title: Evaluating Sequence-Based Genomic Prediction with an Efficient New Simulator.
abstract: The vast amount of sequence data generated to analyze complex traits is posing new challenges in terms of the analysis and interpretation of the results. Although simulation is a fundamental tool to investigate the reliability of genomic analyses and to optimize experimental design, existing software cannot realistically simulate complete genomes. To remedy this, we have developed a new strategy (Sequence-Based Virtual Breeding, SBVB) that uses real sequence data and simulates new offspring genomes and phenotypes in a very efficient and flexible manner. Using this tool, we studied the efficiency of full sequence in genomic prediction compared to SNP arrays. We used real porcine sequences from three breeds as founder genomes of a 2500-animal pedigree and two genetic architectures: "neutral" and "selective." In the neutral architecture, frequencies and allele effects were sampled independently whereas, in the selective case, SNPs were sites putatively under selection after domestication and a negative correlation between effect and frequency was induced. We compared the effectiveness of different genotyping strategies for genomic selection, including the use of full sequence commercial arrays or randomly chosen SNP sets in both outbred and crossbred experimental designs. We found that accuracy increases using sequence instead of commercial chips but modestly, perhaps by ≤ 4%. This result was robust to extreme genetic architectures. We conclude that full sequence is unlikely to offset commercial arrays for predicting genetic value when the number of loci is relatively large and the prior given to each SNP is uniform. Using sequence to improve selection thus requires optimized prior information and, likely, increased population sizes. The code and manual for SBVB are available at https://github.com/mperezenciso/sbvb0.
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.|ICREA, 08010 Barcelona, Spain.
authors.forename: Miguel
authors.identifier: 0000-0003-3524-995X
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.
authors.forename: Natalia
authors.initials: N
authors.lastname: Forneris
authors.affiliation: Department of Epidemiology and Biostatistics, Michigan State University, East Lansing, Michigan 48824.|Department of Statistics, Michigan State University, East Lansing, Michigan 48824.
authors.forename: Gustavo
authors.initials: G
authors.lastname: de Los Campos
authors.affiliation: INRA, UMR 1388 GENPHYSE, Castanet-Tolosan 31326, France.
authors.forename: Andrés
authors.identifier: 0000-0001-8893-7620
authors.initials: A
authors.lastname: Legarra
mesh_terms: D000818:Animals; D001947:Breeding / Q000379:methods*; D016678:Genome; D008957:Models, Genetic; D020641:Polymorphism, Single Nucleotide; D012680:Sensitivity and Specificity; D017422:Sequence Analysis, DNA / Q000379:methods; D012984:Software; D013552:Swine / Q000235:genetics
keywords: GenPred; complex trait; forward simulation; genomic selection; pig; sequence; shared data resource