March 29, 2015
[LMM] literature overview: performance March 27, 2015 [LMM] literature overview: approximate methods March 15, 2015 [FaSTLMM] Proximal contamination March 13, 2015 [FaSTLMM] REML estimate March 11, 2015 [FaSTLMM] comparison with PyLMM (continued) March 10, 2015 [FaSTLMM] comparison with PyLMM (practice) March 9, 2015 [FaSTLMM] comparison with PyLMM (theory) March 3, 2015 [FaSTLMM] fastlmm/inference/lmm_cov.py, part 2 February 27, 2015 [FaSTLMM] highlevel overview, part 2 February 25, 2015 [FaSTLMM] highlevel overview of the codebase, part 1 February 18, 2015 [FaSTLMM] fastlmm/inference/lmm.py February 16, 2015 [FaSTLMM] fastlmm/inference/lmm_cov.py, part 1 
LMM(select) + PCsThe authors claim that under some circumstances this method gives as good results as The algorithm is described in a supplementary note. That note also contains a brief definition of Probabilistic PCA. A more comprehensible description is found in http://www.robots.ox.ac.uk/~cvrg/hilary2006/ppca.pdf. The components are computed by The rest is not implemented in Python yet. Instead, the C++ executable EpistasisAnother available procedure is searching for pairs of correlated SNPs. This is done by module Main function there is The purpose of having such function (instead of having a function which runs on a pair of SNPs) is that these jobs can be distributed on a cluster and thus the granularity has to be somewhat coarsegrained. The job first computes the matrix $X$, containing
Then, for each pair, two models are compared. In the null model, the $ X$ matrix contains only covariates and SNP values of the pair. The alternative model adds to that the additional column with the product of the SNP values. (If there’s no correlation between the two SNPs, it will have mean close to zero.) For both models, loglikelihood is computed, and likelihoodratio test is performed to obtain pvalue. Testing sets of SNPsNot only pairs, but also sets of SNPs can be checked for correlations. This functionality is available in the module TODO: find description of the algorithm TODO
