Developing Statistics Methods to Detect Rare Genetics Variants in Human Complex Pedigrees

开发统计方法来检测人类复杂谱系中的罕见遗传变异

• 批准号: 9152134
• 负责人: Yin Yao
• 金额: $ 21.69万
• 依托单位: NATIONAL INSTITUTE OF MENTAL HEALTH
• 依托单位国家: 美国
• 资助国家: 美国
• 起止时间: 至
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/9152134
• 关键词: Accounting; Affect; Autistic Disorder; Base Sequence; Code; Complex; Coupled; DNA; Data Set; Dependency; Disease; Disease model; Family; Genes; Genetic; Genomic Segment; Haplotypes; Human; Individual; Literature; Mental disorders; Methods; Modeling; Mutation; Obsessive-Compulsive Disorder; Phenotype; Population; Property; Publications; Relative (related person); Research Design; Role; Schizophrenia; Scientist; Statistical Methods; Sum; Technology; Testing; Universities; Variant; Weight; Work; analytical method; base; database of Genotypes and Phenotypes; design; exome sequencing; genetic linkage analysis; genetic pedigree; genetic variant; genome sequencing; genome wide association study; human disease; interest; large scale simulation; medical schools; next generation sequencing; novel; population based; professor; rare variant; statistics

We have developed pedigree-based rare variants analysis approach by treating each affected relative as dependent pairs and the dependency will be accounted for using correlation matrix. This work led to two publications. We are now working on using a haplotype-based approach to identify causal variants for human diseases such as schizophrenia, bipolar, and obsessive compulsive disorder. We have obtained the relevant data sets from dbGap which will allow us to compare the statistical properties given by various types of analytical methods. We have made contributions to a study of obsessive compulsive disorder, led by Professor Gerald Nestadt at the Medical School of Johns Hopkins University.

我们开发了基于家系的罕见变异分析方法，将每个受影响的亲属视为依赖对，并使用相关矩阵来解释依赖关系。这项工作导致了两个出版物。我们现在正致力于使用基于单体型的方法来识别人类疾病的因果变异，如精神分裂症，双相情感障碍和强迫症。我们已经从dbGap获得了相关的数据集，这将使我们能够比较各种类型的分析方法给出的统计特性。 我们为约翰霍普金斯大学医学院的杰拉尔德·内斯特教授领导的一项强迫症研究做出了贡献。