Incorporating a developmental perspective into gene identification models for alcohol outcomes

将发育视角纳入酒精结果的基因识别模型中

• 批准号: 10491732
• 负责人: Nathaniel Stembridge Thomas
• 金额: $ 3.3万
• 依托单位: VIRGINIA COMMONWEALTH UNIVERSITY
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-09-13 至 2023-06-25
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10491732
• 关键词: Adolescence; Adolescent; Adult; Alcohol consumption; Alcohols; Applications Grants; Behavioral Genetics; Cessation of life; Child; Cohort Studies; Collaborations; Communities; Complex; Data; Development; Fostering; Future; Genes; Genetic; Genetic Models; Genetic Variation; Genomics; Goals; Gold; Grant; Health; Lead; Longevity; Longitudinal Studies; Longitudinal cohort study; Manuscripts; Measures; Mentors; Meta-Analysis; Methodology; Methods; Modeling; Molecular Genetics; National Institute on Alcohol Abuse and Alcoholism; National Longitudinal Survey of Adolescent to Adult Health; Outcome; Parents; Phenotype; Publications; Research; Research Personnel; Risk; Sampling; Science; Single Nucleotide Polymorphism; Strategic Planning; Testing; Time; Training; Twin Multiple Birth; Unit of Measure; United States National Institutes of Health; Work; alcohol measurement; alcohol use disorder; behavior test; burden of illness; cost; emerging adulthood; experience; gene discovery; genetics of alcoholism; genome wide association study; improved; indexing; longitudinal analysis; longitudinal dataset; novel; phenotypic data; polygenic risk score; precision medicine; predictive modeling; skills; statistics

Project Summary Frequent alcohol use can lead to alcohol use disorder, which accounts for three million deaths and over 133 million life years lost to disability and death worldwide per year. Alcohol use outcomes are under genetic influence. Genome-wide association represents the state-of-the-science statistical methodology for identifying genes associated with alcohol use outcomes. However, contemporary GWAS methods typically do not account for variability in genetic effects throughout development. For example, when analyzing longitudinal data, most contemporary GWAS are developmentally agnostic and average across timepoints to construct a phenotype that disregards developmental variability in genetic effects. GWAS summary statistics are used as the starting point for phenotype prediction via polygenic risk scores (PRS), which aggregate measured genetic effects on a phenotype into a score that indexes the statistical association between SNPs and the phenotype. Models of genetic influences on alcohol use might be improved if they consider the phenotype as dynamic over time, rather than a fixed maximum or average. No previous studies have constructed PRS from developmentally- informative GWAS, that measure genetic effects that are specific to developmental stage and change over time. In this project, I will apply novel multivariate genomic methods to incorporate developmentally-informative phenotype data into GWAS, create PRS that reflect change over time in alcohol use and PRS that are specific to developmental stage, and validate findings in an independent sample. Longitudinal cohort studies targeted for gene-identification analyses include the Avon Longitudinal Study of Parents and Children (ALSPAC, n~10,000), the Collaborative Study on the Genetics of Alcoholism (COGA, n~2,000), and The National Longitudinal Study of Adolescent to Adult Health (Add Health, n~6,000). Genetic prediction analyses will be conducted in the Finnish Twin Cohort Study (Finn Twin, n~1,400). This project is consistent with NIAAA’s goal to take a lifespan approach to alcohol use and disorder, and provides an analytical approach for doing this for alcohol gene identification efforts. These novel methods will advance the field of behavior genetics beyond the study of aggregated longitudinal phenotypes and represents an important step towards the broader NIH goal of advancing precision medicine strategies for alcohol use outcomes.

项目摘要 经常使用酒精会导致饮酒障碍，造成300万人死亡，超过133 每年在全球范围内为残疾和死亡而丧生。酒精使用结果在遗传之下 影响。全基因组的关联代表了识别的最先进的统计方法 与酒精使用结果相关的基因。但是，当代GWAS方法通常不考虑 例如，当分析纵向数据时，大多数 当代GWA在构造表型的时间点上开发不可知和平均 这无视遗传效应的发育差异。 GWAS摘要统计用作起始 通过多基因风险评分（PRS）进行表型预测的点，汇总测量了对A的遗传作用 表型分为分数，该分数索引了SNP与表型之间的统计关联。模型 如果将表型视为动态，遗传对饮酒的影响可能会得到改善 而不是固定的最大值或平均值。以前没有研究从发展中构建PRS- 信息丰富的GWA，衡量特定于发育阶段并改变的遗传效应 时间。在这个项目中，我将应用新颖的多元基因组方法来纳入发展信息 表型数据进入GWAS，创建反映酒精使用中随时间变化的PR和特定的PR 在独立样本中开发阶段并验证发现。纵向队列研究针对 用于基因识别分析包括对父母和孩子的雅芳纵向研究（AlSPAC， n〜10,000），关于酒精中毒遗传学的合作研究（Coga，n〜2,000）和国家 青少年对成人健康的纵向研究（增加健康，n〜6,000）。遗传预测分析将是 在芬兰双胞胎队列研究（Finn Twin，n〜1,400）中进行。该项目与NIAAA的目标一致 采取寿命的饮酒和混乱方法，并为此提供了一种分析方法 酒精基因识别工作。这些新颖的方法将使行为遗传学领域超越 综合纵向表型的研究，是朝着更广泛的NIH目标迈出的重要一步 推进酒精使用结果的精确医学策略。