Roberto Fritsche-Neto

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Assistant Professor

LSU AgCenter

United States

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About

Roberto Fritsche-Neto is an Assistant Professor at LSU AgCenter in the United States. His research focuses on association mapping, genomic selection, and plant breeding, particularly in relation to rice and maize under environmental stress conditions. Recent publications include studies on the genetic optimization of rice breeding and strategies for enhancing genomic-enabled predictions in plant breeding.

Recent Grants

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TCGA: an tropical corn germplasm assembly for genomic prediction and high-throughput phenotyping studies

Open Date: 2018-03-01

Close Date: 2020-02-01

Grant: Close

Genetic variability between double-haploids in maize obtained from different generations of induction and germplasm

Open Date: 2017-05-01

Close Date: 2020-04-01

Grant: Close

Accuracy of non-additive and GE interaction models of genomic selection for nitrogen use efficiency in tropical maize single-crosses

Open Date: 2015-08-01

Close Date: 2017-07-01

Grant: Close

Acurácia de modelos não-aditivos de seleção genômica de híbridos simples de milho tropical

Open Date: 2015-08-01

Close Date: 2018-12-01

Grant: Close

Acurácia da seleção genômica ampla em linhagens pseudo-endogâmicas de soja para produtividade e aparência geral

Open Date: 2015-01-01

Close Date: 2017-12-01

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Collaborators (4)

Luís Felipe V Ferrão

University of Florida

UNITED STATES

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Adam Famoso

UNITED STATES

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Alison Bentley

Australian National University

AUSTRALIA

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Gota Morota

UNITED STATES

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Roberto Fritsche-Neto

Research Interests

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About

Recent Grants

Articles (24)

On the usefulness of genomic selection for rice ratoon performance in early breeding stages

Association Mapping for Components of Reaction Norms to Environmental Covariates in Public Tropical Maize (Zea mays) Panel Under Water Stress

110 years of rice breeding at LSU: realized genetic gains and future optimization

Unveiling early-stage responses of sensitive traits to water stress in tropical maize: a characterization study of a public panel

Comparing strategies for genomic predictions in interspecific biparental populations: a case study with the Rubus genus

Improving hybrid rice breeding programs via stochastic simulations: number of parents, number of hybrids, tester update, and genomic prediction of hybrid performance

Association mapping for image-based root traits in tropical maize under water stress in semi-arid regions

Data Augmentation Enhances Plant-Genomic-Enabled Predictions

Deep purple - an open-pollinated variety to induce haploids in tropical maize

Phenotypic plasticity and genetic trends in the past 30 years of sugarcane genetic improvement in Louisiana

Elite germplasm introduction, training set composition, and genetic optimization algorithms effect on genomic selection‐based breeding programs

A review of multimodal deep learning methods for genomic-enabled prediction in plant breeding

Complementary approaches to dissect late leaf rust resistance in an interspecific raspberry population

Deciphering the genetic basis of salinity tolerance in a diverse panel of cultivated and wild soybean accessions by genome-wide association mapping

Realized genetic gains via recurrent selection in a tropical maize haploid inducer population and optimizing simultaneous selection for the next cycles

Optimizing quantitative trait loci introgression in elite rice germplasms: Comparing methods and population sizes to develop new recipients via stochastic simulations

SoilType: An R package to interplay soil characterization in plant science

Genome‐wide association analysis of hyperspectral reflectance data to dissect the genetic architecture of growth‐related traits in maize under plant growth‐promoting bacteria inoculation

Envirome-wide associations enhance multi-year genome-based prediction of historical wheat breeding data

On the accuracy of threshold genomic prediction models for leaf miner and leaf rust resistance in arabica coffee

Enviromic-based kernels may optimize resource allocation with multi-trait multi-environment genomic prediction for tropical Maize

Genotyping marker density and prediction models effects in long-term breeding schemes of cross-pollinated crops

A low‐cost greenhouse‐based high‐throughput phenotyping platform for genetic studies: A case study in maize under inoculation with plant growth‐promoting bacteria

Population-tailored mock genome enables genomic studies in species without a reference genome

Collaborators (4)

Luís Felipe V Ferrão

Adam Famoso

Alison Bentley

Gota Morota

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