Keywords: paralog processing, ascorbate, genomic prediction

Latest 8 Papers

Highly polygenic control of photosynthetic responses to nighttime temperature in Arabidopsis studied by genomic prediction

Authors: dos Santos Sa, A. C., Bazarova, A., Fischbach, A., Kesselheim, S., Stich, B., Matsubara, S.

Date: 2025-11-10 · Version: 1

DOI: 10.1101/2025.11.08.687332

nighttime temperature photosynthetic efficiency (Fq/Fm)genome‑wide association study genomic prediction Arabidopsis thaliana

Phylogenomic challenges in polyploid-rich lineages: Insights from paralog processing and reticulation methods using the complex genus Packera (Asteraceae: Senecioneae)

Authors: Moore-Pollard, E. R., Ellestad, P., Mandel, J.

Date: 2025-10-13 · Version: 2

DOI: 10.1101/2025.09.14.676143

Model Organism: Multi-species

phylogenomic discordance polyploidy paralog processing Packera historical biogeography

Population-level super-pangenome reveals genome evolution and empowers precision breeding in watermelon

Authors: Sun, H., Zhang, J., Liao, S., Guo, S., Zhou, Z., Zhao, X., Wu, S., Zhao, J., Gong, G., Wang, J., Li, M., Yu, Y., Ren, Y., Tian, S., Li, S., Zhang, H., Hammar, S. A., McGregor, C., Jarret, R., Wechter, P., Branham, S. E., Kousik, C., Levi, A., Grumet, R., Xu, Y., Fei, Z.

Date: 2025-07-27 · Version: 1

DOI: 10.1101/2025.07.25.666869

Model Organism: Citrullus lanatus (watermelon)

watermelon super-pangenome structural variants genome-wide association study copy number variation genomic prediction

Graph pangenome reveals structural variation dynamics during cucumber breeding

Authors: Zhao, X., Yu, J., Zhang, J., Sun, H., Wu, S., Zhao, J., Zhou, Y., Hammar, S., Lin, Y.-C., Zhang, Z., Huang, S., Dymerski, R., Chen, F., Weng, Y., Grumet, R., Xu, Y., Fei, Z.

Date: 2025-07-21 · Version: 1

DOI: 10.1101/2025.07.17.665377

Model Organism: Cucumis sativus

structural variants graph pangenome cucumber domestication genomic prediction purifying selection

Optimizing population simulations to accurately parallel empirical data for digital breeding

Authors: Burns, M. J., Della Coletta, R., Fernandes, S. B., Bohn, M. O., Lipka, A. E., Hirsch, C. N.

Date: 2025-06-24 · Version: 1

DOI: 10.1101/2025.06.18.660215

Model Organism: Zea mays

digital breeding GWAS-informed simulation maize phenotypic prediction genomic prediction

Arabidopsis lines with modified ascorbate concentrations reveal a link between ascorbate and auxin biosynthesis

Authors: Fenech, M., Zulian, V., Moya-Cuevas, J., Arnaud, D., Morilla, I., Smirnoff, N., Botella, M. A., Stepanova, A. N., Alonso, J. M., Martin-Pizarro, C., Amorim-Silva, V.

Date: 2025-05-16 · Version: 1

DOI: 10.1101/2025.05.15.654287

Model Organism: Arabidopsis thaliana

ascorbate Arabidopsis thaliana auxin biosynthesis redox homeostasis transcriptomics

Genomic insights and breeding strategies for nixtamalization moisture content in hybrid maize

Authors: Burns, M. J., Berry, S. P., Loftus, M., Gilbert, A. M., Hirsch, C. N.

Date: 2025-04-24 · Version: 1

DOI: 10.1101/2025.04.23.650239

Model Organism: Zea mays

nixtamalization moisture content genomic prediction additive and dominance effects maize hybrids NIR spectroscopy

The FIP 1.0 Data Set: Highly Resolved Annotated Image Time Series of 4,000 Wheat Plots Grown in Six Years

Authors: Roth, L., Boss, M., Kirchgessner, N., Aasen, H., Aguirre-Cuellar, B. P., Akiina, P. P. A., Anderegg, J., Gajardo Castillo, J., Chen, X., Corrado, S., Cybulski, K., Keller, B., Kortstee, S. G., Kronenberg, L., Liebisch, F., Nousi, P., Oppliger, C., Perich, G., Pfeifer, J., Yu, K., Storni, N., Tschurr, F., Volpi, M., Treier, S., Zellweger, H., Zumsteg, O., Hund, A., Walter, A.

Date: 2025-03-11 · Version: 3

DOI: 10.1101/2024.10.04.616624

Model Organism: Triticum aestivum

genotype‑environment interaction winter wheat high‑throughput phenotyping genomic prediction crop modeling