Reliable allele detection using SNP-based PCR primers containing Locked Nucleic Acid: application in genetic mapping

BACKGROUND: The diploid, Solanum caripense, a wild relative of potato and tomato, possesses valuable resistance to potato late blight and we are interested in the genetic base of this resistance. Due to extremely low levels of genetic variation within the S. caripense genome it proved impossible to generate a dense genetic map and to assign individual Solanum chromosomes through the use of conventional chromosome-specific SSR, RFLP, AFLP, as well as gene- or locus-specific markers. The ease of detection of DNA polymorphisms depends on both frequency and form of sequence variation. The narrow genetic background of close relatives and inbreds complicates the detection of persisting, reduced polymorphism and is a challenge to the development of reliable molecular markers. Nonetheless, monomorphic DNA fragments representing not directly usable conventional markers can contain considerable variation at the level of single nucleotide polymorphisms (SNPs). This can be used for the design of allele-specific molecular markers. The reproducible detection of allele-specific markers based on SNPs has been a technical challenge. RESULTS: We present a fast and cost-effective protocol for the detection of allele-specific SNPs by applying Sequence Polymorphism-Derived (SPD) markers. These markers proved highly efficient for fingerprinting of individuals possessing a homogeneous genetic background. SPD markers are obtained from within non-informative, conventional molecular marker fragments that are screened for SNPs to design allele-specific PCR primers. The method makes use of primers containing a single, 3'-terminal Locked Nucleic Acid (LNA) base. We demonstrate the applicability of the technique by successful genetic mapping of allele-specific SNP markers derived from monomorphic Conserved Ortholog Set II (COSII) markers mapped to Solanum chromosomes, in S. caripense. By using SPD markers it was possible for the first time to map the S. caripense alleles of 16 chromosome-specific COSII markers and to assign eight of the twelve linkage groups to consensus Solanum chromosomes. CONCLUSION: The method based on individual allelic variants allows for a level-of-magnitude higher resolution of genetic variation than conventional marker techniques. We show that the majority of monomorphic molecular marker fragments from organisms with reduced heterozygosity levels still contain SNPs that are sufficient to trace individual alleles

Genetic Population Structure of Cacao Plantings within a Young Production Area in Nicaragua

Significant cocoa production in the municipality of Waslala, Nicaragua, began in 1961. Since the 1980s, its economic importance to rural smallholders increased, and the region now contributes more than 50% of national cocoa bean production. This research aimed to assist local farmers to develop production of high-value cocoa based on optimal use of cacao biodiversity. Using microsatellite markers, the allelic composition and genetic structure of cacao was assessed from 44 representative plantings and two unmanaged trees. The population at Waslala consists of only three putative founder genotype spectra (lineages). Two (B and R) were introduced during the past 50 years and occur in >95% of all trees sampled, indicating high rates of outcrossing. Based on intermediate allelic diversity, there was large farm-to-farm multilocus genotypic variation. GIS analysis revealed unequal distribution of the genotype spectra, with R being frequent within a 2 km corridor along roads, and B at more remote sites with lower precipitation. The third lineage, Y, was detected in the two forest trees. For explaining the spatial stratification of the genotype spectra, both human intervention and a combination of management and selection driven by environmental conditions, appear responsible. Genotypes of individual trees were highly diverse across plantings, thus enabling selection for farm-specific qualities. On-farm populations can currently be most clearly recognized by the degree of the contribution of the three genotype spectra. Of two possible strategies for future development of cacao in Waslala, i.e. introducing more unrelated germplasm, or working with existing on-site diversity, the latter seems most appropriate. Superior genotypes could be selected by their specific composite genotype spectra as soon as associations with desired quality traits are established, and clonally multiplied. The two Y trees from the forest share a single multilocus genotype, possibly representing the Mayan, ‘ancient Criollo’ cacao

Diversity of cacao trees in Waslala, Nicaragua: Associations between genotype spectra, product quality and yield potential

