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Unraveling the Complexity of Genetic Interactions: A Critical Review of Methods for High-Order Epistasis Detection in Genomic Studies

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Mathematical Methods in Artificial Intelligence
This chapter is in the book Mathematical Methods in Artificial Intelligence

Abstract

High-order epistasis – referring to complex interactions involving more than two genetic loci – plays a pivotal role in unraveling the genetic architecture underlying complex diseases and traits. However, detecting such interactions remains a formidable challenge due to the combinatorial explosion of possible loci combinations and the inherent limitations of existing computational and statistical methodologies. This review offers a comprehensive examination of current approaches to high-order epistasis detection, spanning from traditional statistical models to emerging machine learning techniques. We systematically classify and compare these methods based on their underlying principles, strengths, and weaknesses, with particular attention to scalability, interpretability, and effectiveness on large-scale genomic datasets. In addition, we explore how recent advancements – especially in deep learning and network-based frameworks – are shaping the future of this field. By highlighting critical challenges and proposing future research directions, this review aims to support researchers in selecting appropriate tools for their investigations and to inspire novel strategies for deciphering the complexities of high-order genetic interactions.

Abstract

High-order epistasis – referring to complex interactions involving more than two genetic loci – plays a pivotal role in unraveling the genetic architecture underlying complex diseases and traits. However, detecting such interactions remains a formidable challenge due to the combinatorial explosion of possible loci combinations and the inherent limitations of existing computational and statistical methodologies. This review offers a comprehensive examination of current approaches to high-order epistasis detection, spanning from traditional statistical models to emerging machine learning techniques. We systematically classify and compare these methods based on their underlying principles, strengths, and weaknesses, with particular attention to scalability, interpretability, and effectiveness on large-scale genomic datasets. In addition, we explore how recent advancements – especially in deep learning and network-based frameworks – are shaping the future of this field. By highlighting critical challenges and proposing future research directions, this review aims to support researchers in selecting appropriate tools for their investigations and to inspire novel strategies for deciphering the complexities of high-order genetic interactions.

