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Integrative modeling of schizophrenia rare variant genetic architecture

精神分裂症罕见变异遗传结构的综合建模

基本信息

批准号：
9061026
负责人：
Eli A Stahl
金额：
$ 42.13万
依托单位：
ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
依托单位国家：
美国
项目类别：
财政年份：
2015
资助国家：
美国
起止时间：
2015-06-01 至 2018-02-28
项目状态：
已结题

来源：
https://reporter.nih.gov/project-details/9061026
关键词：
Affect Architecture Area Bayesian Modeling Biological Biological Assay Brain Cells Cessation of life Chromatin Clinical Data Clinical Research Comorbidity Complex Computer software DNA Sequence Data Data Set Detection Development Environmental Exposure Epidemiology Etiology Family Family Study Future Genes Genetic Genetic Models Genetic Risk Genetic study Genomics Glean Individual Link Measures Meditation Mental disorders Methods Modeling Mutation Neurons Nucleotides Palliative Care Patients Penetrance Phenotype Population Population Genetics Probability Proteomics Quantitative Trait Loci Recording of previous events Regulation Research Research Design Risk Sample Size Sampling Schizophrenia Seeds Sequence Analysis Statistical Models Stratification Testing Thinking Tissues Unemployment United States Validation Variant base case control clinical risk cognitive ability disorder risk endophenotype exome sequencing functional genomics gene environment interaction genetic analysis genetic variant genome sequencing genome wide association study genome-wide genomic data histone modification innovation insertion/deletion mutation insight model building next generation next generation sequencing open source power analysis predictive modeling protein complex psychogenetics public health relevance rare variant risk variant simulation sound statistics success trait

项目摘要

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

描述（由申请人提供）：罕见变异导致精神分裂症风险，包括基因破坏大的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，