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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.
Pubmed ID: 40463188
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Multi paradigm numerical computing environment and fourth generation programming language developed by MathWorks. Allows matrix manipulations, plotting of functions and data, implementation of algorithms, creation of user interfaces, and interfacing with programs written in other languages, including C, C++, Java, Fortran and Python. Used to explore and visualize ideas and collaborate across disciplines including signal and image processing, communications, control systems, and computational finance.
View all literature mentionsBiobank provides data collected at Assessment Center and via online questionnaires on participants aged 40-69 years recruited throughout United Kingdom and provides summary information to improve prevention, diagnosis and treatment of serious and life threatening illnesses.
View all literature mentionsLong-term study of brain development and child health in the United States. The study tracks subjects' biological and behavioral development through adolescence into young adulthood to determine how childhood experiences (such as sports, videogames, social media, unhealthy sleep patterns, and smoking) interact with each other and with a child’s changing biology to affect brain development and social, behavioral, academic, health, and other outcomes.
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