Incorporating a developmental perspective into gene identification models for alcohol outcomes

项目摘要 频繁饮酒会导致酒精使用障碍，导致300万人死亡，133多人死亡。 全世界每年因残疾和死亡而损失的生命年数为100万。酒精使用的结果是在遗传 影响力的社会全基因组关联代表了科学统计方法，用于识别 与酒精使用结果相关的基因。然而，当代GWAS方法通常不考虑 在整个发育过程中遗传效应的变异性。例如，在分析纵向数据时，大多数 当代GWAS在发育上是不可知的，并且在不同时间点上取平均值以构建表型 忽略了遗传效应中的发育变异性。GWAS汇总统计数据用作 通过多基因风险评分（PRS）进行表型预测的点，该评分汇总了测量的遗传效应， 表型转化为指数SNP和表型之间的统计关联的得分。模型 如果他们认为表型随时间变化是动态的， 而不是固定的最大值或平均值。以前的研究没有从发育上构建PRS- 信息GWAS，测量特定于发育阶段和变化的遗传效应 时间在这个项目中，我将应用新的多变量基因组学方法， 表型数据输入GWAS，创建反映酒精使用随时间变化的PRS和特定的PRS 发展阶段，并在一个独立的样本验证结果。纵向队列研究 用于基因鉴定分析的包括父母和孩子的雅芳纵向研究（ALSPAC， n~ 10，000）、酒精中毒遗传学合作研究（COGA，n~ 2，000）和国家 青少年到成人健康的纵向研究（添加健康，n~ 6，000）。遗传预测分析将是 芬兰双胞胎队列研究（Finn Twin，n~ 1，400）。该项目与NIAAA的目标一致 对酒精使用和疾病采取寿命方法，并提供了一种分析方法， 酒精基因鉴定工作。这些新的方法将推动行为遗传学领域的发展， 聚合纵向表型的研究，并代表了更广泛的NIH目标的重要一步， 推进酒精使用结果的精准医学策略。

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.