Extend mixed models to multilayer neural networks for genomic prediction including intermediate omics data.
With the growing amount and diversity of intermediate omics data complementary to genomics (e.g. DNA methylation, gene expression, and protein abundance), there is a need to develop methods to incorporate intermediate omics data into conventional genomic evaluation. The omics data help decode the mu...
| Published in: | Genetics Vol. 221; no. 1; pp. 1 - 11 |
|---|---|
| Main Authors: | , , |
| Format: | equations & formulas research tables/charts Journal Article |
| Published: |
Oxford University Press / USA
May2022
|
| Online Access: | View this record in EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=156775173&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 156775173 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00166731 GNT jtl: Genetics issn: 00166731 maglogo: N pubinfo: dt: May2022 vid: 221 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 156775173 156775173 156775173 10.1093/genetics/iyac034 156775173 ppf: 1 ppct: 10 formats: tig: atl: Extend mixed models to multilayer neural networks for genomic prediction including intermediate omics data. aug: au: Tianjing Zhao Jian Zeng Hao Cheng affil: Department of Animal Science, University of California Davis, Davis, CA 95616, USA sug: subj: Neural Networks (Computer) Genomics Bioinformatics Models, Statistical Human Genotype Phenotype Predictive Validity ab: With the growing amount and diversity of intermediate omics data complementary to genomics (e.g. DNA methylation, gene expression, and protein abundance), there is a need to develop methods to incorporate intermediate omics data into conventional genomic evaluation. The omics data help decode the multiple layers of regulation from genotypes to phenotypes, thus forms a connected multilayer network naturally. We developed a new method named NN-MM to model the multiple layers of regulation from genotypes to intermediate omics features, then to phenotypes, by extending conventional linear mixed models ("MM") to multilayer artificial neural networks ("NN"). NN-MM incorporates intermediate omics features by adding middle layers between genotypes and phenotypes. Linear mixed models (e.g. pedigree-based BLUP, GBLUP, Bayesian Alphabet, single-step GBLUP, or single-step Bayesian Alphabet) can be used to sample marker effects or genetic values on intermediate omics features, and activation functions in neural networks are used to capture the nonlinear relationships between intermediate omics features and phenotypes. NN-MM had significantly better prediction performance than the recently proposed single-step approach for genomic prediction with intermediate omics data. Compared to the single-step approach, NN-MM can handle various patterns of missing omics measures and allows nonlinear relationships between intermediate omics features and phenotypes. NN-MM has been implemented in an open-source package called "JWAS". pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|