Variance heterogeneity genome-wide mapping for cadmium in bread wheat reveals novel genomic loci and epistatic interactions

Genome-wide association mapping identifies quantitative trait loci (QTL) that influence the mean differences between the marker genotypes for a given trait. While most loci influence the mean value of a trait, certain loci, known as variance heterogeneity QTL (vQTL) determine the variability of the trait instead of the mean trait value (mQTL). In the present study, we performed a variance heterogeneity genome-wide association study (vGWAS) for grain cadmium (Cd) concentration in bread wheat. We used double generalized linear model and hierarchical generalized linear model to identify vQTL associated with grain Cd. We identified novel vQTL regions on chromosomes 2A and 2B that contribute to the Cd variation and loci that affect both mean and variance heterogeneity (mvQTL) on chromosome 5A. In addition, our results demonstrated the presence of epistatic interactions between vQTL and mvQTL, which could explain variance heterogeneity. Overall, we provide novel insights into the genetic architecture of grain Cd concentration and report the first application of vGWAS in wheat. Moreover, our findings indicated that epistasis is an important mechanism underlying natural variation for grain Cd concentration.

1 1 direct extension of the DGLM that allows joint modelling of the mean and dispersion parts and 1 9 6 introduces random effects as a linear predictor for the mean (Rönnegård and Carlborg, 2007).
The mean part of HGLM was given as follows: We fitted HGLM using the hglm R package (Rönnegård et al., 2010b). We reformulated the term  We investigated the extent of epistasis that was manifested through variance heterogeneity. All 2 1 2 the possible pairwise interaction analyses for markers that were associated with grain Cd 2 1 3 concentration were performed using the following two markers at a time epistatic model: genome annotations to obtain the predicted genes and functional annotations. Although grain Cd concentration is a highly heritable trait, recent GWAS revealed that  We found the single genomic region on chromosome 5A affecting the grain Cd concentration structure. The population structure based on PCA of the HWW association panel is given in Supplemental File S1: Figure S1. QQ plots (Supplemental File S1: Figure S2) show that both We classified the QTL into the following categories: mQTL, which contributes to difference in In the present study, we explored the genetic variants affecting variance heterogeneity of Cd.

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Given the complexity of genetic regulation of Cd in wheat (Guttieri et al., 2015a) and the 3 1 8 influence of epistatic interactions, we anticipated that partial genetic regulation of Cd in wheat 3 1 9 can be detected using methods that have been developed to identify vQTL. As reported by Rönnegård and Valdar, (2012), a potential explanation for variance-controlling QTL is epistatic interactions that are unspecified in the model. Herein, we utilized two approaches, namely, wheat.

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The DGLM framework is a powerful approach for vGWAS analysis. However, in DGLM, GLM 3 2 5 is fit by including only the fixed effects in the linear predictor of mean and dispersion. Therefore, 3 2 6 by using the DGLM approach, population structure can only be accounted for by using the first 3 2 7 few PCs obtained from the SNP matrix; however, this may not completely account for complex power of DGLM and HGLM remains to be explored; further examination is warranted. In the literature, it has been argued that variance heterogeneity can also arise by a simple mean and observed that the means of all the markers were the same (Fig. 4), indicating that the effect 3 4 2 of SNP on variance heterogeneity was not due to the consequences of mean-variance function  Further, variance heterogeneity can also be observed in a population when two or more alleles  In QTL studies, variance heterogeneity arises because of various underlying mechanisms, such the effect of the other loci in the genome, as shown in one pair of interacting markers (Fig. 5).

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Hence, identifying the loci affecting variance heterogeneity through vGWAS means that the loci whether epistasis can explain the identified vQTL and mvQTL in this study, we analyzed all 3 5 7 possible pairwise interactions between the associated markers. We detected significant epistatic interactions between the associated markers (Fig. 2), which can explain the existence of variance approach, many of the requirements necessary for conventional epistasis mapping can be 3 6 2 avoided (e.g., large sample size and extensive multiple testing corrections that reduce power).

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However, Forsberg and Carlborg (2017)  as three functional homoeologous copies (triplicated). This also indicates that genetic complexity phenotypic variation is amplified by the addition of each gene copies can act additively (e.g., additive variation between homoeologous gene has been shown to be an important source of 3 7 8 variation in wheat. However, its relative contribution across the wheat genome as compared to We showed the potential of vGWAS for dissecting the genetic architecture of complex traits and Supplemental File S1 contains Table S1 and Figures S1-S5.   concentration in the hard-red winter wheat association panel.  The authors declare there are no competing interests.  G.M. supervised and directed the study. All authors read and approved the manuscript.