Integrating Gene Expression Data Into Genomic Prediction.

Area of Science:

• Quantitative genetics
• Genomics
• Animal breeding

Background:

• Gene expression profiles offer potential for predicting breeding values and phenotypes.
• Previous studies have explored genomic prediction using single nucleotide polymorphisms (SNPs).

Purpose of the Study:

• To evaluate the utility of transcriptome data for phenotype prediction in Drosophila melanogaster.
• To compare the predictive abilities of genomic best linear unbiased prediction (GBLUP) with transcriptome-augmented methods (GTBLUP and GRBLUP).

Main Methods:

• Utilized transcriptome and SNP data from 185 inbred lines of Drosophila melanogaster across nine traits and two sexes.
• Incorporated transcriptome data into GBLUP using linear (GTBLUP) and Gaussian (GRBLUP) kernels.
• Assessed predictive abilities and phenotypic variance explained by different models.

Main Results:

• GRBLUP and GBLUP showed similar predictive abilities for most traits.
• GRBLUP explained a greater proportion of phenotypic variance compared to GBLUP.
• A significant improvement in predictive ability was observed for olfactory perception to Ethyl Butyrate in females using GRBLUP.

Conclusions:

• Transcriptome data has the potential to improve genomic predictions, especially when integrated on a larger scale.
• The concept of 'omics-augmented broad sense heritability' is relevant for multi-omics predictions.
• Further research is needed to optimize the inclusion of transcriptome data in breeding value prediction.