• Home
  • /
  • AI in Plant Genomics: GWAS, Genomic Prediction & Climate-Resilient Crop Breeding
Biotechnology · Moderate · Live online

AI in Plant Genomics: GWAS, Genomic Prediction & Climate-Resilient Crop Breeding

From Genetic Discovery to Climate-Resilient Crops — Unlock GWAS, AI-Powered Genomic Prediction & Precision Breeding.

18–20 Nov 20265:30 PM IST3 Days (60-90 Min)Certificate

About This Course

This advanced hands-on workshop focuses on integrating plant genomic data, statistical genetics, and machine learning to support modern crop improvement.

Participants will work with real crop genomics datasets to identify trait-associated SNP markers, develop genomic prediction models, and evaluate breeding lines across different environments.

The workshop follows a complete research workflow: Genetic Discovery → Genomic Prediction → Climate-Resilient Crop Selection.

Aim

To provide researchers with practical experience in plant genome-wide association studies (GWAS), AI-based genomic prediction, and genotype-by-environment (G×E) analysis for developing climate-resilient crop breeding strategies.

Workshop Objectives

  • Understand the application of plant genomics, GWAS, and quantitative genetics in modern crop improvement.
  • Perform SNP quality control, population structure analysis, and genome-wide association studies.
  • Apply statistical genetics and machine learning for genomic prediction and genomic selection.
  • Develop and evaluate GBLUP, ridge regression, and Random Forest models for crop trait prediction.
  • Integrate genomic, phenotypic, and environmental datasets to investigate genotype-by-environment interactions.
  • Explore AI-assisted breeding strategies for improving crop productivity, environmental adaptability, and stress resilience.
Next: Day-by-day →

Workshop Structure

Day 1: Plant GWAS, SNP Analysis & Trait-Associated Gene Discovery

Topics Covered

  • Plant genomic datasets, SNP markers and quantitative trait analysis
  • SNP quality control, filtering and population structure analysis
  • Genome-wide association studies (GWAS) using GAPIT
  • Manhattan plots, Q–Q plots and candidate genomic region identification
  • Functional annotation of significant SNPs and candidate gene prioritization

Hands-On: Analyze a real wheat genomic dataset, perform SNP filtering and PCA, run GWAS, visualize genetic associations and identify candidate genomic regions.

Tools: R, GAPIT, tidyverse, RStudio

Day 2: AI-Based Genomic Prediction, Machine Learning & Genomic Selection

Topics Covered

  • Genomic selection and genome-wide marker-based prediction
  • Genomic BLUP (GBLUP) and ridge regression models
  • Random Forest for genomic trait prediction
  • Model validation, predictive accuracy and breeding value estimation
  • Feature importance analysis and interpretation of genomic prediction models

Hands-On: Build genomic prediction models using SNP markers, compare GBLUP and Random Forest performance, evaluate predictive accuracy and rank candidate breeding lines.

Tools: R, rrBLUP, Python, Scikit-learn, Google Colab

Day 3: Genotype-by-Environment Modeling, Climate Adaptation & AI-Driven Crop Selection

Topics Covered

  • Genotype-by-environment (G×E) interactions and crop adaptability
  • Integration of genomic, phenotypic and environmental data
  • Machine learning for multi-environment crop performance prediction
  • Yield stability analysis and AI-assisted breeding line prioritization
  • Multi-environment genomic selection and identification of climate-adaptive breeding candidates

Hands-On: Integrate genomic and environmental datasets, predict crop performance across environments, assess yield stability and prioritize promising breeding lines.

Tools: Python, Pandas, Scikit-learn, Matplotlib, Google Colab

Next: Dates →

Important Dates

Registration Ends

November 20, 2026
IST 4:00 PM

Workshop Dates

November 18, 2026 – November 20, 2026
IST 5:30 PM

Workshop Outcomes

  • Analyze Plant Genomic Data: Process SNP datasets, perform quality control, and investigate population structure.
  • Perform GWAS: Identify trait-associated SNP markers, interpret Manhattan and Q–Q plots, and prioritize candidate genomic regions.
  • Build Genomic Prediction Models: Implement statistical and machine-learning approaches to predict complex crop traits.
  • Evaluate Predictive Performance: Compare genomic prediction models using appropriate validation methods and accuracy metrics.
  • Analyze Environmental Adaptability: Investigate genotype-by-environment interactions and evaluate crop performance across multiple environments.
  • Prioritize Breeding Candidates: Apply genomic prediction and yield-stability analysis to identify promising crop lines for further breeding and validation.
Next: Fees →

Fee Structure

Student Fee

₹2,499 | $65

Enrol as Student

Ph.D. Scholar / Researcher Fee

₹3,499 | $75

Enrol as Ph.D. Scholar / Researcher

Academician / Faculty Fee

₹4,499 | $85

Enrol as Academician / Faculty

Industry Professional Fee

₹5,499 | $115

Enrol as Industry Professional

Next: FAQ →

Frequently asked questions

Will I get the recording?

Yes. Paid participants get the session recordings and study material in their NanoSchool dashboard (My registrations → Recorded Lectures / Study Material).

What if I miss a live session?

Watch the recording in your dashboard and send your questions to the mentor through the support desk.

Do I get a certificate?

Yes, an e-certificate of participation after the workshop, with a QR code anyone can verify and an "Add to LinkedIn" button.

Which fee category applies to me?

Choose the category that matches your current role: Student, Ph.D. Scholar / Researcher, Academician / Faculty or Industry Professional.

How can I pay?

By card, UPI or PayPal on the payment page; international bank transfer is also available there.

What is the refund policy?

See our Returns, Refunds & Cancellation policy: Returns, Refunds & Cancellation policy

Need Help?

We’re here for you!


(+91) 120-4781-217
WhatsApp +91 99581 61117

Registration open

More upcoming workshops

Live, mentor-led and certified. Dates and times in IST.

See all workshops
Support