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

Multiomics and biotechnologies for understanding and influencing cadmium accumulation and stress response in plants

Cadmium (Cd) is one of the most toxic heavy metals faced by plants and, additionally, via the food chain, threatens human health. It is principally dispersed through agro-ecosystems via anthropogenic activities and geogenic sources. Given its high mobility and persistence, Cd, although not required, can be readily assimilated by plants thereby posing a threat to plant growth and productivity as well as animal and human health. Thus, breeding crop plants in which the edible parts contain low to zero Cd as safe food stuffs and harvesting shoots of high Cd-containing plants as a route for decontaminating soils are vital strategies to cope with this problem. Recently, multiomics approaches have been employed to considerably enhance our understanding of the mechanisms underlying (i) Cd toxicity, (ii) Cd accumulation, (iii) Cd detoxification and (iv) Cd acquisition tolerance in plants. This information can be deployed in the development of the biotechnological tools for developing plants with modulated Cd tolerance and detoxification to safeguard cellular and genetic integrity as well as to minimize food chain contamination. The aim of this review is to provide a current update about the mechanisms involved in Cd uptake by plants and the recent developments in the area of multiomics approach in terms of Cd stress responses, as well as in the development of Cd tolerant and low Cd accumulating crops.

Genetic factors of grain cadmium concentration in Polish wheat (Triticum polonicum L.)

Wheat (Triticum aestivum L.) is one of the most important crops worldwide and a major source of human cadmium (Cd) intake. Limiting grain Cd concentration (Gr_Cd_Conc) in wheat is necessary to ensure food safety. However, the genetic factors associated with Cd uptake, translocation and distribution and Gr_Cd_Conc in wheat are poorly understood. Here, we mapped quantitative trait loci (QTLs) for Gr_Cd_Conc and its related transport pathway using a recombinant inbred line (RIL) population derived from 2 Polish wheat varieties (RIL_DT; dwarf Polish wheat [DPW] and tall Polish wheat [TPW]). We identified 29 novel major QTLs for grain and tissue Cd concentration; 14 novel major QTLs for Cd uptake, translocation, and distribution; and 27 major QTLs for agronomic traits. We also analyzed the pleiotropy of these QTLs. Six novel QTLs (QGr_Cd_Conc-1A, QGr_Cd_Conc-3A, QGr_Cd_Conc-4B, QGr_Cd_Conc-5B, QGr_Cd_Conc-6A, and QGr_Cd_Conc-7A) for Gr_Cd_Conc explained 8.16% to 17.02% of the phenotypic variation. QGr_Cd_Conc-3A, QGr_Cd_Conc-6A, and QGr_Cd_Conc-7A pleiotropically regulated Cd transport; 3 other QTLs were organ-specific for Gr_Cd_Conc. We fine-mapped the locus of QGr_Cd_Conc-4B and identified the candidate gene as Cation/Ca exchanger 2 (TpCCX2-4B), which was differentially expressed in DPW and TPW. It encodes an endoplasmic reticulum membrane/plasma membrane-localized Cd efflux transporter in yeast. Overexpression of TpCCX2-4B reduced Gr_Cd_Conc in rice. The average Gr_Cd_Conc was significantly lower in TpCCX2-4BDPW genotypes than in TpCCX2-4BTPW genotypes of the RIL_DT population and 2 other natural populations, based on a Kompetitive allele-specific PCR marker derived from the different promoter sequences between TpCCX2-4BDPW and TpCCX2-4BTPW. Our study reveals the genetic mechanism of Cd accumulation in wheat and provides valuable resources for genetic improvement of low-Cd-accumulating wheat cultivars.

Rice Chalky Grain 5 regulates natural variation for grain quality under heat stress

Heat stress occurring during rice (Oryza sativa) grain development reduces grain quality, which often manifests as increased grain chalkiness. Although the impact of heat stress on grain yield is well-studied, the genetic basis of rice grain quality under heat stress is less explored as quantifying grain quality is less tractable than grain yield. To address this, we used an image-based colorimetric assay (Red, R; and Green, G) for genome-wide association analysis to identify genetic loci underlying the phenotypic variation in rice grains exposed to heat stress. We found the R to G pixel ratio (RG) derived from mature grain images to be effective in distinguishing chalky grains from translucent grains derived from control (28/24°C) and heat stressed (36/32°C) plants. Our analysis yielded a novel gene, rice Chalky Grain 5 (OsCG5) that regulates natural variation for grain chalkiness under heat stress. OsCG5 encodes a grain-specific, expressed protein of unknown function. Accessions with lower transcript abundance of OsCG5 exhibit higher chalkiness, which correlates with higher RG values under stress. These findings are supported by increased chalkiness of OsCG5 knock-out (KO) mutants relative to wildtype (WT) under heat stress. Grains from plants overexpressing OsCG5 are less chalky than KOs but comparable to WT under heat stress. Compared to WT and OE, KO mutants exhibit greater heat sensitivity for grain size and weight relative to controls. Collectively, these results show that the natural variation at OsCG5 may contribute towards rice grain quality under heat stress.

