Owen A. Hoekenga

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.

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 plants 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.

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.

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.

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.

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.

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.

Integrating co-expression networks with GWAS to prioritize causal genes in maize

Genome-wide association studies (GWAS) have identified thousands of loci linked to hundreds of traits in many different species. However, for most loci, the causal genes and the cellular processes they contribute to remain unknown. This problem is especially pronounced in species where functional annotations are sparse. Given little information about a gene, patterns of expression are a powerful tool for inferring biological function. Here, we developed a computational framework called Camoco that integrates loci identified by GWAS with functional information derived from gene co-expression networks. We built co-expression networks from three distinct biological contexts and establish the precision of our method with simulated GWAS data. We applied Camoco to prioritize candidate genes from a large-scale GWAS examining the accumulation of 17 different elements in maize seeds, demonstrating the need to match GWAS datasets with co-expression networks derived from the appropriate biological context. Furthermore, our results show that simply taking the genes closest to significant GWAS loci will often lead to spurious results, indicating the need for proper functional modeling and a reliable null distribution when integrating these high-throughput data types. We performed functional validation on a gene identified by our approach using mutants and annotate other high-priority candidates with ontological enrichment and curated literature support, resulting in a targeted set of candidate genes that drive elemental accumulation in maize grain.Background Genome wide association studies (GWAS) have identified thousands of loci linked to hundreds of traits in many different species. However, because linkage equilibrium implicates a broad region surrounding each identified locus, the causal genes often remain unknown. This problem is especially pronounced in non-human, non-model species where functional annotations are sparse and there is frequently little information available for prioritizing candidate genes. Results To address this issue, we developed a computational approach called Camoco (Co-Analysis of Molecular Components) that systematically integrates loci identified by GWAS with gene co-expression networks to prioritize putative causal genes. We applied Camoco to prioritize candidate genes from a large-scale GWAS examining the accumulation of 17 different elements in maize seeds. Camoco identified statistically significant subnetworks for the majority of traits examined, producing a prioritized list of high-confidence causal genes for several agronomically important maize traits. Two candidate genes identified by our approach were validated through analysis of mutant phenotypes. Strikingly, we observed a strong dependence in the performance of our approach on the type of co-expression network used: expression variation across genetically diverse individuals in a relevant tissue context (in our case, maize roots) outperformed other alternatives. Conclusions Our study demonstrates that co-expression networks can 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 Camoco software and the lessons on integrating GWAS data with co-expression networks generalize to species beyond maize.

Elemental Accumulation in Kernels of the Maize Nested Association Mapping Panel Reveals Signals of Gene by Environment Interactions

Elemental accumulation in seeds is the product of a combination of environment and a wide variety of genetically controlled physiological processes. We measured the kernel elemental composition of the Nested Association Mapping (NAM) of maize (Zea mays L.) grown in 4 different environments. Analysis of variance revealed strong effects of genotype, environment and genotype by environment interactions. Using Joint-linkage mapping on a set of 7000 markers we identified 354 quantitative trait loci (QTL) across 20 elements, four environments and a combination of the environments. Leveraging 20 M SNPs derived from genome resequencing on the parents of the population, genome-wide association mapping studies (GWAS) detected 8573 loci. While most of the GWAS SNPs were located near genes not previously implicated in elemental regulation, several SNPs were located next to orthologs of well-characterized elemental regulation genes.

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.