Neurodivergent parent, racing cars fan, and cycling enthusiast.
I'm a scientist at Syngenta Seeds (North America), working in the global biostatistics team within molecular breeding.
My science domain lies in quantitative genetics and biometrics for modeling complex traits, elucidating their genetic architecture, and predicting their variation across environments. I try to integrate diverse data types like phenomics, genomics, and enviromics/envirotyping for breeding analytics, and techniques, such as machine learning, experimental statistics, hypothesis testing, and simulations.
My mission is to develop cutting-edge data analytics pipelines for plant breeding that enable the development of varieties for a sustainable, productive, and resource-efficient agricultural system.
I also volunteer as a mentor for students and early-career researchers, trying to help in their professional development.
My research focuses on developing mathematical models capable of describing how plants respond to changes in their environment in terms of plasticity, adaptation, and productivity. I utilize applied quantitative genetics models capable of predicting and analyzing complex plant traits, integrating various data types, including phenomics, genomics, weather and soil information, remote sensing, satellite-based data (GIS), and ecophysiology models. Through the integration of my expertise with other fields such as biometrics, computational biology, experimental design/statistics, and breeding, my goal is to assist plant scientists in addressing society's growing demands for a sustainable, productive, and resource-efficient agricultural system.
e-mail: germano.cneto@gmail.com
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Publications: Google Scholar ORCID and Research Gate profile
Most of my projects are in R programming language.
- Envirotyping pipeline using EnvRtype package
- Genomic enabled prediction using non-linear kernel methods
- Enviromic-aided Genomic Prediction (E-GP)
- Environmental-wide association and envirotype-to-phenotype association (EPA)
- Adaptive Allele mining by environmental GWAS (envGWAS)
- Multi-enviromics layers for GxE prediction
- CVandME: Multiple Cross-validation schemes for Prediction-based Breeding
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Enviromics-informed genomic prediction in tropical maize - Zea Evolution Meeting, Maize Genetics Society of United States
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Enviromics for a climate-smart genomic prediction: the good, the bad and the ugly - Brazilian Congress on Plant Breeding, December 2021
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Envirotyping & enviromics in plant breeding / Ambitipagem e Ambiômica no Melhoramento de Plantas - GEMS-R webinar, 28h August 2021. Brazil
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Envirotyping-informed tools for GxE analysis video (english/portuguese) and codes- I INTERGEN, Plant Science Symposia Series, 14h July 2020. Brazil
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Envirotype-to-phenotype modelling in genomic prediction (english/portuguese) , Departament of Genetics, University of São Paulo, 18th Setp 2020, Brazil
- Short Course: EnvRtype v1.0.1 (April 2022, for GenMelhor Study Group, UFV, Brazil) -- Git Hub ([english])
- Short Course: EnvRtype v1.0.0 (Aug 2021, for GEMS) -- Git Hub (english)
- Short Course: Modeling GxE interaction with phenotypic, genomic and enviromic data (portuguese)
- (Linkedin) Collection of daily weather (NASA POWER API) and elevation data (SRTM) globally
- (Brazilian Society of Plant Breeding) Enviromics: bridging different sources of data, building one framework
- (Linkedin) Reading review papers helped me with mental rehabilitation after COVID-19
- (EMBRAPA boletin 56) Environmental Information for modeling GxE in plant breeding (portuguese)
Most of my studies were conducted using tropical maize data from the Allogamous Plant Breeding Laboratory (University of São Paulo). This data can be download at the Mendeley Respository
