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Linear Mixed Model (LMM)

Definition
AI-generated

In GWAS, a linear mixed model regresses the phenotype on each variant while including random effects with covariance proportional to a genetic relationship matrix (kinship), jointly modeling population structure and cryptic relatedness.

Topics
Regression and multivariate statistics
Related
Genetic Relationship Matrix (GRM) Kinship Coefficient LD Pruning

Why it matters in GWAS

Standard regression assumes independent individuals; LMMs control inflation from relatedness and ancestry when retaining relatives or structured cohorts, often improving calibration relative to PC adjustment alone.

Example usage

"The analysis framework includes Linear Mixed Model (LMM) to quantify evidence and compare competing hypotheses."

Genetic Relationship Matrix (GRM) Kinship Coefficient LD Pruning Relatedness and Sample Structure Population Stratification Principal Component Analysis (PCA) Genome-Wide Association Study (GWAS)
Related-term network

Click any node to open that term page.

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

  • Yang J, et al. (2014). Mixed model association methods account for relatedness in genome-wide association studies. Nat Genet.
  • GWASTutorial: Linear mixed model.
← Likelihood ratio test Linkage Disequilibrium (LD) →

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