Developing New Statisical Methods to Detect Common and Rare Variants Involved in Neuropsychiatric Disorders

开发新的统计方法来检测与神经精神疾病有关的常见和罕见变异

• 批准号: 9357308
• 负责人: Yin Yao
• 金额: $ 46.84万
• 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/9357308
• 关键词: Affect; Age; Algorithms; Analysis of Variance; Antidepressive Agents; Anxiety; Beds; Biological; Categories; Citalopram; Collaborations; Complex; Computer software; Computers; Data; Data Set; Dependence; Extramural Activities; Future; Gender; Genes; Genetic; Genetic Variation; Glutamates; Goals; Hispanics; Individual; Ketamine; Link; Linux; Mental Depression; Mental disorders; Methods; Modeling; Obsessive-Compulsive Disorder; Pathway interactions; Performance; Pharmaceutical Preparations; Pharmacotherapy; Publications; Reporting; Role; Running; Schizophrenia; Scientist; Selective Serotonin Reuptake Inhibitor; Sequence Analysis; Serotonergic System; Signal Transduction; Statistical Methods; Study Subject; System; Testing; Time; Work; alternative treatment; antidepressant effect; base; exome sequencing; genetic variant; genome wide association study; human disease; improved; insight; neuropsychiatric disorder; neurotransmission; novel; rare variant; response; simulation; tool; treatment response

Our goal is to develop new analytic tools that can be used to identify genetic variation more efficiently and accurately than the existing methods and then test them in the datasets of psychiatric disorders or other complex human diseases. We have been actively working with both intramural and extramural collaborators. A few examples include 1) Using pathway-bases approaches to detect genetic domains underlying the pharmacotherapy options of serotonin reuptake inhibitors effect in study subjects affected with obsessive compulsive disorder (OCD); 2) Establishing efficient pipelines for exome sequencing analyses in a Latino population (schizophrenia and bipolar); and 3) Developing an efficient test for nonlinear dependence of two continuous variables. This presents a new way of testing nonlinear dependence between two continuous variables. There is a need to improve the current version of the software so that it can be used by individuals who use Linux-based computer or desktops to run their analysis. Summary We collaborated with extramural scientists and developed a new statistical method called CANOVA. Using this method, one can generalize the within category variance in traditional analysis of variance (ANOVA). Using extensive simulations, we extensively evaluated the performance of CANOVA. We then applied CANOVA to a real dataset and showed that the power of CANOVA performs better when the correlation is highly non-linear. In addition, we developed a novel mixture model to estimate the time to antidepressant effect onset and its association with covariates such as age, gender and baseline anxiety. We evaluated the model's overall utility and performance via extensive simulations. We demonstrated its use by application to a longitudinal dataset from the Sequenced treatment Alternatives to Relieve Depression (STAR*D) study. Our algorithm successfully identified age and anxiety status as significant factors in influencing the onset distribution of citalopram. And we developed a pathway-based pipeline which can be used to link genome-wide association study signals to several important biological pathways. We used our own pipelines to analyze drug response on Obsessive Compulsive Disorder (OCD) GWAS data to select significant pathways. In a recent publication, we reported the suggestive roles of genes in the glutamatergic neurotransmission system and the serotonergic system. The results presented may provide new insights into genetic mechanisms underlying treatment response in OCD. More recently, we applied the same approach to seeking genetic variants underlying antidepressants such as ketamine. The potential findings will be reported in the future.

我们的目标是开发新的分析工具，可以比现有的方法更有效、更准确地识别遗传变异，然后在精神疾病或其他复杂的人类疾病的数据集中进行测试。我们一直在与校内和校外的合作者积极合作。一些例子包括：1)利用基于途径的方法检测影响强迫症（OCD）患者血清素再摄取抑制剂药物治疗选择的遗传结构域；2)在拉丁裔人群（精神分裂症和躁郁症）中建立有效的外显子组测序分析管道；3)开发一种有效的检验两个连续变量的非线性相关性的方法。这为检验两个连续变量之间的非线性相关性提供了一种新的方法。有必要改进软件的当前版本，以便使用基于linux的计算机或台式机的个人可以使用它来运行他们的分析。

项目成果

New insights into the genetic mechanism of IQ in autism spectrum disorders.

对自闭症谱系障碍智商遗传机制的新见解。

• DOI: 10.3389/fgene.2013.00195
• 发表时间: 2013
• 期刊: Frontiers in genetics
• 影响因子: 3.7
• 作者: Wang,HaroldZ;Qin,Hai-De;Guo,Wei;Samuels,Jack;Shugart,YinYao
• 通讯作者: Shugart,YinYao

Altered expression of mRNA profiles in blood of early-onset schizophrenia.

早发性精神分裂症血液中 mRNA 表达谱的改变

• DOI: 10.1038/srep16767
• 发表时间: 2016-01-06
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Xu Y;Yao Shugart Y;Wang G;Cheng Z;Jin C;Zhang K;Wang J;Yu H;Yue W;Zhang F;Zhang D
• 通讯作者: Zhang D

Increased Variability of Genomic Transcription in Schizophrenia.

精神分裂症基因组转录变异性增加

• DOI: 10.1038/srep17995
• 发表时间: 2015-12-10
• 期刊: Scientific reports
• 影响因子: 4.6
• 作者: Zhang F;Yao Shugart Y;Yue W;Cheng Z;Wang G;Zhou Z;Jin C;Yuan J;Liu S;Xu Y
• 通讯作者: Xu Y