Genetic analysis of arsenic accumulation in maize using QTL mapping

Genetic dissection of maize (Zea maysL.) trace element traits using genome-wide association studies

Maize (Zea mays L.) is an important food and feed crop worldwide and serves as a a vital source of biological trace elements, which are important breeding targets. In this study, 170 maize materials were used to detect QTNs related to the content of Mn, Fe and Mo in maize grains through two GWAS models, namely MLM_Q + K and MLM_PCA + K. The results identified 87 (Mn), 205 (Fe), and 310 (Mo) QTNs using both methods in the three environments. Considering comprehensive factors such as co-location across multiple environments, strict significance threshold, and phenotypic value in multiple environments, 8 QTNs related to Mn, 10 QTNs related to Fe, and 26 QTNs related to Mo were used to identify 44 superior alleles. Consequently, three cross combinations with higher Mn element, two combinations with higher Fe element, six combinations with higher Mo element, and two combinations with multiple element (Mn/Fe/Mo) were predicted to yield offspring with higher numbers of superior alleles, thereby increasing the likelihood of enriching the corresponding elements. Additionally, the candidate genes identified 100 kb downstream and upstream the QTNs featured function and pathways related to maize elemental transport and accumulation. These results are expected to facilitate the screening and development of high-quality maize varieties enriched with trace elements, establish an important theoretical foundation for molecular marker assisted breeding and contribute to a better understanding of the regulatory network governing trace elements in maize.

The genetic architecture of trait covariation in Populus euphratica, a desert tree

Introduction

The cooperative strategy of phenotypic traits during the growth of plants reflects how plants allocate photosynthesis products, which is the most favorable decision for them to optimize growth, survival, and reproduction response to changing environment. Up to now, we still know little about why plants make such decision from the perspective of biological genetic mechanisms.

Methods

In this study, we construct an analytical mapping framework to explore the genetic mechanism regulating the interaction of two complex traits. The framework describes the dynamic growth of two traits and their interaction as Differential Interaction Regulatory Equations (DIRE), then DIRE is embedded into QTL mapping model to identify the key quantitative trait loci (QTLs) that regulate this interaction and clarify the genetic effect, genetic contribution and genetic network structure of these key QTLs. Computer simulation experiment proves the reliability and practicability of our framework.

Results

In order to verify that our framework is universal and flexible, we applied it to two sets of data from Populus euphratica, namely, aboveground stem length - underground taproot length, underground root number - underground root length, which represent relationships of phenotypic traits in two spatial dimensions of plant architecture. The analytical result shows that our model is well applicable to datasets of two dimensions.

Discussion

Our model helps to better illustrate the cooperation-competition patterns between phenotypic traits, and understand the decisions that plants make in a specific environment that are most conducive to their growth from the genetic perspective.

QTL and Candidate Genes: Techniques and Advancement in Abiotic Stress Resistance Breeding of Major Cereals

At least 75% of the world's grain production comes from the three most important cereal crops: rice (Oryza sativa), wheat (Triticum aestivum), and maize (Zea mays). However, abiotic stressors such as heavy metal toxicity, salinity, low temperatures, and drought are all significant hazards to the growth and development of these grains. Quantitative trait locus (QTL) discovery and mapping have enhanced agricultural production and output by enabling plant breeders to better comprehend abiotic stress tolerance processes in cereals. Molecular markers and stable QTL are important for molecular breeding and candidate gene discovery, which may be utilized in transgenic or molecular introgression. Researchers can now study synteny between rice, maize, and wheat to gain a better understanding of the relationships between the QTL or genes that are important for a particular stress adaptation and phenotypic improvement in these cereals from analyzing reports on QTL and candidate genes. An overview of constitutive QTL, adaptive QTL, and significant stable multi-environment and multi-trait QTL is provided in this article as a solid framework for use and knowledge in genetic enhancement. Several QTL, such as DRO1 and Saltol, and other significant success cases are discussed in this review. We have highlighted techniques and advancements for abiotic stress tolerance breeding programs in cereals, the challenges encountered in introgressing beneficial QTL using traditional breeding techniques such as mutation breeding and marker-assisted selection (MAS), and the in roads made by new breeding methods such as genome-wide association studies (GWASs), the clustered regularly interspaced short palindromic repeat (CRISPR)/Cas9 system, and meta-QTL (MQTL) analysis. A combination of these conventional and modern breeding approaches can be used to apply the QTL and candidate gene information in genetic improvement of cereals against abiotic stresses.

