A new environmental-based tool to support forest breeders in selecting species adapted to current and near-term climate conditions - Ricardo Cavalheiro; Aurelio Mendes Aguiar; Ranga Raju Vatsavai; Mragisha Jain; William M. Hammond; Gary Hodge; Juan Jose Acosta; Roberto Fritsche-Neto (Tree Genetics & Genomes, 2026)
This study presents SST (Species Selection Tool), a climate-based framework designed to support species selection across geographic regions and climate scenarios. The tool was developed to enable breeders to examine species selection scenarios across countries and climate horizons. Beyond identifying promising species for testing, SST prioritizes candidate species, highlights opportunities for germplasm exchange, anticipates climate-driven shifts, and maps environmental clusters and adapted genetic resources to guide near- and long-term breeding strategies. Built with the Shiny framework in R, SST allows users to upload tabular data in CSV format for flexible analyses. We demonstrate its application for a Eucalyptus breeding example in Brazil, using occurrence data from GBIF and environmental covariates from TerraClimate and CHIRPS, and complemented by CMIP6 future climate projections. Temperature and precipitation data were used to compute 19 bioclimatic variables (BIO1–BIO19) for macroenvironmental classification and species suitability assessment. SST integrates five analytical indices, scaled from 0 to 1, producing an overall suitability index and ranking species accordingly. The tool identified E. urophylla , E. brassiana , E. deglupta , and E. pellita as the most suitable for the study area in the Maranhão State, Brazil. Open-source and user-friendly, SST accelerates breeding decisions, supports climate-adaptive planning, and provides access to advanced analytical tools. The tool is a useful contribution to forest management and forest tree breeding, supporting data-driven strategies for sustainable forestry under changing climates.

Correction: Optimizing progeny size and number of crosses under genomic selection: insights into additive and epistatic contributions to long‑term genetic gain - Jesimiel da Silva Viana; Júlio César DoVale; Roberto Fritsche-Neto (Theoretical and Applied Genetics, 2026)

Optimizing progeny size and number of crosses under genomic selection: insights into additive and epistatic contributions to long-term genetic gain - Jesimiel da Silva Viana; Júlio César DoVale; Roberto Fritsche-Neto (Theoretical and Applied Genetics, 2026)
Key message Designing genomic selection to capture the additive and epistatic effects. Abstract Genomic selection (GS) offers great potential to accelerate long-term genetic gain, but strategic decisions such as progeny size and number of crosses remain poorly established, particularly under contrasting resource scenarios. We conducted stochastic simulations of rice breeding programs over 50 years (10 cycles) using progeny sizes of 25, 50, 100, and 200 individuals, under both theoretical (unlimited resources) and practical (budget-constrained to 4000 F 2 individuals) contexts, and considering three levels of epistasis (absent, moderate, high). In theoretical scenarios, larger progenies consistently achieved higher gains. After 50 years, progenies of 200 individuals reached cumulative responses to selection of 2.39 (1.96% yr −1 ) with no epistasis, 3.20 (2.60% yr −1 ) under moderate epistasis, and 3.48 (3.34% yr −1 ) under high epistasis. These schemes also maximized prediction accuracy and efficiently converted additive and epistatic variance into genetic gain. Conversely, under budget constraints, smaller progenies combined with more crosses outperformed larger ones. Progenies of 25 and 50 individuals achieved the greatest responses—up to 2.58 (2.07% yr −1 ) without epistasis, 3.36 (2.76% yr −1 ) under moderate epistasis, and 2.72 (2.45% yr −1 ) under high epistasis—while maintaining higher genetic diversity across cycles. Our results demonstrate that in resource-unlimited conditions, larger progenies (200 individuals) maximize the capture of additive and epistatic effects, whereas in budget-constrained programs, smaller progenies (25–50 individuals) coupled with more crosses provide the most efficient strategy. These findings provide practical guidelines for breeders to design GS schemes that reconcile high long-term genetic gain with operational feasibility, highlighting the decisive role of epistasis in shaping gain trajectories.