The sensory quality and the contents of quality-determining chemical compounds in unfermented and fermented cocoa from 100 cacao trees (individual genotypes) representing groups of nine genotype spectra (GG), grown at smallholder plantings in the municipality of Waslala, Nicaragua, were evaluated for two successive harvest periods. Cocoa samples were fermented using a technique mimicking recommended on-farm practices. The sensory cocoa quality was assessed by experienced tasters, and seven major chemical taste compounds were quantified by near infrared spectrometry (NIRS). The association of the nine, partially admixed, genotype spectra with the analytical and sensory quality parameters was tested. The individual parameters were analyzed as a function of the factors GG and harvest (including the date of fermentation), individual trees within a single GG were used as replications. In fermented cocoa, significant GG-specific differences were observed for methylxanthines, theobromine-to-caffeine (T/C) ratio, total fat, procyanidin B5 and epicatechin, as well as the sensory attributes global score, astringency, and dry fruit aroma, but differences related to harvest were also apparent. The potential cocoa yield was also highly determined by the individual GG, although there was significant tree-to-tree variation within every single GG. Non-fermented samples showed large harvest-to-harvest variation of their chemical composition, while differences between GG were insignificant. These results suggest that selection by the genetic background, represented here by groups of partially admixed genotype spectra, would be a useful strategy toward enhancing quality and yield of cocoa in Nicaragua. Selection by the GG within the local, genetically segregating populations of seedpropagated cacao, followed by clonal propagation of best-performing individuals of the selected GG could be a viable alternative to traditional propagation of cacao by seed from open pollination. Fast and gentle air-drying of the fermented beans and their permanent dry storage were an efficient and comparatively easy precondition for high cocoa quality. (Résumé d'auteur

Increasing the efficiency of potato breeding through marker assisted selection - general thoughts. Molecular markers for late blight resistance - when applied for breeders?

Despite many breathtaking breakthroughs in the area of crop genetics and genomics, plant breeding still widely depends on the methods that had been worked out almost a century ago. This is not because commercial plant breeders are overly conservative but because the new knowledge lacks efficient and economical tools that would permit their application in practice. Breeders desire supporting technologies that would facilitate laborious and time-consuming screening in the field and laboratory. In particular, resistance screening often cannot be performed satisfactorily as the necessary disease pressure and appropriate pathogen populations may be unavailable. In potato breeding, specific and often complex resistances need to be developed, at the same time maintaining high levels of quality and culinary characteristics. Therefore, it is worthwhile to revisit the facts that comprise the progress in genetics of disease resistance and to analyze current technologies of genotyping and marker assisted selection, with the objective to detect those parameters that limit the efficiency of methods for commercial application. Selection in potato for resistance to late blight will be highlighted as an example. Maps, genes and markers for resistance have been identified – how universal are they? Single genes and quantitative trait loci for race-specific and race non-specific resistance are known – how efficient is their use? Marker technologies based on polymerase chain reaction and DNA hybridization have been developed that are far more efficient than first-generation technologies – is their use in commercial breeding economical? By discussing these issues concepts will emerge that help to pave the way for marker assisted selection (MAS) in potato breeding. The most important parameters required for economical MAS include to have a clear idea of the traits to be selected for, to use proven, reliable markers, to have in place a robust system for the collection and management of DNA samples, and to use technologies whose total cost is below or equal to the cost of the conventional methods. The most striking advantages of MAS are that a breeder will obtain more information than by conventional methodology, the information will be more precise, field labour can be saved and in that way the breeding process will be intensified. The implementation of the new technology could lead to even closer collaboration of breeders and scientists. Possible disadvantages include the relative increase of laboratory and computer work within the breeding program, and possibly higher costs during the implementation phase of the new technology

Plots of theobromine/caffeine (T/C) ratio (ordinate) vs. total caffeine content (abscissa) determined for samples of non-fermented cocoa.