Water and soil contaminated by arsenic: the use of microorganisms and plants in bioremediation

Owing to their roles in the arsenic (As) biogeochemical cycle, microorganisms and plants offer significant potential for developing innovative biotechnological applications able to remediate As pollutions. This possible use in bioremediation processes and phytomanagement is based on their ability to catalyse various biotransformation reactions leading to, e.g. the precipitation, dissolution, and sequestration of As, stabilisation in the root zone and shoot As removal. On the one hand, genomic studies of microorganisms and their communities are useful in understanding their metabolic activities and their interaction with As. On the other hand, our knowledge of molecular mechanisms and fate of As in plants has been improved by laboratory and field experiments. Such studies pave new avenues for developing environmentally friendly bioprocessing options targeting As, which worldwide represents a major risk to many ecosystems and human health.

Arsenic uptake and toxicity in wheat (Triticum aestivum L.): A review of multi-omics approaches to identify tolerance mechanisms

Arsenic (As) due to its widespread has become a primary concern for sustainable food production, especially in Southeast Asian countries. In that context, the present review presented a comprehensive detail of the available literature marking an assortment of As-induced impacts on wheat. The conclusive findings of past research suggest that As tends to grossly affect the germination, elongation, biomass, grain yield, and induce oxidative stress. Several human studies are suggestive of higher cancer risks (>1 × 10^-6) due to the ingestion of wheat grains. However, the body of proof is limited and the scarcity of information limited understanding about tolerance mechanism in wheat against As. Therefore, the paper provided a reference from tolerance mechanism based studies in other crops like rice and maize. The generated knowledge of arsenomics would pave the way for plant breeders to develop resistant varieties for As to ensure sustainable food production.

Genetic Mapping of Head Size Related Traits in Common Carp (Cyprinus carpio)

Head size is important economic trait for many aquaculture fish which is directly linked to their carcass yield. The genetic basis of head size trait remains unclear in many widely cultured fish species. Common carp (Cyprinus carpio) is one of the most widely studied fish due to its importance on both economic and environmental aspects. In this study, we performed genome-wide association study using 433 Yellow River carp individuals from multiple families to identify loci and genes potentially associated with head size related traits including head length (HL), head length/body length ratio (HBR), eye diameter (ED), and eye cross (EC). QTL mapping was utilized to filter the effects of population stratification and improve power for the candidates identification in the largest surveyed family with a published genetic linkage map. Twelve SNPs showed significant for head size traits in GWAS and 18 QTLs were identified in QTL mapping. Our study combining both GWAS and QTL mapping could compensate the deficiency from each other and advance our understanding of head size traits in common carp. To acquire a better understanding of the correlation between head size and body growth, we also performed comparisons between QTLs of head size traits and growth-related traits. Candidate genes underlying head size traits were identified surrounding the significant SNPs, including parvalbumin, srpk2, fsrp5, igf1, igf3, grb10, igf1r, notch2, sfrp2. Many of these genes have been identified with potential functions on bone formation and growth. Igf1 was a putative gene associated with both head size and body growth in Yellow River carp. The teleost-specific igf3 was a candidate head size related gene, related to both HL and HBR. Our study also indicated the importance of Igf signaling pathway for both growth and head size determination in common carp, which could be potentially used in future selective breeding in common carp as well as other species.

Genetic analysis of seedling root traits reveals the association of root trait with other agronomic traits in maize

BACKGROUND

Root systems play important roles in crop growth and stress responses. Although genetic mechanism of root traits in maize (Zea mays L.) has been investigated in different mapping populations, root traits have rarely been utilized in breeding programs. Elucidation of the genetic basis of maize root traits and, more importantly, their connection to other agronomic trait(s), such as grain yield, may facilitate root trait manipulation and maize germplasm improvement. In this study, we analyzed genome-wide genetic loci for maize seedling root traits at three time-points after seed germination to identify chromosomal regions responsible for both seedling root traits and other agronomic traits in a recombinant inbred line (RIL) population (Zong3 × Yu87-1).

RESULTS

Eight seedling root traits were examined at 4, 9, and 14 days after seed germination, and thirty-six putative quantitative trait loci (QTLs), accounting for 9.0-23.2% of the phenotypic variation in root traits, were detected. Co-localization of root trait QTLs was observed at, but not between, the three time-points. We identified strong or moderate correlations between root traits controlled by each co-localized QTL region. Furthermore, we identified an overlap in the QTL locations of seedling root traits examined here and six other traits reported previously in the same RIL population, including grain yield-related traits, plant height-related traits, and traits in relation to stress responses. Maize chromosomal bins 1.02-1.03, 1.07, 2.06-2.07, 5.05, 7.02-7.03, 9.04, and 10.06 were identified QTL hotspots for three or four more traits in addition to seedling root traits.

