Biology subjects

Dreisigacker, S.

Publications and source records attributed to Dreisigacker, S..

8 recordsLinked to original sources

bioRxiv2026

A novel matrix multiplication framework for modeling genotype-by-environment interaction in genomic prediction

Accurate modeling of genotype-by-environment (GxE) interaction is critical for genomic prediction in plant breeding but remains challenging due to complex interaction structures. Conventional models often use the Hadamard product of genotype and environment covariance matrices to capture joint similarity, which may not fully represent GxE complexity. Here we propose a novel framework that derives covariance structures from the matrix multiplication of genotype and environment kernels, decomposing these into symmetric components incorporated as random effects in mixed models. Evaluated for 11 wheat and rice multi-environment datasets and across, this approach consistently outperformed the traditional Hadamard-based model, improving prediction accuracy by up to 13.2% in Pearsons correlation and enhancing top-selection accuracy. Combining both methods yielded the highest performance, indicating complementary information capture. This framework offers a flexible, interpretable, and computationally feasible extension for modeling GxE interaction, potentially enhancing genomic selection effectiveness under diverse environmental conditions.

genetics↗

bioRxiv2026

Homoeo-alleles of wheat GNI2 fuel grain yields across input environments

Grain yield in wheat is frequently driven by pre-anthesis growth and how carbon is directed to reproductive parts, determining the potential size of the grain-producing sink. However, the genetic mechanisms controlling this carbon allocation remain unclear. In this study we discovered a series of loci in tetraploid and hexaploid wheats that, when combined, confer an average grain yield advantage in hexaploid winter wheat of approx. 5-7% under high-input farming while approx. 10-15% under low-input conditions. The responsible gene is GRAIN NUMBER INCREASE 2 (GNI2), ancestral to its duplicate GNI1, and represented by the homoeo-allelic series GNI-A2, GNI-B2, and GNI-D2. GNI1 and all GNI2 copies additively affect floral growth and fertility by modulating reproductive allocation and harvest index. Herewith, we describe a series of grain yield-relevant GNI2 homoeo-alleles with a proven track record for being beneficial in both high- and low-input environments. Their deployment offers a sustainable pathway to raise global grain yields in the future.

plant biology↗

bioRxiv2023

Development of a Next Generation SNP Genotyping Array for Wheat

High throughput genotyping arrays have provided a cost effective, reliable and interoperable system for genotyping hexaploid wheat and its related germplasm pool. Existing, highly cited arrays including our 35K Axiom Wheat Breeders genotyping array and the Illumina 90K iSelect array were designed based on a limited amount of varietal sequence diversity and with imperfect knowledge of SNP positions. Recent progress in sequencing wheat varieties and landraces has given us access to a vast pool of SNP diversity, whilst technological improvements in array design has allowed us to fit significantly more probes onto a 384-well format Axiom array than was previously possible. Here we describe a novel High Density Axiom genotyping array, the Triticum aestivum Next Generation array (TaNG), largely derived from whole genome skim sequencing of 204 elite wheat lines and 111 wheat landraces taken from the Watkins "Core Collection". We use a novel "minimal marker" optimisation approach to select up to six SNPs in each 1.5 MB region of the wheat genome with the highest combined varietal discrimination potential. A design iteration step allowed us to test and replace skim-sequence derived SNPs which failed to convert to reliable Axiom markers, resulting in a final design, designated TaNG1.1 with 43,372 SNPs derived from a haplotype-optimised combination of novel SNPs, DArTAG-derived and legacy wheat Axiom markers. We show that this design has an even distribution of SNPs across chromosomes and sub-genomes compared to previous arrays and can be used to generate genetic maps with a significantly higher number of distinct bins than our previous Axiom array. We also demonstrate the improved performance of TaNG1.1 for Genome Wide Association Studies (GWAS) and its utility for Copy Number Variation (CNV) analysis. The array is commercially available, and the marker annotations, initial genotyping results and software used to generate the optimised marker sets are freely available.

genomics↗

bioRxiv2023

The wheat powdery mildew resistance gene Pm4 also confers resistance to wheat blast

Wheat blast, caused by the fungus Magnaporthe oryzae, threatens global cereal production since its emergence in Brazil in 1985 and recently spread to Bangladesh and Zambia. Here we demonstrate that the AVR-Rmg8 effector, common in wheat-infecting isolates, is recognised by the gene Pm4, previously shown to confer resistance to specific races of Blumeria graminis f.sp. tritici, the cause of powdery mildew of wheat. We show that Pm4 alleles differ in their recognition of different AVR-Rmg8 alleles, and some confer resistance only in seedling leaves but not spikes making it important to select for those alleles that function in both tissues. This study has identified a gene recognising an important virulence factor present in wheat blast isolates in Bangladesh and Zambia and represents an important first step towards developing durably resistant wheat cultivars for these regions.

