Applying network and genetic analysis to the potato metabolome.

Compositional traits in potato [Solanum tuberosum L.] are economically important but genetically complex, often controlled by many loci of small effect; new methods need to be developed to accelerate analysis and improvement of such traits, like chip quality. In this study, we used network analysis to organize hundreds of metabolic features detected by mass spectrometry into groups, as a precursor to genetic analysis. 981 features were condensed into 44 modules; module eigenvalues were used for genetic mapping and correlation analysis with phenotype data collected by the Solanaceae Coordinated Agricultural Project. Half of the modules were associated with at least one SNP according to GWAS; 11 of those modules were also significantly correlated with chip color. Within those modules features associated with chipping provide potential targets for selection in addition to selection for reduced glucose. Loci associated with module eigenvalues were not evenly distributed throughout the genome but were instead clustered on chromosomes 3, 7, and 8. Comparison of GWAS on single features and modules of clustered features often identified the same SNPs. However, features with related chemistries (for example, glycoalkaloids with precursor/product relationships) were not found to be near neighbors in the network analysis and did not share common SNPs from GWAS. Instead, the features within modules were often structurally disparate, suggesting that linkage disequilibrium complicates network analyses in potato. This result is consistent with recent genomic studies of potato showing that chromosomal rearrangements that create barriers to recombination are common in cultivated germplasm.

Multi-omics prediction of oat agronomic and seed nutritional traits across environments and in distantly related populations.

KEY MESSAGE: Integration of multi-omics data improved prediction accuracies of oat agronomic and seed nutritional traits in multi-environment trials and distantly related populations in addition to the single-environment prediction. Multi-omics prediction has been shown to be superior to genomic prediction with genome-wide DNA-based genetic markers (G) for predicting phenotypes. However, most of the existing studies were based on historical datasets from one environment; therefore, they were unable to evaluate the efficiency of multi-omics prediction in multi-environment trials and distantly related populations. To fill those gaps, we designed a systematic experiment to collect omics data and evaluate 17 traits in two oat breeding populations planted in single and multiple environments. In the single-environment trial, transcriptomic BLUP (T), metabolomic BLUP (M), G + T, G + M, and G + T + M models showed greater prediction accuracy than GBLUP for 5, 10, 11, 17, and 17 traits, respectively, and metabolites generally performed better than transcripts when combined with SNPs. In the multi-environment trial, multi-trait models with omics data outperformed both counterpart multi-trait GBLUP models and single-environment omics models, and the highest prediction accuracy was achieved when modeling genetic covariance as an unstructured covariance model. We also demonstrated that omics data can be used to prioritize loci from one population with omics data to improve genomic prediction in a distantly related population using a two-kernel linear model that accommodated both likely casual loci with large-effect and loci that explain little or no phenotypic variance. We propose that the two-kernel linear model is superior to most genomic prediction models that assume each variant is equally likely to affect the trait and can be used to improve prediction accuracy for any trait with prior knowledge of genetic architecture.

Genome-Wide Association Study and Genomic Prediction Elucidate the Distinct Genetic Architecture of Aluminum and Proton Tolerance in Arabidopsis thaliana.

Under acid soil conditions, Al stress and proton stress can occur, reducing root growth and function. However, these stressors are distinct, and tolerance to each is governed by multiple physiological processes. To better understand the genes that underlie these coincidental but experimentally separable stresses, a genome-wide association study (GWAS) and genomic prediction (GP) models were created for approximately 200 diverse Arabidopsis thaliana accessions. GWAS and genomic prediction identified 140/160 SNPs associated with Al and proton tolerance, respectively, which explained approximately 70% of the variance observed. Reverse genetics of the genes in loci identified novel Al and proton tolerance genes, including TON1-RECRUITING MOTIF 28 (AtTRM28) and THIOREDOXIN H-TYPE 1 (AtTRX1), as well as genes known to be associated with tolerance, such as the Al-activated malate transporter, AtALMT1. Additionally, variation in Al tolerance was partially explained by expression level polymorphisms of AtALMT1 and AtTRX1 caused by cis-regulatory allelic variation. These results suggest that we successfully identified the loci that regulate Al and proton tolerance. Furthermore, very small numbers of loci were shared by Al and proton tolerance as determined by the GWAS. There were substantial differences between the phenotype predicted by genomic prediction and the observed phenotype for Al tolerance. This suggested that the GWAS-undetectable genetic factors (e.g., rare-allele mutations) contributing to the variation of tolerance were more important for Al tolerance than for proton tolerance. This study provides important new insights into the genetic architecture that produces variation in the tolerance of acid soil.

