Genome-Enabled Prediction Methods Based on Machine Learning

Reinoso-Peláez, Edgar L.; Gianola, Daniel; González-Recio, Óscar

doi:10.1007/978-1-0716-2205-6_7

Cited by 10 publications

(7 citation statements)

References 109 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…In general, there is no consensus on which GP method is best. A recent review by Reinoso-Peláez et al ( 2022 ) points at Reproducible Kernel Hilbert Space (RKHS) as the best overall method in plants. But there is variability.…”

Section: Introductionmentioning

confidence: 99%

Transposable element polymorphisms improve prediction of complex agronomic traits in rice

et al. 2022

View full text Add to dashboard Cite

Key message Transposon insertion polymorphisms can improve prediction of complex agronomic traits in rice compared to using SNPs only, especially when accessions to be predicted are less related to the training set. Abstract Transposon insertion polymorphisms (TIPs) are significant sources of genetic variation. Previous work has shown that TIPs can improve detection of causative loci on agronomic traits in rice. Here, we quantify the fraction of variance explained by single nucleotide polymorphisms (SNPs) compared to TIPs, and we explore whether TIPs can improve prediction of traits when compared to using only SNPs. We used eleven traits of agronomic relevance from by five different rice population groups (Aus, Indica, Aromatic, Japonica, and Admixed), 738 accessions in total. We assess prediction by applying data split validation in two scenarios. In the within-population scenario, we predicted performance of improved Indica varieties using the rest of Indica accessions. In the across population scenario, we predicted all Aromatic and Admixed accessions using the rest of populations. In each scenario, Bayes C and a Bayesian reproducible kernel Hilbert space regression were compared. We find that TIPs can explain an important fraction of total genetic variance and that they also improve genomic prediction. In the across population prediction scenario, TIPs outperformed SNPs in nine out of the eleven traits analyzed. In some traits like leaf senescence or grain width, using TIPs increased predictive correlation by 30–50%. Our results evidence, for the first time, that TIPs genotyping can improve prediction on complex agronomic traits in rice, especially when accessions to be predicted are less related to training accessions.

show abstract

Section: Introductionmentioning

confidence: 99%

Transposable element polymorphisms improve prediction of complex agronomic traits in rice

et al. 2022

View full text Add to dashboard Cite

show abstract

“…This held across all the phenotypic scenarios that were simulated. This contrasting behavior of GBLUP vs deep learning models has been already observed in several studies on whole-genome predictions in animals and plants 53 .…”

Section: Discussionmentioning

confidence: 55%

Stacked kinship CNN vs. GBLUP for genomic predictions of additive and complex continuous phenotypes

Biscarini

2022

Sci Rep

View full text Add to dashboard Cite

Deep learning is impacting many fields of data science with often spectacular results. However, its application to whole-genome predictions in plant and animal science or in human biology has been rather limited, with mostly underwhelming results. While most works focus on exploring alternative network architectures, in this study we propose an innovative representation of marker genotype data and tested it against the GBLUP (Genomic BLUP) benchmark with linear and nonlinear phenotypes. From publicly available cattle SNP genotype data, different types of genomic kinship matrices are stacked together in a 3D pile from where 2D grayscale slices are extracted and fed to a deep convolutional neural network (DNN). We simulated nine phenotype scenarios with combinations of additivity, dominance and epistasis, and compared the DNN to GBLUP-A (computed using only the additive kinship matrix) and GBLUP-optim (additive, dominance, and epistasis kinship matrices, as needed). Results varied depending on the accuracy metric employed, with DNN performing better in terms of root mean squared error (1–12% lower than GBLUP-A; 1–9% lower than GBLUP-optim) but worse in terms of Pearson’s correlation (0.505 for DNN compared to 0.672 and 0.669 of GBLUP-A and GBLUP-optim for fully additive case; 0.274 for DNN, 0.279 for GBLUP-A, and 0.477 for GBLUP-optim for fully dominant case). The proposed approach offers a basis to explore further the application of DNN to tabular data in whole-genome predictions.

show abstract

Section: Metrics: How Prediction Accuracy Is Measuredmentioning

confidence: 55%

Stacked kinship CNN vs. GBLUP for genomic predictions of additive and complex continuous phenotypes

Biscarini

2022

Preprint

View full text Add to dashboard Cite

Deep learning is impacting many fields of data science with often spectacular results. However, its application to whole-genomepredictions in plant and animal science or in human biology has been rather limited, with mostly underwhelming results.While most works focus on exploring alternative network architectures, in this study we propose an innovative representation ofmarker genotype data. Different types of genomic kinship matrices are stacked together in a 3D pile from where 2D grayscaleslices are extracted and fed to a deep convolutional neural network (DNN). We tested nine scenarios of simulated phenotypeswith combinations of additivity, dominance and epistasis, and compared the DNN to GBLUP-A (Genomic BLUP computedusing only the additive kinship matrix) and GBLUP-optim (Genomic BLUP with additive, dominance, and epistasis kinshipmatrices, as needed).Results varied depending on the accuracy metric employed, with DNN performing better in terms of Root Mean Squared Error(1%-12% lower than GBLUP-A; 1%-9% lower than GBLUP-optim) but worse in terms of Pearson’s correlation (0.505 for DNNcompared to 0.672 and 0.669 of GBLUP-A and GBLUP-optim for fully additive case; 0.274 for DNN, 0.279 for GBLUP-A, and0.477 for GBLUP-optim for fully dominant case).

show abstract

Genome-Enabled Prediction Methods Based on Machine Learning

Cited by 10 publications

References 109 publications

Transposable element polymorphisms improve prediction of complex agronomic traits in rice

Transposable element polymorphisms improve prediction of complex agronomic traits in rice

Stacked kinship CNN vs. GBLUP for genomic predictions of additive and complex continuous phenotypes

Stacked kinship CNN vs. GBLUP for genomic predictions of additive and complex continuous phenotypes

Contact Info

Product

Resources

About