Parekh Pravesh, Parker Nadine, Pecheva Diliana, Frei Evgeniia, Vaudel Marc, Smith Diana M, Rigby Alison, Jahołkowski Piotr, Sønderby Ida Elken, Birkenæs Viktoria, Bakken Nora Refsum, Fan Chun Chieh, Makowski Carolina, Kopal Jakub, Loughnan Robert, Hagler Donald J, van der Meer Dennis, Johansson Stefan, Njølstad Pål Rasmus, Jernigan Terry L, Thompson Wesley K, Frei Oleksandr, Shadrin Alexey A, Nichols Thomas E, Andreassen Ole A, Dale Anders M
Centre for Precision Psychiatry, Division of Mental Health and Addiction, University of Oslo and Oslo University Hospital, Oslo, Norway.
Center for Multimodal Imaging and Genetics, J. Craig Venter Institute, La Jolla, CA, USA.
bioRxiv. 2025 May 15:2025.05.09.653146. doi: 10.1101/2025.05.09.653146.
Linear mixed-effects (LME) models are commonly used for analyzing longitudinal data. However, most applications of LME models rely on random intercepts or simple, e.g., stationary, covariance. Here, we extend the Fast and Efficient Mixed-Effects Algorithm (FEMA) and present FEMA-Long, a computationally tractable approach to flexibly modeling longitudinal covariance suitable for high-dimensional data. FEMA-Long can i) model unstructured covariance, ii) model non-linear fixed effects using splines, iii) discover time-dependent fixed effects with spline interactions, and iv) perform genome-wide association studies (GWAS) supporting discovery of time-dependent genetic effects. We applied FEMA-Long to perform a longitudinal GWAS with non-linear SNP-by-time interaction on length, weight, and body mass index of 68,273 infants with up to six measurements in the first year of life. We found dynamic patterns of random effects including time-varying heritability and correlations, as well as several genetic variants showing time-dependent effects, highlighting the applicability of FEMA-Long to enable novel discoveries.
线性混合效应（LME）模型常用于分析纵向数据。然而，LME模型的大多数应用依赖于随机截距或简单的（例如，平稳的）协方差。在此，我们扩展了快速高效混合效应算法（FEMA），并提出了FEMA-Long，这是一种计算上易于处理的方法，用于灵活地对适用于高维数据的纵向协方差进行建模。FEMA-Long可以：i）对非结构化协方差进行建模；ii）使用样条函数对非线性固定效应进行建模；iii）通过样条函数交互发现时间依赖性固定效应；iv）进行全基因组关联研究（GWAS），以支持发现时间依赖性遗传效应。我们应用FEMA-Long对68273名婴儿在生命的第一年进行多达六次测量的身长、体重和体重指数进行了具有非线性单核苷酸多态性（SNP）-时间交互作用的纵向GWAS。我们发现了随机效应的动态模式，包括随时间变化的遗传力和相关性，以及几个显示时间依赖性效应的基因变异，突出了FEMA-Long在实现新发现方面的适用性。
