Nonoy B. Bandillo

Reaction Norm Modeling of High-Dimensional Genomic and Environmental Data Improves Prediction Accuracy in Winter Wheat

This paper improves genomic prediction accuracy in winter wheat by integrating genotype-by-environment interactions and environmental covariates across multiple environments.

Environmentally-mediated yield effects of vernalization and photoperiod alleles in historic winter wheat trials

The paper analyzes historic wheat trials to show how vrn1 and Ppd1 alleles affect yield through environmental interactions, finding that vrn1 effects are more variable than Ppd1 effects.

Genomic and Transcriptomic Basis of Salinity Tolerance in Dry Pea

This study identifies genomic and transcriptomic factors contributing to salinity tolerance in dry pea using genetic mapping and transcriptomic analysis of diverse germplasm sets.

Multi-trait multi-environment genomic prediction of preliminary yield trials in pulse crops

The paper evaluates a multi-trait multi-environment genomic prediction model for improving yield and protein content in field peas under stress and non-stress conditions using breeding lines from a pulse crop program.

High-Throughput Phenotyping of Seed Quality Traits Using Imaging and Deep Learning in Dry Pea

This paper develops an automated seed quality assessment tool for dry peas using imaging and deep learning, achieving high accuracy and efficiency compared to traditional methods.

Effective Population Size in Field Pea

The paper estimates effective population size (Ne) in pea using linkage disequilibrium, finding higher Ne in the USDA diversity panel compared to NDSU breeding lines.

Genomic-inferred cross-selection metrics for multi-trait improvement in a recurrent selection breeding program

This paper develops genomic-inferred cross-selection metrics to improve multi-trait genetic gain in a breeding program while maintaining additive genetic variance.

Genomic selection performs as effectively as phenotypic selection for increasing seed yield in soybean

This study shows genomic selection (GS) is as effective as phenotypic selection (PS) for increasing soybean seed yield, with similar gains and reduced breeding costs.

Multi-trait genomic prediction improves selection accuracy for enhancing seed mineral concentrations in pea (Pisum sativum L.)

This study shows that multi-trait genomic selection with sparse phenotyping improves prediction accuracy for seed mineral concentrations in pea compared to univariate genomic selection.

Harnessing genetic diversity in the USDA pea (Pisum sativum L.) germplasm collection through genomic prediction

The paper evaluates genomic prediction models on pea germplasm to improve selection efficiency using SNP markers and phenotypic data.

Comparative evolutionary analysis and prediction of deleterious mutation patterns between sorghum and maize

This study compares deleterious mutation patterns between sorghum and maize, finding sorghum diverges from the domestication cost hypothesis and develops a gene deleterious index with accuracy over 0.5.

Transcriptome-wide association supplements genome-wide association in Zea mays

The paper shows that combining transcriptome-wide association studies (TWAS) with genome-wide association studies (GWAS) improves the detection of genes linked to agronomic and seed quality traits in maize.

Metabolome-scale genome-wide association studies reveal chemical diversity and genetic control of maize specialized metabolites

The paper uses metabolome-scale GWAS to identify genetic loci controlling maize specialized metabolites, revealing chemical diversity and gene-metabolite associations.

Leveraging mutational burden for complex trait prediction in sorghum

This study estimates deleterious mutation burden in sorghum and assesses its impact on agronomic traits, finding moderate genetic variance explanation but limited improvement in breeding accuracy due to linkage.