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 effec...

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Autores principales:	Jordan Ubbens, Isobel Parkin, Christina Eynck, Ian Stavness, Andrew G. Sharpe
Formato:	article
Lenguaje:	EN
Publicado:	Wiley 2021
Materias:	Plant culture SB1-1110 Genetics QH426-470
Acceso en línea:	https://doaj.org/article/33a2fce533d7499b90a08552cbaacd06
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id	oai:doaj.org-article:33a2fce533d7499b90a08552cbaacd06
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spelling	oai:doaj.org-article:33a2fce533d7499b90a08552cbaacd062021-12-05T07:50:11ZDeep neural networks for genomic prediction do not estimate marker effects1940-337210.1002/tpg2.20147https://doaj.org/article/33a2fce533d7499b90a08552cbaacd062021-11-01T00:00:00Zhttps://doi.org/10.1002/tpg2.20147https://doaj.org/toc/1940-3372Abstract 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 result in better predictive capability. In this work, we propose the theory that, because of a previously described principle called shortcut learning, deep neural networks tend to base their predictions on overall genetic relatedness rather than on the effects of particular markers such as epistatic effects. Using several datasets of crop plants [lentil (Lens culinaris Medik.), wheat (Triticum aestivum L.), and Brassica carinata A. Braun], we demonstrate the network's indifference to the values of the markers by showing that the same network, provided with only the locations of matches between markers for two individuals, is able to perform prediction to the same level of accuracy.Jordan UbbensIsobel ParkinChristina EynckIan StavnessAndrew G. SharpeWileyarticlePlant cultureSB1-1110GeneticsQH426-470ENThe Plant Genome, Vol 14, Iss 3, Pp n/a-n/a (2021)
institution	DOAJ
collection	DOAJ
language	EN
topic	Plant culture SB1-1110 Genetics QH426-470
spellingShingle	Plant culture SB1-1110 Genetics QH426-470 Jordan Ubbens Isobel Parkin Christina Eynck Ian Stavness Andrew G. Sharpe Deep neural networks for genomic prediction do not estimate marker effects
description	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 result in better predictive capability. In this work, we propose the theory that, because of a previously described principle called shortcut learning, deep neural networks tend to base their predictions on overall genetic relatedness rather than on the effects of particular markers such as epistatic effects. Using several datasets of crop plants [lentil (Lens culinaris Medik.), wheat (Triticum aestivum L.), and Brassica carinata A. Braun], we demonstrate the network's indifference to the values of the markers by showing that the same network, provided with only the locations of matches between markers for two individuals, is able to perform prediction to the same level of accuracy.
format	article
author	Jordan Ubbens Isobel Parkin Christina Eynck Ian Stavness Andrew G. Sharpe
author_facet	Jordan Ubbens Isobel Parkin Christina Eynck Ian Stavness Andrew G. Sharpe
author_sort	Jordan Ubbens
title	Deep neural networks for genomic prediction do not estimate marker effects
title_short	Deep neural networks for genomic prediction do not estimate marker effects
title_full	Deep neural networks for genomic prediction do not estimate marker effects
title_fullStr	Deep neural networks for genomic prediction do not estimate marker effects
title_full_unstemmed	Deep neural networks for genomic prediction do not estimate marker effects
title_sort	deep neural networks for genomic prediction do not estimate marker effects
publisher	Wiley
publishDate	2021
url	https://doaj.org/article/33a2fce533d7499b90a08552cbaacd06
work_keys_str_mv	AT jordanubbens deepneuralnetworksforgenomicpredictiondonotestimatemarkereffects AT isobelparkin deepneuralnetworksforgenomicpredictiondonotestimatemarkereffects AT christinaeynck deepneuralnetworksforgenomicpredictiondonotestimatemarkereffects AT ianstavness deepneuralnetworksforgenomicpredictiondonotestimatemarkereffects AT andrewgsharpe deepneuralnetworksforgenomicpredictiondonotestimatemarkereffects
_version_	1718372546923986944

Deep neural networks for genomic prediction do not estimate marker effects

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