Assessment of two statistical approaches for variance genome-wide association studies in plants

Genomic loci that control the variance of agronomically important traits are increasingly important due to the profusion of unpredictable environments arising from climate change. The ability to identify such variance-controlling loci in association studies will be critical for future breeding efforts. Two statistical approaches that have already been used in the variance genome-wide association study (vGWAS) paradigm are the Brown-Forsythe test (BFT) and the double generalized linear model (DGLM). To ensure that these approaches are deployed as effectively as possible, it is critical to study the factors that influence their ability to identify variance-controlling loci. We used genome-wide marker data in maize (Zea mays L.) and Arabidopsis thaliana to simulate traits controlled by epistasis, genotype by environment (GxE) interactions, and variance quantitative trait nucleotides (vQTNs). We then quantified true and false positive detection rates of the BFT and DGLM across all simulated traits. We also conducted a vGWAS using both the BFT and DGLM on plant height in a maize diversity panel. The observed true positive detection rates at the maximum sample size considered (N = 2815) suggest that both of these vGWAS approaches are capable of identifying epistasis and GxE for sufficiently large sample sizes. We also noted that the DGLM decisively outperformed the BFT for simulated traits controlled by vQTNs at sample sizes of N = 500. Although we conclude that there are still certain aspects of vGWAS approaches that need further refinement, this study suggests that the BFT and DGLM are capable of identifying variance-controlling loci in current state-of-the-art plant or agronomic data sets.

Evaluation of the phenotypic and genomic background of variability based on litter size of Large White pigs

BACKGROUND

The genetic background of trait variability has captured the interest of ecologists and animal breeders because the genes that control it could be involved in buffering various environmental effects. Phenotypic variability of a given trait can be assessed by studying the heterogeneity of the residual variance, and the quantitative trait loci (QTL) that are involved in the control of this variability are described as variance QTL (vQTL). This study focuses on litter size (total number born, TNB) and its variability in a Large White pig population. The variability of TNB was evaluated either using a simple method, i.e. analysis of the log-transformed variance of residuals (LnVar), or the more complex double hierarchical generalized linear model (DHGLM). We also performed a single-SNP (single nucleotide polymorphism) genome-wide association study (GWAS). To our knowledge, this is only the second study that reports vQTL for litter size in pigs and the first one that shows GWAS results when using two methods to evaluate variability of TNB: LnVar and DHGLM.

RESULTS

Based on LnVar, three candidate vQTL regions were detected, on Sus scrofa chromosomes (SSC) 1, 7, and 18, which comprised 18 SNPs. Based on the DHGLM, three candidate vQTL regions were detected, i.e. two on SSC7 and one on SSC11, which comprised 32 SNPs. Only one candidate vQTL region overlapped between the two methods, on SSC7, which also contained the most significant SNP. Within this vQTL region, two candidate genes were identified, ADGRF1, which is involved in neurodevelopment of the brain, and ADGRF5, which is involved in the function of the respiratory system and in vascularization. The correlation between estimated breeding values based on the two methods was 0.86. Three-fold cross-validation indicated that DHGLM yielded EBV that were much more accurate and had better prediction of missing observations than LnVar.

CONCLUSIONS

The results indicated that the LnVar and DHGLM methods resulted in genetically different traits. Based on their validation, we recommend the use of DHGLM over the simpler method of log-transformed variance of residuals. These conclusions can be useful for future studies on the evaluation of the variability of any trait in any species.

Genetic architecture affecting maize agronomic traits identified by variance heterogeneity association mapping

Conventional genome-wide association studies (GWAS) focused on the phenotypic mean differences (mGWAS) but often ignored genetic variants influencing differences in the variance between genotypes. In this study, we performed variance heterogeneity GWAS (vGWAS) analysis for 13 previously measured agronomic traits in a maize population. We discovered a total of 129 significant SNPs. We demonstrated that the genetic loci influencing mean differences and variance heterogeneity formed distinct groups, suggesting that breeders were able to independently select for phenotype mean and variance values. Moreover, vGWAS served as a tractable approach to effectively identify 214 epistatic interaction pairs. In addition, we documented four agronomic traits with decreasing phenotype variance during modern maize breeding history and identified the potential genetic variants influencing this process. In summary, we discovered additional non-additive effects contributing to missing heritability and valuable genetic variants used for breeding varieties with desired phenotypic variance.