Expanding genomic prediction in plant breeding: harnessing big data, machine learning, and advanced software - José Crossa; Johannes W.R. Martini; Paolo Vitale; Paulino Pérez-Rodríguez; Germano Costa-Neto; Roberto Fritsche-Neto; Daniel Runcie; Jaime Cuevas; Fernando Toledo; H. Li; Pasquale De Vita; Guillermo Gerard; et al. (Trends in Plant Science, 2025)

110 years of rice breeding at LSU: realized genetic gains and future optimization - Allison Vieira da Silva; Adam Famoso; Steve Linscombe; Roberto Fritsche-Neto (Theoretical and Applied Genetics, 2025)
Key message Strategic resource allocation in breeding programs is key to balancing cost-effectiveness and genetic improvement. Abstract This research aimed to understand the critical role of adopting advanced breeding tools and optimizing breeding strategies to ensure the sustainability and success of public breeding programs in meeting future food security challenges. In this context, there are two main objectives: estimate the genetic gains achieved over 110 years in the rice breeding program of Louisiana State University (LSU); evaluate through stochastic simulations the impacts of modern selection tools such as genomic selection (GS) and high-throughput phenotyping (HTP) on future genetic gains. Considering the 110 years, the average increase was 4.55 kg/ha per generation (23 breeding cycles). However, from 1994 to 2018, we observed more substantial trends in genetic gains, particularly for grain yield, which increased by approximately 56.54 kg/ha per year. Based on simulations, integrating GS and HTP demonstrated significant advantages, including shorter breeding cycles, enhanced selection accuracy, and reduced costs. Also, simulation results showed that this approach yielded the highest response to selection (4.68% per year) due to the synergistic effects of combining advanced phenotyping techniques with GS. Finally, we assessed the effects of balancing the number of parents, crosses, and progeny sizes to maximize genetic gains and maintain genetic variability. Variance component analysis indicated that progeny size had the greatest impact on total variance (36%), followed by the number of crosses (23%) and the number of parents (3.4%). The findings highlight the need for strategic resource allocation in breeding programs to balance cost-effectiveness and genetic improvement.

Correction: Genomic prediction and QTL analysis for grain Zn content and yield in Aus-derived rice populations - Tapas Kumer Hore; C. H. Balachiranjeevi; Mary Ann Inabangan-Asilo; C. A. Deepak; Alvin D. Palanog; Jose E. Hernandez; Glenn B. Gregorio; Teresita U. Dalisay; Maria Genaleen Q. Diaz; Roberto Fritsche Neto; Md. Abdul Kader; Partha Sarathi Biswas; et al. (Journal of Plant Biochemistry and Biotechnology, 2025)

Machine learning algorithms translate big data into predictive breeding accuracy - José Crossa; Osval A. Montesinos-Lopez; Germano Costa-Neto; Paolo Vitale; Johannes W.R. Martini; Daniel Runcie; Roberto Fritsche-Neto; Abelardo Montesinos-Lopez; Paulino Pérez-Rodríguez; Guillermo Gerard; Susanna Dreisigacker; Leonardo Crespo-Herrera; et al. (Trends in Plant Science, 2025)

Unveiling early-stage responses of sensitive traits to water stress in tropical maize: a characterization study of a public panel - Ingrid Pinheiro Machado; Fernanda Carla Ferreira de Pontes; Maria Valnice de Souza Silveira; Antônio Lucas Aguiar Lobo; Michele Jorge Silva Siqueira; Roberto Fritsche-Neto; Júlio César DoVale (Euphytica, 2025)

