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Novel Methods for Longitudinal Study of Gene-Environment Interplay in Alcoholism

Novel Methods for Longitudinal Study of Gene-Environment Interplay in Alcoholism
酒精中毒基因与环境相互作用纵向研究的新方法
批准号:
9267409
负责人:
Zuoheng Wang
金额:
$17.25万
依托单位:
依托单位国家:
美国
项目类别:
财政年份:
2015
资助国家:
美国
项目状态:
已结题
起止时间:
2015-05-01 至 2020-04-30

项目摘要

项目成果

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中文摘要
翻译
 描述(由申请人提供):候选人是耶鲁大学公共卫生学院生物统计学系的遗传统计学家。她的研究兴趣集中在统计方法和计算算法的开发和应用,从遗传学的角度了解成瘾行为的脆弱性和进展。该K 01奖旨在为候选人提供必要的支持,以实现以下目标:(1)建立对酒精中毒及其临床表现的更深入的了解;(2)获得关于艾滋病毒使用酒精的特征的知识。 受感染人口;(3)提高她在精神病学遗传学方面的熟练程度;(4)提高她在统计建模和计算方面的技能;(5)在统计学,遗传学和精神病学的界面上开发独立的研究计划。为了实现这些目标,候选人组建了一个多学科的指导团队,由一位主要导师,耶鲁大学生物统计学系主任,统计遗传学,计算生物学和人类遗传学专家Hongyu Zhao博士和三位共同导师组成:Joel Gelernter博士,他拥有精神疾病遗传学方面的专业知识; John Krystal博士是将转化神经科学方法应用于酗酒研究的先驱; Amy Justice博士是慢性HIV感染结果研究专家。酒精使用障碍是HIV感染人群的主要问题。本申请中的拟议研究是通过开发新的统计方法,更好地利用纵向遗传数据来改善基因和基因-环境相互作用的检测。所提出的方法将被用来描述退伍军人老化队列研究(VACS)中酒精暴露的过程。在这项研究中,将开发一系列核-机非参数方法并应用于全基因组研究,以(1)在基因,区域和途径等生物信息的各个水平上寻找联合遗传效应;(2)检测影响酒精使用时间模式的基因;(3)探索G×E与社会环境因素(例如,婚姻状况,无家可归和就业),这些都是与酒精相关的问题,以及在酒精使用过程中潜在的G×HIV相互作用。新的遗传和环境相互作用的发现可能会提供新的见解酒精使用障碍的病因,特别是在艾滋病毒感染人群。从拟议的研究中获得的培训和研究经验将作为独立研究计划的基础,重点是方法学工作,以帮助了解酒精使用和共病疾病的遗传和环境机制。
英文摘要
 DESCRIPTION (provided by applicant): The candidate is a genetic statistician in the Department of Biostatistics at Yale School of Public Health. Her research interests focus on the development and applications of statistical methods and computational algorithms for understanding the vulnerability and progression of addiction behaviors, from a genetic perspective. This K01 Award is designed to provide the candidate with the support necessary to accomplish the following goals: (1) to establish a more in-depth understanding of alcoholism and its clinical manifestations; (2) to gain knowledge on the characteristics of alcohol use in HIV infected population; (3) to increase her proficiency in psychiatric genetics; (4) to advance her skills in statistical modeling and computation; and (5) to develop an independent research program in the interface of statistics, genetics and psychiatry. To achieve these goals, the candidate has assembled a multi-disciplinary mentoring team comprised of a primary mentor, Dr. Hongyu Zhao, Chair of the Biostatistics Department at Yale, an expert in statistical genetics, computational biology and human genetics, and three co-mentors: Dr. Joel Gelernter, who has expertise in the genetics of psychiatric disorders; Dr. John Krystal, a pioneer in the application of translational neuroscience approaches to the study of alcoholism; and Dr. Amy Justice, an expert on outcome research in chronic HIV infection. Alcohol use disorder is a predominant problem in HIV infected population. The proposed research in this application is to improve the detection of genes and gene-environment interactions by developing novel statistical methods making better use of the longitudinal genetic data. The proposed methods will be used to characterize the course of alcohol exposure in the Veterans Aging Cohort Study (VACS). In this study, a series of kernel- machine nonparametric methods will be developed and applied to a genome-wide study to (1) search for the joint genetic effects at various levels of biological information such as genes, regions and pathways; (2) detect genes affecting the temporal patterns of alcohol use; and (3) explore G×E interactions with social- environmental factors (e.g., marital status, homelessness and employment) that are well established correlated of alcohol related problems, and potential G×HIV interactions in the course of alcohol use. The novel genetic and environmental interaction findings will likely provide new insights into the etiology of alcohol use disorder, particularly in HIV infected population. The training and research experience gained from the proposed study will serve as the groundwork for an independent research program with a focus on methodological work to help understand the genetic and environmental mechanisms underlying alcohol use and comorbid disorders.
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Graph Learning of Cell-cell Communications in Spatial Transcriptomics
  • 批准号:
    10661087
  • 项目类别:
  • 资助金额:
    $34.28万
  • 财政年份:
    2022
  • 负责人:
    Zuoheng Wang
  • 依托单位:
Graph Learning of Cell-cell Communications in Spatial Transcriptomics
  • 批准号:
    10672669
  • 项目类别:
  • 资助金额:
    $12.56万
  • 财政年份:
    2022
  • 负责人:
    Zuoheng Wang
  • 依托单位:
Graph Learning of Cell-cell Communications in Spatial Transcriptomics
  • 批准号:
    10504269
  • 项目类别:
  • 资助金额:
    $34.28万
  • 财政年份:
    2022
  • 负责人:
    Zuoheng Wang
  • 依托单位:
Novel Methods for Longitudinal Study of Gene-Environment Interplay in Alcoholism
  • 批准号:
    9057927
  • 项目类别:
  • 资助金额:
    $17.36万
  • 财政年份:
    2015
  • 负责人:
    Zuoheng Wang
  • 依托单位:
海外基金