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Generalized Linear Mixed Model (GLMM)

Definition
AI-generated

A generalized linear mixed model extends a generalized linear model by adding random effects, allowing non-Gaussian outcomes such as binary or count traits while still modeling correlation among observations.

Topics
Regression and multivariate statistics
Related
Case-Control Imbalance Firth Regression Genetic Relationship Matrix (GRM)

Why it matters in GWAS

Logistic GLMMs are a standard choice for large binary-trait association studies when ordinary regression is poorly calibrated because of related samples, population structure, or case-control imbalance. Methods such as SAIGE build directly on this framework.

Example usage

"Binary-trait association was fit with a logistic GLMM using a sparse GRM and covariates for age, sex, and ancestry PCs."

Case-Control Imbalance Firth Regression Genetic Relationship Matrix (GRM) Linear Mixed Model (LMM) Odds Ratio (OR) Scalable and Accurate Implementation of Generalized Mixed Model (SAIGE)
Related-term network

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Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Breslow NE, Clayton DG. (1993). Approximate inference in generalized linear mixed models. J Am Stat Assoc. https://doi.org/10.1080/01621459.1993.10594284
  • Zhou W, et al. (2018). Efficiently controlling for case-control imbalance and sample relatedness in large-scale genetic association studies. Nat Genet. https://doi.org/10.1038/s41588-018-0184-y
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