Genetic effects on changes in human traits over time are understudied and may have important pathophysiological impact. We propose a framework that enables data quality control, implements mixed models to evaluate trajectories of change in traits, and estimates phenotypes to identify age-varying genetic effects in genome-wide association studies (GWASs). Using childhood body mass index (BMI) as an example, we included 71,336 participants from six cohorts and estimated the slope and area under the BMI curve within four time periods (infancy, early childhood, late childhood and adolescence) for each participant, in addition to the age and BMI at the adiposity peak and the adiposity rebound. GWAS on each of the estimated phenotypes identified 28 genome-wide significant variants at 13 loci across the 12 estimated phenotypes, one of which was novel (in DAOA) and had not been previously associated with childhood or adult BMI. Genetic studies of changes in human traits over time could uncover novel biological mechanisms influencing quantitative traits.

中文翻译：

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进行随时间变化的全基因组关联研究的框架：在六个多种族队列中儿童时期体重指数的应用

随着时间的推移，遗传对人类特征变化的影响尚未得到充分研究，并且可能具有重要的病理生理学影响。我们提出了一个框架，可以实现数据质量控制，实施混合模型来评估性状变化的轨迹，并估计表型以识别全基因组关联研究（GWAS）中随年龄变化的遗传效应。以儿童体重指数 (BMI) 为例，我们纳入了来自 6 个队列的 71,336 名参与者，并估计了每个参与者在四个时间段（婴儿期、幼儿期、幼儿期和青春期）内的 BMI 曲线下的斜率和面积，此外肥胖高峰时的年龄和体重指数以及肥胖反弹。对每个估计表型的 GWAS 在 12 个估计表型的 13 个位点上识别出 28 个全基因组显着变异，其中一个是新颖的（在 DAOA 中），并且之前与儿童或成人 BMI 没有关联。对人类性状随时间变化的遗传学研究可以揭示影响数量性状的新生物机制。