Image1_Evaluation of low-density SNP panels and imputation for cost-effective genomic selection in four aquaculture species.pdf

• Main Authors: Christina Kriaridou, Smaragda Tsairidou, Clémence Fraslin, Gregor Gorjanc, Mark E. Looseley, Ian A. Johnston, Ross D. Houston, Diego Robledo
• Format: Still Image
• Language: unknown
• Published: 2023
• Subjects: Genetics; Genetic Engineering; Biomarkers; Developmental Genetics (incl. Sex Determination); Epigenetics (incl. Genome Methylation and Epigenomics); Gene Expression (incl. Microarray and other genome-wide approaches); Genome Structure and Regulation; Genomics; Genetically Modified Animals; Livestock Cloning; Gene and Molecular Therapy; selective breeding; imputation; genomic prediction; aquaculture; fish; bivalve; Atlantic salmon; Pacific oyster; Turbot
• Online Access: https://doi.org/10.3389/fgene.2023.1194266.s001; https://figshare.com/articles/figure/Image1_Evaluation_of_low-density_SNP_panels_and_imputation_for_cost-effective_genomic_selection_in_four_aquaculture_species_pdf/22810124

Genomic selection can accelerate genetic progress in aquaculture breeding programmes, particularly for traits measured on siblings of selection candidates. However, it is not widely implemented in most aquaculture species, and remains expensive due to high genotyping costs. Genotype imputation is a promising strategy that can reduce genotyping costs and facilitate the broader uptake of genomic selection in aquaculture breeding programmes. Genotype imputation can predict ungenotyped SNPs in populations genotyped at a low-density (LD), using a reference population genotyped at a high-density (HD). In this study, we used datasets of four aquaculture species (Atlantic salmon, turbot, common carp and Pacific oyster), phenotyped for different traits, to investigate the efficacy of genotype imputation for cost-effective genomic selection. The four datasets had been genotyped at HD, and eight LD panels (300–6,000 SNPs) were generated in silico. SNPs were selected to be: i) evenly distributed according to physical position ii) selected to minimise the linkage disequilibrium between adjacent SNPs or iii) randomly selected. Imputation was performed with three different software packages (AlphaImpute2, FImpute v.3 and findhap v.4). The results revealed that FImpute v.3 was faster and achieved higher imputation accuracies. Imputation accuracy increased with increasing panel density for both SNP selection methods, reaching correlations greater than 0.95 in the three fish species and 0.80 in Pacific oyster. In terms of genomic prediction accuracy, the LD and the imputed panels performed similarly, reaching values very close to the HD panels, except in the pacific oyster dataset, where the LD panel performed better than the imputed panel. In the fish species, when LD panels were used for genomic prediction without imputation, selection of markers based on either physical or genetic distance (instead of randomly) resulted in a high prediction accuracy, whereas imputation achieved near maximal prediction accuracy independently of ...