Heritable temporal gene expression patterns correlate with metabolomic seed content in developing hexaploid oat seed.

Oat ranks sixth in world cereal production and has a higher content of health-promoting compounds compared with other cereals. However, there is neither a robust oat reference genome nor transcriptome. Using deeply sequenced full-length mRNA libraries of oat cultivar Ogle-C, a de novo high-quality and comprehensive oat seed transcriptome was assembled. With this reference transcriptome and QuantSeq 3' mRNA sequencing, gene expression was quantified during seed development from 22 diverse lines across six time points. Transcript expression showed higher correlations between adjacent time points. Based on differentially expressed genes, we identified 22 major temporal co-expression (TCoE) patterns of gene expression and revealed enriched gene ontology biological processes. Within each TCoE set, highly correlated transcripts, putatively commonly affected by genetic background, were clustered and termed genetic co-expression (GCoE) sets. Seventeen of the 22 TCoE sets had GCoE sets with median heritabilities higher than 0.50, and these heritability estimates were much higher than that estimated from permutation analysis, with no divergence observed in cluster sizes between permutation and non-permutation analyses. Linear regression between 634 metabolites from mature seeds and the PC1 score of each of the GCoE sets showed significantly lower p-values than permutation analysis. Temporal expression patterns of oat avenanthramides and lipid biosynthetic genes were concordant with previous studies of avenanthramide biosynthetic enzyme activity and lipid accumulation. This study expands our understanding of physiological processes that occur during oat seed maturation and provides plant breeders the means to change oat seed composition through targeted manipulation of key pathways.

Multivariate Genome-Wide Association Analyses Reveal the Genetic Basis of Seed Fatty Acid Composition in Oat (Avena sativa L.).

Oat (Avena sativa L.) has a high concentration of oils, comprised primarily of healthful unsaturated oleic and linoleic fatty acids. To accelerate oat plant breeding efforts, we sought to identify loci associated with variation in fatty acid composition, defined as the types and quantities of fatty acids. We genotyped a panel of 500 oat cultivars with genotyping-by-sequencing and measured the concentrations of ten fatty acids in these oat cultivars grown in two environments. Measurements of individual fatty acids were highly correlated across samples, consistent with fatty acids participating in shared biosynthetic pathways. We leveraged these phenotypic correlations in two multivariate genome-wide association study (GWAS) approaches. In the first analysis, we fitted a multivariate linear mixed model for all ten fatty acids simultaneously while accounting for population structure and relatedness among cultivars. In the second, we performed a univariate association test for each principal component (PC) derived from a singular value decomposition of the phenotypic data matrix. To aid interpretation of results from the multivariate analyses, we also conducted univariate association tests for each trait. The multivariate mixed model approach yielded 148 genome-wide significant single-nucleotide polymorphisms (SNPs) at a 10% false-discovery rate, compared to 129 and 73 significant SNPs in the PC and univariate analyses, respectively. Thus, explicit modeling of the correlation structure between fatty acids in a multivariate framework enabled identification of loci associated with variation in seed fatty acid concentration that were not detected in the univariate analyses. Ultimately, a detailed characterization of the loci underlying fatty acid variation can be used to enhance the nutritional profile of oats through breeding.

Mass spectrometry-based analytical developments to link iron speciation to iron bioavailability in maize.

A sequential fractionation procedure based on (i) water extraction, (ii) hexane extraction, (iii) saccharification, and (iv) proteolysis was developed to provide the first ever data on the molecular distribution of iron in maize. This was completed by the operational determination of the iron bioavailability using an in-vitro simulated model for gastro-intestinal digestion. The coupling of hydrophilic interaction chromatography (HILIC) and size exclusion chromatography (SEC) with the parallel detection by inductively coupled plasma mass spectrometry (ICP-MS) and high resolution electrospray mass spectrometry (HR-ESI-MS) allowed the identification of water-soluble Fe(III)-mugineate, Fe(III)-(citrate)2, and Fe(III)2-(phytate)2. The procedures were applied to study some well characterized maize varieties having shown previously differences in iron bioavailability during cell culture and animal model feeding studies. The combined analytical methods developed in this work could unambiguously discriminate low from high Fe bioavailable seeds in these closely related maize varieties.

