Desacidificación de aceite de oliva lampante

Integration of conventional and molecular maps

During the period 1980–1990 molecular maps were developed for many plant species.

The first generation of molecular maps have been integrated with conventional genetic maps constructed using morphological and isozyme markers through cytological mark-ers and markmark-ers shared by different maps.

The 12 molecular linkage groups in rice (McCouch et al., 1988) were assigned to clas-sical linkage groups using trisomics for each of the 12 rice chromosomes. Shared markers and those which segregate in the population

can be integrated with the molecular link-age map by using the same population for both conventional and molecular markers.

As only very few morphological markers can segregate simultaneously in one popu-lation, integration of many of these mark-ers requires multiple populations each with an available preliminary molecular map. If a complete linkage map for morpholgical markers is available, the positions of these markers relative to molecular markers can be inferred from the linkage relationship revealed by both morphological and molec-ular markers. In addition, morphological markers, including some traits of agronomic importance, can be mapped much more precisely if they are integrated with a dense molecular map and this has now become an integral step in trait and gene mapping.

Integration of conventional and molecu-lar maps has been very successful for crop plants for which relatively complete genetic linkage maps are available as a result of the use of morphological markers.

Some representative examples of such maps include rice, maize, tomato and soy-bean. In rice, 39 morphological markers and 82 RFLP markers were mapped together based on the segregation analysis of 19 F₂ populations derived from the crosses between indica cultivar IR24 and japonica lines with different morphological markers (Ideta et al., 1996). In tomato, a number of morphologi-cal and isozyme markers were mapped with respect to RFLP markers by orienting the molecular linkage map to both morphologi-cal and cytologimorphologi-cal maps. An integrated high-density RFLP-AFLP map of tomato based on two independent Lycopersicon esculentum

× Lycopersicon pennellii F2 populations was constructed (Haanstra et al., 1999), which spanned 1482 cM and contained 67 RFLP and 1175 AFLP markers. Integrated maps were also developed for maize (Neuffer et al., 1997; Lee et al., 2002) and soybean (Cregan et al., 1999).

Integration of multiple molecular maps For many crop plants, several molecular maps have been constructed using differ-ent populations. These populations are of

variable size and structure and maps have been created using different numbers and types of markers. To build an integrated reference or consensus map, the order and genetic distance between specific markers is compared across populations and maps.

Stam (1993) developed a computer pro-gram, JOINMAP, for the construction of genetic linkage maps for several types of mapping populations: BC₁, F₂, RILs, DHs and out-breeder full-sib family. JOINMAP can be used to combine (‘join’) data derived from several sources into an integrated map.

For each crop all the molecular maps developed from different populations will finally be integrated into a consensus map.

This process has been very successful for several major crops and it can be expected that it will be extended to all crops when sufficient maps become available. In wheat, an SSR consensus map was constructed by fusing several genetic maps to maximize the integration of genetic mapping information from different sources (Somers et al., 2004).

In cotton, chromosome identities were assigned to 15 linkage groups in the RFLP joinmap developed from four intraspecific cotton (Gossypium hirsutum L.) popula-tions with different genetic backgrounds (Ulloa et al., 2005). In maize, two popula-tions of intermated RILs (IRILs) were used to build a consensus map, the first panel (IBM) was derived from B73 × Mo17 and the second panel (LHRF) from F2 × F252.

Framework maps of 237 loci were built from the IBM panel and 271 loci from the LHRF panel. Both maps were used to locate 1454 loci (1056 on map IBM_Gnp2004 and 398 on map LHRF_Gnp2004) that corresponded to 954 previously unmapped cDNA probes (Falque et al., 2005). In barley, Wenzl et al.

(2006) built a high-density consensus link-age map from the combined data sets of ten populations, most of which were simultane-ously typed with DArT and SSR, RFLP and/

or STS markers. The map comprised 2935 loci (2085 DArT, 850 other loci), spanned 1161 cM and contained a total of 1629 ‘bins’

(unique loci). The arrangement of loci was very similar to, and almost as optimal as, the arrangement of loci in component maps created for individual populations.

Integration of genetic and physical maps Integrated genetic and physical genome maps are extremely valuable for map-based gene isolation, comparative genome analysis and as sources of sequence-ready clones for genome sequencing projects.

A well-defined correlation between the physical and genetic maps will greatly facilitate molecular breeding efforts through associating candidate genes with important biological or agronomic traits, positional cloning and comparative analy-sis across populations and species, and whole genome sequences, which will in turn facilitate the development of various molecular breeding tools.

Various methods have been developed for assembling physical maps of complex genomes and integrating them with genetic maps. To create an integrated genetic and physical map resource for maize, a compre-hensive approach was used that included three core components (Cone et al., 2002).

The first was a high-resolution genetic map that provided essential genetic anchor points for ordering the physical map and for utilizing comparative information from other smaller genome plants. The physical map component consisted of contigs (sets of overlapping fingerprint clones) assem-bled from clones from three deep-coverage genomic libraries. The third core compo-nent was a set of informatics tools designed to analyse, search and display the mapping data. In rice, most of the genome (90.6%) was anchored genetically by overgo hybrid-ization, DNA gel blot hybridization and in silico anchoring (Chen et al., 2002).

In wheat, the genetic–physical map rela-tionship of microsatellite markers was established using the deletion bin system (Sourdille et al., 2004). In sorghum, Klein et al. (2000) developed a high-throughput PCR-based method for building bacterial artificial chromosome (BAC) contigs and locating BAC clones on the genetic map in order to construct an integrated genetic and physical map. It was found that 30%

of the overlapping BACs aligned by AFLP analysis provided information for merg-ing contigs and smerg-ingletons that could not

be joined using fingerprint data alone. In the grasses Lolium perenne and Festuca pratensis, the physical map was integrated with a genetic map using genomic in situ hybridization, which was composed of 104 F. pratensis-specific AFLPs. The integrated map demonstrated the large-scale analy-sis of the physical distribution of AFLPs and variation in the relationship between genetic and physical distance from one part of the F. pratensis chromosome to another (King et al., 2002).

An integrated genetic and physi-cal mapping tool has been developed by the Maize Mapping Project, Columbia, Missouri, USA (http://www.maizemap.

org/iMapDB/iMap.html). Contigs that were assembled by fingerprinting and the

automated matching of BACs were then anchored on to IBM2 and IBM2 neighbour maps. In the Gramene database, a web-based tool, CMAP, was developed to allow users to view comparisons of genetic and physical maps (Ware et al., 2002). In addi-tion, an integrated bioinformatic tool, the Comparative Map and Trait Viewer (CMTV), was developed to construct consensus maps and compare QTL and functional genomics data across genomes and exper-iments (Sawkins et al., 2004). All these tools can be used to build integrated maps based on shared markers and a reference map to initiate the process. The integra-tion of genetic, cytological and physical maps is illustrated in the example shown in Fig. 3.6.

©Yunbi Xu 2010. Molecular Plant Breeding (Yunbi Xu) 59

Molecular Breeding Tools: