25gmds005 10.3205/25gmds005 urn:nbn:de:0183-25gmds0053 Meeting Abstract Grabert Grabert Gordon G

Data Science in Biomedicine, TU Braunschweig, Braunschweig, Germany Braunschweig Integrated Centre of Systems Biology, TU Braunschweig, Braunschweig, Germany The Danish Twin Registry and Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, Denmark

author Mohammadnejad Mohammadnejad Afsaneh A

Department of Mathematics and Computer Science, University of Southern Denmark, Odense, Denmark

author Bonat Bonat Wagner Hugo WH

Paraná Federal University, Curitiba, Brazil

author von Bornemann Hjelmborg von Bornemann Hjelmborg Jacob J

The Danish Twin Registry and Epidemiology, Biostatistics and Biodemography, Department of Public Health, University of Southern Denmark, Odense, Denmark

author Kacprowski Kacprowski Tim T

Data Science in Biomedicine, TU Braunschweig, Braunschweig, Germany Braunschweig Integrated Centre of Systems Biology, TU Braunschweig, Braunschweig, Germany

author German Medical Science GMS Publishing House

Düsseldorf

610 genetics generalized linear models heritability 20251103 engl This is an Open Access article distributed under the terms of the Creative Commons Attribution 4.0 License. Dieser Artikel ist ein Open-Access-Artikel und steht unter den Lizenzbedingungen der Creative Commons Attribution 4.0 License (Namensnennung). M0631 005 Deutsche Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie 70. Jahrestagung der Deutschen Gesellschaft für Medizinische Informatik, Biometrie und Epidemiologie e. V. (GMDS) V: Bioinformatics, Omics data and precision medicine Jena 20250907 20250911 Abstr. 243 TextIntroduction: Linear mixed models are widely used in genetics to estimate genetic variance components from SNP data. These models typically assume that phenotypes follow a (multivariate) normal distribution. However, in many practical settings, phenotype distributions deviate from normality, limiting the applicability of standard linear mixed models.State of the art: Phenotypes such as binary disease status, gene expression counts, or DNA methylation levels are better modeled using distributions like Bernoulli, Poisson, or Beta, respectively. Generalized linear mixed models (GLMMs) are more appropriate in these cases. However, most implementations focus on univariate outcomes, and extensions to multivariate phenotypes – especially when each phenotype follows a different distribution – remain computationally challenging.Concept: We address this issue using the multivariate covariance generalized linear model (McGLM) framework introduced by Bonat & Jørgensen , which accounts for non-normality by explicitly modeling the relationship between the mean and variance. Within this unified and flexible framework, genetic variance components can be estimated based on genetic relationship matrices (GRM).Implementation: To make this approach scalable, especially with increasing sample sizes, we apply low-rank approximation techniques to the GRM. These approximations significantly reduce the computational cost of matrix operations involved in parameter estimation, enabling efficient heritability estimation under complex, mixed distributional settings.Lessons learned: Our results show that low-rank approximations provide substantial computational speed-ups without sacrificing accuracy. This makes the McGLM framework practical for large-scale genetic studies involving diverse types of phenotypes. The approach opens the door to more flexible and computationally feasible heritability estimation across a wide range of real-world applications.The authors declare that they have no competing interests.The authors declare that an ethics committee vote is not required. Bonat WH Jørgensen B Multivariate covariance generalized linear models 2016 Journal of the Royal Statistical Society Series C: Applied Statistics 649-75 Bonat WH, Jørgensen B. Multivariate covariance generalized linear models. Journal of the Royal Statistical Society Series C: Applied Statistics. 2016 Nov;65(5):649-75. 0 0 0 0