Tag: Mixed-Model

EMMAX Beta – Efficient Mixed-Model Association eXpedited

EMMAX Beta

:: DESCRIPTION

EMMAX is a statistical test for large scale human or model organism association mapping accounting for the sample structure. In addition to the computational efficiency obtained by EMMA algorithm, EMMAX takes advantage of the fact that each loci explains only a small fraction of complex traits, which allows us to avoid repetitive variance component estimation procedure, resulting in a significant amount of increase in computational time of association mapping using mixed model.

::DEVELOPER

Hyun Min Kang

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux

:: DOWNLOAD

 EMMAX

:: MORE INFORMATION

Citation

Nat Genet. 2010 Apr;42(4):348-54. Epub 2010 Mar 7.
Variance component model to account for sample structure in genome-wide association studies.
Kang HM, Sul JH, Service SK, Zaitlen NA, Kong SY, Freimer NB, Sabatti C, Eskin E.

LMM-Lasso – Lasso Multi-Marker Mixed Model for Association Mapping with Population Structure Correction

LMM-Lasso

:: DESCRIPTION

LMM-Lasso is a linear mixed models that allows for both multi-locus mapping and correction for confounding effects.

::DEVELOPER

Machine Learning and Computational Biology Research Group

:: SCREENSHOTS

N/A

:: REQUIREMENTS

  • Linux / Windows/ MacOsX
  • Python

:: DOWNLOAD

 LMM-Lasso

:: MORE INFORMATION

Citation

Bioinformatics. 2013 Jan 15;29(2):206-14. doi: 10.1093/bioinformatics/bts669.
A Lasso multi-marker mixed model for association mapping with population structure correction.
Rakitsch B, Lippert C, Stegle O, Borgwardt K.

spaMM 3.0.0 – Mixed Models, Particularly Spatial GLMMs

spaMM 3.0.0

:: DESCRIPTION

spaMM is an standard R package , implementing a collection of functions for inference in mixed models, including GLMMs with spatial correlations and models with non-Gaussian random effects (e.g., Beta Binomial, or negative-binomial mixed models).

::DEVELOPER

Francois Rousset (Rousset@isem.univ-montp2.fr)

:: SCREENSHOTS

N/A

::REQUIREMENTS

  • Linux  / Windows / MacOsX
  • R

:: DOWNLOAD

 spaMM

:: MORE INFORMATION

Citation

Ecography Volume 37, Issue 8, pages 781–790, August 2014
Testing environmental and genetic effects in the presence of spatial autocorrelation
François Rousset andJean-Baptiste Ferdy DOI: 10.1111/ecog.00566