Abstract
Genome-wide association studies (GWAS) have been widely used to dissect the complex biosynthetic processes of plant metabolome. Most studies have used single-locus GWAS approaches, such as mixed linear model (MLM), and little is known about more efficient algorithms to implement multi-locus GWAS. Here, we report a comprehensive GWAS of 20 free amino acid (FAA) levels in kernels of bread wheat (Triticum aestivum L.) based on 14,646 SNPs by six multi-locus models (FASTmrEMMA, FASTmrMLM, ISISEM-BLASSO, mrMLM, pKWmEB, and pLARmEB). Our results showed that 328 significant quantitative trait nucleotides (QTNs) were identified in total (38, 8, 92, 45, 117, and 28, respectively, for the above six models). Among them, 66 were repeatedly detected by more than two models, and 155 QTNs appeared only in one model, indicating the reliability and complementarity of these models. We also found that the number of significant QTNs for different FAAs varied from 8 to 41, which revealed the complexity of the genetic regulation of metabolism, and further demonstrated the necessity of the multi-locus GWAS. Around these significant QTNs, 15 candidate genes were found to be involved in FAA biosynthesis, and one candidate gene (TraesCS1D01G052500, annotated as tryptophan decarboxylase) was functionally identified to influence the content of tryptamine in vitro. Our study demonstrated the power and efficiency of multi-locus GWAS models in crop metabolome research and provided new insights into understanding FAA biosynthesis in wheat.
Highlights
Genome-wide association studies (GWAS) have largely been applied to the genetic dissection of complex traits in plants
We identified 328 significant quantitative trait nucleotides (QTNs) (LOD > 3.0) with six multi-locus mGWAS models and assigned 15 candidate genes involved in free amino acid (FAA) biosynthesis
Our study proved the efficiency of multi-locus GWAS models in metabolome research and provided new insights into understanding of FAA biosynthesis in wheat, which may facilitate metabolomics-based breeding for quality improvement
Summary
Genome-wide association studies (GWAS) have largely been applied to the genetic dissection of complex traits in plants. GWAS of FAA Levels in Wheat approach, MLM leads to missing some significant loci because of the conservative Bonferroni correction (0.05/me, where me is the number of effective markers) and the stringent criterion of the significance test (Wang et al, 2016) To address this issue, several multi-locus models have been developed, such as Bayesian LASSO (Hoggart et al, 2008), ISISEM-BLASSO (Tamba et al, 2017), pLARmEB (Zhang et al, 2017), and pKWmEB (Ren et al, 2018). Our study proved the efficiency of multi-locus GWAS models in metabolome research and provided new insights into understanding of FAA biosynthesis in wheat, which may facilitate metabolomics-based breeding for quality improvement
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