An ultradense genetic linkage map with .10,000 AFLP loci was constructed from a heterozygous diploid potato population. To our knowledge, this is the densest meiotic recombination map ever constructed. A fast marker-ordering algorithm was used, based on the minimization of the total number of recombination events within a given marker order in combination with genotyping error-detection software. This resulted in ''skeleton bin maps,'' which can be viewed as the most parsimonious marker order. The unit of distance is not expressed in centimorgans but in ''bins.'' A bin is a position on the genetic map with a unique segregation pattern that is separated from adjacent bins by a single recombination event. Putative centromeres were identified by a strong clustering of markers, probably due to cold spots for recombination. Conversely, recombination hot spots resulted in large intervals of up to 15 cM without markers. The current level of marker saturation suggests that marker density is proportional to physical distance and independent of recombination frequency. Most chromatids (92%) recombined once or never, suggesting strong chiasma interference. Absolute chiasma interference within a chromosome arm could not be demonstrated. Two examples of contig construction and map-based cloning have demonstrated that the marker spacing was in accordance with the expected physical distance: approximately one marker per BAC length. Currently, the markers are used for genetic anchoring of a physical map of potato to deliver a sequence-ready minimal tiling path of BAC contigs of specific chromosomal regions for the potato genome sequencing consortium (http:/ /www.potatogenome.net).

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BackgroundIntegrating QTL results from independent experiments performed on related species helps to survey the genetic diversity of loci/alleles underlying complex traits, and to highlight potential targets for breeding or QTL cloning. Potato (Solanum tuberosum L.) late blight resistance has been thoroughly studied, generating mapping data for many Rpi-genes (R-genes to Phytophthora infestans) and QTLs (quantitative trait loci). Moreover, late blight resistance was often associated with plant maturity. To get insight into the genomic organization of late blight resistance loci as compared to maturity QTLs, a QTL meta-analysis was performed for both traits.ResultsNineteen QTL publications for late blight resistance were considered, seven of them reported maturity QTLs. Twenty-one QTL maps and eight reference maps were compiled to construct a 2,141-marker consensus map on which QTLs were projected and clustered into meta-QTLs. The whole-genome QTL meta-analysis reduced by six-fold late blight resistance QTLs (by clustering 144 QTLs into 24 meta-QTLs), by ca. five-fold maturity QTLs (by clustering 42 QTLs into eight meta-QTLs), and by ca. two-fold QTL confidence interval mean. Late blight resistance meta-QTLs were observed on every chromosome and maturity meta-QTLs on only six chromosomes.ConclusionsMeta-analysis helped to refine the genomic regions of interest frequently described, and provided the closest flanking markers. Meta-QTLs of late blight resistance and maturity juxtaposed along chromosomes IV, V and VIII, and overlapped on chromosomes VI and XI. The distribution of late blight resistance meta-QTLs is significantly independent from those of Rpi-genes, resistance gene analogs and defence-related loci. The anchorage of meta-QTLs to the potato genome sequence, recently publicly released, will especially improve the candidate gene selection to determine the genes underlying meta-QTLs. All mapping data are available from the Sol Genomics Network (SGN) database.

The root-knot nematode (Meloidogyne spp.) is a major plant pathogen, affecting several solanaceous crops worldwide. In Capsicum annuum, resistance to this pathogen is controlled by several independent dominant genes--the Me genes. Six Me genes have previously been shown to be stable at high temperature in three highly resistant and genetically distant accessions: PI 322719, PI 201234, and CM334 (Criollo de Morelos 334). Some genes (Me4, Mech1, and Mech2) are specific to certain Meloidogyne species or populations, whereas others (Me1, Me3, and Me7) are effective against a wide range of Meloidogyne species, including M. arenaria, M. javanica, and M. incognita, the most common species in Mediterranean and tropical areas. These genes direct different response patterns in root cells depending on the pepper line and nematode species. Allelism tests and fine mapping using the BSA-AFLP approach showed these genes to be different but linked, with a recombination frequency of 0.02-0.18. Three of the PCR-based markers identified in several genetic backgrounds were common to the six Me genes. Comparative mapping with CarthaGene software indicated that these six genes clustered in a single genomic region within a 28 cM interval. Four markers were used to anchor this cluster on the P9 chromosome on an intraspecific reference map for peppers. Other disease resistance factors have earlier been mapped in the vicinity of this cluster. This genomic area is colinear to chromosome T12 of tomato and chromosome XII of potato. Four other nematode resistance genes have earlier been identified in this area, suggesting that these nematode resistance genes are located in orthologous genomic regions in Solanaceae.

A QTL analysis was performed to determine the genetic basis of 13 horticultural traits conditioning yield in pepper (Capsicum annuum). The mapping population was a large population of 297 recombinant inbred lines (RIL) originating from a cross between the large-fruited bell pepper cultivar 'Yolo Wonder' and the small-fruited chilli pepper 'Criollo de Morelos 334'. A total of 76 QTLs were detected for 13 fruit and plant traits, grouped in 28 chromosome regions. These QTLs explained together between 7% (internode growth time) and 91% (fruit diameter) of the phenotypic variation. The QTL analysis was also performed on two subsets of 141 and 93 RILs sampled using the MapPop software. The smaller populations allowed for the detection of a reduced set of QTLs and reduced the overall percentage of trait variation explained by QTLs. The frequency of false positives as well as the individual effect of QTLs increased in reduced population sets as a result of reduced sampling. The results from the QTL analysis permitted an overall glance over the genetic architecture of traits considered by breeders for selection. Colinearities between clusters of QTLs controlling fruit traits and/or plant development in distinct pepper species and in related solanaceous crop species (tomato and eggplant) suggests that shared mechanisms control the shape and growth of different organs throughout these species.