Integrating Coexpression Networks with GWAS to Prioritize Causal Genes in Maize.

Genome-wide association studies (GWAS) have identified loci linked to hundreds of traits in many different species. Yet, because linkage equilibrium implicates a broad region surrounding each identified locus, the causal genes often remain unknown. This problem is especially pronounced in nonhuman, nonmodel species, where functional annotations are sparse and there is frequently little information available for prioritizing candidate genes. We developed a computational approach, Camoco, that integrates loci identified by GWAS with functional information derived from gene coexpression networks. Using Camoco, we prioritized candidate genes from a large-scale GWAS examining the accumulation of 17 different elements in maize (Zea mays) seeds. Strikingly, we observed a strong dependence in the performance of our approach based on the type of coexpression network used: expression variation across genetically diverse individuals in a relevant tissue context (in our case, roots that are the primary elemental uptake and delivery system) outperformed other alternative networks. Two candidate genes identified by our approach were validated using mutants. Our study demonstrates that coexpression networks provide a powerful basis for prioritizing candidate causal genes from GWAS loci but suggests that the success of such strategies can highly depend on the gene expression data context. Both the software and the lessons on integrating GWAS data with coexpression networks generalize to species beyond maize.

NLR locus-mediated trade-off between abiotic and biotic stress adaptation in Arabidopsis.

Osmotic stress caused by drought, salt or cold decreases plant fitness. Acquired stress tolerance defines the ability of plants to withstand stress following an initial exposure1. We found previously that acquired osmotolerance after salt stress is widespread among Arabidopsis thaliana accessions2. Here, we identify ACQOS as the locus responsible for ACQUIRED OSMOTOLERANCE. Of its five haplotypes, only plants carrying group 1 ACQOS are impaired in acquired osmotolerance. ACQOS is identical to VICTR, encoding a nucleotide-binding leucine-rich repeat (NLR) protein3. In the absence of osmotic stress, group 1 ACQOS contributes to bacterial resistance. In its presence, ACQOS causes detrimental autoimmunity, thereby reducing osmotolerance. Analysis of natural variation at the ACQOS locus suggests that functional and non-functional ACQOS alleles are being maintained due to a trade-off between biotic and abiotic stress adaptation. Thus, polymorphism in certain plant NLR genes might be influenced by competing environmental stresses.

Chromatographic Methods to Evaluate Nutritional Quality in Oat.

Oats (A. sativa L.) have an important and positive role in human diet and health. The health benefits of oats are attributed to its multifunctional characteristic and nutritional profile, being an important source of soluble dietary fiber, well-balanced proteins, unsaturated fatty acids, vitamins, essential minerals, and a good source of natural antioxidants. These antioxidants include the avenanthramides (Avns) and avenalumic acids, which are unique to oats among cereals. High-performance liquid chromatography allows a simultaneous quantification of free amino acids and biogenic amines in oat samples as their OPA/FMOC-CL (o-phthalaldehyde/9-fluorenylmethoxycarbonyl chloride) derivatives. In addition, an ultra-performance liquid chromatography/mass spectrometry method was developed to quantify and characterize avenanthramides contained in oat samples.

The Interaction of Genotype and Environment Determines Variation in the Maize Kernel Ionome.

Plants obtain soil-resident elements that support growth and metabolism from the water-flow facilitated by transpiration and active transport processes. The availability of elements in the environment interacts with the genetic capacity of organisms to modulate element uptake through plastic adaptive responses, such as homeostasis. These interactions should cause the elemental contents of plants to vary such that the effects of genetic polymorphisms will be dramatically dependent on the environment in which the plant is grown. To investigate genotype by environment interactions underlying elemental accumulation, we analyzed levels of elements in maize kernels of the Intermated B73 × Mo17 (IBM) recombinant inbred population grown in 10 different environments, spanning a total of six locations and five different years. In analyses conducted separately for each environment, we identified a total of 79 quantitative trait loci (QTL) controlling seed elemental accumulation. While a set of these QTL was found in multiple environments, the majority were specific to a single environment, suggesting the presence of genetic by environment interactions. To specifically identify and quantify QTL by environment interactions (QEIs), we implemented two methods: linear modeling with environmental covariates, and QTL analysis on trait differences between growouts. With these approaches, we found several instances of QEI, indicating that elemental profiles are highly heritable, interrelated, and responsive to the environment.

