Daniel E. Runcie

The paper quantifies anthropogenic effects on maize genetic diversity in the Americas using genotyping, human genomic data, and linguistic maps, finding weak genome-wide effects but localized language-associated loci.

This study investigates the role of maize inversion Inv4m in adaptation to phosphorus-deficient soils and finds that phosphorus deficiency does not drive its adaptive value.

This paper identifies the chromosomal inversion Inv4m in teosinte mexicana and shows it affects maize flowering time, plant height, and gene networks through JMJ histone demethylases.

This study identifies cell-specific Na+ accumulation patterns in tomato leaves and links them to symplastic transport regulation via PDLP1.

The paper uses a MAGIC population to study genetic architecture of clinal traits in Mexican maize, finding one major QTL for tassel branching and multiple QTL for flowering time with complex environmental associations.

The paper argues that cross-validation overestimates genomic selection's effectiveness in plant breeding, especially when used for accuracy improvement rather than selection intensity.

This study investigates the origins and adaptive consequences of polyploidy in the prairie grass Andropogon gerardi, finding that whole genome duplication is common and provides fitness advantages in xeric climates.

The paper identifies genetic variants at transcription factor binding sites that explain a large portion of phenotypic heritability in maize under different water conditions.

This study evaluates the use of environmental and genomic data to identify climate-adaptive maize varieties, finding that genomic data better predict yield traits than environmental data alone.

The paper argues that benchmarking phenomic prediction against genomic prediction using cross-validation is inappropriate because accuracy differences do not reliably reflect breeder's equation parameters and other factors influence selection outcomes.

The paper evaluates the usefulness criterion for mate selection in breeding programs and finds it provides little benefit over selecting parents based on predicted family means.

The paper reports on a competition to predict maize yield using genetic and environmental data, showing that diverse modeling approaches can achieve good results.

MegaLMM improves genomic predictions in new environments by incorporating environmental covariates into a statistical model, showing higher accuracy than traditional methods in maize breeding.

The paper investigates convergent evolution of histone reader domains fused to MSH6 across eukaryotes, showing evidence of natural selection and links to genome size and mutation rate heterogeneity.

The paper introduces MegaBayesianAlphabet, a Bayesian regression framework for genome-wide prediction and association studies, showing improved accuracy in genetic value prediction and marker discovery using high-dimensional phenotypic data.

The paper uses Bayesian Sparse Factor Genetic modelling to analyze gene expression traits in two Drosophila species, finding that most heritable trait covariation is due to a few extreme genotypes, suggesting the House of Cards model applies to co-expression phenotypes.

This study compares 3' RNA sequencing and whole transcript RNA-seq, finding similar power and precision, with whole transcript RNA-seq offering additional benefits in non-traditional species.

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

MegaLMM improves genetic value prediction accuracy by analyzing thousands of traits simultaneously in genomic studies.

This study investigates the gene regulatory effects of a 13 Mb chromosomal inversion in highland maize using transcriptomic data to identify its adaptive significance.

The paper identifies issues with cross-validation in multi-trait genomic prediction and proposes three solutions to improve model selection.

This paper introduces Grid-LMM, a fast and flexible linear mixed model framework for genome-wide genetics, improving computational efficiency and accuracy in genetic association analyses.