Two QTL characterized for soft scald and soggy breakdown in apple (Malus × domestica) through pedigree-based analysis of a large population of interconnected families

Howard, Nicholas P.; Weg, Eric van de; Tillman, John R.; Tong, Cindy B. S.; Silverstein, Kevin A.T.; Luby, James J.

doi:10.1007/s11295-017-1216-y

Cited by 28 publications

(44 citation statements)

References 71 publications

(81 reference statements)

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“…These high estimates signified a large opportunity to identify the underlying genetic factors segregating in this germplasm. While it is difficult to harvest apple fruit in a standardized manner from genetically variable populations (Evans et al 2012;Howard et al 2018), the high heritability estimates obtained indicate that any discrepancies in determining standardized maturity across trees within and among years were minimal for fruit acidity compared to genetic influences. Kouassi et al (2009) reported narrow-sense heritabilities for four storage treatments of 0.79 ± 0.01 to 0.81 ± 0.01 for TA on 2207 pedigreed individuals in 29 families from breeding programs of six European countries.…”

Section: General Genetic Influences On Fruit Acidity From Harvest Thrmentioning

confidence: 98%

Two large-effect QTLs, Ma and Ma3, determine genetic potential for acidity in apple fruit: breeding insights from a multi-family study

Verma

Evans

Guan

et al. 2019

Tree Genetics & Genomes

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Acidity is a critical component of the apple fruit consumption experience. In previous biparental family studies, two large-effect acidity QTLs were reported using freshly harvested fruit. Objectives of this study were to determine the number and location of QTLs for acidity variation in a large apple breeding program and ascertain the quantitative effects and breeding relevance of QTL allelic combinations at harvest and after commercially relevant periods of cold storage. Pedigree-connected germplasm of 16 full-sib families representing nine important breeding parents, genotyped for the 8K SNP array, was assessed for titratable acidity at harvest and after 10-and 20-week storage treatments, for three successive seasons. Using pedigree-based QTL mapping software, FlexQTL™, evidence was found for only two QTLs, on linkage groups 16 (the reported Ma locus) and LG 8 (here called Ma3) that jointly explained 66 ± 5% of the phenotypic variation. An additive allele dosage model for the two QTLs effectively explained most acidity variation, with an average of + 1.8 mg/L at harvest per high-acidity allele. The more high-acidity alleles, the faster the depletion with storage, with all combinations appearing to eventually converge to a common baseline. All parent cultivars and selections had one or two of the four possible high-acidity alleles. Each QTL had a rare second high-acidity allele with stronger or reduced effect. Diagnostic SNP markers were identified for QTL alleles derived from distinct sources. Combined QTL effects highlighted utility of the DNAbased information in new cultivar development for targeting desired fruit acidity levels before or after storage.

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Section: General Genetic Influences On Fruit Acidity From Harvest Thrmentioning

confidence: 98%

Two large-effect QTLs, Ma and Ma3, determine genetic potential for acidity in apple fruit: breeding insights from a multi-family study

Verma

Evans

Guan

et al. 2019

Tree Genetics & Genomes

View full text Add to dashboard Cite

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“…The Bayesian QTL mapping approach implemented by FlexQTL ™ (Bink et al, 2008 andBink et al, 2014) allows analyzing multiple pedigreelinked progenies at the same time; reducing the limitations derived from QTL analyses using single populations. This approach has been successfully used in recent years for QTL analyses of different traits in Rosaceae species, such as sweet cherry (Rosyara et al, 2013), apple (Bink et al, 2014;Guan et al, 2015;Allard et al, 2016;Di Guardo et al, 2017;Howard et al, 2018), peach (Fresnedo-Ramírez et al, 2015;Fresnedo-Ramírez et al, 2016;Hernández Mora et al, 2017), and strawberry (Roach et al, 2016;Mangandi et al, 2017;Anciro et al, 2018).…”