Joint genetic and network analyses identify loci associated with root growth under NaCl stress in Arabidopsis thaliana.

Plants have evolved a series of tolerance mechanisms to saline stress, which perturbs physiological processes throughout the plant. To identify genetic mechanisms associated with salinity tolerance, we performed linkage analysis and genome-wide association study (GWAS) on maintenance of root growth of Arabidopsis thaliana in hydroponic culture with weak and severe NaCl toxicity. The top 200 single-nucleotide polymorphisms (SNPs) determined by GWAS could cumulatively explain approximately 70% of the variation observed at each stress level. The most significant SNPs were linked to the genes of ATP-binding cassette B10 and vacuolar proton ATPase A2. Several known salinity tolerance genes such as potassium channel KAT1 and calcium sensor SOS3 were also linked to SNPs in the top 200. In parallel, we constructed a gene co-expression network to independently verify that particular groups of genes work together to a common purpose. We identify molecular mechanisms to confer salt tolerance from both predictable and novel physiological sources and validate the utility of combined genetic and network analysis. Additionally, our study indicates that the genetic architecture of salt tolerance is responsive to the severity of stress. These gene datasets are a significant information resource for a following exploration of gene function.

Single-kernel ionomic profiles are highly heritable indicators of genetic and environmental influences on elemental accumulation in maize grain (Zea mays).

The ionome, or elemental profile, of a maize kernel can be viewed in at least two distinct ways. First, the collection of elements within the kernel are food and feed for people and animals. Second, the ionome of the kernel represents a developmental end point that can summarize the life history of a plant, combining genetic programs and environmental interactions. We assert that single-kernel-based phenotyping of the ionome is an effective method of analysis, as it represents a reasonable compromise between precision, efficiency, and power. Here, we evaluate potential pitfalls of this sampling strategy using several field-grown maize sample sets. We demonstrate that there is enough genetically determined diversity in accumulation of many of the elements assayed to overcome potential artifacts. Further, we demonstrate that environmental signals are detectable through their influence on the kernel ionome. We conclude that using single kernels as the sampling unit is a valid approach for understanding genetic and environmental effects on the maize kernel ionome.

Compositional analysis of genetically modified (GM) crops: key issues and future needs.

Effective symposia need two strong legs to stand upon: informative presentations of recent research paired with lively discussion of these topics. Although it is easy for the organizers of a symposium to predict the usefulness of the former, as they select the speakers and their topic areas, guaranteeing productive discussion is a far more difficult task. For the Crop Composition Workshop sponsored by the International Life Sciences Institute's Committee on Food and Biotechnology (ILSI IFBIC), the organizers scheduled four roundtable discussions with preselected questions and with rapporteurs drawn from governmental organizations and public-sector research institutes (the authors). It was also the organizers' intent to let these discussions flow on the basis of the experiences of the participants and pressing issues within the overall debate on the role of crop compositional analysis within safety assessment of biotechnology as it exists now and in the future. The goal of this perspective is to summarize the issues raised, providing references when possible, and to describe the consensus statements reached through the course of these discussions.

Leveraging non-targeted metabolite profiling via statistical genomics.

One of the challenges of systems biology is to integrate multiple sources of data in order to build a cohesive view of the system of study. Here we describe the mass spectrometry based profiling of maize kernels, a model system for genomic studies and a cornerstone of the agroeconomy. Using a network analysis, we can include 97.5% of the 8,710 features detected from 210 varieties into a single framework. More conservatively, 47.1% of compounds detected can be organized into a network with 48 distinct modules. Eigenvalues were calculated for each module and then used as inputs for genome-wide association studies. Nineteen modules returned significant results, illustrating the genetic control of biochemical networks within the maize kernel. Our approach leverages the correlations between the genome and metabolome to mutually enhance their annotation and thus enable biological interpretation. This method is applicable to any organism with sufficient bioinformatic resources.

High bioavailability iron maize (Zea mays L.) developed through molecular breeding provides more absorbable iron in vitro (Caco-2 model) and in vivo (Gallus gallus).

