Turbocharging introgression breeding of perennial fruit crops: a case study on apple

Abstract

The allelic diversity of primitive germplasm of fruit crops provides a useful resource for introgressing novel genes to meet consumer preferences and environmental challenges. Pre-breeding facilitates the identification of novel genetic variation in the primitive germplasm and expedite its utilisation in cultivar breeding programmes. Several generations of pre-breeding could be required to minimise linkage drag from the donor parent and to maximise the genomic content of the recipient parent. In this study we investigated the potential of genomic selection (GS) as a tool for rapid background selection of parents for the successive generation. A diverse set of 274 accessions was genotyped using random-tag genotyping-by-sequencing, and phenotyped for eight fruit quality traits. The relationship between 'own phenotypes' of 274 accessions and their general combining ability (GCA) was also examined. Trait heritability influenced the strength of correspondence between own phenotype and the GCA. The average (across eight traits) accuracy of predicting own phenotype was 0.70, and the correlations between genomic-predicted own phenotype and GCA were similar to the observed correlations. Our results suggest that genome-assisted parental selection (GAPS) is a credible alternative to phenotypic parental selection, so could help reduce the generation interval to allow faster accumulation of favourable alleles from donor and recipient parents.

The apple REFPOP—a reference population for genomics-assisted breeding in apple

“…Genomic prediction in apple was tested for the first time by Kumar et al in 2012 8 . However, this and further studies have been based on a limited number of genetic markers and/or been carried out at a local scale 9 – 14 .…”

Inheritance of Rootstock Effects in Avocado (Persea americana Mill.) cv. Hass

“…To expand our knowledge on the extent of the rootstock–scion interaction and speed up fruit tree breeding programs ( Kumar et al, 2020 ; Peng et al, 2020 ; Santantonio and Robbins, 2020 ), further heritability estimates should be gathered on contrasting traits using multi-environment ( Crossa et al, 2019 ; Costa-Neto et al, 2020 ) provenance (“common garden”) and progeny trials with diverse panels of seedling and clonal rootstocks. The “genetic prediction” model used here to estimate pedigree-free heritabilities ( Milner et al, 2000 ; Kruuk, 2004 ; Frentiu et al, 2008 ; Wilson et al, 2010 ; Berenos et al, 2014 ), or alternatively indirect genetic effect (IGE) models ( Bijma, 2010 , 2013 ; Fisher and Mcadam, 2019 ), may be extended to field trials at a low genotyping cost, as few polymorphic SSR markers are enough to span the genetic relatedness gradient.…”

Harnessing multivariate, penalized regression methods for genomic prediction and QTL detection to cope with climate change affecting grapevine

“…In contrast, GP methods, by focusing on prediction, are less restrictive on the number of useful markers, sometimes resulting in all markers being retained as predictive with a non-zero effect. That is why GP methods are more efficient to predict genotypic values (Goddard and Hayes 2007) and therefore have become more and more popular with breeders (Heffner et al 2010; Crossa 2017; Kumar et al 2020).…”