Genome-Wide Association Studies of Free Amino Acid Levels by Six Multi-Locus Models in Bread Wheat

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.

Genome-Wide Association Study and QTL Meta-Analysis Identified Novel Genomic Loci Controlling Potassium Use Efficiency and Agronomic Traits in Bread Wheat

“…Another problem with MLM based models is that the stringent criterion of significance for marker selection such as Bonferroni correction does not allow many significant markers to be detected Chang et al, 2018). Multi-locus mixed linear models have been developed to address this problem because they can be used to detect powerful marker-trait associations (MTA) using lower significance criterion as no Bonferroni correction is applied Chang et al, 2018;Lü et al, 2018;Ma et al, 2018;Peng et al, 2018;. Ever since the first multi-locus random-SNP-effect mixed linear model (mrMLM) was proposed by Wang et al (2016), a series of models has been published in various studies, e.g., pLARmEB (Zhang et al, 2017), FASTmrMLM (Zhang and Tamba, 2018), and FASTmrEMMA (Wen et al, 2018).…”

Genome-Wide Association Studies and Whole-Genome Prediction Reveal the Genetic Architecture of KRN in Maize

“…Therefore, multi-locus models are valuable alternative methods for GWASs of KRN in maize. Additionally, the small number of common QTNs codetected by different methods was also observed in the study of Peng et al [30] for free amino acid levels in bread wheat.…”

“…Recently, there have been a few studies focusing on the above GWAS methods to detect important loci controlling different traits in rice [27], maize [28], ax [29], bread wheat [30] and upland cotton [31,32].…”

Genome-Wide Association Studies and Whole-Genome Prediction Reveal the Genetic Architecture of KRN in Maize

“…To date, the GWAS approach has been widely used to investigate the genetic basis of important traits in many species by calculating the association between genotypic and corresponding phenotypic variations [43]. [30] for free amino acid levels in bread wheat.…”

“…Recently, a few studies have implemented the above GWAS methods to detect important loci controlling different traits in rice [27], maize [28], ax [29], bread wheat [30] and upland cotton [31,32].…”