Integrating genomics and multiplatform metabolomics enables metabolite quantitative trait loci detection in breeding‐relevant apple germplasm. Issue 5 (4th October 2021)

• Record Type: Journal Article
• Authors: Bilbrey, Emma A.; Williamson, Kathryn; Hatzakis, Emmanuel; Miller, Diane Doud; Fresnedo‐Ramírez, Jonathan; Cooperstone, Jessica L.
• Is Part Of: New phytologist. Volume 232:Issue 5(2021)
• Page Start: 1944
• Page End: 1958
• Publication Date: 2021-10-04
• Subjects: apple breeding -- chlorogenic acid -- genome‐wide association study (GWAS) -- Malus × domestica (apple) -- omics -- pedigree‐based analysis -- quantitative trait loci (QTL) -- specialty crop; Botany -- Periodicals; 580
• DOI: 10.1111/nph.17693
• Languages: English
• ISSNs: 0028-646X

Abstract

Summary: Apple ( Malus × domestica ) has commercial and nutritional value, but breeding constraints of tree crops limit varietal improvement. Marker‐assisted selection minimises these drawbacks, but breeders lack applications for targeting fruit phytochemicals. To understand genotype–phytochemical associations in apples, we have developed a high‐throughput integration strategy for genomic and multiplatform metabolomics data. Here, 124 apple genotypes, including members of three pedigree‐connected breeding families alongside diverse cultivars and wild selections, were genotyped and phenotyped. Metabolite genome‐wide association studies (mGWAS) were conducted with c. 10 000 single nucleotide polymorphisms and phenotypic data acquired via LC–MS and 1 H NMR untargeted metabolomics. Putative metabolite quantitative trait loci (mQTL) were then validated via pedigree‐based analyses (PBA). Using our developed method, 519, 726 and 177 putative mQTL were detected in LC–MS positive and negative ionisation modes, and NMR, respectively. mQTL were indicated on each chromosome, with hotspots on linkage groups 16 and 17. A chlorogenic acid mQTL was discovered on chromosome 17 via mGWAS and validated with a two‐step PBA, enabling discovery of novel candidate gene–metabolite relationships. Complementary data from three metabolomics approaches and dual genomics analyses increased confidence in validity of compound annotation and mQTL detection. Our platform demonstrates the utility of multiomic integration to advance data‐driven, phytochemical‐based plant breeding.