Trait Specific Breeding Tool Development

CITRLS HLB Genomic Selection Workflow

1. DNA Sequencing
   Citrus samples are sequenced to generate genome-wide variation data.

2. Marker Discovery (SNP Identification)
   Key SNPs associated with HLB tolerance are identified from sequencing data.

3. Genomic Prediction Model
   Machine learning/statistical models use SNP data to predict resistance and breeding performance.

4. Trait Scoring & Accuracy Validation
   Each plant is assigned a resistance score (e.g., high resistance ~94%), and model accuracy is evaluated (e.g., R² ≈ 0.87).

5. Breeding Decision Dashboard
   Top parent plants are selected based on predicted performance, optimizing crossing strategies.

6. Resistance/Yield Gain Projection
   Expected improvement in yield and resistance is estimated (e.g., +20% gain).

7. Experimental Validation
   Selected SNPs are validated through lab assays.

8. CRISPR / Targeted Editing (Optional)
   Candidate genes are edited to enhance HLB resistance.