Targeted and untargeted approaches unravel novel candidate genes and diagnostic SNPs for quantitative resistance of the potato (Solanum tuberosum L.) to Phytophthora infestans causing the late blight disease

This work was carried out with the aid of a grant from Canada’s International Development Research Centre (IDRC), and with financial support from the Government of Canada, provided through Global Affairs Canada (GAC)Results of this study provide new insight into the molecular genetic basis of quantitative resistance in potato, and provide a diagnostic toolbox to identify single nucleotide polymorphisms (SNPs): markers that are associated with maturity corrected resistance (MCR) to late blight, for future breeding applications. The SNPs are strong candidates for: being directly involved in the control of quantitative resistance to late blight uncompromised by late plant maturity; for further functional characterization; and for validation of diagnostic power in different breeding populations and environments. The paper describes a detailed breakdown and assessment of marker-trait associations

Comparative transcript profiling by SuperSAGE identifies novel candidate genes for controlling potato quantitative resistance to late blight not compromised by late maturity.

Resistance to pathogens is essential for survival of wild and cultivated plants. Pathogen susceptibility causes major losses of crop yield and quality. Durable field resistance combined with high yield and other superior agronomic characters are therefore important objectives in every crop breeding program. Precision and efficacy of resistance breeding can be enhanced by molecular diagnostic tools, which result from knowledge of the molecular basis of resistance and susceptibility. Breeding uses resistance conferred by single R genes and polygenic quantitative resistance. The latter is partial but considered more durable. Molecular mechanisms of plant pathogen interactions are elucidated mainly in experimental systems involving single R genes, whereas most genes important for quantitative resistance in crops like potato are unknown. Quantitative resistance of potato to Phytophthora infestans causing late blight is often compromised by late plant maturity, a negative agronomic character. Our objective was to identify candidate genes for quantitative resistance to late blight not compromised by late plant maturity. We used diagnostic DNA-markers to select plants with different field levels of maturity corrected resistance (MCR) to late blight and compared their leaf transcriptomes before and after infection with P. infestans using SuperSAGE (serial analysis of gene expression) technology and next generation sequencing. We identified 2034 transcripts up or down regulated upon infection, including a homolog of the kiwi fruit allergen kiwellin. 806 transcripts showed differential expression between groups of genotypes with contrasting MCR levels. The observed expression patterns suggest that MCR is in part controlled by differential transcript levels in uninfected plants. Functional annotation suggests that, besides biotic and abiotic stress responses, general cellular processes such as photosynthesis, protein biosynthesis and degradation play a role in MCR

Chromosome-level genome assembly and population genomic resource to accelerate orphan crop lablab breeding

Abstract Under-utilised orphan crops hold the key to diversified and climate-resilient food systems. Here, we report on orphan crop genomics using the case of Lablab purpureus (L.) Sweet (lablab) - a legume native to Africa and cultivated throughout the tropics for food and forage. Our Africa-led plant genome collaboration produces a high-quality chromosome-scale assembly of the lablab genome. Our assembly highlights the genome organisation of the trypsin inhibitor genes - an important anti-nutritional factor in lablab. We also re-sequence cultivated and wild lablab accessions from Africa confirming two domestication events. Finally, we examine the genetic and phenotypic diversity in a comprehensive lablab germplasm collection and identify genomic loci underlying variation of important agronomic traits in lablab. The genomic data generated here provide a valuable resource for lablab improvement. Our inclusive collaborative approach also presents an example that can be explored by other researchers sequencing indigenous crops, particularly from low and middle-income countries (LMIC)

Number of SolCAP SNPs and corresponding loci showing putative associations with MCR, rAUDPC and PM based on different ranking criteria.

<p>Number of SolCAP SNPs and corresponding loci showing putative associations with MCR, rAUDPC and PM based on different ranking criteria.</p

SNPs in candidate genes showing association with rAUDPC, MCR and/or PM in three or four association models with -Log10(P) > 2.

<p>Full data are available in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156254#pone.0156254.s004" target="_blank">S4</a>–<a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0156254#pone.0156254.s006" target="_blank">S6</a> Files.</p

Genes with functions in the jasmonate pathway that were tested for association with MCR, rAUDPC and PM.

<p>Genes with functions in the jasmonate pathway that were tested for association with MCR, rAUDPC and PM.</p

Number of genes containing from 1 to 60 SNPs with different allele frequency in the R8 and S8 genotype pools.

<p>Number of genes containing from 1 to 60 SNPs with different allele frequency in the R8 and S8 genotype pools.</p

Genomic segments harboring QTL for MCR, rAUDPC and PM based on GWAS.

<p>Genomic segments harboring QTL for MCR, rAUDPC and PM based on GWAS.</p

Frequency distribution of the <i>tbr</i> (<i>S</i>. <i>tuberosum</i>) SNP alleles with different allele frequencies (q < 0.01) in the R8 and S8 genotype pools (significant SNPs) and without significant differences (q > 0.01, non-significant SNPs).

<p>Frequency distribution of the <i>tbr</i> (<i>S</i>. <i>tuberosum</i>) SNP alleles with different allele frequencies (q < 0.01) in the R8 and S8 genotype pools (significant SNPs) and without significant differences (q > 0.01, non-significant SNPs).</p