Harnessing underutilized gene bank diversity and genomic prediction of cross usefulness to enhance resistance to Phytophthora cactorum in strawberry

Abstract

The development of strawberry (Fragaria × ananassa Duchesne ex Rozier) cultivars resistant to Phytophthora crown rot (PhCR), a devastating disease caused by the soil‐borne pathogen Phytophthora cactorum (Lebert & Cohn) J. Schröt., has been challenging partly because the resistance phenotypes are quantitative and only moderately heritable. To develop deeper insights into the genetics of resistance and build the foundation for applying genomic selection, a genetically diverse training population was screened for resistance to California isolates of the pathogen. Here we show that genetic gains in breeding for resistance to PhCR have been negligible (3% of the cultivars tested were highly resistant and none surpassed early 20th century cultivars). Narrow‐sense genomic heritability for PhCR resistance ranged from 0.41 to 0.75 among training population individuals. Using multivariate genome‐wide association studies (GWAS), we identified a large‐effect locus (predicted to be RPc2) that explained 43.6–51.6% of the genetic variance, was necessary but not sufficient for resistance, and was associated with calcium channel and other candidate genes with known plant defense functions. The addition of underutilized gene bank resources to our training population doubled additive genetic variance, increased the accuracy of genomic selection, and enabled the discovery of individuals carrying favorable alleles that are either rare or not present in modern cultivars. The incorporation of an RPc2‐associated single‐nucleotide polymorphism (SNP) as a fixed effect increased genomic prediction accuracy from 0.40 to 0.55. Finally, we show that parent selection using genomic‐estimated breeding values, genetic variances, and cross usefulness holds promise for enhancing resistance to PhCR in strawberry.

A medium‐density genotyping platform for cultivated strawberry using DArTag technology

“…The recent availability of high‐quality reference genomes and sequence datasets for strawberry has provided essential resources for trait mapping, functional genomics, and genetic marker development in this allo‐octoploid hybrid species (Bassil et al., 2015; Edger et al., 2019; Hardigan et al., 2020, 2021; Lee et al., 2021; Hardigan et al., 2021; Mao et al., 2023; Shirasawa et al., 2021). The growing molecular breeding toolbox for strawberry has nevertheless needed cost‐effective high‐throughput marker genotyping solutions to enable genomic selection and routine forensic identification of individuals and parents on a large scale (Gezan et al., 2017; Jiménez et al., 2023; Osorio et al., 2021; Pincot et al., 2020, 2021; Whitaker et al., 2020). MIP‐like technologies offer the advantages of high sample throughput and the ability to sequence target amplicon regions at a high read depth, reducing the likelihood of missing genotypes or genotyping errors at heterozygous sites (Wang et al., 2023).…”

“…While biological ascertainment bias between wild and cultivated subpopulations or different cultivated subpopulations (i.e., breeding programs) can lead to misinterpretation of total genome-wide diversity (Heslot et al, 2013;Malomane et al, 2018), technical ascertainment bias in the amplification of homologous allelic sequences increases the likelihood of genotyping errors at loci targeted by markers (Lighten et al, 2014;Zhang et al, 2015). This can result from non-copy specific oligo binding to both target and off-target genome sequences, or alternatively, weaker oligo binding to non-reference haplotypes within on-target regions due to interference from OTVs around the target SNP.…”

“…(2020). The publication of multiple single nucleotide polymorphism (SNP) arrays for strawberry from 2015 to 2020 enabled genetic and physical mapping of new loci controlling traits related to disease resistance and fruit quality (Bassil et al., 2015; Barbey et al., 2021; Castillejo et al., 2020; Jiménez et al., 2023; Noh et al., 2018; Nelson et al., 2021; Nellist et al., 2019; Oh et al., 2019; Osorio et al., 2021; Oh et al., 2021; Pincot et al., 2018, 2020, 2021, 2022; Pillet et al., 2017; Petrasch et al., 2022; Salinas et al., 2019). Progress in this area has only increased following the publication of multiple high‐quality, chromosome‐scale assemblies of octoploid Fragaria genomes in the last 5 years (Edger et al., 2019; Hardigan et al., 2021; Lee et al., 2021; Mao et al., 2023; Shirasawa et al., 2021).…”

“…However, small marker panels are unable to capture broad allelic diversity across large numbers of haploblocks in the approximately 800‐Mb strawberry genome. Other economically important strawberry traits have been shown to be highly quantitative and controlled by many genetic loci with individually small effects (Gezan et al., 2017; Jiménez et al., 2023; Osorio et al., 2021; Pincot et al., 2020; Petrasch et al., 2022). Genomic prediction has the potential to improve selection efficiency for heritable but genetically complex traits over multiple generations and has part of the molecular breeding toolbox for parent and seedling selection in strawberry (Gezan et al., 2017; Osorio et al., 2021; Whitaker et al., 2020).…”

Accelerating genetic gains for quantitative resistance to verticillium wilt through predictive breeding in strawberry

“…Heirloom strawberry cultivars and wild relatives have been identified as potential sources of novel favorable alleles; however, these accessions also contain a high frequency of unfavorable alleles that would impact several horticulturally important traits, such as yield, shelf‐life, fruit quality, and resistance to other diseases, relative to modern‐day cultivars (Bringhurst et al., 1968; Jiménez et al., 2022; Pincot et al., 2020). There is great interest in identifying the genetic basis of V. dahliae resistance to facilitate the efficient incorporation of novel alleles into breeding materials without diminishing horticultural value.…”