Biology subjects

Schoen, C.-C.

Publications and source records attributed to Schoen, C.-C..

6 recordsLinked to original sources

bioRxiv2026

Genetic analysis of maize seedling root traits under chilling highlights their importance for early field development

Characterizing the genetic basis of root system architecture and its role in early plant development is essential for developing maize varieties with improved nutrient uptake, enhanced early vigour, and higher yield potential in temperate regions. Landraces represent an invaluable source of allelic diversity that can be leveraged to enrich the genetic basis of modern breeding material. In this study, we used a high throughput phenotyping platform to characterize genetic variation for seedling root traits under chilling conditions relevant for early plant establishment in a large doubled haploid (DH) library derived from two European maize landraces. We dissected the quantitative genetic architecture of twelve seedling root traits using a haplotype-based genome-wide association study, identifying large-effect haplotypes specific to the individual landraces as well as numerous small-effect haplotypes present in both landraces. We validated the effects of four QTL in a biparental population, demonstrating their stability across genetic backgrounds. We found highly significant correlations between haplotype effects on seedling root traits evaluated in the phenotyping platform and early plant height evaluated in multi environment field trials, demonstrating the relevance of seedling root architecture for early plant establishment. In particular, haplotypes associated with seminal and lateral root length were the major determinants of early plant height under field conditions. Several of the haplotypes increasing seedling root length were absent from a broad panel of flint breeding lines, highlighting their potential as targets for introgression to improve early plant establishment under temperate growing conditions. Key messageSeedling root QTL discovered in a high throughput phenotyping platform under chilling conditions influence early plant development in the field.

genetics↗

bioRxiv2025

Genetic architecture and functional consequences of lateral root length in maize (Zea mays L.)

Understanding the genetic basis of root architecture and its relevance for crop productivity can contribute to the sustainable intensification of agriculture. Leveraging the phenotypic and allelic diversity of an Austrian maize landrace, we dissected the genetic basis of lateral root (LR) length across developmental stages. LR length, a relevant trait for breeding resource-efficient varieties, showed high heritability in our experiments. We discovered eight quantitative trait loci (QTL) for LR length at the reproductive stage R2, overlapping with four QTL at stage R6 but not with QTL detected at vegetative stage V6, suggesting that the genetic regulation of LR length might differ in vegetative and reproductive stages. We fine-mapped qlr1, the most significant QTL for LR length, to a region of 2.3 Mb containing 46 annotated genes. Based on whole-genome sequence and comparative genomics analyses we suggest a candidate gene underlying qlr1. Additionally, we examined the impact of nitrogen, phosphorus, and irrigation treatments on root and shoot development, finding that LR length positively correlates with biomass accumulation under optimal nutrient supply but not under nitrogen stress. Our work provides insights into the genetic regulation of LR length in maize and its relevance for the adaptation to specific growing environments. HighlightMultiple QTL affect lateral root length in maize at different developmental stages. The correlation between lateral root length and biomass accumulation varies under diverse field conditions.

genetics↗

bioRxiv2024

Embracing native diversity to enhance maximum quantum efficiency of photosystem II in maize (Zea mays L.)

Sustainability of maize cultivation would benefit tremendously from early sowing but is hampered by low temperatures during early development in temperate climate. We show that allelic variation of subunit M of NADH-dehydrogenase-like (NDH) complex (ndhm1), discovered in a European maize landrace affects several quantitative traits relevant during early development in cold climates through NDH-mediated cyclic electron transport (CET) around photosystem I, a process crucial for photosynthesis. Starting from a genome-wide association study (GWAS) for maximum potential quantum yield of photosystem II in dark-adapted leaves (Fv/Fm) we capitalized on large phenotypic effects of a hAT transposon insertion in ndhm1 on quantitative traits early plant height (EPH), Fv/Fm, chlorophyll content and cold tolerance caused by reduced protein levels of NDHM and associated NDH components. Analysis of the native allelic series of ndhm1 revealed a rare allele of ndhm1 which is associated with small albeit significant effects on maximum potential quantum yield of photosystem II in dark- and light adapted leaves (Fv/Fm, {Phi}PSII) and early plant height compared to common alleles. Our work showcases the extraction of novel, favorable alleles from locally adapted landraces, offering an efficient strategy for broadening the genetic variation of elite germplasm by breeding or genome editing.

plant biology↗

bioRxiv2020

Accounting for epistasis improves genomic prediction of phenotypes with univariate and bivariate models across environments

