Transcriptome-Based Prediction of Complex Traits in Maize

Abstract

The ability to predict traits from genome-wide sequence information (i.e., genomic prediction) has improved our understanding of the genetic basis of complex traits and transformed breeding practices. Transcriptome data may also be useful for genomic prediction. However, it remains unclear how well transcript levels can predict traits, particularly when traits are scored at different development stages. Using maize (Zea mays) genetic markers and transcript levels from seedlings to predict mature plant traits, we found that transcript and genetic marker models have similar performance. When the transcripts and genetic markers with the greatest weights (i.e., the most important) in those models were used in one joint model, performance increased. Furthermore, genetic markers important for predictions were not close to or identified as regulatory variants for important transcripts. These findings demonstrate that transcript levels are useful for predicting traits and that their predictive power is not simply due to genetic variation in the transcribed genomic regions. Finally, genetic marker models identified only 1 of 14 benchmark flowering-time genes, while transcript models identified 5. These data highlight that, in addition to being useful for genomic prediction, transcriptome data can provide a link between traits and variation that cannot be readily captured at the sequence level.

Leveraging Multiple Layers of Data To Predict Drosophila Complex Traits

“…These results indicate that the genome and transcriptome may contribute largely overlapping information, with the key player being dependent on the trait analyzed. This contrasts with previous results ( Guo et al 2016 ; Li et al 2019 ; Azodi et al 2020 ) in which the genome consistently explained more variance than the transcriptome in all models, and the interaction term contributed to explaining more variance for almost all of traits analyzed ( Guo et al 2016 ).…”

“…In summary, this study has confirmed that using transcriptomic data to predict quantitative trait phenotypes is promising for some traits. Our work, together with other studies ( Finucane et al 2015 ; Edwards et al 2016 ; Abdollahi-Arpanahi et al 2017 ; Azodi et al 2020 ), has shown that integrating omic data together with functional annotation can identify features that are important to understand and predict complex traits. However, there are several improvements to the experimental design that can be made in the future that may further increase predictive ability and consequently our understanding of the genetic basis of variation in quantitative traits.…”

Merging Genomics and Transcriptomics for Predicting Fusarium Head Blight Resistance in Wheat

“…A fundamental question when using transcription data for making predictions is the choice of an appropriate time-point [ 35 , 50 ] and tissue for RNA sampling [ 25 ], since transcriptome analyses are only reflecting snapshots of the concerted gene expression. Nevertheless, samples for transcriptomic-based predictions were taken from seeds [ 19 ], whole-seedlings [ 51 , 52 ], flag leaves of adult plants [ 22 ], or from ears/heads as in the study at hand. The potential targets of such predictions are ranging from characteristics assessed in the seedling stage, e.g., juvenile growth to agronomic traits measured on mature plants like grain yield.…”

Expression of fatty acid and triacylglycerol synthesis genes in interspecific hybrids of oil palm

“…Expression profiles of the specific isoforms of candidate genes like SAD, FATB and LACS4 provide another layer of support for the association of the markers defining the QTL regions to FAC in oil palm interspecific hybrids and their backcrosses. However, as pointed out by Azodi et al 51 , genomic regions defined by genetic markers may not capture the entire variation associated with complex traits. Transcriptome analysis in combination with DNA markers using appropriate statistical models is probably Figure 4.…”