Biology subjects

Schoen, C. C.

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

6 recordsLinked to original sources

bioRxiv2026

Genetic Dissection of Grain Yield and Correlated Proxy Traits Under Suboptimal Conditions

Securing sustainable crop production requires the genetic improvement of abiotic stress tolerance. Due to the broad range of environmental factors causing abiotic stress and complex genotype-by-environment interactions, it is crucial to understand the genetic basis of crop yield under suboptimal conditions. Here, we developed a dent maize Multi-parent Advanced Generation Inter-Cross (MAGIC) population comprising 388 doubled haploid (DH) lines. The population was derived from eight founders with varying stress tolerance, selected from a dent diversity panel evaluated for yield performance across a wide range of European environments. The MAGIC DH lines were genotyped via whole-genome sequencing ([~]5X coverage) and evaluated in seven testcross and 14 line per se trials, for grain dry matter yield, leaf senescence, leaf rolling, anthesis-silking interval, and six additional agronomic traits. Genetic dissection identified 22 grain yield QTL, explaining 45% of the genetic variance. Under heat and drought stress, testcross grain yield correlated significantly with leaf senescence and leaf rolling measured in line per se trials. Bivariate multi-trait analysis showed that alleles for delayed senescence and reduced rolling at detected QTL generally exhibited positive effects on grain yield, suggesting that accumulating these favorable alleles could enhance yield performance. Incorporating these proxies into multi-trait genomic prediction models improved yield prediction accuracy, although gains were constrained by modest trait correlations. Given the comprehensive data, we also provide recommendations for optimizing sequencing depth and QTL mapping strategies in experimental maize populations. Key messageThis eight-founder MAGIC population represents a powerful resource for dissecting complex traits in maize, assessing the utility of drought proxy traits, and optimizing low-coverage whole-genome sequencing approaches.

genetics↗

bioRxiv2026

Time series analysis in a maize landrace reveals rapid fixation of beneficial alleles

Identifying loci in the genome that allow a population to respond to selection pressure is essential to understand evolution and improve crops. Temporally consecutive generations under selection offer the opportunity to identify signatures of selection. Maize, as one of the most important crops worldwide is rich in genetic diversity and a model for breeding advances. Therefore, it is an ideal system to study genetic changes in response to selection. Here, we study the genetic changes in two replicates of a selection experiment in a European maize landrace, which showed rapid trait improvement over three cycles of selection. We identified an increase in genetic divergence across successive doubled-haploid populations derived from each selection cycle, consistent with the effect of strong directional selection. The genetic divergence observed between the replicates was greater than that between generations. In addition to the genome-wide signal, we identified multiple candidate loci under selection through temporal FST outlier analysis comparing the original landrace population to subsequent cycles. These loci showed a significant overlap with genomic regions, controlling intentionally selected traits and other traits. The significant overlap of selected loci between the two replicates shows the importance of major loci in response to directional selection, while the large number of non-overlapping loci demonstrates the polygenic response. Our work shows that the temporal dimension in plant breeding time-series enables the identification of candidate loci under selection and the genome-wide dynamics of change in response to selection.

evolutionary biology↗

bioRxiv2025

Structural variation at lhcb6 underlies genetic variation in photosystem II maximum quantum efficiency in maize

Targeted utilization of native genetic diversity can expand the genetic basis of traits that exhibit limited genetic variation in elite breeding material and can enhance our understanding of the genotype-phenotype relationships associated with complex traits in crops. In a genome-wide association study in a European maize landrace we identified quantitative trait loci (QTL) that affected the maximum quantum efficiency of photosystem II (Fv/Fm) in field experiments. In a forward genetic approach, we focused on a QTL on chromosome 10, explaining a large proportion of the genetic variance for Fv/Fm in growth stages V4 (35%) and V6 (47%), for genetic dissection and candidate gene discovery. Integrating molecular and physiological information we show that allelic variation at the gene encoding LIGHT HARVESTING CHLOROPHYLL A/B BINDING PROTEIN6 (LHCB6), a component of the photosystem II (PSII) light-harvesting complex (LHCII) antenna, underlies the variation in Fv/Fm. We demonstrate that the allelic variation results from a hAT transposon insertion at lhcb6 and is associated with differential accumulation of the LHCII antenna components LHCB6 and LHCB3, leading to differences in non-photochemical quenching (NPQ) and plant biomass accumulation. Based on proteomic analyses we propose candidate genes that compensate the unfavorable effects caused by impaired LHCII antenna assembly. Our work provides novel insights into the function of lhcb6 in the context of LHCII antenna assembly and demonstrates the value of natural variation in landraces for the understanding and genetic improvement of complex photosynthetic processes.

genetics↗

bioRxiv2021

Increasing calling accuracy, coverage, and read depth in sequence data by the use of haplotype blocks

