Genomic prediction using DArT-Seq technology for yellowtail kingfish Seriola lalandi

Area of Science:

• Aquaculture
• Genomics
• Animal Breeding

Background:

• Genomic prediction using Diversity Arrays Technology (DArT) genotype by sequencing has not been previously reported in yellowtail kingfish (Seriola lalandi).
• A knowledge gap exists regarding the application of genomic selection for commercially important traits in this species.

Purpose of the Study:

• To assess the predictive ability of genomic Best Linear Unbiased Prediction (gBLUP) for growth traits in yellowtail kingfish.
• To evaluate the effectiveness of gBLUP using DArT sequencing data in a population of 752 individuals.

Main Methods:

• Genomic Best Linear Unbiased Prediction (gBLUP) was employed for its computational efficiency.
• Phenotypic and DNA sequence data for growth traits (body weight, fork length, condition index) were analyzed.
• Prediction accuracy was estimated using a five-fold cross-validation approach.

Main Results:

• The gBLUP model demonstrated moderate to high prediction accuracy (0.44–0.69) for growth-related traits.
• Imputing missing genotype data improved predictive ability for body weight by 17.0% (0.69 to 0.83).
• The coefficient of determination (R²) ranged from 0.49 to 0.71, indicating good performance for growth traits.

Conclusions:

• Genomic selection using gBLUP shows significant potential for future breeding programs in yellowtail kingfish (Seriola lalandi).
• This study provides the first evidence of applying genomic selection for growth-related traits in this species.