Genetic, Physical, and Informatics Resources for Maize. On the Road to an Integrated Map

Maize (Zea mays) is among the most important crop plants in the world. For any crop plant, an integrated genetic and physical map serves as the foundation for numerous studies, especially those aimed at improving the agronomic characteristics of the plant. Once a phenotypically defined locus controlling a trait of interest has been mapped genetically, the integrated map facilitates isolating the underlying gene by positional cloning. Gene isolation is the prelude to studies aimed at first elucidating how the gene functions to control the targeted trait and then applying this knowledge to crop improvement. The maize genome presents a complex challenge to the development of an integrated genetic and physical map. The genome is large, approximately 2,500 megabases (Arumuganathan and Earle, 1991); it is laden with numerous families of transposable elements, whose copy numbers can be in the tens of thousands (Bennetzen, 2000; Myers et al., 2001); and sequence information is limited. Previous large-scale mapping projects, driven by the goal of genome sequencing, were aimed at selecting a minimal tiling path of genomic clones; as clones were sequenced, they were useful in verifying and merging contigs (International Human Genome Mapping Consortium, 2001; Chen et al., 2002). To develop an integrated genetic and physical map resource for maize, we are using a comprehensive approach that includes three core components. The first is a high-resolution genetic map that provides essential genetic anchor points for ordering the physical map and for utilizing comparative information from other smaller genome plants. The physical map component consists of contigs assembled from clones from three deep-coverage genomic libraries. The third core component is a set of informatics tools designed to analyze, search, and display the mapping data. As the genetic foundation of the integrated map for maize, we are using the intermated B73/Mo17 (IBM) map with more than 1,800 markers. Details of the IBM map are provided elsewhere (Davis et al., 2001; Lee et al., 2002). An enhanced version of the IBM map is being generated to take advantage of the many markers that were mapped on previous mapping populations, but were not mapped on the IBM because of lack of detectable polymorphisms between B73 and Mo17. We are implementing a “neighbors” map approach in which we extrapolate locations of loci from non-IBM maps to their nearest neighbors on the IBM map, such that the framework loci on the IBM serve as a fixed backbone onto which additional loci are added. To extrapolate, we look for shared loci on the two maps that define an interval containing a locus of interest, calculate the distance between the shared and target loci on the non-IBM map as a ratio of the distance for the interval, and use the ratio to estimate a map coordinate for the target locus in that interval on the IBM. In choosing which neighbors to extrapolate, we consider the depth of the genetic data and the confidence levels for locus assignment to the non-IBM map. The new map is called “IBM Neighbors.” Comparison of a region of chromosome 6 from the IBM map (left) and the IBM Neighbors map (right). Loci in red are shared between the maps. Loci in black have been extrapolated from other maps to the IBM Neighbors map. This display was created using the Compare Maps (cMap) tool. The integrated map currently offers display of the IBM map, but plans are under way to present an additional view based on the IBM Neighbors map. Three genomic libraries were constructed in bacterial artificial chromosomes (BACs) using DNA from the inbred line B73. This line was used because it is one of the parents of the IBM genetic mapping population; thus, probes mapped on the IBM and used to screen the BAC libraries could provide direct anchors for integrating the genetic and physical maps. To provide for deep coverage of the genome and to minimize gaps in sequence representation, three restriction enzymes were used for library construction: HindIII,EcoRI, and MboI. Specifications of all three libraries were described previously (Coe et al., 2002) and detailed characterization of the HindIII library has been published (Tomkins et al., 2002). Together, the libraries represent 27-fold coverage of the genome with average insert sizes ranging from 137 to 167 kb. The BACs are being assembled into contigs using the fingerprinting method of Marra et al. (1997). In brief, BACs are digested with HindIII, the digests are fractionated on high-resolution agarose gels, and banding patterns are catalogued to formulate a fingerprint using IMAGE software (Sulston et al., 1989). The HindIII banding patterns are then subjected to analysis to detect overlaps among them using the FingerPrintedContig (FPC) software package (Soderlund et al., 1997, 2000). Contigs are being generated automatically with a cutoff value of 1 × 10−12. This cutoff value is similar to that used in assembly for other genomes. Contigs for the human (Homo sapiens) genome were assembled using a 3 × 10−12 cutoff (International Human Genome Mapping Consortium, 2001); contigs for the mouse (Mus musculus) genome were assembled initially using a 1 × 10−16 cutoff, but after aligning the mouse contigs with the human genome, the stringency was reduced to 1 × 10−12 for further assembly (Gregory et