phenix imputes missing entries in a phenotype matrix using a low-rank multi-trait mixed model and a genetic similarity matrix (R package, Dahl 2016 Nat Genet). For large datasets, it is better to use methods like softImpute (Mazumder 2010 JMLR, Dahl 2023 Nat Genet) or AutoComplete (An et al 2023 Nat Genet)

rgwas finds and validates subtypes in multitrait data by (1) clustering phenotypes using a mixture of regressions (MFMR) and (2) testing for subtype-specific genetic and nongenetic effects. MFMR is a Gaussian mixture model that accounts for confounders like genetic PCs. (R package, Dahl et al 2019 PG)

gxemm fits polygenic gene-context interaction (R package). Applies to any low-dimensional "context," e.g. stress (Dahl 2020 AJHG), treatment status (Sadowski 2024 Cell Genomics), or endophentoypes (Dahl 2026 JACI). GENIE dramatically scales up GxEMM's IID model (Pazokitoroudi 2024 AJHG).

singher estimates heritability explained by singleton variants. It cannot fit standard genetic relatedness matrices (R package, Hernandez et al 2019 NG)

EFA fits the Epistasis Factor Analysis model, a coordinated form of epistasis. EFA scales to large biobanks and jointly fits dozens-hundreds of SNPs (python package, R script, Tang 2023 AJHG)

CTMM learns cell type-specific and -shared variation across individuals from single cell RNA-seq data fit on dozens-hundreds of individuals and several cell types (python package, Chen and Dahl 2024 NC)

CIGMA quantifies cell type-specific and -shared genetic variance in single cell RNA-seq data. Requires hundreds of individuals (python package, Chen 2026 Nature)

SIQreg learns an optimal phenotype scale transformation by minimizing quantile-dependent heterogeneity, which improves power and reduces scale-dependent heterogeneity (code, Huang et al 2026 biorxiv)