2021
DOI: 10.1101/2021.05.20.445038
|View full text |Cite
Preprint
|
Sign up to set email alerts
|

Deep Neural Networks for Genomic Prediction Do Not Estimate Marker Effects

Abstract: Genomic prediction is a promising technology for advancing both plant and animal breeding, with many different prediction models evaluated in the literature. It has been suggested that the ability of powerful nonlinear models such as deep neural networks to capture complex epistatic effects between markers offers advantages for genomic prediction. However, these methods tend not to outperform classical linear methods, leaving it an open question why this capacity to model nonlinear effects does not seem to res… Show more

Help me understand this report
View published versions

Search citation statements

Order By: Relevance

Paper Sections

Select...
2
1
1
1

Citation Types

2
3
0

Year Published

2022
2022
2022
2022

Publication Types

Select...
3
1

Relationship

0
4

Authors

Journals

citations
Cited by 4 publications
(5 citation statements)
references
References 20 publications
2
3
0
Order By: Relevance
“…Instead, it performs comparably to other non-linear methods, such as the non-linear SVM and the Decision Tree Classifier, even with the regularization features ( i.e ., batch normalization and dropout) that aid the same architecture in achieving top-5 success. These results are consistent with other work that show linear techniques outperform or match performance of more complex non-linear methods [5], [10], [11]. We believe that linear models outperform non-linear ones because the relationship between dog SNP sequences and breeds, given enough genomic positions, is additive.…”
Section: Resultssupporting
confidence: 91%
See 1 more Smart Citation
“…Instead, it performs comparably to other non-linear methods, such as the non-linear SVM and the Decision Tree Classifier, even with the regularization features ( i.e ., batch normalization and dropout) that aid the same architecture in achieving top-5 success. These results are consistent with other work that show linear techniques outperform or match performance of more complex non-linear methods [5], [10], [11]. We believe that linear models outperform non-linear ones because the relationship between dog SNP sequences and breeds, given enough genomic positions, is additive.…”
Section: Resultssupporting
confidence: 91%
“…On the other hand, non-linear techniques show strong performance for regression tasks. Recent work confirms these trends [5], [10], [11].…”
Section: Introductionsupporting
confidence: 58%
“…Another consideration is recent evidence to suggest that DL models do not estimate complex marker effects, but rather use the genetic relatedness between markers to make predictions. This may partially explain the underperformance of DL in crop genomic prediction problems [38].…”
Section: Machine Learning Performancementioning
confidence: 99%
“…In a recent study 29 it has been proposed the principle of shortcut learning to explain why deep learning does not outperform penalized regression methods. Briefly, it is theorized that neural networks tend to base their predictions on overall genetic relatedness rather than on the effects of specific markers with, say, epistatic effects.…”
Section: Comparison To the State Of The Artmentioning
confidence: 99%