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Matrix Factorization for Pleiotropy

Definition
AI-generated

Matrix factorization approaches in pleiotropy analysis decompose GWAS effect matrices (variants × traits) or related structures into lower-rank components—latent factors or sparse patterns—that summarize shared cross-trait genetic effects.

Topics
Post-GWAS
Related
Genetic Correlation Genomic Structural Equation Modeling (Genomic SEM) Latent Genetic Factors in Pleiotropy

Why it matters in GWAS

They offer a compact description of widespread pleiotropy, help discover groups of related traits or variant modules, and complement pairwise genetic correlation and Genomic SEM.

Example usage

"A replication step checks whether Matrix Factorization for Pleiotropy assumptions remain stable across cohorts."

Genetic Correlation Genomic Structural Equation Modeling (Genomic SEM) Latent Genetic Factors in Pleiotropy Multi-Trait GWAS Pleiotropy Pleiotropy Dissection
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

  • Jee J, et al. (2026). The pleiotropic landscape of the human genome. Nat Rev Genet. https://doi.org/10.1038/s41576-025-00908-0
  • Uffelmann E, et al. (2021). Genome-wide association studies. Nat Rev Methods Primers. https://doi.org/10.1038/s43586-021-00056-9
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