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，超过133人 全世界每年因残疾和死亡而损失的生命年为100万年。饮酒的后果与基因有关 影响力。全基因组关联代表了用于识别 与酒精使用结果相关的基因。然而，当代的全球气候变化分析方法通常没有考虑到 在整个发育过程中遗传效应的可变性。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，并在不同时间点上平均构建表型 这忽略了遗传效应的发育变异性。使用GWAS汇总统计数据作为起点 通过多基因风险分数(PR)进行表型预测的点，该分数聚集了测量到的对 表型转化为指数SNPs和表型之间的统计关联的分数。模型 如果他们认为酒精使用的表型随着时间的推移是动态的，那么对酒精使用的遗传影响可能会得到改善， 而不是固定的最大值或平均值。以前的研究还没有从发育的角度构建PR- 信息性GWAs，它测量特定于发育阶段和变化的遗传效应 时间到了。在这个项目中，我将应用新的多变量基因组学方法来整合发育信息 将表型数据输入GWAS，创建反映酒精使用随时间变化的PR和特定的PR 到发育阶段，并在独立样本中验证研究结果。有针对性的纵向队列研究 基因鉴定分析包括雅芳父母和孩子纵向研究(ALSPAC， N~10,000)，酒精中毒遗传学合作研究(COGA，n~2,000)，以及国家 青少年与成人健康的纵向研究(Add Health，n~6,000)。基因预测分析将是 在芬兰双胞胎队列研究中进行(芬兰双胞胎，n~1400)。这个项目与NIAAA的目标是一致的 对酒精使用和紊乱采取终生方法，并提供了一种分析方法 酒精基因鉴定的努力。这些新方法将推动行为遗传学领域超越 研究聚集的纵向表型，代表着朝着更广泛的NIH目标迈出的重要一步 推进酒精使用结果的精准医学战略。

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.