Alencar Xavier

Quantitative Genetics and Breeding Analytics

Welcome to my GitHub page! I am a dedicated researcher specializing in the application of quantitative genetics, machine learning, and advanced statistical methods to enhance plant breeding and agricultural systems. My work focuses on developing innovative analytical tools and methodologies to accelerate genetic gains and improve crop performance.

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🔗 Connect with Me

• Google Scholar
• LinkedIn
• ORCID
• Purdue University Profile
• Email
• Phone
• Old Website

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📄 Resumes

• Academic Resume
• Descriptive Resume

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💻 Software solutions

Package/App	Description	Status
bWGR	Bayesian Whole Genome Regression and modeling tools.	🟢
mas	Tools for Multivariate/Multipopulation Association Studies.	🟢
pegs	Pseudo-Expectation Gauss-Seidel light solver.	🟡
NAM	Nested Association Mapping and breeding tools.	🟡
SoyNAM	Soybean Nested Association Mapping dataset.	🟡
G2F	2025 Genomes-to-Field GxE prediction dataset.	🟡
SGC	USDA Soybean Germplasm Collection and Passport data.	🟡
MET accuracy app	Shiny app for assessing the accuracy of multi-environment trials.	🔴

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🔬 Research Highlights

Background material

• Lecture directory - Repo of slide decks from lectures and presentations
• Lecture on variance components - R code, estimate variances and metrics (Nice one)
• Lecture on R basics - Example of R base code, including functions and plots
• Mixed models lecture - Basic background on models, predictions, and signal detection
• Presentation on YouTube - Recordings from lectures and events

Methods and Reviews

• GIS-based GxE modeling of maize hybrids through enviromic markers engineering
• Milestones after a decade of machine learning in GS
• Megavariate methods to capture GxE ⭐
• Efficient implementation of multivariate methods ⭐
• Efficient computation of marker effects
• Technical nuances of machine learning in GS
• Statistical black boxes of plant breeding ⭐

Investigation Papers

• Global GxE prediction competition reveals modeling strategies deliver satisfactory yield estimates
• Quantitative genetics of yield components in soybeans
• Genetic architecture of phenomic-enabled canopy coverage in soy
• Unsupervised learning to infer tradeoffs among quantitative traits
• Quantitative metrics from the USDA soybean germplasm collection
• Joint modeling of genetics and field variation
• Factors impacting long-term gains in breeding

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🎤 Talks and Lectures

A selection of recent and notable presentations:

Year	Title	Event/Institution
2025	Introduction to scalable megavariate models	NAPPN 2025
2023	Trends of predictive breeding	ASTA 2023
2023	Machine learning-based breeding	ASA-CSSA-SSSA 2023
2023	Machine learning-based AI applied to breeding	Purdue University
2022	Leveraging correlated information under multivariate settings	University of Georgia
2021	Overview on Plant Breeding Analytics	Purdue University
2019	Good learners, faster learning	PAG
2015	Technical Nuances of Machine Learning	Iowa State University

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🎓 Research Areas of Interest

• Pipeline and portfolio optimization, breeding flowcharts, genetic trends
• Genome-wide Association Studies and QTL mapping
• Genome-wide prediction and genomic selection
• Multivariate and single-step mixed linear models
• Indirect Selection, Genetic Correlations, Improvement of Heritability and Genetic Gains
• Phenomics and High-Throughput Technologies in Plant Breeding
• Spatial Statistics, Adjustment of Field Variation and Imputation Methods
• Machine Learning, Data Mining, Computational Breeding, MCMC and Bayesian Analysis
• Algorithm development and high-performance computing
• Reaction norms for genotype-by-environment interaction modeling and stability
• Environmental classification and Test Network Optimization
• Signatures of Selection and Applied Population Genetics
• Field Breeding Optimization and Efficient Data Collection