Guides
These guides explain individual methods and inputs in more detail than the Tutorials. For results that assess the implementations, see Software validation.
Genomic methods
Heritability and genetic correlation: prepare summary statistics and LD, choose score or regional methods, and interpret the returned estimates.
Bayesian regression: specify priors, learn parameters and inspect chains and marker effects.
Fine-mapping and colocalisation: prepare regional evidence, compare marker support and assess shared signals.
Gene sets and pathways: construct gene evidence and select an enrichment or Bayesian pathway method.
Mendelian randomisation: prepare instruments and inspect estimates and diagnostics.
Covariance models: fit SEM and graphical models to summary-statistic covariance estimates.
Genomic scoring and additional summary interfaces: prepare summaries, construct scores and apply labelled weights. ## Data preparation
Genotype resources: create and reopen a Glist.
Marker filtering: apply explicit quality criteria.
Sparse LD resources: construct and reuse LDlist data.
Models and reference
- Standalone mixed models: install the Linux command and run a complete YAML model with automatic scratch and resource reporting.
- YAML model specification: inputs, effects, covariance matrices, estimation controls and supported combinations.
- Capabilities and current R access
- Mixed-model catalog
- Formulas and covariance components
- R interface reference
- Documentation index