Publications by authors named "D Runcie"
Mate selection plays an important role in breeding programs. The Usefulness Criterion was proposed to improve mate selection, combining information on both the mean and standard deviation of the potential offspring of a cross, particularly in clonally propagated species where large family sizes are possible. Predicting the mean value of a cross is generally easier than predicting the standard deviation, especially in outbred species when the linkage of alleles is unknown and phasing is required.
View Article and Find Full Text PDFPredicting phenotypes from a combination of genetic and environmental factors is a grand challenge of modern biology. Slight improvements in this area have the potential to save lives, improve food and fuel security, permit better care of the planet, and create other positive outcomes. In 2022 and 2023 the first open-to-the-public Genomes to Fields (G2F) initiative Genotype by Environment (GxE) prediction competition was held using a large dataset including genomic variation, phenotype and weather measurements and field management notes, gathered by the project over nine years.
View Article and Find Full Text PDFArticle Synopsis
- Genomic prediction models help forecast how certain genotypes will perform in different environments, but they can be hard to compute effectively.
- This study presents three advanced algorithms (MegaLMM, MegaSEM, and PEGS) designed to tackle genotype-by-environment interactions and assesses their accuracy and runtime efficiency through simulated scenarios.
- Results showed that MegaLMM and PEGS models achieved high accuracy with many testing environments, and PEGS was significantly faster than traditional methods, with MegaSEM excelling in speed when handling large datasets.
Multienvironment trials (METs) are crucial for identifying varieties that perform well across a target population of environments. However, METs are typically too small to sufficiently represent all relevant environment-types, and face challenges from changing environment-types due to climate change. Statistical methods that enable prediction of variety performance for new environments beyond the METs are needed.
View Article and Find Full Text PDFArticle Synopsis
- Statistical machine learning (ML) analyzes large volumes of genomic, phenotypic, and environmental data to uncover patterns and improve prediction models in plant breeding.
- By investigating genotype-by-environment (G×E) interactions, ML helps identify genetic factors that influence performance in various environments.
- This review emphasizes how big data and ML enhance prediction accuracy and streamline breeding strategies through comprehensive analysis of diverse datasets.