Integrative modeling of schizophrenia rare variant genetic architecture

 描述(申请人提供)：罕见的变异有助于精神分裂症的风险，包括基因干扰大插入和缺失和单核苷酸变异;然而，这些突变和它们破坏的原因基因已被证明很难准确定位和表征，并且它们的影响大小在广泛的变异和基因组中被估计。来自新一代的新数据 DNA测序应用使罕见的变异检测和关联成为可能。我们建议开发统计模型，从大规模外显子组测序数据中推断因果基因和变异，以及它们对精神分裂症风险的影响。 精神分裂症是一种常见的复杂的精神疾病，影响多达1%的 人口，美国有200多万人。精神分裂症伴随着虚弱 从失业到早逝的各种并发症，没有治愈的办法，只有姑息治疗 成功率适中。用于常见疾病临床风险预测的基因组模型的发展 复杂疾病风险是一个新兴的研究领域，具有长期的潜在

 DESCRIPTION (provided by applicant): Rare variants contribute to schizophrenia risk, including gene disrupting large insertions and deletions and single nucleotide variants; however, these mutations and the causal genes they disrupt have proven difficult to pinpoint and characterize, and their effect sizes are estimated across broad classes of variants and gene sets. New data from next generation DNA sequencing applications enable rare variant detection and association. We propose to develop statistical models to infer causal genes and variants, and their effects on schizophrenia risk, from large scale exome sequencing data. Schizophrenia is a common, complex psychiatric disorder that affects as much as 1% of the population, over two million people in the United States. Schizophrenia comes with debilitating comorbidities ranging from unemployment to early death, and there is no cure, only palliative treatment with moderate success rates. Development of genomic models for clinical risk prediction of common, complex disease risk is a burgeoning area of research, with long-term potential for