Gota Morota

Phenotypic plasticity, stalk geometry, and noncoding variation underpin stalk lodging resistance in maize

This study identifies genetic and phenotypic factors contributing to stalk lodging resistance in maize by analyzing intermediate traits and their genetic correlations.

Impact of trait measurement error on quantitative genetic analysis of computer vision derived traits

This study investigates the impact of trait measurement error on quantitative genetic analysis of pig body weight, finding no genetic influence on the error and equivalent heritability between scale-based and image-based measurements.

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.

Genomic prediction of stalk lodging resistance and the associated intermediate phenotypes in maize using whole-genome resequence and multi-environmental data

This study improves genomic prediction of stalk lodging resistance in maize by integrating multi-environment data, showing that multi-environment models significantly enhance prediction accuracy for most traits.

AnimalMotionViz: an interactive software tool for tracking and visualizing animal motion patterns using computer vision

AnimalMotionViz tracks and visualizes dairy cattle movement patterns using computer vision to generate motion heatmaps and analyze space utilization.

Genomic prediction of metabolic content in rice grain in response to warmer night conditions

This study evaluates genomic prediction of metabolic content in rice grains under control and high night temperature conditions, finding moderate heritability and improved accuracy with non-additive models.

Multi-environment analysis enhances genomic prediction accuracy of agronomic traits in sesame

This study compares single- and multi-environment genomic prediction models for agronomic traits in sesame, finding that multi-environment analysis improves prediction accuracy by 15%-58%.

Evaluating metabolic and genomic data for predicting grain traits under high night temperature stress in rice

This study uses metabolites and genomics to predict grain traits under high night temperature stress in rice, finding genomic prediction outperforms metabolic prediction.

Classification of plant growth-promoting bacteria inoculation status and prediction of growth-related traits in tropical maize using hyperspectral image and genomic data

This study uses hyperspectral and genomic data to classify plant growth-promoting bacteria inoculation status and predict growth traits in maize, finding that hyperspectral data better predict shoot dry mass and stalk diameter while genomic data better predict plant height.

A low-cost greenhouse-based high-throughput phenotyping platform for genetic studies: a case study in maize under inoculation with plant growth-promoting bacteria

This study develops a low-cost greenhouse-based high-throughput phenotyping platform to assess maize growth under plant growth-promoting bacteria inoculation, showing moderate to high genomic heritability and correlation with manual measurements.

Simulation study evaluating the ability of two statistical approaches to identify variance quantitative trait loci Arabidopsis and maize

This study simulates traits controlled by variance quantitative trait nucleotides in Arabidopsis and maize to evaluate the performance of two statistical methods for identifying variance quantitative trait loci.

Near-infrared spectroscopy outperforms genomic selection for predicting sugarcane feedstock quality traits

This study compares genomic selection and near-infrared spectroscopy for predicting sugarcane feedstock quality traits, finding NIR outperforms genomics and their integration does not improve accuracy.

GCA: An R package for genetic connectedness analysis using pedigree and genomic data

The paper introduces the GCA R package for genetic connectedness analysis using pedigree and genomic data, providing tools to calculate connectedness statistics and assess data comparability.

Modeling multiple phenotypes in wheat using data-driven genomic exploratory factor analysis and Bayesian network learning

This study uses data-driven exploratory factor analysis and Bayesian networks to identify latent phenotypes and their genetic relationships in wheat, revealing how traits like yield and disease resistance are interrelated.

A Multiple-trait Bayesian Variable Selection Regression Method for Integrating Phenotypic Causal Networks in Genome-Wide Association Studies

This paper introduces SEM-BayesC{Pi}, a multi-trait Bayesian regression method for GWAS that incorporates causal structures, showing improved genotype-phenotype mapping compared to multi-trait BayesC{Pi}.

Deciphering cattle temperament measures derived from a four-platform standing scale using genetic factor analytic modeling

This study uses genetic factor analytic modeling to explore the genetic relationships among cattle temperament measures, validating an objective movement-based method as an alternative to subjective scoring.

Leveraging genome-enabled growth models to study shoot growth responses to water deficit in rice Oryza sativa

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.

Regional genomic heritability mapping for agronomic traits in sugarcane

This study uses regional heritability mapping to identify genomic regions associated with agronomic traits in sugarcane, finding high-heritability windows linked to fiber, sucrose, and yield traits.

Variance heterogeneity genome-wide mapping for cadmium in bread wheat reveals novel genomic loci and epistatic interactions

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

Multi-trait random regression models increase genomic prediction accuracy for a temporal physiological trait derived from high-throughput phenotyping

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.

Examining the relationships between phenotypic plasticity and local environments with genomic structural equation models

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

Utilizing trait networks and structural equation models as tools to interpret multi-trait genome-wide association studies

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.

Predicting longitudinal traits derived from high-throughput phenomics in contrasting environments using genomic Legendre polynomials and B-splines

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.

Leveraging breeding values obtained from random regression models for genetic inference of longitudinal traits

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.

Genomic Bayesian confirmatory factor analysis and Bayesian network to characterize a wide spectrum of rice phenotypes

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

Including phenotypic causal networks in genome-wide association studies using mixed effects structural equation models

The paper applies structural equation models to GWAS in chickens to decompose SNP effects into direct, indirect, and total effects, showing greater insight into causal relationships than traditional multi-trait methods.

ShinyAIM: Shiny-based Application of Interactive Manhattan Plots for Longitudinal Genome-Wide Association Studies

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

Utilizing random regression models for genomic prediction of a longitudinal trait derived from high-throughput phenotyping

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.

ShinyGPAS: Interactive genomic prediction accuracy simulator based on deterministic formulas

ShinyGPAS simulates genomic prediction accuracy using deterministic formulas and provides interactive visualizations of prediction accuracy versus genetic factors.

Genomic Relatedness Strengthens Genetic Connectedness Across Management Units

This study evaluates how genomic information improves genetic connectedness across management units compared to pedigree-based methods using simulation and real data from mice and cattle.