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. Theme 1: Intelligent Systems
  4. Theme 1: Intelligent Systems 1
  5. AI-Driven Mobile Marketplaces for Agriculture: A Real-Time Intelligent Trading System 5
  6. AI-Based Framework for Demand Forecasting, Real-Time Stock Alert, and Automated Reordering in Supermarkets 13
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  11. Real-Time AI-Powered Monitoring and Automation System for Industrial Graywater Recycling 59
  12. Early Detection of Eye Cancer Through Deep Learning and Image Classification 67
  13. A Flask-Enabled Web Platform for Multilingual Sentiment Mining Using AI-Driven Mathematical Models 77
  14. AI-Assisted Mathematical Techniques for Sustainable Waste Control in Industrial Environments 85
  15. AI-Enabled Intelligent Signal System for Pedestrian and Ambulance Prioritization 93
  16. CIVICMIND: AI-Powered Tool for Efficient Petition Management and Tracking 105
  17. AI-Integrated Healthcare Social Media Application 111
  18. Generative and Deep Learning Models for AI-Assisted Dermatological Diagnostics 117
  19. AI-Integrated Migrant Worker Support Platform (AIM-WSP) 125
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  21. AI-Enhanced IoT Smart Baby Monitoring System: Mathematical Models for Predictive Infant Care 143
  22. Deep Learning with Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) for AI-Driven Smart Home Automation 153
  23. MMDT-DM: Multimodal Foundation Models with Digital Twin Technology for Predictive Disaster Management in Smart Cities 165
  24. AI-Driven Energy Management in Smart Homes Using Deep Q-Learning and LightGBM Algorithms 177
  25. AmbiNeuro-Home: Ambient Intelligence with Neuromorphic Spiking Networks for Invisible Disaster Prevention in Smart Homes 189
  26. Deep Learning-Based Traffic Flow Predictions with Transformer Models and Recurrent Neural Networks (RNN) 201
  27. Exploring AI for Smart Homes: Combining Capsule Networks and Bayesian Neural Networks for Enhanced Efficiency 215
  28. Mathematical Modeling and AI-Driven Forecasting of Consumer Behavior Trends in E-Commerce Platforms 227
  29. Development and Evaluation of Machine Learning Models for Parkinson’s Disease Detection Using Speech Features: A Comparative Study 241
  30. Enhanced Parkinson’s Disease Detection Using Bi-LSTM with Attention Mechanism: A Deep Learning Approach for Speech-Based Early Diagnosis and Classification 255
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  32. AI-Powered UAV-Based Selective Spraying System for Precision Agriculture 281
  33. AI-Enabled Target Tracking Drone for Surveillance and Defense Applications 293
  34. Real-Time Energy Management for Electric Vehicles: A Web-Integrated Neural Network Framework for Hybrid Energy Storage Systems 305
  35. Enhancing Critical Thinking Assessment Through AI-Student Dialogue Analysis 319
  36. AI-Powered Blood Cell Analysis: The Role of Mathematical Models in Hematological Diagnostics 331
  37. Application of Industrial Waste in Self-Compacting Concrete: A Machine Learning and Genetic Algorithm Approach 339
  38. Real-Time Air Quality Assessment Using AI-Driven Mathematical Models and Data Science Tools 355
  39. Fabrication of AI-Enhanced ADAS Vehicles Using Mathematical Modeling Techniques 363
  40. Revolutionizing Smart City Infrastructure with U-Net and GRU Networks: A Machine Learning Approach 371
  41. A Semantic-Related Ontology Model for Legal Document Classification and Summarization 383
  42. A Systematic Review on Image Reconstruction from fMRI Using Deep Learning 395
  43. Advancing String Matching Using Graph Neural Networks and Meta-Learning with Interpretability 405
  44. EEG-Based Detection of Autism Spectrum Disorder Using CNN-Driven Spectrogram Analysis: A Simpler Approach with Mathematical Feature Enhancement 415
  45. Simulation and Performance Analysis of a Hybrid Load Balancing Algorithm in a Python-Based Cloud Environment 427
  46. Emotionally Enhanced Text-to-Speech Synthesis Using CNN 441
  47. Automatic Steering Wheel Control Using CNN 453
  48. Unraveling the Complexity of Genetic Interactions: A Critical Review of Methods for High-Order Epistasis Detection in Genomic Studies 465
  49. A PSAM-Enhanced Sentiment Classification Framework Using Particle Swarm Optimized Feature Selection and Deep LSTM Networks for Text Mining Applications 477
  50. Enhanced Text Classification Using Machine Learning Techniques for Self-Learning Platform Data 489
https://doi.org/10.1515/9783112234952-045

