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FlexLMM: a Nextflow linear mixed model framework for GWAS

2024-10-02 · Saul Pierotti, Tomas Fitzgerald, Ewan Birney

Summary: Linear mixed models are a commonly used statistical approach in genome-wide association studies when population structure is present. However, naive permutations to empirically estimate the null distribution of a statistic of interest are not appropriate in the presence of population structure, because the samples are not exchangeable with each other. For this reason we developed FlexLMM, a Nextflow pipeline that runs linear mixed models while allowing for flexibility in the definition of the exact statistical model to be used. FlexLMM can also be used to set a significance threshold via permutations, thanks to a two-step process where the population structure is first regressed out, and only then are the permutations performed. We envision this pipeline will be particularly useful for researchers working on multi-parental crosses among inbred lines of model organisms or farm animals and plants. Availability and implementation: The source code and documentation for the FlexLMM is available at https://github.com/birneylab/flexlmm.

📄 PDF Abstract BibTeX arXiv:2410.01533

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birneylab/flexlmm 공식 구현

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SET Dynamic Sparse Training method where weight mask is updated randomly periodically

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