Using convolutional neural networks for image-based genomic prediction in mice

B.C. Perez, A. Savchuk, P. Duenk, M.P.L. Calus, M.C.A.M. Bink

Research output: Chapter in Book/Report/Conference proceedingConference paperAcademic

Abstract

Convolutional neural networks (CNN) are well suited image recognition tools for their ability to recognize latent patterns from images. Here, we investigated whether CNN can be used for genomic prediction. We created genomic images from genotype data and used them to predict phenotypes in mice. This approach was compared with traditional GBLUP and gradient boosting machine (GBM) models. For the two traits analysed, CNN was competitive in terms of predictive performance. The resolution of genomic images impacted model performance where, for this dataset, optimum results were obtained with 100×100 pixels. These first results demonstrate the potential of genomic images for genomic prediction using CNNs and merit investigation on adding layers of information to further increase accuracy of prediction.
Original languageEnglish
Title of host publicationProceedings of 12th World Congress on Genetics Applied to Livestock Production (WCGALP)
Subtitle of host publicationTechnical and species orientated innovations in animal breeding, and contribution of genetics to solving societal challenges
EditorsR.F. Veerkamp, Y. de Haas
Place of PublicationWageningen
PublisherWageningen Academic Publishers
Pages1233-1236
ISBN (Electronic)9789086869404
DOIs
Publication statusPublished - 2022
EventWorld Congress on Genetics Applied to Livestock Production: WCGALP 2022 - Rotterdam, Netherlands
Duration: 3 Jul 20228 Jul 2022

Conference/symposium

Conference/symposiumWorld Congress on Genetics Applied to Livestock Production: WCGALP 2022
Country/TerritoryNetherlands
CityRotterdam
Period3/07/228/07/22

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