Deciphering the genetic basis of salinity tolerance in a diverse panel of cultivated and wild soybean accessions by genome-wide association mapping - Rajat Pruthi; Chanderkant Chaudhary; Sandeep Chapagain; Mostafa Mohamed Elbasuoni Abozaid; Prabhat Rana; Ravi Kiran Reddy Kondi; Roberto Fritsche-Neto; Prasanta K. Subudhi (Theoretical and Applied Genetics, 2024)
Key message In a genome-wide association study involving 269 cultivated and wild soybean accessions, potential salt tolerance donors were identified along with significant markers and candidate genes, such as GmKUP6 and GmWRKY33 . Abstract Salt stress remains a significant challenge in agricultural systems, notably impacting soybean productivity worldwide. A comprehensive genome-wide association study (GWAS) was conducted to elucidate the genetic underpinnings of salt tolerance and identify novel source of salt tolerance among soybean genotypes. A diverse panel comprising 269 wild and cultivated soybean accessions was subjected to saline stress under controlled greenhouse conditions. Phenotypic data revealed that salt tolerance of soybean germplasm accessions was heavily compromised by the accumulation of sodium and chloride, as indicated by highly significant positive correlations of leaf scorching score with leaf sodium/chloride content. The GWAS analysis, leveraging a dataset of 32,832 SNPs, unveiled 32 significant marker-trait associations (MTAs) across seven traits associated with salt tolerance. These markers explained a substantial portion of the phenotypic variation, ranging from 14 to 52%. Notably, 11 markers surpassed Bonferroni’s correction threshold, exhibiting highly significant associations with the respective traits. Gene Ontology enrichment analysis conducted within a 100 Kb range of the identified MTAs highlighted candidate genes such as potassium transporter 6 ( GmKUP6 ), cation hydrogen exchanger ( GmCHX15 ), and GmWRKY33 . Expression levels of GmKUP6 and GmWRKY33 significantly varied between salt-tolerant and salt-susceptible soybean accessions under salt stress. The genetic markers and candidate genes identified in this study hold promise for developing soybean varieties resilient to salinity stress, thereby mitigating its adverse effects.

Comparing strategies for genomic predictions in interspecific biparental populations: a case study with the Rubus genus - Allison Vieira da Silva; Melina Prado; Gabriela Romêro Campos; Karina Lima Reis Borges; Rafael Massahiro Yassue; Gustavo Husein; Marcel Bellato Sposito; Lilian Amorim; José Crossa; Roberto Fritsche-Neto (Euphytica, 2024)

Genomic prediction and QTL analysis for grain Zn content and yield in Aus-derived rice populations - Tapas Kumer Hore; C. H. Balachiranjeevi; Mary Ann Inabangan-Asilo; C. A. Deepak; Alvin D. Palanog; Jose E. Hernandez; Glenn B. Gregorio; Teresita U. Dalisay; Maria Genaleen Q. Diaz; Roberto Fritsche Neto; Md. Abdul Kader; Partha Sarathi Biswas; et al. (Journal of Plant Biochemistry and Biotechnology, 2024)
Zinc (Zn) biofortification of rice can address Zn malnutrition in Asia. Identification and introgression of QTLs for grain Zn content and yield (YLD) can improve the efficiency of rice Zn biofortification. In four rice populations we detected 56 QTLs for seven traits by inclusive composite interval mapping (ICIM), and 16 QTLs for two traits (YLD and Zn) by association mapping. The phenotypic variance (PV) varied from 4.5% ( qPN 4.1 ) to 31.7% ( qPH 1.1 ). qDF 1.1 , qDF 7.2 , qDF 8.1 , qPH 1.1 , qPH 7.1 , qPL 1.2 , qPL 9.1, qZn 5.1 , qZn 5.2 , qZn 6.1 and qZn 7.1 were identified in both dry and wet seasons; qZn 5.1 , qZn 5.2 , qZn 5.3, qZn 6.2, qZn 7.1 and qYLD 1.2 were detected by both ICIM and association mapping. qZn 7.1 had the highest PV (17.8%) and additive effect (2.5 ppm). Epistasis and QTL co-locations were also observed for different traits. The multi-trait genomic prediction values were 0.24 and 0.16 for YLD and Zn respectively. qZn 6.2 was co-located with a gene ( OsHMA2 ) involved in Zn transport. These results are useful for Zn biofortificatiton of rice.

