Genome to Phenome Mapping in Apple Using Historical Data. Issue 2 (1st July 2016)

• Record Type: Journal Article
• Authors: Migicovsky, Zoë; Gardner, Kyle M.; Money, Daniel; Sawler, Jason; Bloom, Joshua S.; Moffett, Peter; Chao, C. Thomas; Schwaninger, Heidi; Fazio, Gennaro; Zhong, Gan‐Yuan; Myles, Sean
• Is Part Of: œplant genome. Volume 9:Issue 2(2016)
• Page Start: n/a
• Page End: n/a
• Publication Date: 2016-07-01
• Subjects: Plant genomes -- Periodicals; Plant genome mapping -- Periodicals; 572.862
• DOI: 10.3835/plantgenome2015.11.0113
• Languages: English
• ISSNs: 1940-3372

Abstract

Abstract : Apple ( Malus X . domestica Borkh.) is one of the world's most valuable fruit crops. Its large size and long juvenile phase make it a particularly promising candidate for marker‐assisted selection (MAS). However, advances in MAS in apple have been limited by a lack of phenotype and genotype data from sufficiently large samples. To establish genotype‐phenotype relationships and advance MAS in apple, we extracted over 24, 000 phenotype scores from the USDA‐Germplasm Resources Information Network (GRIN) database and linked them with over 8000 single nucleotide polymorphisms (SNPs) from 689 apple accessions from the USDA apple germplasm collection clonally preserved in Geneva, NY. We find significant genetic differentiation between Old World and New World cultivars and demonstrate that the genetic structure of the domesticated apple also reflects the time required for ripening. A genome‐wide association study (GWAS) of 36 phenotypes confirms the association between fruit color and the MYB1 locus, and we also report a novel association between the transcription factor, NAC18.1, and harvest date and fruit firmness. We demonstrate that harvest time and fruit size can be predicted with relatively high accuracies ( r > 0.46) using genomic prediction. Rapid decay of linkage disequilibrium (LD) in apples means millions of SNPs may be required for well‐powered GWAS. However, rapid LD decay also promises to enable extremely high resolution mapping of causal variants, which holds great potential for advancing MAS.