Benjamin Stich | PubMed Health

PubMed2026

The potential of considering photosynthesis parameters in crop yield breeding by genomic prediction.

To meet the growing demand for agricultural products, optimizing photosynthesis is a promising strategy to improve crop yields. Phenotypic variance in photosynthesis has been observed within or between species. To explore the potential of integrating photosynthetic parameters into crop breeding programs, we explored the genetic variation in photosynthesis by assessing photosynthesis-related parameters across plant development in 631 barley recombinant inbred lines (RILs) from eight HvDRR subpopulations under field conditions. The genetic complexity of these parameters was resolved by analyses of bi-parental and multi-parental quantitative trait loci (QTLs). Finally, we examined the merit of integrating photosynthesis-related parameters in genomic prediction of yield and its components. Significant genotypic variations of the photosynthesis-related parameters were found among the RILs, with their heritability ranging from 0.38 to 0.54. The multiple QTLs and dynamic QTLs for photosynthesis observed across different developmental stages underlined the complexity of the genetics of photosynthesis in barley. The considerably higher percentage of phenotypic variance explained for genomic prediction than multi-parental QTL analysis illustrates that the photosynthesis-related parameters are inherited in a more complex way than classical agronomic traits. Notably, the prediction ability for yield was increased by integrating the photosynthesis-related parameters of some developmental stages into genomic prediction models. Thus, our results suggest a novel perspective on increasing the efficiency of crop breeding programs by integrating photosynthesis-related parameters into prediction models.

Photosynthesis↗

PubMed2025

Assessment of genomic prediction capabilities of transcriptome data in a barley multi-parent RIL population.

Low-cost and high-throughput RNA sequencing data for barley RILs achieved GP performance comparable to or better than traditional SNP array datasets when combined with parental whole-genome sequencing SNP data. The field of genomic selection (GS) is advancing rapidly on many fronts including the utilization of multi-omics datasets with the goal of increasing prediction ability and becoming an integral part of an increasing number of breeding programs ensuring future food security. In this study, we used RNA sequencing (RNA-Seq) data to perform genomic prediction (GP) on three related barley RIL populations. We investigated the potential of increasing prediction ability by combining genomic and transcriptomic datasets, adding whole-genome sequencing (WGS) SNP data, functional annotation-based filtering, and empirical quality filtering. Our RNA-Seq data were generated cost-efficiently using small-footprint plant cultivation, high-throughput RNA extraction, and Library preparation miniaturization. We also examined sequencing depth reduction as an additional cost-saving measure. We used fivefold cross-validation to evaluate the prediction ability of the gene expression dataset, the RNA-Seq SNP dataset, and the consensus SNP dataset between the RNA-Seq and parental WGS data, resulting in prediction abilities between 0.73 and 0.78. The consensus SNP dataset performed best, with five out of eight traits performing significantly better compared to a 50K SNP array, which served as a benchmark. The advantage of the consensus SNP dataset was most prominent in the inter-population predictions, in which the training and validation sets originated from different RIL sub-populations. We were therefore able to not only show that RNA-Seq data alone are able to predict various complex traits in barley using RILs, but also that the performance can be further increased with WGS data for which the public availability will steadily increase.

Hordeum↗