*Fa*ctored *S*pectrally *T*ransformed *L*inear *M*ixed *M*odels

FaST-LMM:
Genetic analysis in structured populations used mixed linear models where the variance matrix of the error term is a linear combination of an identity matrix and a positive definite matrix.

The linear model is of the familiar form: 𝑦 = 𝑋 β + ϵ.

- 𝑦: phenotype
- 𝑋: covariates
- β: fixed effects
- ϵ: error term

Further, V(ϵ) = τ²𝐾+ σ²𝐼, where τ² is the genetic variance, σ² is the environmental variance, 𝐾 is the kinship matrix, and 𝐼 is the identity matrix.

The key idea in speeding up computations here is that by rotating the phenotypes by the eigenvectors of 𝐾 we can transform estimation to a weighted least squares problem.

This code is under development.

Guide to the directories:

`src`

: Julia source code`data`

: Example data for development and testing`test`

: Code for testing`docs`

: Notes on comparisons with other implementations