plant biology↗

bioRxiv2022

ENVIROME-WIDE ASSOCIATIONS ENHANCE MULTI-YEAR GENOME-BASED PREDICTION OF HISTORICAL WHEAT BREEDING DATA

Linking high-throughput environmental data (enviromics) into genomic prediction (GP) is a cost-effective strategy for increasing selection intensity under genotype-by-environment interactions (GxE). This study developed a data-driven approach based on Environment-Phenotype Associations (EPA) aimed at recycling important GxE information from historical breeding data. EPA was developed in two applications: (1) scanning a secondary source of genetic variation, weighted from the shared reaction-norms of past-evaluated genotypes; (2) pinpointing weights of the similarity among trial-sites (locations), given the historical impact of each envirotyping data variable for a given site. Then, the EPA outcomes were integrated into multi-environment GP models through a new single-step GBLUP. The wheat trial data used included 36 locations, 8 years and 3 target populations of environments (TPE) in India. Four prediction scenarios and 6 kernel-models within/across TPEs were tested. Our results suggest that the conventional GBLUP, without enviromic data or when omitting EPA, is inefficient in predicting the performance of wheat lines in future years. However, when EPA was introduced as an intermediary learning step to reduce the dimensionality of the GxE kernels while connecting phenotypic and environmental-wide variation, a significant enhancement of GxE prediction accuracy was evident. EPA revealed that the effect of seasonality makes strategies such as "covariable selection" unfeasible because GxE is year-germplasm specific. We propose that the EPA effectively serves as a "reinforcement learner" algorithm capable of uncovering the effect of seasonality over the reaction-norms, with the benefits of better forecasting the similarities between past and future trialing sites. EPA combines the benefits of dimensionality reduction while reducing the uncertainty of genotype-by-year predictions and increasing the resolution of GP for the genotype-specific level.

genetics↗

bioRxiv2022

The effects of brief heat during early booting on reproductive, developmental and physiological performance in common wheat (Triticum aestivum L.)

Rising temperatures due to climate change threaten agricultural crop productivity. As a cool-season crop wheat is heat sensitive, but often exposed to high temperatures during cultivation. In the current study, a bread wheat panel of spring wheat genotypes, including putatively heat-tolerant Australian and CIMMYT genotypes, was exposed to a 5-day mild (34{degrees}C/28{degrees}C, day/night) or extreme (37{degrees}C/27{degrees}C) heat stress during the sensitive pollen developmental stage. Worsening effects on anther morphology were observed as heat stress increased from mild to extreme. Even under mild heat a significant decrease in pollen viability and grain number per spike from primary spike was observed compared with the control (21{degrees}C/15{degrees}C), with Sunstar and two CIMMYT breeding lines performing well. A heat-specific positive correlation between the two traits indicates the important role of pollen fertility for grain setting. Interestingly, both mild and extreme heat induced development of new tillers after the heat stress, providing an alternative sink for accumulated photosynthates and significantly contributing to the final yield. Measurements of flag leaf maximum potential quantum efficiency of Photosystem II (Fv/Fm) showed an initial inhibition after the heat treatment, followed by a full recovery within a few days. Despite this, model fitting using chlorophyll SPAD measurements showed an earlier onset or faster senescence rate under heat stress. The data presented here provide interesting entry points for further research into pollen fertility, tillering dynamics and leaf senescence under heat. The identified tolerant wheat genotypes can be used to dissect the underlying mechanisms and breed climate-resilient wheat.

plant biology↗

bioRxiv2019

Genetic image-processing using regularized selection indices

High-throughput phenotyping (HTP) technologies can produce data on thousands of phenotypes per unit being monitored. These data can be used to breed for economically and environmentally relevant traits (e.g., drought tolerance); however, incorporating high-dimensional phenotypes in genetic analyses and in breeding schemes poses important statistical and computational challenges. To address this problem, we developed regularized selection indices; the methodology integrates techniques commonly used in high-dimensional phenotypic regressions (including penalization and rank-reduction approaches) into the selection index (SI) framework. Using extensive data from CIMMYTs (International Maize and Wheat Improvement Center) wheat breeding program we show that regularized SIs derived from hyper-spectral data offer consistently higher accuracy for grain yield than those achieved by canonical SIs, and by vegetation indices commonly used to predict agronomic traits. Regularized SIs offer an effective approach to leverage HTP data that is routinely generated in agriculture; the methodology can also be used to conduct genetic studies using high-dimensional phenotypes that are often collected in humans and model organisms including body images and whole-genome gene expression profiles.

genetics↗

bioRxiv2018

Use of Hyperspectral Reflectance-Derived Relationship Matrices for Genomic Prediction of Grain Yield in Wheat

Hyperspectral reflectance phenotyping and genomic selection are two emerging technologies that have the potential to increase plant breeding efficiency by improving prediction accuracy for grain yield. Hyperspectral cameras quantify canopy reflectance across a wide range of wavelengths that are associated with numerous biophysical and biochemical processes in plants. Genomic selection models utilize genome-wide marker or pedigree information to predict the genetic values of breeding lines. In this study, we propose a multi-kernel GBLUP approach to genomic selection that uses genomic marker-, pedigree-, and hyperspectral reflectance-derived relationship matrices to model the genetic main effects and genotype x environment (G x E) interactions across environments within a bread wheat (Triticum aestivum L.) breeding program. We utilized an airplane equipped with a hyperspectral camera to phenotype five differentially managed treatments of the yield trials conducted by the Bread Wheat Improvement Program, International Maize and Wheat Improvement Center (CIMMYT) at Ciudad Obregon, Mexico over four breeding cycles. We observed that single-kernel models using hyperspectral reflectance-derived relationship matrices performed similarly or superior to marker-and pedigree-based genomic selection models when predicting within and across environments. Multi-kernel models combining marker/pedigree information with hyperspectral reflectance phentoypes had the highest prediction accuracies; however, improvements in accuracy over marker-and pedigree-based models were marginal when correcting for days to heading. Our results demonstrates the potential of hyperspectral imaging in predicting grain yield within a multi-environment context, it also supports further studies on integration of hyperspectral reflectance phenotyping in breeding programs.

genomics↗