Epistatic fire blight resistance QTL alleles in the apple cultivar ‘Enterprise’ and selection X-6398 discovered and characterized through pedigree-informed analysis

Weg, Eric van de; Guardo, Mario Di; Jänsch, Melanie; Socquet-Juglard, Didier; Costa, Fabrizio; Baumgartner, Isabelle; Broggini, Giovanni A. L.; Kellerhals, M.; Troggio, Michela; Laurens, François; Durel, Charles Eric; Patocchi, Andrea

doi:10.1007/s11032-017-0755-0

Cited by 65 publications

(69 citation statements)

References 72 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Fiesta was classified as MS to MR whereas Enterprise was HR. The range from MS to HR among these cultivars, which share the FB_F7 resistance allele, supports the conclusions of van de Weg et al (2018) that the resistance of Enterprise is complex and cannot be wholly explained by the few QTLs that have been identified. The variability of cultivars' resistance responses in different tests could partially be explained by the presence of multiple interacting loci, which can be differentially impacted by variable environmental and/or physiological factors.…”

Section: Discussionsupporting

confidence: 77%

“…Calenge et al (2005) identified a major QTL on chromosome 7 (34.3-46.6% phenotypic variation) in two progenies from crosses with Fiesta. This QTL was validated by van de Weg et al (2018) in a Gala 9 Enterprise population and several putative QTLs identified whose effects were not detected on their own, indicating potential epistatic interactions. Through pedigree-informed analysis, van de Weg et al (2018) determined that the favourable resistance allele FB_F7 of the major QTL was derived from Cox's Orange Pippin; however, without the presence of additional putative QTLs, the effect of FB_F7 was relatively minor.…”

Section: Discussionmentioning

confidence: 89%

“…There is evidence that multiple loci in additive and/or epistatic interaction contribute to a cultivar's resistance (e.g. Calenge et al, 2005;van de Weg et al, 2018). Calenge et al (2005) identified a major QTL on chromosome 7 (34.3-46.6% phenotypic variation) in two progenies from crosses with Fiesta.…”

Section: Discussionmentioning

confidence: 99%

“…High heterozygosity, gametophytic self-incompatibility and long generation times are challenges associated with apple breeding and breeding for resistance to fire blight is further complicated by quantitative resistance and the difficulties associated with phenotyping fire blight resistance/susceptibility (Peil et al, 2009;Brown, 2012). Several quantitative trait loci (QTLs) associated with resistance/susceptibility to fire blight have been identified in Malus species and cultivars, accounting for <15-80% of the phenotypic variation (Calenge et al, 2005;Peil et al, 2007;Durel et al, 2009;Le Roux et al, 2010;Gardiner et al, 2012;Emeriewen et al, 2014Emeriewen et al, , 2017Desnoues et al, 2018;van de Weg et al, 2018). Most have been identified in genetic backgrounds that are characterized by small, astringent, crab appletype fruit (e.g.…”

Section: Introductionmentioning

confidence: 99%

See 3 more Smart Citations

Novel metrics to classify fire blight resistance of 94 apple cultivars

2019

View full text Add to dashboard Cite

Fire blight (Erwinia amylovora), a potentially devastating disease in apple, can cause floral, fruit and structural damage and even tree death. Most commercial apple cultivars are susceptible and the resistance/susceptibility of many modern cultivars has not been evaluated. Fire blight resistance/susceptibility is difficult to phenotype due to quantitative resistance, impacts of tree vigour and environment on susceptibility, and the erratic nature of the disease. Resistance/susceptibility levels were determined for 94 apple cultivars and important breeding parents. In 2016 and 2017, multiple actively growing shoots per tree (about three trees per cultivar) were challenged with E. amylovora Ea153n via a cut‐leaf inoculation method. Proportion of current season's shoot length blighted (SLB) was calculated for each shoot. To classify cultivar responses, estimated marginal SLB means were compared to four controls, representing highly susceptible (HS) to highly resistant (HR), via Dunnett's tests. Cultivar responses ranged from HS to HR with estimated marginal SLB means of 0.001–0.995 in 2016 and 0.000–0.885 in 2017. Most cultivars demonstrated similar resistance/susceptibility levels in both years (ρ = 0.657, P < 0.0001). K‐means clustering was used to classify cultivars into three resistance/susceptibility groups based on incidence, average severity (SLB), and maximum severity values (maximum SLB and age of wood infected). Sixteen cultivars were consistently moderately resistant (MR) to HR while the remainder ranged from HS to MR. An updated comparison of susceptibility of important cultivars is provided. Resistance/susceptibility information gained could be used to identify genetic loci associated with resistance/susceptibility and/or inform parental selection in apple scion breeding programmes.

show abstract

Section: Discussionsupporting

confidence: 77%

Section: Discussionmentioning

confidence: 89%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Novel metrics to classify fire blight resistance of 94 apple cultivars

2019

View full text Add to dashboard Cite

show abstract

“…After loading all functions defined in the R-script, the ‘CheckParAll()’ function was used to identify Mendelian-inconsistent errors for individuals with at least one known parent in the data set. When an individual’s supposed parent was not genotyped but the supposed grandparents were genotyped, the grandparents-grandchild relationship was tested with the AB+AA-AA test in Excel using the template provided by van de Weg and co-workers (2018) [23].…”

