Optimal breeding-value prediction using a sparse selection index.
Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and linkage disequilibrium patterns can lead to heterogeneity in...
| Publicado en: | Genetics Vol. 218; no. 1; pp. 1 - 11 |
|---|---|
| Autores principales: | , |
| Formato: | equations & formulas research tables/charts Journal Article |
| Publicado: |
Oxford University Press / USA
May2021
|
| Acceso en línea: | Ver este registro en EBSCOhost |
| fields | @attributes: recordID: 1 pdfLink: plink: https://search.ebscohost.com/login.aspx?direct=true&db=ccm&AN=151323067&site=ehost-live header: @attributes: shortDbName: ccm uiTerm: 151323067 longDbName: CINAHL Complete uiTag: AN controlInfo: bkinfo: dissinfo: jinfo: jid: 00166731 GNT jtl: Genetics issn: 00166731 maglogo: N pubinfo: dt: May2021 vid: 218 iid: 1 pid: 622 pub: Oxford University Press / USA artinfo: ui: 151323067 151323067 151323067 10.1093/genetics/iyab030 151323067 ppf: 1 ppct: 10 formats: tig: atl: Optimal breeding-value prediction using a sparse selection index. aug: au: Lopez-Cruz, Marco de los Campos, Gustavo affil: Department of Plant, Soil and Microbial Sciences, Michigan State University, East Lansing, MI 48824, USA sug: subj: Reproduction Techniques Genomics DNA Sequence Analysis Methods Computer Simulation Methods Prediction Models Genetics Human Alleles Sensitivity and Specificity Phenotype Polymorphism, Single Nucleotide Reproducibility of Results Regression ab: Genomic prediction uses DNA sequences and phenotypes to predict genetic values. In homogeneous populations, theory indicates that the accuracy of genomic prediction increases with sample size. However, differences in allele frequencies and linkage disequilibrium patterns can lead to heterogeneity in SNP effects. In this context, calibrating genomic predictions using a large, potentially heterogeneous, training data set may not lead to optimal prediction accuracy. Some studies tried to address this sample size/homogeneity trade-off using training set optimization algorithms; however, this approach assumes that a single training data set is optimum for all individuals in the prediction set. Here, we propose an approach that identifies, for each individual in the prediction set, a subset from the training data (i.e., a set of support points) from which predictions are derived. The methodology that we propose is a sparse selection index (SSI) that integrates selection index methodology with sparsity-inducing techniques commonly used for high-dimensional regression. The sparsity of the resulting index is controlled by a regularization parameter (k); the G-Best Linear Unbiased Predictor (G-BLUP) (the prediction method most commonly used in plant and animal breeding) appears as a special case which happens when λ = 0. In this study, we present the methodology and demonstrate (using two wheat data sets with phenotypes collected in 10 different environments) that the SSI can achieve significant (anywhere between 5 and 10%) gains in prediction accuracy relative to the G-BLUP. pubtype: Academic Journal doctype: equations & formulas research tables/charts Journal Article ougenre: Article language: English refInfo: holdings: @attributes: islocal: N |
|---|