Transcriptome-based prediction of complex traits in maize

Abstract

The ability to predict traits from genome-wide sequence information (Genomic Prediction, GP), has improved our understanding of the genetic basis of complex traits and transformed breeding practices. Transcriptome data may also be useful for GP. However, it remains unclear how well transcript levels can predict traits, particularly when traits are scored at different development stages. Using maize genetic markers and transcript levels from seedlings to predict mature plant traits, we found transcript and genetic marker models have similar performance. Surprisingly, genetic markers important for predictions were not close to or identified as regulatory variants for important transcripts. Thus, transcript levels are predictive not simply due to genetic variation. Furthermore, genetic marker models identified only one of 14 benchmark flowering time genes, while transcript models identified five. Our findings highlight that transcriptome data is useful for GP and can provide a link between traits and variation that cannot be readily captured at the sequence level.