al., 2002; S. Gregory, personal communication). As more clones are analyzed, the chance of a false positive overlap increases as a result of association of questionable clones whose banding patterns do not align precisely with the map. A new FPC function called the de-Qer reassembles all contigs with questionable clones using a more stringent (lower) cutoff value so as to automatically remove the majority of falsely assembled clones (C. Soderlund, unpublished data). Once all fingerprints are collected, the contigs will be edited manually to merge adjacent contigs; postponing manual editing till the end of fingerprinting will make this process more efficient. Decrease in contig number with increasing number of BACs fingerprinted. Integrating the genetic and physical map depends on anchoring BAC contigs by association with genetically mapped probes. Two strategies have been used for screening of the BAC libraries: hybridization of radioactive probes to BAC filters and PCR-based analysis of BAC DNA pools. For the hybridization experiments, subsets of theHindIII and EcoRI libraries were arrayed on high-density filters. For the PCR-based screening, DNA was isolated from a subset of the HindIII BACs that were pooled using a six-dimensional scheme (Klein et al., 2000). In collaboration with DuPont (Wilmington, DE) and Incyte Genomics (Palo Alto, CA), overgo probes derived from a unigene set representing over 10,000 maize expressed sequence tags (ESTs) were hybridized to BAC filter arrays. The unigene set was generated by DuPont; it is called the Cornsensus, to distinguish it from other maize unigene sets. At Incyte Genomics, the unigene sequences were masked for repetitive sequences, and overgo probes were designed for each EST contig (Ross et al., 1999). Another group of overgo probes was derived from other grass sequences, including cDNA coding regions and hypomethylated genomic sequences that have been mapped genetically in sorghum (Sorghum bicolor) and/or other cereal genomes, including maize. The probes were radioactively labeled and hybridized to BAC filter arrays using a multiplex pooling strategy. The results allow us to assign BAC addresses to specific overgo sequences. Details of the Cornsensus development and overgo hybridization will be described elsewhere. All overgo:BAC association data are available at the Maize Mapping Project Web site (http://www.maizemap.org/resources.htm). The BAC filters were also hybridized with probes representing mapped RFLP markers (Yim et al., 2002). Among the set of 90 core RFLP markers, most of the core probes yielded clear probe:BAC assignments, and the 22 that are single copy in the genome will serve as potential anchors linking the physical and genetic maps. To increase the number of possible anchors, the BAC pools are being screened by PCR with primers developed from sequences corresponding to additional single-copy RFLPs and with SSR primers. A contig can be assigned unambiguously to a specific genetic location if the contig is associated with (hit by) a probe that is mapped to one genetic location and the probe does not hit BACs in another contig. This is a simple concept, but in practice, a number of factors make contig anchoring a challenging prospect. In an ideal world, the most direct route to anchoring the physical map to the genetic map would be to use single-copy probes derived from all mapped loci to screen the BAC libraries. This would require a completed high-resolution genetic map. In our case, genetic mapping and physical mapping were occurring simultaneously and we did not have the luxury of choosing anchoring probes based on their genetic location. Instead, our probes—mainly derived from the Cornsensus—were chosen for two reasons: They constituted a large number of genes for which sequence information was available and they represented expressed genes and, therefore, possible direct links to traits of interest to many researchers. Anchoring strategy. Sources of markers for integrating the genetic and physical maps are shown on the left. Overgo probes from the Cornsensus unigene set and from a variety of grass markers were used to hybridize to the BAC filters, providing BAC:marker associations. Probes derived from mapped maize markers not in the Cornsensus are being screened on the BAC pools to derive additional BAC:marker associations. Contigs associated with mapped markers can serve as anchors for the integrated map. Unmapped markers associated with contigs are targets for mapping via single nucleotide polymorphisms (SNPs) to convert them to potential anchors. Many genetically mapped SSR and RFLP markers are not represented by the Cornsensus unigene set and, thus, were not used as probes in the initial BAC screening. As an additional anchoring strategy (Fig. 3), BAC addresses are being obtained for these sequences by PCR-based screening of the BAC DNA pools using SSR primers or primers generated from mapped RFLP sequences. This information is then incorporated into FPC for contig assembly. To assign contigs to the genetic map, the most obvious rule is that one contig should have one genetic location. Unfortunately, the patterns of probe:BAC:contig associations obtained from FPC contig assembly reveal several possible types of conflict that are at odds with this rule. Although manual editing of the contigs will resolve some of these conflicts, this step is not scheduled to begin