CONCLUSIONS

Our identification of co-localization of root trait QTLs at, but not between, each of the three time-points suggests that maize seedling root traits are regulated by different sets of pleiotropic-effect QTLs at different developmental stages. Furthermore, the identification of QTL hotspots suggests the genetic association of seedling root traits with several other traits and reveals maize chromosomal regions valuable for marker-assisted selection to improve root systems and other agronomic traits simultaneously.

Differential transcriptome modulation leads to variation in arsenic stress response in Arabidopsis thaliana accessions

Arsenic (As) is a ubiquitous metalloid and a health hazard to millions of people worldwide. The presence of As in groundwater poses a threat as it not only affects crop productivity but also contaminates food chain. Therefore, it is essential to understand molecular mechanisms underlying uptake, transport and accumulation of As in plants. In recent past, natural variation in Arabidopsis thaliana has been utilized to understand molecular and genetic adaptation under different stresses. In this study, responses of Arabidopsis accessions were analyzed at biochemical and molecular levels towards arsenate [As(V)] stress. On the basis of reduction in root length, accessions were categorized into tolerant and sensitive ones towards As(V). Root length analysis led to the identification of Col-0 (<10% reduction) and Slavi-1 (>60% reduction) as the most tolerant and sensitive accessions, respectively. Comparative genome-wide expression analysis revealed differential expression of 168 and 548 genes in Col-0 and Slavi-1, respectively, with 120 common differentially expressed genes. A number of genes associated with defense and stress-response, transport system, regulatory mechanisms and biochemical processes showed differential expression in contrasting accessions. The study provides an insight into the molecular mechanisms associated with stress response and processes involved in adaptation strategies towards As stress.

Genetic-based dissection of arsenic accumulation in maize using a genome-wide association analysis method

Understanding the mechanism of arsenic (As) accumulation in plants is important in reducing As's toxicity to plants and its potential risks to human health. Here, we performed a genome-wide association study to dissect the genetic basis of the As contents of different maize tissues in Xixian, which was irrigated with As-rich surface water, and Changge using an association population consisting of 230 representative maize inbred lines. Phenotypic data revealed a wide normal distribution and high repeatability for the As contents in maize tissues. The As concentrations in maize tissues followed the same trend in the two locations: kernels < axes < stems < bracts < leaves. In total, 15, 16 and 15 non-redundant quantitative trait loci (QTLs) associated with As concentrations were identified (P ≤ 2.04 × 10-6 ) in five tissues from Xixian, Changge, and the combination of the locations, respectively, explaining 9.70%-24.65% of the phenotypic variation for each QTL, on average. Additionally, four QTLs [involving 15 single nucleotide polymorphisms (SNPs)] were detected in the single and the combined locations, indicating that these loci/SNPs might be stable across different environments. The candidate genes associated with these four loci were predicted. In addition, four non-redundant QTLs (6 SNPs), including a QTL that was detected in multiple locations according to the genome-wide association study, were found to co-localize with four previously reported QTL intervals. These results are valuable to understand the genetic architecture of As mechanism in maize and facilitate the genetic improvement of varieties without As toxicity.

Genome-wide association analysis of lead accumulation in maize

Large phenotypic variations in the lead (Pb) concentration were observed in grains and leaves of maize plants. A further understanding of inheritance of Pb accumulation may facilitate improvement of low-Pb-accumulating cultivars in maize. A genome-wide association study was conducted in a population of 269 maize accessions with 43,737 single-nucleotide polymorphisms (SNPs). The Pb concentrations in leaves and kernels of 269 accessions were collected in pot-culture and field experiments in years of 2015 and 2016. Significant differences in Pb accumulation were found among individuals under different environments. Using the structure and kinship model, a total of 21 SNPs significantly associated with the Pb accumulation were identified with P < 2.28 × 10^-5 and FDR < 0.05 in the pot-culture and field experiments across 2 years. Three SNPs on chromosome 4 had significant associations simultaneously with the Pb concentrations of kernels and leaves and were co-localized with the previously detected quantitative trait loci. Through ridge regression best linear unbiased prediction Pb accumulation in the association population, the prediction accuracies by cross validation were 0.18-0.59 and 0.17-0.64, depending on the k-fold and the size of the training population. The results are helpful for genetic improvement and genomic prediction of Pb accumulation in maize.