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Dilkes, B.

Publications and source records attributed to Dilkes, B..

8 recordsLinked to original sources
bioRxiv2025

A panel of near-isogenic lines derived from locally adapted populations of a wild plant: A powerful tool for dissecting additive and non-additive effects on ecologically important traits

Identifying and estimating the effects of loci contributing to natural variation in ecologically important traits can be hampered by quantitative inheritance, dominance, epistasis, and environmentally dependent trait expression. Here we announce the availability of germplasm and sequence data for a reciprocal panel of near-isogenic lines (NILs) derived from locally adapted natural populations of Arabidopsis thaliana, for investigating the genetic basis of ecologically important traits. We created a panel of 54 NILs and performed whole genome sequencing to precisely locate introgression segments(s) in each NIL. Deep sequencing largely confirmed prior knowledge of NIL genotypes but also identified multiple novel small introgressions and regions of residual heterozygosity. To illustrate the utility of this panel, we identified genomic regions underlying ecotypic differences in flowering time in a laboratory common garden experiment. We detected strong additive effects on flowering time in multiple NILs with segments at the top of chromosome 5 implicating the floral regulator FLC, as expected based on previous quantitative trait locus studies. We also detected novel and complex contributions to ecotypic differences in flowering time, only visible in the NILs, suggesting the possibility of epistasis. Our results highlight the utility of this panel for dissecting the genetic architecture of ecologically important traits, including the future potential for fine mapping of additive effects and testing for epistasis and linkage using NILs derived from the panel. This panel can be used by any member of the research community to investigate any of a broad suite of traits for which the parents differ.

evolutionary biology↗
bioRxiv2023

A comparative approach for selecting orthologous candidate genes underlying signal in genome-wide association studies across multiple species

Advances in quantitative genetics have enabled researchers to identify genomic regions associated with changes in phenotype. However, genomic regions can contain hundreds to thousands of genes, and progressing from genomic regions to causative genes is still challenging. In genome-wide association studies (GWAS) measuring elemental accumulation (ionomic) traits, only 5% of loci overlap genes known to control the ionome - indicating that many causal genes are still unknown. To identify candidates for the remaining 95% loci, we developed a method that uses GWAS studies across multiple species to identify conserved causative genes. By Filtering the Results of Multi-species, Analogous, GWAS Experiments (FiREMAGE) we were able to take the GWAS of 19 ionomic traits in Arabidopsis, soybean, rice, maize, and sorghum, and identify alleles affecting trait variation at conserved genes. Permutation testing demonstrated that GWAS loci affecting the same trait contained homologs more often than expected by chance. Most of the top 10% most significant conserved candidate sets encoded alleles in all five species, highlighting the conservation of ionomic genetic regulators in flowering plants. The candidates include proteins with known biochemical functions that regulate the ionome, validating the approach. In addition to genes with known functions, this approach also identified many conserved genes underlying GWAS loci affecting the same trait in multiple species that have no previously identified function in regulating the ionome, providing a path to discover the as yet unknown mechanisms of element accumulation in plants. This method enables the identification of conserved genes of previously unknown function via GWAS. Author summaryQuantitative genetics identifies a genomic region of interest but not the causal gene. We developed an approach to narrow these gene lists using genetic loci affecting elemental (i.e., calcium, iron, zinc) accumulation. Comparative genomics and GWAS demonstrates that alleles at evolutionarily conserved genes alter the same phenotype in multiple species. This produced a list of conserved candidate genes including previously known elemental regulators and genes whose elemental accumulation mechanism has yet to be determined. Combining datasets across species boosted the signal at these loci. This approach accelerates the discovery of new functional roles of genes.

genetics↗
bioRxiv2020

Variation in maize chlorophyll biosynthesis alters plant architecture

Chlorophyll is a tetrapyrrole metabolite essential for photosynthesis in plants. The oil yellow1 (oy1) gene of maize encodes subunit I of Magnesium chelatase, the enzyme catalyzing the first committed step of chlorophyll biosynthesis. A range of chlorophyll contents and net CO2 assimilation rates can be achieved in maize by combining a semi-dominant mutant allele, Oy1-N1989, and cis-regulatory alleles encoded by the Mo17 inbred called very oil yellow1 (vey1). We previously demonstrated that these allelic interactions can delay reproductive maturity. In this study, we demonstrate that multiple gross morphological traits respond to a reduction in chlorophyll. We found that stalk width, number of lateral branches (tillers), and branching of the inflorescence decline with a decrease in chlorophyll level. Chlorophyll variation suppressed tillering in multiple maize mutants including teosinte branched1, grassy tiller1, and Tillering1 as well as the tiller number1 QTL responsible for tillering in many sweet corn varieties. In contrast to these traits, plant height showed a non-linear response to chlorophyll levels. Weak suppression of Oy1-N1989 by vey1B73 resulted in a significant increase in mutant plant height. This was true in multiple mapping populations, isogenic inbreds, and hybrid backgrounds. Enhancement of the Oy1-N1989 mutants by the vey1Mo17 allele reduced chlorophyll contents and plant height in mapping populations and isogenic inbred background. We demonstrate that the effects of reduced chlorophyll content on plant growth and development are complex and that the genetic relationship depends on the trait. We propose that growth control for branching and architecture are downstream of energy balance sensing.

