title: Sequence- vs. chip-assisted genomic selection: accurate biological information is advised.
abstract: BACKGROUND
  The development of next-generation sequencing technologies (NGS) has made the use of whole-genome sequence data for routine genetic evaluations possible, which has triggered a considerable interest in animal and plant breeding fields. Here, we investigated whether complete or partial sequence data can improve upon existing SNP (single nucleotide polymorphism) array-based selection strategies by simulation using a mixed coalescence - gene-dropping approach.
  
  
  RESULTS
  We simulated 20 or 100 causal mutations (quantitative trait nucleotides, QTN) within 65 predefined 'gene' regions, each 10 kb long, within a genome composed of ten 3-Mb chromosomes. We compared prediction accuracy by cross-validation using a medium-density chip (7.5 k SNPs), a high-density (HD, 17 k) and sequence data (335 k). Genetic evaluation was based on a GBLUP method. The simulations showed: (1) a law of diminishing returns with increasing number of SNPs; (2) a modest effect of SNP ascertainment bias in arrays; (3) a small advantage of using whole-genome sequence data vs. HD arrays i.e. ~4%; (4) a minor effect of NGS errors except when imputation error rates are high (≥20%); and (5) if QTN were known, prediction accuracy approached 1. Since this is obviously unrealistic, we explored milder assumptions. We showed that, if all SNPs within causal genes were included in the prediction model, accuracy could also dramatically increase by ~40%. However, this criterion was highly sensitive to either misspecification (including wrong genes) or to the use of an incomplete gene list; in these cases, accuracy fell rapidly towards that reached when all SNPs from sequence data were blindly included in the model.
  
  
  CONCLUSIONS
  Our study shows that, unless an accurate prior estimate on the functionality of SNPs can be included in the predictor, there is a law of diminishing returns with increasing SNP density. As a result, use of whole-genome sequence data may not result in a highly increased selection response over high-density genotyping.
authors.affiliation: Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB Consortium, 08193, Bellaterra, Barcelona, Spain. miguel.perez@uab.es.|Departament de Ciència Animal i dels Aliments, Universitat Autònoma de Barcelona, 08193, Bellaterra, Barcelona, Spain. miguel.perez@uab.es.|Institut Català de Recerca i Estudis Avançats (ICREA), Carrer de Lluís Companys 23, Barcelona, 08010, Spain. miguel.perez@uab.es.
authors.forename: Miguel
authors.initials: M
authors.lastname: Pérez-Enciso
authors.affiliation: Centre for Research in Agricultural Genomics (CRAG), CSIC-IRTA-UAB-UB Consortium, 08193, Bellaterra, Barcelona, Spain. jcrincon@unal.edu.co.|Universidad Nacional de Colombia, Sede Medellín, Facultad de Ciencias Agrarias, Departamento de producción Animal, Medellín, Colombia. jcrincon@unal.edu.co.
authors.forename: Juan C
authors.initials: JC
authors.lastname: Rincón
authors.affiliation: INRA, UMR 1388 GENPHYSE, Génétique, Physiologie et Systèmes d'Elevage, Castanet-Tolosan, 31326, France. andres.legarra@toulouse.inra.fr.
authors.forename: Andrés
authors.initials: A
authors.lastname: Legarra
mesh_terms: D000818:Animals; D001947:Breeding; D002417:Cattle; D023281:Genomics / Q000379:methods; D060005:Genotyping Techniques; D059014:High-Throughput Nucleotide Sequencing; D009154:Mutation; D020411:Oligonucleotide Array Sequence Analysis; D020641:Polymorphism, Single Nucleotide; D015203:Reproducibility of Results; D017422:Sequence Analysis, DNA