High-throughput genotyping of large numbers of lines remains a key challenge in plant genetics, requiring geneticists and breeders to find a balance between data quality and the number of genotyped lines under a variety of different existing technologies when resources are limited. In this work, we are proposing a new imputation pipeline ("HBimpute") that can be used to generate high-quality genomic data from low read-depth whole-genome-sequence data. The key idea of the pipeline is the use of haplotype blocks from the software HaploBlocker to identify locally similar lines and merge their reads locally. The effectiveness of the pipeline is showcased on a dataset of 321 doubled haploid lines of a European maize landrace, which were sequenced with 0.5X read-depth. Overall imputing error rates are cut in half compared to the state-of-the-art software BEAGLE, while the average read-depth is increased to 83X, thus enabling the calling of structural variation. The usefulness of the obtained imputed data panel is further evaluated by comparing the performance in common breeding applications to that of genomic data from a 600k array. In particular for genome-wide association studies, the sequence data is shown to be performing slightly better. Furthermore, genomic prediction based on the overlapping markers from the array and sequence is leading to a slightly higher predictive ability for the imputed sequence data, thereby indicating that the data quality obtained from low read-depth sequencing is on par or even slightly higher than high-density array data. When including all markers for the sequence data, the predictive ability is slightly reduced indicating overall lower data quality in non-array markers. Author summaryHigh-throughput genotyping of large numbers of lines remains a key challenge in plant genetics and breeding. Cost, precision, and throughput must be balanced to achieve optimal efficiencies given available technologies and finite resources. Although genotyping arrays are still considered the gold standard in high-throughput quantitative genetics, recent advances in sequencing provide new opportunities for this. Both the quality and cost of genomic data generated based on sequencing are highly dependent on the used read depth. In this work, we are proposing a new imputation pipeline ("HBimpute") that uses haplotype blocks to detect individuals of the same genetic origin and subsequently uses all reads of those individuals in the variant calling. Thus, the obtained virtual read depth is artificially increased, leading to higher calling accuracy, coverage, and the ability to all copy number variation based on relatively cheap low-read depth sequencing data. Thus, our approach makes sequencing a cost-competitive alternative to genotyping arrays with the additional benefit of the potential use of structural variation.

genetics↗

bioRxiv2020

Bivariate genomic prediction of phenotypes by selecting epistatic interactions across years

The importance of accurate genomic prediction of phenotypes in plant breeding is undeniable, as higher prediction accuracy can increase selection responses. In this study, we investigated the ability of three models to improve prediction accuracy by including phenotypic information from the last growing season. This was done by considering a single biological trait in two growing seasons (2017 and 2018) as separate traits in a multi-trait model. Thus, bivariate variants of the Genomic Best Linear Unbiased Prediction (GBLUP) as an additive model, Epistatic Random Regression BLUP (ERRBLUP) and selective Epistatic Random Regression BLUP (sERRBLUP) as epistasis models were compared with respect to their prediction accuracies for the second year. The results indicate that bivariate ERRBLUP is slightly superior to bivariate GBLUP in predication accuracy, while bivariate sERRBLUP has the highest prediction accuracy in most cases. The average relative increase in prediction accuracy from bivariate GBLUP to maximum bivariate sERRBLUP across eight phenotypic traits and studied dataset from 471/402 doubled haploid lines in the European maize landrace Kemater Landmais Gelb/Petkuser Ferdinand Rot, were 7.61 and 3.47 percent, respectively. We further investigated the genomic correlation, phenotypic correlation and trait heritability as the factors affecting the bivariate models predication accuracy, with genetic correlation between growing seasons being the most important one. For all three considered model architectures results were far worse when using a univariate version of the model, e.g. with an average reduction in prediction accuracy of 0.23/0.14 for Kemater/Petkuser when using univariate GBLUP. Key MassageBivariate models based on selected subsets of pairwise SNP interactions can increase the prediction accuracy by utilizing phenotypic data across years under the assumption of high genomic correlation across years.

genetics↗

bioRxiv2018

HaploBlocker: Creation of subgroup specific haplotype blocks and libraries

The concept of haplotype blocks has been shown to be useful in genetics. Fields of application range from the detection of regions under positive selection to statistical methods that make use of dimension reduction. We propose a novel approach (\"HaploBlocker\") for defining and inferring haplotype blocks that focuses on linkage instead of the commonly used population-wide measures of linkage disequilibrium (LD) which fail to identify segments shared by individuals in only a subset of the population. We define a haplotype block as a sequence of alleles that has a predefined minimum frequency in the population and only haplotypes with a similar sequence of alleles are considered to be carrying that block, effectively screening a dataset for group-wise identity-by-descent (IBD). Different to most other approaches these blocks are not restricted to shared start or end positions, but can overlap or even contain each other. From these haplotype blocks we construct a haplotype library that represents a large proportion of genetic variability of a population with a limited number of blocks. Our method is implemented in the associated R-package HaploBlocker and provides flexibility to not only optimize the structure of the obtained haplotype library for subsequent analyses (e.g., identification of shared segments between different populations), but is also able to handle datasets of different marker density and genetic diversity. By using haplotype blocks instead of SNPs, local epistatic interactions can be naturally modelled and the reduced number of parameter enables a wide variety of new methods for further genomic analyses. We illustrate our methodology with a dataset comprising 501 doubled haploid lines in a European maize landrace genotyped at 501124 SNPs. With the suggested approach, we identified 2851 haplotype blocks with an average length of 2633 SNPs (compared to 27.8 SNPs per block in HaploView) that together represent 94% of the dataset.\n\nAuthor summaryWhereas it is quite easy to identify segments of shared DNA between pairs of individuals, the problem becomes far more complex when analyzing a population. Especially for livestock and crop populations under strong selection one can observe long and possibly favourable segments that are segregating at high frequency. We propose here an adaptive and flexible approach to identify such segments (\"haplotype blocks\"). The main conceptual difference to other approaches is that we allow haplotype blocks to overlap so that patterns shared by a subset of the population can be mapped adequately. Afterwards, we select a set of those haplotype blocks that form a representation of the whole population (\"haplotype library\"). This haplotype library can be used similar to a SNP-dataset for subsequent genomic approaches with the advantage of a massive reduction of the number of parameters compared to standard haplotyping approaches. Since many breeding goals (e.g. grain yield, milk production) are known to be caused by complex interactions in genomic regions (or even the whole genome) using haplotype blocks instead of single base pairs provides a natural model for local interactions and enables the use of more complex models to incorporate distant interactions between genes, for instance.

genetics↗