Section: Methodsmentioning

confidence: 99%

High-quality, genome-wide SNP genotypic data for pedigreed germplasm of the diploid outbreeding species apple, peach, and sweet cherry through a common workflow

Vanderzande

Howard

Cai

et al. 2019

Preprint

View full text Add to dashboard Cite

26High-quality genotypic data is a requirement for many genetic analyses. For any crop, errors in genotype 27 calls, phasing of markers, linkage maps, pedigree records, and unnoticed variation in ploidy levels can 28 lead to spurious marker-locus-trait associations and incorrect origin assignment of alleles to individuals. 29High-throughput genotyping requires automated scoring, as manual inspection of thousands of scored 30 loci is too time-consuming. However, automated SNP scoring can result in errors that should be 31 corrected to ensure recorded genotypic data are accurate and thereby ensure confidence in 32 downstream genetic analyses. To enable quick identification of errors in a large genotypic data set, we 33 have developed a comprehensive workflow. This multiple-step workflow is based on inheritance 34 principles and on removal of markers and individuals that do not follow these principles, as 35 demonstrated here for apple, peach, and sweet cherry. Genotypic data was obtained on pedigreed 36 germplasm using 6-9K SNP arrays for each crop and a subset of well-performing SNPs was created using 37 ASSIsT. Use of correct (and corrected) pedigree records readily identified violations of simple inheritance 38 principles in the genotypic data, streamlined with FlexQTL TM software. Retained SNPs were grouped into 39 haploblocks to increase the information content of single alleles and reduce computational power 40 needed in downstream genetic analyses. Haploblock borders were defined by recombination locations 41 detected in ancestral generations of cultivars and selections. Another round of inheritance-checking was 42 conducted, for haploblock alleles (i.e., haplotypes). High-quality genotypic data sets were created using 43 this workflow for pedigreed collections representing the U.S. breeding germplasm of apple, peach, and 44 sweet cherry evaluated within the RosBREED project. These data sets contain 3855, 4005, and 1617 45 SNPs spread over 932, 103, and 196 haploblocks in apple, peach, and sweet cherry, respectively. The 46 highly curated phased SNP and haplotype data sets, as well as the raw iScan data, of germplasm in the 47 apple, peach, and sweet cherry Crop Reference Sets is available through the Genome Database for 48 Rosaceae. 3 49 50 103 Manual p17 [34]). The presence of one or more additional SNPs, insertions, or deletions in the probe-104 binding region can lead to reduced or loss of binding affinity for the SNP's probe and thereby to the 105 presence of additional clusters, both of which can lead to incorrect genotype scoring of some SNPs [33]. 106 107 No systematic workflow exists to efficiently detect and resolve all types of errors from a genotypic data 108 set for pedigreed germplasm. Methods and software exist to tackle specific types of errors. For example, 109 the ASSIsT software was developed for use with Illumina Infinium® arrays to identify which SNPs show 110 robust results, which SNPs might have genotype calling errors due to alleles with reduced affinity or null 111 alleles, and which...

show abstract

Efficiency of Bayesian quantitative trait loci mapping with full‐sib progeny

et al. 2020

View full text Add to dashboard Cite

Quantitative trait loci (QTL) mapping with perennial crops is based on one or more full‐sib progeny because homozygous genotypes are difficult to obtain. The objective of this study was to assess the efficiency of Bayesian QTL mapping with full‐sib progeny. The analyses used two simulated data sets, one assuming genotyping for 292 simple sequence repeats (SSR) loci (density of 10 cM) and the other assuming genotyping for 2969 single nucleotide polymorphisms (SNPs) (density of 1 cM). Each data set consisted of 50 replications of 40 full‐sib progeny of size 400. We assumed a broad sense heritability of 60%, genetic control by 10 QTLs and 90 minor genes, and positive dominance. The QTL heritability values ranged from 1 to 12%. The scenarios included one and multiple progeny. In the best scenarios for the low (four progeny of size 400) and high marker density (one progeny of size 400), the average power of detection was 52 and 67% for the low heritability QTLs, 83 and 95% for the average heritability QTLs, and 95 and 94% for the high heritability QTLs, respectively. The observed false discovery rate (FDR) was 15 and 9% with low and high density, respectively. The Bayesian QTL mapping provides a precise localization of candidate genes with a bias in the QTL position of approximately 4–6 cM. The polygenic effect is important to control the false discovery rate (FDR) and to provide a higher power of QTL detection with multiple progeny.

show abstract

Epistatic fire blight resistance QTL alleles in the apple cultivar ‘Enterprise’ and selection X-6398 discovered and characterized through pedigree-informed analysis

Cited by 65 publications

References 72 publications

Novel metrics to classify fire blight resistance of 94 apple cultivars

Novel metrics to classify fire blight resistance of 94 apple cultivars

High-quality, genome-wide SNP genotypic data for pedigreed germplasm of the diploid outbreeding species apple, peach, and sweet cherry through a common workflow

Efficiency of Bayesian quantitative trait loci mapping with full‐sib progeny

Contact Info

Product

Resources

About