Hiroyoshi Iwata

Progeny-based genomic selection reveals untapped genetic potential in an underutilized medicinal plant, Perilla frutescens

This study applies genomic selection to improve medicinal compounds in red perilla, showing that GS-based crosses outperform phenotypic selection in target compound levels.

Metabolome genome-wide association study reveals hierarchical and epistatic genetic control of flavonoid metabolism in soybean

Cross Potential Selection for Multiple Traits Considering the Progeny Distribution of Future Inbred Lines in Plant Breeding Programs

This paper introduces CPS-MT, a breeding strategy that improves a target trait while maintaining essential traits within ranges by evaluating cross-pairs based on predicted progeny distributions.

Efficient Optimization of Genotype Pairs for Intercropping using Genomic Prediction and Bayesian Optimization

This paper develops a framework combining genomic prediction and Bayesian optimization to efficiently identify high-performing genotype pairs for intercropping.

Interpretable multi-omics machine learning reveals drought-driven shifts in plant-microbe interactions

The paper uses interpretable machine learning to analyze multi-omics data from soybean under drought stress, identifying key biomarkers and interactions influencing plant phenotypes.

Integration of Proxy Intermediate Omics traits into a Nonlinear Two-Step model for accurate phenotypic prediction

The paper proposes a nonlinear two-step model integrating proxy intermediate omics traits to improve phenotypic prediction in soybean, showing better accuracy than linear models.

Optimizing progeny allocation strategies in breeding schemes while updating genomic prediction models

The paper develops a framework that optimizes breeding strategies by updating genomic prediction models during breeding schemes, showing improved performance under moderate to low selection intensities.

Haplotype Bias Detection Using Pedigree-Based Transmission Simulation: Traces of Selection That Occurred in Apple Breeding

The paper develops a method to detect haplotype bias using pedigree-based gene drop simulations in apple cultivars, identifying SNPs associated with fruit traits and potential selection signals.

Leveraging species-wide variation and patterns of adaptation to inform pecan crop improvement efforts

This study uses over 700 pecan genotypes and a haplotype-resolved genome to identify gene pools, assess environmental adaptation, and predict future maladaptation risks.

Reciprocal BLUP: A Predictability-Guided Multi-Omics Framework for Plant Phenotype Prediction

The paper introduces Reciprocal BLUP, a multi-omics framework that improves plant phenotype prediction by analyzing cross-layer relationships between genome, metabolome, and microbiome data.

DiffBreed: Automatic differentiation enables efficient gradient-based optimization of breeding strategies

DiffBreed uses automatic differentiation to optimize breeding strategies, achieving higher genetic gains than equal allocation through gradient-based optimization.

Estimating fruit tree growth curves in breeding field using fragmented longitudinal data: An application to citrus hybrid seedlings

The paper estimates growth curves for citrus hybrid seedlings using fragmented longitudinal data and Bayesian nonlinear models, showing improved parameter estimation with genomic integration.

I-SVVS: Integrative stochastic variational variable selection to explore joint patterns of multi-omics microbiome data

I-SVVS improves clustering accuracy and reduces computation time for multi-omics microbiome data across soybean, mice, and human datasets.

Ridge Redundancy Analysis for High-Dimensional Omics Data

This paper introduces an efficient framework for ridge RDA to analyze high-dimensional omics data by addressing computational challenges through SVD and reduced memory storage.

Optimization of crossing strategy based on the usefulness criterion in inter-population crosses considering different genetic effects among populations

This study proposes a method to select optimal cross pairs in inter-population crosses by considering genetic effects across populations, showing increased genetic gains in multiple traits.

ConvCGP: A Convolutional Neural Network to Predict Genotypic Values of Rice Traits from Compressed Genome-Wide Polymorphisms

The paper introduces ConvCGP, a deep learning framework combining CNNs and autoencoders to predict rice trait genotypic values from compressed genome-wide polymorphism data, outperforming existing methods.

Modeling soybean growth: A mixed model approach

The paper proposes a nonlinear mixed-effects model to analyze genetic and environmental effects on soybean growth using simulated and experimental data.

Quantifying Forest Biomass and Genetic Contribution using Light Detection and Ranging

This study uses LiDAR to measure tree traits and assess their heritability in larch trees, finding backpack LiDAR provides more accurate DBH estimates than UAV LiDAR.

Cross Potential Selection: A Proposal for Optimizing Crossing Combinations in Recurrent Selection Based on the Ability of Future Inbred Lines

This paper introduces Cross Potential Selection (CPS), a strategy for selecting crossing pairs in plant breeding to accelerate novel variety production, showing higher genetic improvements in early cycles of simulated breeding programs.

AI-assisted selection of mating pairs through simulation-based optimized progeny allocation strategies in plant breeding

This paper develops an AI method using simulation and optimization to improve genetic gain in plant breeding by optimizing progeny allocation strategies for mating pairs.

Development of a novel GWAS method to detect QTL effects interacting with the discrete and continuous population structure

This paper develops two new GWAS models, SNPxGB and HBxGB, to detect QTLs interacting with discrete and continuous population structures without requiring prior population structure information.

High-throughput Phenotyping of Soybean Biomass: Conventional Trait Estimation and Novel Latent Feature Extraction Using UAV Remote Sensing and Deep Learning Models

This study uses UAV remote sensing and deep learning to estimate soybean biomass traits and extract latent features for breeding applications.

Assessing the potential for genome-assisted breeding in red perilla using quantitative trait locus analysis and genomic prediction

This study evaluates genomic selection and marker-assisted selection for improving medicinal compound production in red perilla using quantitative trait locus analysis and genomic prediction.

Evaluating drought tolerance stability in soybean by theresponse of irrigation change captured from time-seriesmultispectral data

This study evaluates drought tolerance stability in soybean using time-series multispectral data and genomic prediction models, finding higher accuracy with changing irrigation treatments.

Stochastic variational variable selection for high-dimensional microbiome data

The paper introduces SVVS, a method for analyzing high-dimensional microbiome data, showing improved performance and faster computation compared to existing methods.

Bayesian optimization of multivariate genomic prediction models based on secondary traits for improved accuracy gains and phenotyping costs

The paper proposes a Bayesian optimization method to optimize multivariate genomic prediction models using secondary traits, achieving better accuracy while reducing phenotyping costs.

Coupling Day Length Data and Genomic Prediction tools for Predicting Time-Related Traits under Complex Scenarios

The paper develops methods that integrate day length data with genomic prediction tools to improve prediction of time-related traits like days to heading in unobserved environments.

Choosing the optimal population for a genome-wide association study: a simulation using whole-genome sequences from rice

This study shows that combining target populations with more diverse populations improves GWAS power for detecting QTNs, depending on genetic diversity metrics like FST and He.

Haplotype-based genome wide association study using a novel SNP-set method : RAINBOW

RAINBOW is a novel SNP-set GWAS method that improves detection of causal variants and controls false positives, especially for nearby variants with opposite effects.