Integrative modeling of schizophrenia rare variant genetic architecture

 描述（由申请人提供）：罕见变异导致精神分裂症风险，包括基因破坏性大插入和缺失以及单核苷酸变异，然而，这些突变及其破坏的致病基因已被证明难以精确定位和表征，并且其效应大小在广泛的变异和基因集类别中估计。新一代数据 DNA测序应用能够实现罕见变异检测和关联。我们建议开发统计模型来推断因果基因和变异，以及它们对精神分裂症风险的影响，从大规模外显子组测序数据。 精神分裂症是一种常见的，复杂的精神疾病， 美国有超过两百万人。精神分裂症伴随着衰弱 从失业到早逝的共病，没有治愈方法，只有姑息治疗 成功率适中。发展基因组模型用于常见的， 复杂疾病风险是一个新兴的研究领域，具有长期的潜力，

 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