BACKGROUND: Iron (Fe) deficiency is the most common micronutrient deficiency worldwide. Iron biofortification is a preventative strategy that alleviates Fe deficiency by improving the amount of absorbable Fe in crops. In the present study, we used an in vitro digestion/Caco 2 cell culture model as the guiding tool for breeding and development of two maize (Zea mays L.) lines with contrasting Fe bioavailability (ie. Low and High). Our objective was to confirm and validate the in vitro results and approach. Also, to compare the capacities of our two maize hybrid varieties to deliver Fe for hemoglobin (Hb) synthesis and to improve the Fe status of Fe deficient broiler chickens. METHODS: We compared the Fe-bioavailability between these two maize varieties with the presence or absence of added Fe in the maize based-diets. Diets were made with 75% (w/w) maize of either low or high Fe-bioavailability maize, with or without Fe (ferric citrate). Chicks (Gallus gallus) were fed the diets for 6 wk. Hb, liver ferritin and Fe related transporter/enzyme gene-expression were measured. Hemoglobin maintenance efficiency (HME) and total body Hb Fe values were used to estimate Fe bioavailability from the diets. RESULTS: DMT-1, DcytB and ferroportin expressions were higher (P<0.05) in the "Low Fe" group than in the "High Fe" group (no added Fe), indicating lower Fe status and adaptation to less Fe-bioavailability. At times, Hb concentrations (d 21,28,35), HME (d 21), Hb-Fe (as from d 14) and liver ferritin were higher in the "High Fe" than in the "Low Fe" groups (P<0.05), indicating greater Fe absorption from the diet and improved Fe status. CONCLUSIONS: We conclude that the High Fe-bioavailability maize contains more bioavailable Fe than the Low Fe-bioavailability maize, presumably due to a more favorable matrix for absorption. Maize shows promise for Fe biofortification; therefore, human trials should be conducted to determine the efficacy of consuming the high bioavailable Fe maize to reduce Fe deficiency.

Weighted correlation network analysis (WGCNA) applied to the tomato fruit metabolome.

BACKGROUND: Advances in "omics" technologies have revolutionized the collection of biological data. A matching revolution in our understanding of biological systems, however, will only be realized when similar advances are made in informatic analysis of the resulting "big data." Here, we compare the capabilities of three conventional and novel statistical approaches to summarize and decipher the tomato metabolome. METHODOLOGY: Principal component analysis (PCA), batch learning self-organizing maps (BL-SOM) and weighted gene co-expression network analysis (WGCNA) were applied to a multivariate NMR dataset collected from developmentally staged tomato fruits belonging to several genotypes. While PCA and BL-SOM are appropriate and commonly used methods, WGCNA holds several advantages in the analysis of highly multivariate, complex data. CONCLUSIONS: PCA separated the two major genetic backgrounds (AC and NC), but provided little further information. Both BL-SOM and WGCNA clustered metabolites by expression, but WGCNA additionally defined "modules" of co-expressed metabolites explicitly and provided additional network statistics that described the systems properties of the tomato metabolic network. Our first application of WGCNA to tomato metabolomics data identified three major modules of metabolites that were associated with ripening-related traits and genetic background.

Genetic and physiological analysis of iron biofortification in maize kernels.

BACKGROUND: Maize is a major cereal crop widely consumed in developing countries, which have a high prevalence of iron (Fe) deficiency anemia. The major cause of Fe deficiency in these countries is inadequate intake of bioavailable Fe, where poverty is a major factor. Therefore, biofortification of maize by increasing Fe concentration and or bioavailability has great potential to alleviate this deficiency. Maize is also a model system for genomic research and thus allows the opportunity for gene discovery. Here we describe an integrated genetic and physiological analysis of Fe nutrition in maize kernels, to identify loci that influence grain Fe concentration and bioavailability. METHODOLOGY: Quantitative trait locus (QTL) analysis was used to dissect grain Fe concentration (FeGC) and Fe bioavailability (FeGB) from the Intermated B73 × Mo17 (IBM) recombinant inbred (RI) population. FeGC was determined by ion coupled argon plasma emission spectroscopy (ICP). FeGB was determined by an in vitro digestion/Caco-2 cell line bioassay. CONCLUSIONS: Three modest QTL for FeGC were detected, in spite of high heritability. This suggests that FeGC is controlled by many small QTL, which may make it a challenging trait to improve by marker assisted breeding. Ten QTL for FeGB were identified and explained 54% of the variance observed in samples from a single year/location. Three of the largest FeGB QTL were isolated in sister derived lines and their effect was observed in three subsequent seasons in New York. Single season evaluations were also made at six other sites around North America, suggesting the enhancement of FeGB was not specific to our farm site. FeGB was not correlated with FeGC or phytic acid, suggesting that novel regulators of Fe nutrition are responsible for the differences observed. Our results indicate that iron biofortification of maize grain is achievable using specialized phenotyping tools and conventional plant breeding techniques.

