Role of genomics on regulating rice grain metabolic variability under warmer nights: A statistical and image-based deep learning approach
This website is a project for analysis of the Genomic Selection for Drought Tolerance Using Genome Wide GBS and/or DART in Cassava by EMBRAPA Mandioca.
The package is a CLI tool for benchmarking genomic selection models in plant breeding.
Genetic evaluation in R: AI-REML variance components, breeding values and single-step genomics (ssGBLUP, APY), with reaction norms, indirect genetic effects, multi-trait, threshold and survival models. The numerics are C++ inside the package, with no run-time dependency.
Solves kernel ridge regression within the the mixed model framework. All the estimated components and parameters, e.g. BLUP of dual variables and BLUP of random predictor effects for the linear kernel (also known as RR-BLUP), are available.
Tutorial on GWAS-GS
Reproducible genomic prediction study comparing L1/L2-regularized artificial neural networks with GBLUP-ADE under a frozen cross-validation design, using canonical simulated-data workflows, workflowr, and renv.
Evaluating metabolic and genomic data for predicting grain traits under high night temperature stress in rice
Stage-3 dosage-aware genomic selection pipeline for hexaploid sweetpotato, simulation-validated; code accompanying Ecological Genetics and Genomics 41 (2026) 100530
Complete reproduction of 'Generation-Updated Orthogonal Epistatic Kernels for Cross-Generation Genomic Prediction' - NOIA encoding, multi-kernel AI-REML, forward simulation, ablation study. 137 tests, 100% coverage.
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