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1.
Pathogens ; 9(11)2020 Nov 20.
Article in English | MEDLINE | ID: mdl-33233616

ABSTRACT

Polyploidy is a key driver of significant evolutionary changes in plant species. The genus Actinidia (kiwifruit) exhibits multiple ploidy levels, which contribute to novel fruit traits, high yields and resistance to the canker-causing dieback disease incited by Pseudomonas syringae pv. actinidiae (Psa) biovar 3. However, the genetic mechanism for resistance to Psa observed in polyploid kiwifruit is not yet known. In this study we performed detailed genetic analysis of a tetraploid Actinidia chinensis var. chinensis population derived from a cross between a female parent that exhibits weak tolerance to Psa and a highly Psa-resistant male parent. We used the capture-sequencing approach across the whole kiwifruit genome and generated the first ultra-dense maps in a tetraploid kiwifruit population. We located quantitative trait loci (QTLs) for Psa resistance on these maps. Our approach to QTL mapping is based on the use of identity-by-descent trait mapping, which allowed us to relate the contribution of specific alleles from their respective homologues in the male and female parent, to the control of Psa resistance in the progeny. We identified genes in the diploid reference genome whose function is suggested to be involved in plant defense, which underly the QTLs, including receptor-like kinases. Our study is the first to cast light on the genetics of a polyploid kiwifruit and suggest a plausible mechanism for Psa resistance in this species.

2.
Hortic Res ; 6: 101, 2019.
Article in English | MEDLINE | ID: mdl-31645956

ABSTRACT

Pseudomonas syringae pv. actinidiae (Psa) biovar 3, a virulent, canker-inducing pathogen is an economic threat to the kiwifruit (Actinidia spp.) industry worldwide. The commercially grown diploid (2×) A. chinensis var. chinensis is more susceptible to Psa than tetraploid and hexaploid kiwifruit. However information on the genetic loci modulating Psa resistance in kiwifruit is not available. Here we report mapping of quantitative trait loci (QTLs) regulating resistance to Psa in a diploid kiwifruit population, derived from a cross between an elite Psa-susceptible 'Hort16A' and a resistant male breeding parent P1. Using high-density genetic maps and intensive phenotyping, we identified a single QTL for Psa resistance on Linkage Group (LG) 27 of 'Hort16A' revealing 16-19% phenotypic variance and candidate alleles for susceptibility and resistance at this loci. In addition, six minor QTLs were identified in P1 on distinct LGs, exerting 4-9% variance. Resistance in the F1 population is improved by additive effects from 'Hort16A' and P1 QTLs providing evidence that divergent genetic pathways interact to combat the virulent Psa strain. Two different bioassays further identified new QTLs for tissue-specific responses to Psa. The genetic marker at LG27 QTL was further verified for association with Psa resistance in diploid Actinidia chinensis populations. Transcriptome analysis of Psa-resistant and susceptible genotypes in field revealed hallmarks of basal defense and provided candidate RNA-biomarkers for screening for Psa resistance in greenhouse conditions.

3.
Springerplus ; 3: 547, 2014.
Article in English | MEDLINE | ID: mdl-26034671

ABSTRACT

Linear Mixed models (LMMs) that incorporate genetic and spatial covariance structures have been used for many years to estimate genetic parameters and to predict breeding values in animal and plant breeding. Although the theoretical aspects for extending LMM to generalised linear mixed models (GLMMs) have been around for some time, suitable software has been developed only within the last decade or so. The GLIMMIX procedure in SAS® is becoming popular for fitting GLMMs in various disciplines. Applications of GLMMs to genetic analysis have been limited, probably because of the complexity of the models used. This is particularly so for Proc GLIMMIX because, unlike ASReml software, it is not specifically tailored for analysis of breeding data and some pre-procedure coding is necessary. Binary data that fits the GLMM framework is commonly encountered in breeding experiments, such as when evaluating individuals for resistance by observing the presence or absence of disease. Bacterial canker (Psa) caused by Pseudomonas syringae pv. actinidiae is a serious disease of kiwifruit in New Zealand and other kiwifruit-producing countries. Data from a progeny test trial was available to identify parents with high breeding values for resistance. We successfully applied the GLIMMIX procedure for this purpose. Heritability for resistance was moderate, and we identified two parents and their family as having high potential for Psa resistance breeding. There are several potential pitfalls when using GLMMs with binary data and these are briefly discussed.

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