title: Dissimilarity based Partial Least Squares (DPLS) for genomic prediction from SNPs.
abstract: Genomic prediction (GP) allows breeders to select plants and animals based on their breeding potential for desirable traits, without lengthy and expensive field trials or progeny testing. We have proposed to use Dissimilarity-based Partial Least Squares (DPLS) for GP. As a case study, we use the DPLS approach to predict Bacterial wilt (BW) in tomatoes using SNPs as predictors. The DPLS approach was compared with the Genomic Best-Linear Unbiased Prediction (GBLUP) and single-SNP regression with SNP as a fixed effect to assess the performance of DPLS.
  
  
  
  Eight genomic distance measures were used to quantify relationships between the tomato accessions from the SNPs. Subsequently, each of these distance measures was used to predict the BW using the DPLS prediction model. The DPLS model was found to be robust to the choice of distance measures; similar prediction performances were obtained for each distance measure. DPLS greatly outperformed the single-SNP regression approach, showing that BW is a comprehensive trait dependent on several loci. Next, the performance of the DPLS model was compared to that of GBLUP. Although GBLUP and DPLS are conceptually very different, the prediction quality (PQ) measured by DPLS models were similar to the prediction statistics obtained from GBLUP. A considerable advantage of DPLS is that the genotype-phenotype relationship can easily be visualized in a 2-D scatter plot. This so-called score-plot provides breeders an insight to select candidates for their future breeding program.
  
  
  
  DPLS is a highly appropriate method for GP. The model prediction performance was similar to the GBLUP and far better than the single-SNP approach. The proposed method can be used in combination with a wide range of genomic dissimilarity measures and genotype representations such as allele-count, haplotypes or allele-intensity values. Additionally, the data can be insightfully visualized by the DPLS model, allowing for selection of desirable candidates from the breeding experiments. In this study, we have assessed the DPLS performance on a single trait.
authors.affiliation: Department of Bioinformatics, Genetwister Technologies B.V., Wageningen, The Netherlands.|Radboud University Nijmegen, Institute for Molecules and Materials, Nijmegen, The Netherlands.
authors.forename: Priyanka
authors.initials: P
authors.lastname: Singh
authors.affiliation: Radboud University Nijmegen, Institute for Molecules and Materials, Nijmegen, The Netherlands.
authors.forename: Jasper
authors.initials: J
authors.lastname: Engel
authors.affiliation: Radboud University Nijmegen, Institute for Molecules and Materials, Nijmegen, The Netherlands.
authors.forename: Jeroen
authors.initials: J
authors.lastname: Jansen
authors.affiliation: Department of Bioinformatics, Genetwister Technologies B.V., Wageningen, The Netherlands.
authors.forename: Jorn
authors.initials: J
authors.lastname: de Haan
authors.affiliation: Radboud University Nijmegen, Institute for Molecules and Materials, Nijmegen, The Netherlands. l.buydens@science.ru.nl.
authors.forename: Lutgarde Maria Celina
authors.initials: LM
authors.lastname: Buydens
mesh_terms: D000465:Algorithms; D018745:Genome, Plant; D023281:Genomics / Q000379:methods*; D005838:Genotype; D016018:Least-Squares Analysis; D018551:Solanum lycopersicum / Q000235:genetics* / Q000382:microbiology; D010641:Phenotype; D010935:Plant Diseases / Q000382:microbiology*; D020641:Polymorphism, Single Nucleotide; D040641:Quantitative Trait Loci
keywords: Bacterial wilt; Dissimilarity based Partial Least Squares; Genetic distance; Genomic prediction; Phenotype prediction