plant biology↗
bioRxiv2019

Interaction between induced and natural variation at oil yellow1 delays flowering in maize

We previously demonstrated that maize (Zea mays) locus very oil yellow1 (vey1) encodes a putative cis-regulatory expression polymorphism at the magnesium chelatase subunit I gene (aka oil yellow1) that strongly modifies the chlorophyll content of the semi-dominant Oy1-N1989 mutants. The vey1 allele of Mo17 inbred line reduces chlorophyll content in the mutants leading to reduced photosynthetic output. Oy1-N1989 mutants in B73 reached reproductive maturity four days later than wild-type siblings. Enhancement of Oy1-N1989 by the Mo17 allele at the vey1 QTL delayed maturity further, resulting in detection of a flowering time QTL in two bi-parental mapping populations crossed to Oy1-N1989. The near isogenic lines of B73 harboring the vey1 allele from Mo17 delayed flowering of Oy1-N1989 mutants by twelve days. Just as previously observed for chlorophyll content, vey1 had no effect on reproductive maturity in the absence of the Oy1-N1989 allele. Loss of chlorophyll biosynthesis in Oy1-N1989 mutants and enhancement by vey1 reduced CO2 assimilation. We attempted to separate the effects of photosynthesis on the induction of flowering from a possible impact of chlorophyll metabolites and retrograde signaling by manually reducing leaf area. Removal of leaves, independent of the Oy1-N1989 mutant, delayed flowering but surprisingly reduced chlorophyll contents of emerging leaves. Thus, defoliation did not completely separate the identity of the signal(s) that regulates flowering time from changes in chlorophyll content in the foliage. These findings illustrate the necessity to explore the linkage between metabolism and the mechanisms that connect it to flowering time regulation.

plant biology↗
bioRxiv2017

Multivariate analysis reveals environmental and genetic determinants of element covariation in the maize grain ionome

Plants obtain elements from the soil through genetic and biochemical pathways responsive to physiological state and environment. Most perturbations affect multiple elements which leads the ionome, the full complement of mineral nutrients in an organism, to vary as an integrated network rather than a set of distinct single elements. To examine the genetic basis of covariation in the accumulation of multiple elements, we analyzed maize kernel ionomes from Intermated B73 x Mo17 (IBM) recombinant inbred populations grown in 10 environments. We compared quantitative trait loci (QTL) determining single-element variation to QTL that predict variation in principal components (PCs) of multiple-element covariance. Single-element and multivariate approaches detected partially overlapping sets of loci. In addition to loci co-localizing with single-element QTL, multivariate traits within environments were controlled by loci with significant multi-element effects not detectable using single-element traits. Gene-by-environment interactions underlying multiple-element covariance were identified through QTL analyses of principal component models of ionome variation. In addition to interactive effects, growth environment had a profound effect on the elemental profiles and multi-element phenotypes were significantly correlated with specific environmental variables.\n\nAuthor SummaryA multivariate approach to the analysis of element accumulation in the maize kernel shows that elements are not regulated independently. By describing relationships between element accumulation we identified new genetic loci invisible to single-element approaches. The mathematical combinations of elements distinguish groups of plants based on environment, demonstrating that observed variation derives from interactions between genetically controlled factors and environmental variables. These results suggest that successful application of ionomics to improve human nutrition and plant productivity requires simultaneous consideration of multiple-element effects and variation of such effects in response to environment.

plant biology↗
bioRxiv2017

Integrating co-expression networks with GWAS detects genes driving elemental accumulation in maize seeds

BackgroundGenome 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.\n\nResultsTo 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.\n\nConclusionsOur 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.

systems biology↗
bioRxiv2017

Dark period transcriptomic and metabolic profiling of two diverse Eutrema salsugineum accessions

Eutrema salsugineum is a model species for the study of plant adaptation to abiotic stresses. Two accessions of E. salsugineum, Shandong (SH) and Yukon (YK), exhibit contrasting morphology, biotic, and abiotic stress tolerance. Transcriptome and metabolic profiling from tissue samples collected during the dark period were used to investigate the molecular and metabolic bases of these contrasting phenotypes. RNA sequencing identified 17,888 expressed genes, of which 157 were not in the published reference genome and 65 were detected for the first time. Differential expression was detected for only 31 genes. The RNA sequencing data contained 14,808 single nucleotide polymorphisms (SNPs) in transcripts, 3,925 of which are newly identified. Among the differentially expressed genes, there were no obvious candidates for the physiological or morphological differences between SH and YK. Metabolic profiling indicated that YK accumulates free fatty acids and long-chain fatty acid derivatives as compared to SH; whereas sugars are more abundant in SH. Metabolite levels suggest that carbohydrate and respiratory metabolism, including starch degradation, is more active during the first half of the dark period in SH. These metabolic differences may explain the greater biomass accumulation in YK over SH. The accumulation of 56% of the identified metabolites was lower in F1 hybrids than the mid-parent averages and the accumulation of 17% of the metabolites in F1 plants transgressed the level in both parents. Concentrations of several metabolites in F1 hybrids agree with previous studies and suggest a role for primary metabolism in heterosis. The improved annotation of the E. salsugineum genome and newly-identified high-quality SNPs will permit accelerated studies using the standing variation in this species to elucidate the mechanisms of its diverse adaptations to the environment.

plant biology↗