Editorial: The Applications of New Multi-Locus GWAS Methodologies in the Genetic Dissection of Complex Traits

摘要

Since the establishment of the mixed linear model (MLM) method for genome-wide association studies (GWAS) by Zhang et al. ( 2005 ) and Yu et al. ( 2006 ), a series of new MLM-based methods have been proposed (Feng et al., 2016 ). These methods have been widely used in genetic dissection of complex and omics-related traits ( Figure 1 ), especially in conjunction with the development of advanced genomic sequencing technologies. However, most existing methods are based on single marker association in genome-wide scans with population structure and polygenic background controls. To control false positive rate, Bonferroni correction for multiple tests is frequently adopted. This stringent correction results in the exclusion of important loci, especially for large experimental error inherent in field experiments of crop genetics. To address this issue, multi-locus GWAS methodologies have been recommended, i.e., mrMLM (Wang et al., 2016 ), ISIS EM-BLASSO (Tamba et al., 2017 ), pLARmEB (Zhang et al., 2017 ), FASTmrEMMA (Wen et al., 2018a ), pKWmEB (Ren et al., 2018 ), and FASTmrMLM (Zhang and Tamba, 2018 ). Here we summarize their advantages and potential limitations for using these methods ( Table 1 ). Figure 1 The pipeline framework of genome-wide association studies and their application. Table 1 Comparison of single- and multi-locus GWAS methodologies. Single-locus GWAS Multi-locus GWAS ~(*) QTN detection power Low High P -value threshold of significant QTN 5 × 10 ~(?8) (human genetics for common variants) 0.05/ m ~ 1/ m (crop genetics; m is no. of markers) 2 × 10 ~(?4) (or LOD = 3.0) False positive rate Low (with Bonferroni correction) Low (with LOD = 3.0 or P = 2 × 10 ~(?4)) Multiple test correction Yes No Polygenic background control Yes Yes (First step); No (Second step; all the potential genes have been included) Population structure control Yes Yes SNP effect Fixed Random No. of variance components Two (polygenic background and residual variances) Three (QTN, polygenic background and residual variances; First step) Multi-locus genetic model No Yes (second step) How to reduce no. of variances a) To fix the polygenic-to-residual variance ratio b) To estimate residual variance along with fixed effects a) To fix the polygenic-to-residual variance ratio (1~5) b) To estimate residual variance along with fixed effects (1~4) c) Let the number of non-zero eigenvalues of X C X C T be one (3~5) d) To whiten the covariance matrix of polygenic K and noise (3~5) Running time Fast (GEMMA & EMMAX), slow (EMMA) Fast (2, 6), slow (5), moderate (others) Software GEMMA: http://www.xzlab.org/software.html EMMAX: http://genetics.cs.ucla.edu/emmax mrMLM: https://cran.r-project.org/web/packages/mrMLM/index.html mrMLM.GUI: https://cran.r-project.org/web/packages/mrMLM.GUI/index.html Parallel calculation with multiple CPU; quickly read big datasets; graphical user interface (GUI); To continuously run the programs for multiple traits * mrMLM, FASTmrMLM, FASTmrEMMA, pLARmEB, pKWmEB, and ISIS EM-BLASSO are marked by 1, 2, 3, 4, 5, and 6 respectively . Multi-locus Genome-wide Association Studies for Complex Traits Comparison of GWAS Methodologies Our methodological papers have showed their advantages in terms of quantitative trait nucleotide (QTN) detection power and QTN effect estimation accuracy over existing methods (Wang et al., 2016 ; Tamba et al., 2017 ; Zhang et al., 2017 ; Ren et al., 2018 ; Wen et al., 2018a ). This conclusion has been echoed in a number of other applied studies in this Research Topic. For example, Ma et al. and Zhang et al. indicated that mrMLM, FASTmrEMMA, pLARmEB, and ISIS EM-BLASSO outperform the R package GAPIT, with ISIS EM-BLASSO being the most powerful multi-locus approach. Xu et al. compared one single-locus method (GEMMA) and three multi-locus methods (FASTmrEMMA, FarmCPU, and LASSO) in the genetic dissection of starch pasting properties in maize. As a result, FASTmrEMMA detected the most QTNs (29), followed by FarmCPU (19) and LASSO (12), and GEMMA detected the least QTNs (7). In the genetic dissection of salt tolerance traits in rice, Cui et al. compared all the six multi-locus approaches and identified the most co-detected QTNs from ISIS EM-BLASSO. Peng et al. used our six multi-locus GWAS methods to analyze 20 free amino acid levels in kernels of bread wheat ( Triticum aestivum L.) and found the reliability and complementarity of these methods. In the detection of small-effect QTNs for fiber-quality related traits in the early-maturity varieties of upland cotton, Su et al. claimed that the multi-locus GWAS methods are more powerful and robust than the MLM method in TASSEL v5.0. Hou et al. demonstrated that 20 QTNs were associated with drought stress response using mrMLM, while three QTNs were associated with resistance to Verticillium wilt using EMMAX. Although the above studies have shown the advantages of multi-locus GWAS methods over single-locus GWAS methods, Chang et al. , He et al. ,