<p>a; first harvest period 2008–9, b; second harvest (2009–11), c; two-harvest average.</p

Fermented cocoa from 100 individual trees (genotypes) representing 9 cacao Genotype Groups. Relative amounts of chemical compounds and scores of sensory attributes.

<p>Two-harvest averages by Genotype Group (GG) are indicated in the top part. GGs coded by colors according to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054079#pone.0054079-Trognitz2" target="_blank">[3]</a> (B; blue, Y; yellow, O; orange, E; gray, G; green, S; steel gray, A; aqua, P; purple, R; red).</p><p>Bottom part of table; individual GGs are ranked for each character (in descending order from positive to negative contribution to high quality cocoa). The probability P (*; <0.05, **; <0.01, ***; <0.001) of a character as under control by the GG is indicated in the last line. To decide which GG represents the greatest potential (as a decision support in selection) one could consider those that occur most frequently among the (highlighted in bold-face) top three ranks. These would be GGs S and R, both occur 5 times.</p

Summary of analyses of variance, average total quantities of chemical compounds, and sensory quality scores of cocoa samples.

<p>A quality attribute was considered as a function of Genotype Group (GG), Harvest, GG-Harvest interaction, and Trees (as replicates within a GG). The level of significance of variance components (F-test) is indicated (***; P<0.001, **; P<0.01, *; P<0.05, n.s.; not significant). There were 9 Genotype Groups (GG) and 95 (in harvest 1, non-fermented and fermented), 87 (harvest 2, non-fermented), and 78 (fermented) samples from individual trees. Df; error degrees of freedom (in analysis of variance). Grand mean quantities of compounds and their minima and maxima are presented in percent of dry matter.</p

Bar plots of the 15-SSR-genotype of 106 cacao trees from farms at Waslala that were used for assessment of cocoa quality and yield potential.

<p>A single bar represents a tree’s cumulative genotype consisting of fractions of ancestral genotype spectra. The in total nine genotype spectra were determined in <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054079#pone.0054079-Trognitz2" target="_blank">[3]</a> via Bayesian inference of population structure. Color coding of individual genotype spectra according to <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054079#pone.0054079-Trognitz2" target="_blank">[3]</a>. S; steel gray, G; green, P; purple, O; orange, R; red, E; gray, A; aqua, B; blue, Y; yellow. Their most probable ancestors and traditional cocoa types include for S; lower Amazon Forastero, G, P, O, and R; upper Amazon Forastero, E; upper Amazon Forastero isolated in coastal Ecuador, A, B; Trinitario, and Y; Criollo from Mesoamerica. The trees were grouped by their dominating genotype spectra. Group names of genotypes are indicated prior to tree name (as an example, tree W358 belongs to the S genotype group).</p

Pairwise correlations of analytical (F; fermented and N; non-fermented samples) and sensory data of fermented samples obtained on up to 172 individual observations collected over two harvests of cocoa from 100 trees of smallholder farms around Waslala, Nicaragua.

<p>Color and shading intensity indicate levels of significance of correlations. Theobromine/caffeine (T/C) ratio and theobromine and fat contents of fermented cocoa (highlighted by green background) were the most important characteristics as determined in the analytics (see text).</p

Potential cocoa yield (<8% water content) of nine Genotype Groups (GG) and their ranking and differentiation by comparison of multiple means (t-test).

1<p>GG; Genotype Group, compare <a href="http://www.plosone.org/article/info:doi/10.1371/journal.pone.0054079#pone-0054079-g003" target="_blank">Figure 3</a>, Samplings; Number of observations made on the trees. The objective of 2 samplings per tree was not achieved for all trees. # Trees; Number of trees within a GG, Yield; Least-squares mean potential yield per tree (in kg). The potential maximum single-tree yield was estimated as (no fruits past cherelle wilt stage) * (no beans/fruit) * (bean weight). Bean weight was averaged on 100 dry (<8% water content) beans. t-Test; least significant difference of yield by GG determined by multiple t-Tests. Values with identical letters are not significantly different.</p