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Scalable and Accurate Implementation of Generalized Mixed Model (SAIGE)

Definition
AI-generated

SAIGE is a mixed-model association method and software package designed for large-scale GWAS with related samples and highly unbalanced binary traits.

Topics
GWAS
URL
https://saigegit.github.io/SAIGE-doc/
Related
Case-Control Imbalance Firth Regression Generalized Linear Mixed Model (GLMM)

Why it matters in GWAS

In biobank studies of rare diseases, ordinary logistic regression can produce inflated p-values because of case-control imbalance and relatedness. SAIGE became a standard solution because it scales to large cohorts while maintaining calibration for binary-trait analyses that would otherwise be difficult to trust.

Example usage

"We tested the binary phenotype with SAIGE using a sparse GRM and saddlepoint-corrected p-values."

Case-Control Imbalance Firth Regression Generalized Linear Mixed Model (GLMM) Genetic Relationship Matrix (GRM) REGENIE Saddlepoint Approximation
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

  • 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
  • SAIGE documentation: https://saigegit.github.io/SAIGE-doc/
← Saddlepoint Approximation Sequence Alignment/Map (SAM) →

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