We compared the predictive ability of various prediction models for a maize dataset derived from 910 doubled haploid lines from European landraces (Kemater Landmais Gelb and Petkuser Ferdinand Rot), which were tested in six locations in Germany and Spain. The compared models were Genomic Best Linear Unbiased Prediction (GBLUP) as an additive model, Epistatic Random Regression BLUP (ERRBLUP) accounting for all pairwise SNP interactions, and selective Epistatic Random Regression BLUP (sERRBLUP) accounting for a selected subset of pairwise SNP interactions. These models have been compared in both univariate and bivariate statistical settings within and across environments. Our results indicate that modeling all pairwise SNP interactions into the univariate/bivariate model (ERRBLUP) is not superior in predictive ability to the respective additive model (GBLUP). However, incorporating only a selected subset of interactions with the highest effect variances in univariate/bivariate sERRBLUP can increase predictive ability significantly compared to the univariate/bivariate GBLUP. Overall, bivariate models consistently outperform univariate models in predictive ability. Over all studied traits, locations, and landraces, the increase in prediction accuracy from univariate GBLUP to univariate sERRBLUP ranged from 5.9 to 112.4 percent, with an average increase of 47 percent. For bivariate models, the change ranged from -0.3 to +27.9 percent comparing the bivariate sERRBLUP to the bivariate GBLUP. The average increase across traits and locations was 11 percent. This considerable increase in predictive ability achieved by sERRBLUP may be of interest for "sparse testing" approaches in which only a subset of the lines/hybrids of interest is observed at each location. Key MessageThe prediction accuracy of genomic prediction of phenotypes can be increased by only including top ranked pairwise SNP interactions into the prediction models.

genetics↗

bioRxiv2020

Discovery of novel haplotypes for complex traits in landraces

Genetic variation is of crucial importance for selection and genetic improvement of crops. Landraces are valuable sources of diversity for germplasm improvement, but for quantitative traits efficient strategies for their targeted utilization are lacking. Here, we propose a genome-based strategy for making native diversity accessible for traits with limited genetic variation in elite germplasm. We generated ~ 1,000 doubled-haploid (DH) lines from three European maize landraces, pre-selected based on molecular and phenotypic information. Using GWAS, we mapped haplotype-trait associations for early development traits at high resolution in eleven environments. Molecular haplotype inventories of landrace derived DH libraries and a broad panel of 65 breeding lines based on 501,124 SNPs revealed novel variation for target traits in the landraces. DH lines carrying these novel haplotypes outperformed breeding lines not carrying the respective haplotypes. Most haplotypes associated with target traits showed stable effects across populations and environments and only limited correlated effects with undesired traits making them ideal for introgression into elite germplasm. Our strategy was successful in linking molecular variation to meaningful phenotypes and identifying novel variation for quantitative traits in plant genetic resources.

genetics↗

bioRxiv2016

A large scale joint analysis of flowering time reveals independent temperate adaptations in maize

Modulating days to flowering is a key mechanism in plants for adapting to new environments, and variation in days to flowering drives population structure by limiting mating. To elucidate the genetic architecture of flowering across maize, a quantitative trait, we mapped flowering in five global populations, a diversity panel (Ames) and four half-sib mapping designs, Chinese (CNNAM), US (USNAM), and European Dent (EUNAM-Dent) and Flint (EUNAM-Flint). Using whole-genome projected SNPs, we tested for joint association using GWAS, resampling GWAS and two regional approaches; Regional Heritability Mapping (RHM) (1, 2) and a novel method, Boosted Regional Heritability Mapping (BRHM). Direct overlap in significant regions detected between populations and flowering candidate genes was limited, but whole-genome cross-population predictive abilities were [≤]0.78. Poor predictive ability correlated with increased population differentiation (r = 0.41), unless the parents were broadly sampled from across the North American temperate-tropical germplasm gradient; uncorrected GWAS results from populations with broadly sampled parents were well predicted by temperate-tropical FSTs in machine learning. Machine learning between GWAS results also suggested shared architecture between the American panels and, more distantly, the European panels, but not the Chinese panel. Machine learning approaches can reconcile non-linear relationships, but the combined predictive ability of all of the populations did not significantly enhance prediction of physiological candidates. While the North American-European temperate adaption is well studied, this study suggest independent temperate adaptation evolved in the Chinese panel, most likely in China after 1500, a finding supported by differential gene ontology term enrichment between populations.

genetics↗