Julia implementation of Factored Spectrally Transformed Linear Mixed Models
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Updated Last
2 Years Ago
Started In
March 2016

FaST-LMM: Factored Spectrally Transformed Linear Mixed Models

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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

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