until fingerprinting is complete. In the meantime, we have analyzed the patterns of probe:BAC:contig association to develop a set of guidelines that we can use now to construct a working version of the integrated map that has minimal internal conflicts. Effect of filtering out single probe:contig associations that are based on a single BAC hit in a contig. Graph shows the number of markers that detect one to five or more BACs. The first column in each pair represents non-filtered data. The second column represents the marker:contig associations after filtering. Another potential source of conflict arises when a probe that maps to one genetic location hits BACs that have been assembled into more than one contig. This could occur because the probe represents a duplicated sequence in the genome and, therefore, is truly associated with more than one genetic location, perhaps only one of which has been mapped. This possibility is being addressed by developing gene-specific probes for the duplicated sequences and screening the BAC DNA pools to clarify the chromosomal assignment. Another explanation for one probe hitting two contigs could be that the two contigs have not yet been merged into one. This might be true especially if the probed BACs are at the ends of the contigs. This conflict will be resolved during manual editing by examining the fingerprints of the end clones of the contigs to ask if a merge is warranted. An additional type of conflict occurs when two probes corresponding to distinct genetic locations associate with a single contig. This type of conflict could be because of errors in any of several steps: BAC addressing, in silico determination of overgo:mapped marker identity, genetic mapping, or contig assembly. Resolution involves verifying results of BAC hybridization and pool analysis, double checking genetic mapping assignments, and editing contigs manually to test the possibility that the contig is chimeric and should be split apart. Finally, as contigs with genetically mapped markers are merged, a “majority” rule can be applied to assign the contig to a genetic position corresponding to the markers with the highest number of hits in the contig. An important step in refining the integrated map will be to test its robustness. Several types of experiments are under way. For example, the BAC pools are being screened by PCR using primers derived from some of the same cDNA clones from which overgos were derived for hybridization-based screening of the BAC filters. Preliminary comparisons of the BACs identified by the two methods indicate that there is 80% agreement among the methods (Y. Yim and G. Davis, unpublished data). To verify the accuracy of anchoring, we are looking, within a contig, at the markers other than the anchoring marker to ask if there is any additional type of information available to indicate that they are associated with the same genetic location as the anchoring marker. In some cases, this confirming evidence may come from data derived from other genetic mapping experiments. In other cases, colocalization may be inferred from information gleaned from sorghum or rice (Oryza sativa) maps and extrapolated to maize, based on presumed syntenic relationships. Finally, as individual researchers in the maize community use the integrated map resources to isolate their genes of interest, their feedback will help validate the map. To retrieve and display the integrated map data, several Web-based tools have been developed. Table I lists the tools and their Web addresses. iMap provides the unambiguous contig:chromosome assignments in the context of side-by-side views of the genetic map and associated contigs. cMap allows comparison of locus order in different genetic maps from maize, with extensions to rice and sorghum. WebFPC displays the maize BAC contig assemblies. WebChrom provides a chromosome-centered view of the WebFPC data. Because WebFPC and WebChrom present the data with little or no filtration, there may be ambiguity in contig:chromosome assignments. MSL searches an input sequence and returns map location and sequence homology to the Cornsensus and other maize sequences. Together these tools offer unprecedented access to the integrated genetic and physical mapping data for maize and allow the user to enter the access arena from a number of different points. The following sections describe the features of each tool and some typical applications for their use. Web tools for genetic, physical, and integrated maize map data Web tools for genetic, physical, and integrated maize map data iMap presents a simultaneous display of the IBM genetic map and associated BAC contigs that have been unambiguously assigned to their respective genetic locations. The iMap tool was developed by adapting GIOT software originally written by the Rice Genome Project. The iMap “front end” is part of a three-tier architecture for information retrieval in which iMap operates as an applet residing in a Web browser. Data are stored in a dedicated database populated with genetic map data and with FPC-derived physical map data. To retrieve information, requests are sent from the iMap applet (client) to a waiting servlet running on a server. The servlet retrieves the information from the database and delivers it to the client, which then displays the information. The database is updated regularly to ensure congruent connections to the most