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Genome-wide barebones regression scan for mixed-model association analysis

文献类型： 外文期刊

作者： Gao, Jin 1 ; Zhou, Xuefei 2 ; Hao, Zhiyu 3 ; Jiang, Li 4 ; Yang, Runqing 1 ;

作者机构： 1.Nanjing Agr Univ, Wuxi Fisheries Coll, Wuxi 214081, Jiangsu, Peoples R China

2.Guangzhou Zhongbo Educ Corp Ltd, Zhongbo Int Business Sch, Guangzhou 511458, Peoples R China

3.Northeast Agr Univ, Coll Anim Sci & Technol, Harbin 150030, Peoples R China

4.Chinese Acad Fishery Sci, Res Ctr Aquat Biotechnol, Beijing 100141, Peoples R China

期刊名称：THEORETICAL AND APPLIED GENETICS （ 2020影响因子：5.699； 五年影响因子：5.565 ）

ISSN： 0040-5752

年卷期： 2020 年 133 卷 1 期

页码：

收录情况： SCI

摘要： 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. 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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