Malachy T. Campbell

The paper develops genomic prediction methods for plant metabolites using metabolite GWAS and kernel models, improving accuracy for LC-MS metabolites but not GC-MS.

This study evaluates multi-omics prediction models for oat traits across environments and populations, showing improved accuracy over genomic prediction with SNP data.

This study uses metabolomics and genomic approaches to improve prediction of healthful compounds in oat seeds by identifying latent factors associated with lipid metabolism.

This study uses a genome-enabled growth model to analyze shoot growth responses to water deficit in rice, identifying genes associated with growth inflection points under drought.

This study applies variance heterogeneity genome-wide association mapping to identify novel genomic loci and epistatic interactions influencing grain cadmium concentration in bread wheat.

The paper applies multi-trait random regression models to predict daily water usage in rice using high-throughput phenotyping data, showing improved genomic prediction accuracy when integrating shoot biomass measurements.

The paper introduces a genomic structural equation model to examine relationships between phenotypic plasticity and local environments using Bayesian networks and mixed-effects SEM.

This paper introduces SEM-GWAS, a method that incorporates phenotypic networks into GWAS to partition SNP effects into direct and indirect effects, using rice traits as an example.

This study applies random regression models with genomic Legendre polynomials and B-splines to predict rice shoot area under control and water-limited conditions, showing improved prediction accuracy compared to a baseline model.

This paper uses random regression models to improve genetic inference of longitudinal traits in rice by identifying both persistent and time-specific quantitative trait loci.

This paper uses Bayesian confirmatory factor analysis and Bayesian networks to analyze rice phenotypes and identify genetic correlations among traits.

This study characterizes transcriptome and cis-regulatory divergence between rice subspecies Indica and Japonica using transcriptome data from 91 accessions, identifying subspecies-specific eQTL and patterns of genetic diversity.

ShinyAIM provides an interactive tool for visualizing longitudinal GWAS results through Manhattan plots and dynamic data exploration.

This paper applies random regression models to predict longitudinal shoot growth traits in rice using genomic data, showing improved accuracy over single time point predictions.

The paper applies LASSO to a gene co-expression network in rice under salt stress to identify gene interactions linked to salt tolerance.