Abstract
Key message
Based on the simplified FaST-LMM, wherein genomic variance is replaced with heritability, we have significantly improved computational efficiency by implementing rapid R/fastLmPure to statistically infer the genetic effects of tested SNPs and focus on large or highly significant SNPs obtained using the EMMAX algorithm.
Abstract
For a genome-wide mixed-model association analysis, we introduce a barebones linear model fitting function called fastLmPure from the R/RcppArmadillo package for the rapid estimation of single nucleotide polymorphism (SNP) effects and the maximum likelihood values of factored spectrally transformed linear mixed models (FaST-LMM). Starting from the estimated genomic heritability of quantitative traits under a null model without quantitative trait nucleotides, maximum likelihood estimations of the polygenic heritabilities of candidate markers consume the same time as approximately four rounds of genome-wide regression scans. When focusing only on SNPs with large effects or high significance levels, as estimated by the efficient mixed-model association expedited algorithm, the run time of genome-wide mixed-model association analysis is reduced to at most two rounds of genome-wide regression scans. We have developed a novel software application called Single-RunKing to transform nonlinear mixed-model association analyses into barebones linear regression scans. Based on a realised relationship matrix calculated using genome-wide markers, Single-RunKing saves significantly computation time, as compared with the FaST-LMM that optimises the variance ratios of polygenic variances to residual variances using the R/lm function.
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Acknowledgements
This work was supported by the Special Scientific Research Funds for Central Non-profit Institutes, Chinese Academy of Fishery Sciences (2017A001), and China Marine Fish Industry Technology System (CARS-47-G02).
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RQY conceived the proposed method and designed the associated computer software. JG and ZYH developed the source code. XFZ and LJ collected real datasets and participated in simulations and case analyses. All authors read and approved the final manuscript.
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Communicated by Mikko J. Sillanpaa.
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Gao, J., Zhou, X., Hao, Z. et al. Genome-wide barebones regression scan for mixed-model association analysis. Theor Appl Genet 133, 51–58 (2020). https://doi.org/10.1007/s00122-019-03439-5
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DOI: https://doi.org/10.1007/s00122-019-03439-5