Integrative methods for the identification of causal variants in mental disorder

 描述（由申请人提供）：大规模平行测序技术的巨大进步使研究人员能够以快速和经济高效的方式在全基因组范围内获得低至单碱基分辨率的遗传信息。尽管在数据生成方面取得了进展，但分析和解释这些数据仍然非常具有挑战性。由此产生的数据集是高维的，非常稀疏，有数百万个遗传变异，其中绝大多数在人群中是罕见的。确定感兴趣区域中的许多遗传变异中的哪些是真正的因果变异是非常困难的。事实上，尽管在全基因组关联研究（GWAS）中发现了强大的关联，但绝大多数GWAS基因座的潜在因果变异仍是未知的。识别潜在的因果变异的问题对于理解精确的生物学机制具有根本的重要性。虽然实验功能研究是黄金标准，但它们昂贵且难以对大量变体实施。在这里，我们建议开发最先进的和强大的统计方法，将全基因组功能注释数据与来自自闭症和精神分裂症的全外显子组测序和GWAS研究的大量个体的遗传数据整合在一起，以帮助我们在发生在特定感兴趣位点的丰富自然变异中识别真正的因果变异。拟议的统计方法将纳入一个公开提供的软件包。我们认为，拟议的研究非常及时，并有可能通过直接应用于自闭症和精神分裂症，以及更广泛的其他精神疾病，具有重大的公共卫生重要性。

 DESCRIPTION (provided by applicant): The tremendous progress in massively parallel sequencing technologies enables investigators to obtain genetic information down to single base resolution on a genome-wide scale in a rapid and cost efficient manner. Despite this progress in data generation, it remains very challenging to analyze and interpret these data. The resulting datasets are high dimensional and very sparse, with millions of genetic variants, the vast majority of which are rare in the population. Identifying which of the many genetic variants in a region of interest are true causal variants is very difficult. Indeed, despite enormous progress in identifying robust associations in genome-wide association studies (GWAS) studies, the underlying causal variants for the vast majority of GWAS loci are unknown. The problem of identifying the underlying causal variants is of fundamental importance for understanding precise biological mechanisms. While experimental functional studies are the gold standard, they are expensive and difficult to implement for a large number of variants. Here we propose to develop state of the art and powerful statistical methods that integrate genome-wide functional annotation data with genetic data on a large number of individuals from whole-exome sequencing and GWAS studies of autism and schizophrenia to help us identify the true causal variants among the abundant natural variation that occurs at a particular locus of interest. The proposed statistical methods will be implemented into a publicly available software package. We believe that the proposed research is very timely and has the potential to be of great public health importance through direct application to autism and schizophrenia, and more broadly to other psychiatric diseases.