疾病数据集。我们将使用数百个 来自大型精神病GWAS、WES和WGS疾病数据集的数千个样本。

Analyses of common and rare genetic variation have produced key biological insights for many complex diseases. However, for most diseases, including psychiatric disease, the bulk of heritability remains unexplained. The genetics community is increasingly focusing on rare variants, motivated by improvements in technology that are enabling the generation of large whole-genome and whole exome sequencing (WGS and WES) data sets. A growing number of high-profile studies on rare and common variant analysis have been published, including studies published by the PIs of this renewal application and funded by R01MH101244. Nonetheless, there are many unanswered questions about the genetic architecture of complex diseases. Here, we propose a research program that will investigate complex disease architectures and develop methods to optimally leverage rare and common variant contributions to produce new biological discoveries. We will assess contributions to disease heritability across the allele frequency spectrum; identify gene sets and functional annotations that are enriched for disease heritability; and leverage these findings to increase statistical power in studies of rare and common variants while controlling for confounding. Our collaboration has multiple strengths: our statistical and computational expertise; our extensive publication record in the previous funding cycle; our track record of producing practical software that is widely used by the community; and our data- driven approach, which ensures that the methods we develop will be broadly applied to psychiatric and other disease data sets. We will guide our research using hundreds of thousands of samples from large psychiatric GWAS, WES and WGS disease data sets.