AIMC Topic: Gene-Environment Interaction

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Effects of using deep learning to predict the geographic origin of barley genebank accessions on genome-environment association studies.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik Aug 12, 2025
Genome-environment association (GEA) is an approach for identifying adaptive loci by combining genetic variation with environmental parameters, offering potential for improving crop resilience. However, its application to genebank accessions is limit...
Hordeum Geography Genome, Plant Gene-Environment Interaction Deep Learning Polymorphism, Single Nucleotide
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Performance of deep-learning-based approaches to improve polygenic scores.

Nature communications Jun 2, 2025
Polygenic scores, which estimate an individual's genetic propensity for a disease or trait, have the potential to become part of genomic healthcare. Neural-network based deep-learning has emerged as a method of intense interest to model complex, nonl...
Phenotype Deep Learning Genome-Wide Association Study Genetic Predisposition to Disease Humans Neural Networks, Computer Multifactorial Inheritance Polymorphism, Single Nucleotide Linear Models Models, Genetic Linkage Disequilibrium United Kingdom Gene-Environment Interaction
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Bridging Genomic Research Disparities in Osteoporosis GWAS: Insights for Diverse Populations.

Current osteoporosis reports May 24, 2025
PURPOSE OF REVIEW: Genome-wide association studies (GWAS) have significantly advanced osteoporosis research by identifying genetic loci associated with bone mineral density (BMD) and fracture risk. However, disparities persist due to the underreprese...
Humans White People Machine Learning Gene-Environment Interaction Low Density Lipoprotein Receptor-Related Protein-5 Genome-Wide Association Study Osteoporotic Fractures Genetic Predisposition to Disease Bone Density Genomics African People Core Binding Factor Alpha 1 Subunit Osteoporosis Asian People
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Leveraging Automated Machine Learning for Environmental Data-Driven Genetic Analysis and Genomic Prediction in Maize Hybrids.

Advanced science (Weinheim, Baden-Wurttemberg, Germany) Mar 6, 2025
Genotype, environment, and genotype-by-environment (G×E) interactions play a critical role in shaping crop phenotypes. Here, a large-scale, multi-environment hybrid maize dataset is used to construct and validate an automated machine learning framewo...
Zea mays Environment Genome, Plant Genome-Wide Association Study Genomics Genotype Gene-Environment Interaction Machine Learning Phenotype
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Data-driven discovery of the interplay between genetic and environmental factors in bacterial growth.

Communications biology Dec 24, 2024
A complex interplay of genetic and environmental factors influences bacterial growth. Understanding these interactions is crucial for insights into complex living systems. This study employs a data-driven approach to uncover the principles governing ...
Machine Learning Gene Regulatory Networks Gene Expression Regulation, Bacterial Environment Escherichia coli Culture Media Gene-Environment Interaction
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Machine learning algorithms translate big data into predictive breeding accuracy.

Trends in plant science Oct 26, 2024
Statistical machine learning (ML) extracts patterns from extensive genomic, phenotypic, and environmental data. ML algorithms automatically identify relevant features and use cross-validation to ensure robust models and improve prediction reliability...
Phenotype Big Data Genomics Gene-Environment Interaction Plant Breeding Machine Learning Algorithms
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Gene-diet interactions in carbonated sugar-sweetened beverage consumption and metabolic syndrome risk: A machine learning analysis in a large hospital-based cohort.

Clinical nutrition ESPEN Oct 11, 2024
BACKGROUND AND AIM: Carbonated sugar-sweetened beverages (CSSB) intake has been increasingly linked to metabolic diseases. To investigate the association between CSSB intake and metabolic syndrome (MetS) risk, and the interaction between genetic pred...
Gene-Environment Interaction Sugar-Sweetened Beverages Feeding Behavior Carbonated Beverages Female Middle Aged Risk Factors Humans Diet Adult Polymorphism, Single Nucleotide Machine Learning Cohort Studies Genome-Wide Association Study Male Metabolic Syndrome Genetic Predisposition to Disease
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Using machine learning to combine genetic and environmental data for maize grain yield predictions across multi-environment trials.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik Jul 23, 2024
Incorporating feature-engineered environmental data into machine learning-based genomic prediction models is an efficient approach to indirectly model genotype-by-environment interactions. Complementing phenotypic traits and molecular markers with hi...
Plant Breeding Edible Grain Environment Gene-Environment Interaction Genotype Models, Genetic Machine Learning Zea mays Phenotype Genomics
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Residual networks without pooling layers improve the accuracy of genomic predictions.

TAG. Theoretical and applied genetics. Theoretische und angewandte Genetik May 21, 2024
Residual neural network genomic selection is the first GS algorithm to reach 35 layers, and its prediction accuracy surpasses previous algorithms. With the decrease in DNA sequencing costs and the development of deep learning, phenotype prediction ac...
Algorithms Phenotype Deep Learning Neural Networks, Computer Gene-Environment Interaction Genomics Models, Genetic Selection, Genetic
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Classifying Alzheimer's disease and normal subjects using machine learning techniques and genetic-environmental features.

Journal of the Formosan Medical Association = Taiwan yi zhi Dec 2, 2023
BACKGROUND: Alzheimer's disease (AD) is complicated by multiple environmental and polygenetic factors. The accuracy of artificial neural networks (ANNs) incorporating the common factors for identifying AD has not been evaluated.
Sensitivity and Specificity Gene-Environment Interaction Female Humans Male Mitochondrial Precursor Protein Import Complex Proteins Support Vector Machine Neural Networks, Computer ROC Curve Aged Case-Control Studies Polymorphism, Single Nucleotide Alzheimer Disease Aged, 80 and over Machine Learning
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