Genomic and population characterization of a diversity panel of dwarf and tall coconut accessions from the International Coconut Genebank for Latin America and Caribbean - Allison Vieira da Silva; Emiliano Fernandes Nassau Costa; Leandro Eugenio Cardamone Diniz; Semíramis Rabelo Ramalho Ramos; Roberto Fritsche-Neto (Genetic Resources and Crop Evolution, 2024)

Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance - Roberto Fritsche-Neto; Jauhar Ali; Erik Jon De Asis; Mehrzad Allahgholipour; Marlee Rose Labroo (Theoretical and Applied Genetics, 2024)
Key message Schemes that use genomic prediction outperform others, updating testers increases hybrid genetic gain, and larger population sizes tend to have higher genetic gain and less depletion of genetic variance Abstract One of the most common methods to improve hybrid performance is reciprocal recurrent selection (RRS). Genomic prediction (GP) can be used to increase genetic gain in RRS by reducing cycle length, but it is also possible to use GP to predict single-cross hybrid performance. The impact of the latter method on genetic gain has yet to be previously reported. Therefore, we compared via stochastic simulations various phenotypic and genomics-assisted RRS breeding schemes which used GP to predict hybrid performance rather than reducing cycle length, which allows minimal changes to traditional breeding schemes. We also compared three breeding sizes scenarios that varied the number of genotypes crossed within heterotic pools, the number of genotypes crossed between heterotic pools, the number of hybrids evaluated, and the number of genomic predicted hybrids. Our results demonstrated that schemes that used genomic prediction of hybrid performance outperformed the others for the average interpopulation hybrid population and the best hybrid performance. Furthermore, updating the testers increased hybrid genetic gain with phenotypic RRS. As expected, the largest breeding size tested had the highest rates of genetic improvement and the lowest decrease in additive genetic variance due to the drift. Therefore, this study demonstrates the usefulness of single-cross prediction, which may be easier to implement than rapid-cycling RRS and cyclical updating of testers. We also reiterate that larger population sizes tend to have higher genetic gain and less depletion of genetic variance.

On the accuracy of threshold genomic prediction models for leaf miner and leaf rust resistance in arabica coffee - Humberto Fanelli Carvalho; Luís Felipe Ventorim Ferrão; Giovanni Galli; Juliana Vieira Almeida Nonato; Lilian Padilha; Mirian Perez Maluf; Márcio Fernando Ribeiro de Resende; Roberto Fritsche-Neto; Oliveiro Guerreiro-Filho (Tree Genetics & Genomes, 2023)

Quantitative trait loci related to growth and wood quality traits in Eucalyptus grandis W. Hill identified through single- and multi-trait genome-wide association studies - Lucas Fernandes Rocha; Thiago Romanos Benatti; Leandro de Siqueira; Izabel Christina Gava de Souza; Isadora Bianchin; Aguinaldo José de Souza; Aline Cristina Miranda Fernandes; Shinitiro Oda; José Luiz Stape; Rafael Massahiro Yassue; Humberto Fanelli Carvalho; Niels A. Müller; et al. (Tree Genetics & Genomes, 2022)

Genotyping marker density and prediction models effects in long-term breeding schemes of cross-pollinated crops - Júlio César DoVale; Humberto Fanelli Carvalho; Felipe Sabadin; Roberto Fritsche-Neto (Theoretical and Applied Genetics, 2022)

Population-tailored mock genome enables genomic studies in species without a reference genome - Felipe Sabadin; Humberto Fanelli Carvalho; Giovanni Galli; Roberto Fritsche-Neto (Molecular Genetics and Genomics, 2022)

Association mapping reveals genomic regions associated with bienniality and resistance to biotic stresses in arabica coffee - Juliana Vieira Almeida Nonato; Humberto Fanelli Carvalho; Karina Lima Reis Borges; Lilian Padilha; Mirian Perez Maluf; Roberto Fritsche-Neto; Oliveiro Guerreiro Filho (Euphytica, 2021)

Correction to: Female reproductive organs of Brassica napus are more sensitive than male to transient heat stress - Sheng Chen; Renu Saradadevi; Miriam S. Vidotti; Roberto Fritsche-Neto; Jose Crossa; Kadambot H. M. Siddique; Wallace A. Cowling (Euphytica, 2021)

