Adding gene transcripts into genomic prediction improves accuracy and reveals sampling time dependence

Abstract

Recent developments allowed generating multiple high quality ‘omics’ data that could increase predictive performance of genomic prediction for phenotypes and genetic merit in animals and plants. Here we have assessed the performance of parametric and non-parametric models that leverage transcriptomics in genomic prediction for 13 complex traits recorded in 478 animals from an outbred mouse population. Parametric models were implemented using best linear unbiased prediction (BLUP), while non-parametric models were implemented using the gradient boosting machine algorithm (GBM). We also propose a new model named GTCBLUP that aims to remove between-omics-layer covariance from predictors, whereas its counterpart GTBLUP does not do that. While GBM models captured more phenotypic variation, their predictive performance did not exceed the BLUP models for most traits. Models leveraging gene transcripts captured higher proportions of the phenotypic variance for almost all traits when these were measured closer to the moment of measuring gene transcripts in the liver. In most cases, the combination of layers was not able to outperform the best single-omics models to predict phenotypes. Using only gene transcripts, the GBM model was able to outperform BLUP for most traits except body weight, but the same pattern was not observed when using both SNP genotypes and gene transcripts. Although the GTCBLUP model was not able to produce the most accurate phenotypic predictions, it showed highest accuracies for breeding values for 9 out of 13 traits. We recommend using the GTBLUP model for prediction of phenotypes and using the GTCBLUP for prediction of breeding values.

Genomic evaluation methods to include intermediate correlated features such as high-throughput or omics phenotypes

“…Now we provide some examples with actual and invented values. For instance, Guo et al (2016) analyzed a trait (days to silking) with for which the heritability estimate dropped to after fitting transcriptome measurements, which were highly explanatory and were themselves quite heritable In a study in mice, Perez et al (2022) report for the trait BW10, whereas and from which we deduced …”

“…Finally, there is abundant literature related to phenotype prediction ( Guo et al, 2016 ; Lane et al, 2020 ; Perez et al, 2022 ) but the genetic interpretation of the phenotype prediction in that literature is very scarce. In crop breeding ( Guo et al, 2016 ; Hayes et al, 2017 ; Rincent et al, 2018 ), obtaining biochemical measures from grains is easy.…”

Leveraging Transcriptomics-Based Approaches to Enhance Genomic Prediction: Integrating SNP weights and gene-networks for Cotton Fibre Quality Improvement

“…One strategy for this is to measure the transcriptome data for the lines available for the GP study and then integrate the transcriptome and SNP data to jointly conduct prediction analysis (e.g. Hu et al 2019; Perez et al 2022).…”

A Bayesian model for genomic prediction using metabolic networks

“…It has also been explored how to utilize information other than the genome to enhance prediction accuracy. The typical additional information is “omics data.” For example, transcriptome (Li et al ., 2019; Perez et al ., 2022) and metabolome (Riedelsheimer et al ., 2012; Campbell et al ., 2021) are often used together with the genome, and some studies have used both (Xu et al ., 2016; Schrag et al ., 2018). Furthermore, because data in biology are essentially multivariate (i.e., multiple traits can be measured for a genotype at multiple environments), it has been of interest to learn how to utilize multivariate information to improve prediction accuracy for target traits (e.g., Jia and Jannink, 2012; Jarquin et al ., 2016; Atanda et al ., 2022).…”