Physiological and genetic characterization of end-of-day far-red light response in maize seedlings.

Developmental responses associated with end-of-day far-red light (EOD-FR) signaling were investigated in maize (Zea mays subspecies mays) seedlings. A survey of genetically diverse inbreds of temperate and tropical/semitropical origins, together with teosinte (Zea mays subspecies parviglumis) and a modern hybrid, revealed distinct elongation responses. A mesocotyl elongation response to the EOD-FR treatment was largely absent in the tropical/semitropical lines, but both hybrid and temperate inbred responses were of the same magnitude as in teosinte, suggesting that EOD-FR-mediated mesocotyl responses were not lost during the domestication or breeding process. The genetic architecture underlying seedling responses to EOD-FR was investigated using the intermated B73 x Mo17 mapping population. Among the different quantitative trait loci identified, two were consistently detected for elongation and responsiveness under EOD-FR, but none were associated with known light signaling loci. The central role of phytochromes in mediating EOD-FR responses was shown using a phytochromeB1 phytochromeB2 (phyB1 phyB2) mutant series. Unlike the coleoptile and first leaf sheath, EOD-FR-mediated elongation of the mesocotyl appears predominantly controlled by gibberellin. EOD-FR also reduced abscisic acid (ABA) levels in the mesocotyl for both the wild type and phyB1 phyB2 double mutants, suggesting a FR-mediated but PHYB-independent control of ABA accumulation. EOD-FR elongation responses were attenuated in both the wild type and phyB1 phyB2 double mutants when a chilling stress was applied during the dark period, concomitant with an increase in ABA levels. We present a model for the EOD-FR response that integrates light and hormonal control of seedling elongation.

Natural genetic variation in selected populations of Arabidopsis thaliana is associated with ionomic differences.

Controlling elemental composition is critical for plant growth and development as well as the nutrition of humans who utilize plants for food. Uncovering the genetic architecture underlying mineral ion homeostasis in plants is a critical first step towards understanding the biochemical networks that regulate a plant's elemental composition (ionome). Natural accessions of Arabidopsis thaliana provide a rich source of genetic diversity that leads to phenotypic differences. We analyzed the concentrations of 17 different elements in 12 A. thaliana accessions and three recombinant inbred line (RIL) populations grown in several different environments using high-throughput inductively coupled plasma- mass spectroscopy (ICP-MS). Significant differences were detected between the accessions for most elements and we identified over a hundred QTLs for elemental accumulation in the RIL populations. Altering the environment the plants were grown in had a strong effect on the correlations between different elements and the QTLs controlling elemental accumulation. All ionomic data presented is publicly available at www.ionomicshub.org.

Association and linkage analysis of aluminum tolerance genes in maize.

BACKGROUND: Aluminum (Al) toxicity is a major worldwide constraint to crop productivity on acidic soils. Al becomes soluble at low pH, inhibiting root growth and severely reducing yields. Maize is an important staple food and commodity crop in acidic soil regions, especially in South America and Africa where these soils are very common. Al exclusion and intracellular tolerance have been suggested as two important mechanisms for Al tolerance in maize, but little is known about the underlying genetics. METHODOLOGY: An association panel of 282 diverse maize inbred lines and three F2 linkage populations with approximately 200 individuals each were used to study genetic variation in this complex trait. Al tolerance was measured as net root growth in nutrient solution under Al stress, which exhibited a wide range of variation between lines. Comparative and physiological genomics-based approaches were used to select 21 candidate genes for evaluation by association analysis. CONCLUSIONS: Six candidate genes had significant results from association analysis, but only four were confirmed by linkage analysis as putatively contributing to Al tolerance: Zea mays AltSB like (ZmASL), Zea mays aluminum-activated malate transporter2 (ALMT2), S-adenosyl-L-homocysteinase (SAHH), and Malic Enzyme (ME). These four candidate genes are high priority subjects for follow-up biochemical and physiological studies on the mechanisms of Al tolerance in maize. Immediately, elite haplotype-specific molecular markers can be developed for these four genes and used for efficient marker-assisted selection of superior alleles in Al tolerance maize breeding programs.