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Modelling G×E with historical weather information improves genomic prediction in new environments

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dc.contributor Aalto-yliopisto fi
dc.contributor Aalto University en
dc.contributor.author Gillberg, Jussi
dc.contributor.author Marttinen, Pekka
dc.contributor.author Mamitsuka, Hiroshi
dc.contributor.author Kaski, Samuel
dc.date.accessioned 2019-11-07T12:06:38Z
dc.date.available 2019-11-07T12:06:38Z
dc.date.issued 2019-10-15
dc.identifier.citation Gillberg , J , Marttinen , P , Mamitsuka , H & Kaski , S 2019 , ' Modelling G×E with historical weather information improves genomic prediction in new environments ' , Bioinformatics (Oxford, England) , vol. 35 , no. 20 , pp. 4045-4052 . https://doi.org/10.1093/bioinformatics/btz197 en
dc.identifier.issn 1367-4803
dc.identifier.issn 1460-2059
dc.identifier.other PURE UUID: a0cacdce-96ea-4e97-8281-27ed6bba941a
dc.identifier.other PURE ITEMURL: https://research.aalto.fi/en/publications/a0cacdce-96ea-4e97-8281-27ed6bba941a
dc.identifier.other PURE LINK: http://www.scopus.com/inward/record.url?scp=85073183083&partnerID=8YFLogxK
dc.identifier.other PURE FILEURL: https://research.aalto.fi/files/38170570/btz197.pdf
dc.identifier.uri https://aaltodoc.aalto.fi/handle/123456789/41129
dc.description.abstract MOTIVATION: Interaction between the genotype and the environment (G×E) has a strong impact on the yield of major crop plants. Although influential, taking G×E explicitly into account in plant breeding has remained difficult. Recently G×E has been predicted from environmental and genomic covariates, but existing works have not shown that generalization to new environments and years without access to in-season data is possible and practical applicability remains unclear. Using data from a Barley breeding programme in Finland, we construct an in silico experiment to study the viability of G×E prediction under practical constraints. RESULTS: We show that the response to the environment of a new generation of untested Barley cultivars can be predicted in new locations and years using genomic data, machine learning and historical weather observations for the new locations. Our results highlight the need for models of G×E: non-linear effects clearly dominate linear ones, and the interaction between the soil type anddaily rain is identified as the main driver for G×E for Barley in Finland. Our study implies that genomic selection can be used to capture the yield potential in G×E effects for future growth seasons, providing a possible means to achieve yield improvements, needed for feeding the growing population. AVAILABILITY AND IMPLEMENTATION: The data accompanied by the method code (http://research.cs.aalto.fi/pml/software/gxe/bioinformatics_codes.zip) is available in the form of kernels to allow reproducing the results. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online. en
dc.format.extent 8
dc.format.extent 4045-4052
dc.format.mimetype application/pdf
dc.language.iso en en
dc.relation.ispartofseries Bioinformatics (Oxford, England) en
dc.relation.ispartofseries Volume 35, issue 20 en
dc.rights openAccess en
dc.title Modelling G×E with historical weather information improves genomic prediction in new environments en
dc.type A1 Alkuperäisartikkeli tieteellisessä aikakauslehdessä fi
dc.description.version Peer reviewed en
dc.contributor.department Centre of Excellence in Computational Inference, COIN
dc.contributor.department Professorship Marttinen P.
dc.contributor.department Probabilistic Machine Learning
dc.contributor.department Department of Computer Science
dc.identifier.urn URN:NBN:fi:aalto-201911076134
dc.identifier.doi 10.1093/bioinformatics/btz197
dc.type.version publishedVersion

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