Chapters in this book

  1. Frontmatter I
  2. Contents V
  3. Theme 1: Intelligent Systems
  4. Theme 1: Intelligent Systems 1
  5. AI-Driven Mobile Marketplaces for Agriculture: A Real-Time Intelligent Trading System 5
  6. AI-Based Framework for Demand Forecasting, Real-Time Stock Alert, and Automated Reordering in Supermarkets 13
  7. Smart Light Management System with Auto-Brightness Adjustment Using ESP32 25
  8. A Hybrid Edge AI Framework for Real-Time Sign Language Recognition Using Mathematical Models in AI 31
  9. AI-Driven LSRW Assessment Framework for Real-Time Language Learning Assistance for School Children 41
  10. Integrated Video Intercom with Analog IP Connectivity Models Using AI Vibration Sensing 51
  11. Real-Time AI-Powered Monitoring and Automation System for Industrial Graywater Recycling 59
  12. Early Detection of Eye Cancer Through Deep Learning and Image Classification 67
  13. A Flask-Enabled Web Platform for Multilingual Sentiment Mining Using AI-Driven Mathematical Models 77
  14. AI-Assisted Mathematical Techniques for Sustainable Waste Control in Industrial Environments 85
  15. AI-Enabled Intelligent Signal System for Pedestrian and Ambulance Prioritization 93
  16. CIVICMIND: AI-Powered Tool for Efficient Petition Management and Tracking 105
  17. AI-Integrated Healthcare Social Media Application 111
  18. Generative and Deep Learning Models for AI-Assisted Dermatological Diagnostics 117
  19. AI-Integrated Migrant Worker Support Platform (AIM-WSP) 125
  20. Integrated System of Solar Tracking, Li-Fi Technology, and Industrial Energy Automation for Smart Sustainable Solutions 133
  21. AI-Enhanced IoT Smart Baby Monitoring System: Mathematical Models for Predictive Infant Care 143
  22. Deep Learning with Long Short-Term Memory (LSTM) and Convolutional Neural Networks (CNN) for AI-Driven Smart Home Automation 153
  23. MMDT-DM: Multimodal Foundation Models with Digital Twin Technology for Predictive Disaster Management in Smart Cities 165
  24. AI-Driven Energy Management in Smart Homes Using Deep Q-Learning and LightGBM Algorithms 177
  25. AmbiNeuro-Home: Ambient Intelligence with Neuromorphic Spiking Networks for Invisible Disaster Prevention in Smart Homes 189
  26. Deep Learning-Based Traffic Flow Predictions with Transformer Models and Recurrent Neural Networks (RNN) 201
  27. Exploring AI for Smart Homes: Combining Capsule Networks and Bayesian Neural Networks for Enhanced Efficiency 215
  28. Mathematical Modeling and AI-Driven Forecasting of Consumer Behavior Trends in E-Commerce Platforms 227
  29. Development and Evaluation of Machine Learning Models for Parkinson’s Disease Detection Using Speech Features: A Comparative Study 241
  30. Enhanced Parkinson’s Disease Detection Using Bi-LSTM with Attention Mechanism: A Deep Learning Approach for Speech-Based Early Diagnosis and Classification 255
  31. ML-Driven Pronunciation Analysis and LLM-Powered Adaptive Learning Framework 269
  32. AI-Powered UAV-Based Selective Spraying System for Precision Agriculture 281
  33. AI-Enabled Target Tracking Drone for Surveillance and Defense Applications 293
  34. Real-Time Energy Management for Electric Vehicles: A Web-Integrated Neural Network Framework for Hybrid Energy Storage Systems 305
  35. Enhancing Critical Thinking Assessment Through AI-Student Dialogue Analysis 319
  36. AI-Powered Blood Cell Analysis: The Role of Mathematical Models in Hematological Diagnostics 331
  37. Application of Industrial Waste in Self-Compacting Concrete: A Machine Learning and Genetic Algorithm Approach 339
  38. Real-Time Air Quality Assessment Using AI-Driven Mathematical Models and Data Science Tools 355
  39. Fabrication of AI-Enhanced ADAS Vehicles Using Mathematical Modeling Techniques 363
  40. Revolutionizing Smart City Infrastructure with U-Net and GRU Networks: A Machine Learning Approach 371
  41. A Semantic-Related Ontology Model for Legal Document Classification and Summarization 383
  42. A Systematic Review on Image Reconstruction from fMRI Using Deep Learning 395
  43. Advancing String Matching Using Graph Neural Networks and Meta-Learning with Interpretability 405
  44. EEG-Based Detection of Autism Spectrum Disorder Using CNN-Driven Spectrogram Analysis: A Simpler Approach with Mathematical Feature Enhancement 415
  45. Simulation and Performance Analysis of a Hybrid Load Balancing Algorithm in a Python-Based Cloud Environment 427
  46. Emotionally Enhanced Text-to-Speech Synthesis Using CNN 441
  47. Automatic Steering Wheel Control Using CNN 453
  48. Unraveling the Complexity of Genetic Interactions: A Critical Review of Methods for High-Order Epistasis Detection in Genomic Studies 465
  49. A PSAM-Enhanced Sentiment Classification Framework Using Particle Swarm Optimized Feature Selection and Deep LSTM Networks for Text Mining Applications 477
  50. Enhanced Text Classification Using Machine Learning Techniques for Self-Learning Platform Data 489
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