Female reproductive organs of Brassica napus are more sensitive than male to transient heat stress - Sheng Chen; Renu Saradadevi; Miriam S. Vidotti; Roberto Fritsche-Neto; Jose Crossa; Kadambot H. M. Siddique; Wallace A. Cowling (Euphytica, 2021)
Oilseed rape ( Brassica napus L.) is sensitive to heat stress during the reproductive stage, but it is not clear whether the male and female reproductive organs differ in their sensitivity to heat stress. In this study, full diallel crossing experiments were conducted among four genotypes of B. napus under control, moderate and high heat stress conditions for five days immediately before and two days after crossing. General combining ability (GCA), specific combining ability (SCA) and reciprocal effects were analyzed to evaluate the genetic basis of heat stress tolerance in male and female reproductive organs. High female temperature (Tf) and high male temperature (Tm) reduced the number of fertile pods and seeds set per floret, and the significant Tf × Tm interaction indicated that female reproductive organs were more sensitive to heat stress than male reproductive organs. There were no overall GCA, SCA or reciprocal effects across all combinations of Tf and Tm. However, a significant reciprocal × Tf effect was found, suggesting that genotypes differed in their ability to set fertile pods and seeds as Tf increased. The relative heat tolerance of G1 as a female increased as Tf increased, and the relative heat tolerance of G2 as a male decreased as Tf increased. In summary, reciprocal diallel crossing has demonstrated that female reproductive organs of B. napus are more sensitive than male to transient heat stress at the early flowering stage, and genotypes differ in relative heat tolerance in the male and female reproductive organs as Tf increases.

CV-α: designing validations sets to increase the precision and enable multiple comparison tests in genomic prediction - Rafael Massahiro Yassue; Felipe Sabadin; Giovanni Galli; Filipe Couto Alves; Roberto Fritsche-Neto (Euphytica, 2021)

Impact of the complexity of genotype by environment and dominance modeling on the predictive accuracy of maize hybrids in multi-environment prediction models - Filipe Couto Alves; Giovanni Galli; Filipe Inácio Matias; Miriam Suzane Vidotti; Júlia Silva Morosini; Roberto Fritsche-Neto (Euphytica, 2021)

A novel way to validate UAS-based high-throughput phenotyping protocols using in silico experiments for plant breeding purposes - Giovanni Galli; Felipe Sabadin; Germano Martins Ferreira Costa-Neto; Roberto Fritsche-Neto (Theoretical and Applied Genetics, 2021)

The effect of bienniality on genomic prediction of yield in arabica coffee - Humberto Fanelli Carvalho; Giovanni Galli; Luís Felipe Ventorim Ferrão; Juliana Vieira Almeida Nonato; Lilian Padilha; Mirian Perez Maluf; Márcio Fernando Ribeiro de Resende Jr; Oliveiro Guerreiro Filho; Roberto Fritsche-Neto (Euphytica, 2020)

Increasing accuracy and reducing costs of genomic prediction by marker selection - Massaine Bandeira e Sousa; Giovanni Galli; Danilo Hottis Lyra; Ítalo Stefanini Correia Granato; Filipe Inácio Matias; Filipe Couto Alves; Roberto Fritsche-Neto (Euphytica, 2019)

Modeling copy number variation in the genomic prediction of maize hybrids - Danilo Hottis Lyra; Giovanni Galli; Filipe Couto Alves; Ítalo Stefanine Correia Granato; Miriam Suzane Vidotti; Massaine Bandeira e Sousa; Júlia Silva Morosini; José Crossa; Roberto Fritsche-Neto (Theoretical and Applied Genetics, 2019)

Correction to: Accuracy of genomic selection to predict maize single-crosses obtained through different mating designs - Roberto Fritsche-Neto; Deniz Akdemir; Jean-Luc Jannink (Theoretical and Applied Genetics, 2018)

Association mapping for traits related to nitrogen use efficiency in tropical maize lines under field conditions - Júlia Silva Morosini; Leandro de Freitas Mendonça; Danilo Hottis Lyra; Giovanni Galli; Miriam Suzane Vidotti; Roberto Fritsche-Neto (Plant and Soil, 2017)