Section: Introductionmentioning

confidence: 99%

Genetic Dissection of Bloom Time in Low Chilling Sweet Cherry (Prunus avium L.) Using a Multi-Family QTL Approach

et al. 2020

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Bloom time in sweet cherry (Prunus avium L.) is a highly heritable trait that varies between genotypes and depends on the environmental conditions. Bud-break occurs after chill and heat requirements of each genotype are fulfilled, and dormancy is released. Bloom time is a critical trait for fruit production as matching cultivar adaptation to the growing area is essential for adequate fruit set. Additionally, low chilling cultivars are of interest to extend sweet cherry production to warmer regions, and for the crop adaptation to increasing winter and spring temperatures. The aim of this work is to investigate the genetic control of this trait by analyzing multiple families derived from the low chilling and extra-early flowering local Spanish cultivar 'Cristobalina' and other cultivars with higher chilling requirements and medium to late bloom times. Bloom time evaluation in six related sweet cherry populations confirmed a high heritability of this trait, and skewed distribution toward late flowering, revealing possible dominance of the late bloom alleles. SNP genotyping of the six populations (n = 406) resulted in a consensus map of 1269 SNPs. Quantitative trait loci (QTL) analysis using the Bayesian approach implemented by FlexQTL™ software revealed two major QTLs on linkage groups 1 and 2 (qP-BT1.1m and qP-BT2.1m) that explained 47.6% of the phenotypic variation. The QTL on linkage group 1 was mapped to a 0.26 Mbp region that overlaps with the DORMANCY ASSOCIATED MADS-BOX (DAM) genes. This finding is consistent with peach results that indicate that these genes are major determinants of chilling requirement in Prunus. Haplotype analysis of the linkage group 1 and 2 QTL regions showed that 'Cristobalina' was the only cultivar tested that contributed early bloom time alleles for these two QTLs. This work contributes to knowledge of the genetic control of chilling requirement and bloom date and will enable marker-assisted selection for low chilling in sweet cherry breeding programs.

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“…The software package FlexQTL™ utilizes a Bayesian pedigree-based analyses (PBA) approach for QTL discovery and characterization via the software Pedimap (Voorrips et al 2012 ) and Visual FlexQTL™ (van de Weg et al 2004 , 2015 ) which support the graphical representation of FlexQTL™ outputs (van de Weg et al 2004 ). Loci for economically important traits have been characterized using this software in fruit crops including sweet cherry (Rosyara et al 2013 ; Cai et al 2017 ), peach (Fresnedo-Ramirez et al 2015 , 2016 ; Hernández-Mora et al 2017a , b ) and apple (Allard et al 2016 ; Di Guardo et al 2017 ; Durand et al 2017 ; Howard et al 2017 ; van de Weg et al 2017 ).…”

Section: Introductionmentioning

confidence: 99%

FaRCa1: a major subgenome-specific locus conferring resistance to Colletotrichum acutatum in strawberry

et al. 2018

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Optimal strategies for genetic improvement in crops depend on accurate assessments of the genetic architecture of traits. The overall objective of the present study was to determine the genetic architecture of anthracnose fruit rot (AFR) resistance caused by the fungus Colletotrichum acutatum in the University of Florida strawberry ( Fragaria × ananassa ) breeding germplasm. In 2016–2017, 33 full-sib families resulting from crosses between parents with varying levels of AFR resistance were tested. In 2017–2018, six full-sib families resulting from putative heterozygous resistant parents and homozygous susceptible parents were tested. Additionally, a validation population consisting of 77 advanced selections and ten cultivars was tested in the second season. Inoculation was performed using a mixture of three local isolates of the C. acutatum species complex. Phenotypes were scored weekly, and genotyping was performed using the IStraw35 Affymetrix Axiom ® SNP array. A pedigree-based QTL analysis was performed using FlexQTL™ software. A major resistance locus, which we name FaRCa1 , was detected in both seasons with a peak located at 55–56 cM on LG 6B and explaining at least 50% of the phenotypic variation across trials and seasons. The resistant allele exhibited partial dominance in all trials. The FaRCa1 locus is distinct from the previously discovered Rca2 locus, which mapped to LG 7B. While Rca2 is effective against European isolates from pathogenicity group 2, FaRCa1 appears to confer resistance to isolates of pathogenicity group 1. Electronic supplementary material The online version of this article (10.1007/s00122-018-3263-7) contains supplementary material, which is available to authorized users.

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Two QTL characterized for soft scald and soggy breakdown in apple (Malus × domestica) through pedigree-based analysis of a large population of interconnected families

Cited by 28 publications

References 71 publications

Two large-effect QTLs, Ma and Ma3, determine genetic potential for acidity in apple fruit: breeding insights from a multi-family study

Two large-effect QTLs, Ma and Ma3, determine genetic potential for acidity in apple fruit: breeding insights from a multi-family study

Genetic Dissection of Bloom Time in Low Chilling Sweet Cherry (Prunus avium L.) Using a Multi-Family QTL Approach

FaRCa1: a major subgenome-specific locus conferring resistance to Colletotrichum acutatum in strawberry

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