recent WebFPC data. Representative display for iMap and linked resources. The iMap display includes side-by-side views of the genetic map on the left and associated BAC contigs (circles) on the right. Highlighting a locus on the genetic map leads to display of marker types and physical object types. Clicking on any of these words causes a pop-up window to appear with information and links to additional data. Representative links to MaizeDB for genetic marker information and to WebFPC for contig information are shown. In the WebFPC window, the marker highlighted in blue detects several BACs, which are highlighted in green. iMap is the starting point for a user who has genetic map information and wishes to find anchored contigs associated with a favorite locus or neighboring loci. The user enters the name of the locus on the first page. The returns a display of the genetic map of the chromosome with the locus the highlighted links returns information associated contigs. cMap is a tool for the order of loci in different maize maps. also to comparisons in sorghum and rice maps. cMap was developed by adapting GIOT The display presents side-by-side views of of genetic and lists the loci or probes they have in and shows linking the shared loci. 1 shows a of a cMap provide access to the underlying map data in a user has genetic information a rice or sorghum gene and wishes to find out if a corresponding gene in maize has been associated with BAC the starting point is rice or sorghum maps with the IBM will indicate if the gene of interest has a in maize. the maize gene can be used as a to iMap for anchored contigs. developed as a Web-based version of displays contigs and associated BAC shows a contig are via links to The contigs in WebFPC are approximately as new fingerprinting data are WebFPC is by BAC probe or contig, and it is the starting point for a user who has the name of a probe unigene or or the name of a BAC and wishes to find associated contigs. A is by the probe name the marker or BAC name the The returns a of contigs. WebChrom is a tool to WebFPC and provides a view of FPC contigs. WebChrom features a display of the chromosomes of on any chromosome shows the chromosome with contigs and markers. views of probe or BAC markers the WebChrom different than iMap for contigs on chromosomes and contig:chromosome assignments may be different between the two in it is possible that more than one contig might be for a single chromosomal location based on association with a marker at that location. a user has genetic map information for a gene of interest and wishes to find any associated anchored unambiguously or the starting point is selecting a chromosome of interest, the user can view all contigs that have been associated with that chromosome on the of the genetic location of probes that hit the contigs. The MSL is a tool that an DNA sequence as a the Cornsensus or any other maize sequence and returns sequence and map location MSL is a starting point to find out if a DNA sequence of interest is represented in the Cornsensus which overgo probes were developed to screen the BAC The sequence is into the and a is The will display the to Cornsensus sequences, sequence and genetic map location, if a locus is it can be used as a in iMap to for an anchored contig. maps have been assembled for a with the genomic of maize, large genome and of repetitive that are into large arrays. Mapping that the of contig assembly and anchoring with a large number of genetically mapped markers will a integrated map. each FPC contig number is and we this to the number of contigs will be is but a simple provides a BACs, representing genome coverage with average of are and assembled at a stringency overlap among the then contigs could be and The use of the de-Qer function of which false and the of genetic anchoring information, which facilitates merging of contigs hit by adjacent markers, should ensure that the contig are To derive genetic anchors, we are a approach (Fig. Sources of anchors mapped markers and identified in silico from the Cornsensus and from other grass genome maps or sequences, Cornsensus sequences to be targeted for development and mapping, and mapped markers and to be on the physical map by screening of the BAC DNA pools. goal is to on large, contigs to two anchors for each contig as a for verifying map assignments and the order of the contigs to the genetic map. The in rice and sorghum will provide an additional resource for integrating the maize map et al., 2001; Chen et al., 2002). The syntenic among these that map associations in rice or sorghum can be used as points for or confirming similar in maize. As a starting point for these homology searches of the Cornsensus sequences have that many sequence to genes in rice that have been on the integrated genetic and physical map. can be in the context of the rice map, using a tool available at which displays an of the maize Cornsensus other grass sequences including sorghum with rice genomic such as this will be in information from rice and sorghum to maize. Table I presents the Web addresses for the tools developed for and the maize resources. information is available at the Maize Mapping Project Web site and at MaizeDB We of for and BAC DE) and Genomics, Alto, for their in the to the BAC clones by hybridization with Cornsensus of and for and the Rice Genome Project for use of We the of our of of CA), and