Jean Luc Jannink: The road to genomic prediction in cassava

Introduction

This tutorial provides a step-by-step guide based on Dr. Jean Luc Jannink's presentation about genomic prediction in cassava. Understanding this process is crucial for improving cassava breeding programs and optimizing yield and disease resistance in this vital crop.

Step 1: Understanding Genomic Prediction

• Definition: Genomic prediction is a method that uses genetic information to predict the performance of individuals in a breeding program.
• Importance: This approach leads to more accurate selection of desirable traits, reducing the time and resources needed for traditional breeding.
• Applications: Utilize genomic prediction to enhance traits such as disease resistance, yield, and adaptability to different environments.

Step 2: Collecting Genomic Data

• Genotype Selection: Choose representative samples of cassava that reflect the diversity within the population.
• DNA Extraction: Use standardized methods for extracting DNA to ensure quality and reliability of the genomic data.
• Genotyping: Perform genotyping using high-throughput sequencing or SNP arrays to gather comprehensive genetic information.

Step 3: Phenotype Assessment

• Trait Measurement: Collect data on relevant phenotypic traits (e.g., growth rate, disease resistance).
• Field Trials: Conduct field trials to observe the performance of cassava varieties under different conditions.
• Data Recording: Ensure meticulous recording of phenotypic data for subsequent analysis.

Step 4: Statistical Modeling

• Model Selection: Choose appropriate statistical models for genomic prediction, such as GBLUP (Genomic Best Linear Unbiased Prediction).
• Software Tools: Utilize software like R or ASReml for data analysis and model implementation.
• Parameter Estimation: Estimate genetic parameters that are essential for accurate predictions.

Step 5: Prediction and Validation

• Predictive Analysis: Use the statistical models to predict the performance of new cassava genotypes based on their genomic data.
• Validation: Compare predicted results with actual performance in field trials to validate the accuracy of the predictions.
• Refinement: Continuously refine models based on validation outcomes to improve prediction reliability.

Step 6: Integration into Breeding Programs

• Selection Process: Integrate genomic predictions into the breeding selection process to enhance decision-making.
• Training Breeders: Educate plant breeders on how to interpret genomic data and predictions effectively.
• Monitoring Progress: Regularly monitor and evaluate the progress of breeding programs using genomic prediction insights.

Conclusion

Genomic prediction represents a transformative approach in cassava breeding, enabling more efficient selection of desirable traits. By understanding and applying the steps outlined—from data collection to integration into breeding programs—you can significantly enhance the performance and sustainability of cassava crops. Consider exploring additional resources or training opportunities to deepen your understanding of genomic techniques in plant breeding.