A HANDY REFERENCE FOR DESCRIPTIONS OF GENETICS MAPS IN MAIZEGDB
(from Lisa Harper's Maize Newsletter article)
(see old descriptions of maps)

MaizeGDB.org currently stores over 1789 genetic maps created by community members. A list of all maps is here. In the most general sense, genetic maps are made by setting up a testcross or mapping population and measuring the amount of recombination between any two markers for which two different alleles segregate in the mapping population. Many genetic maps stored at MaizeGDB have been made from single testcrosses or single mapping populations. However, the most commonly used maps are "composite" genetic maps, meaning that they have data from many different mapping populations merged into one map.


Genetic: This is the 2008 release of the composite genetic map developed and maintained by Ed Coe. For the past 50 years, Ed has compiled reported mapping information into one genetic map. Thus, this map contains information from crosses between many different inbreds, hybrids, and "mongrels". It also contains information from crosses where recombination occurred in the female parent or in the male parent or both. Thus, any two markers on this map may not have been mapped in the same population. All recombination information used to place markers on this map can be found by clicking on the marker within the MaizeGDB map display. Comments with each map give information on the strategies for updating and curating the map to include genes that are incorporated based on genome sequencing and sequence matching.


IBM (Intermated B73 x Mo17) Genetic Maps: This map is called "high resolution" because the method used to generate the mapping population created more detectable recombinations per chromosomes after several generations. To generate the mapping population (Fig 1), the two inbreds B73 and Mo17 were crossed together to make the F1 hybrid. This was self-pollinated. F2 progeny were then intermated for four generations, followed by repeated selfing to generate Recombinant Inbred Lines (RILs). This type of population is often referred to as Intermated Recombinant Inbred Lines (IRILs). Every cross after the F1 provided more opportunity for recombination between linked loci. These recombinations remain detectable at any genomic position where B73 and Mo17 are polymorphic for the markers used in the subsequenct mapping steps. The fact that these inbreds are so polymorphic relative to each other allows a greater number of markers to break linkage. This leads to a "high resolution" genetic map. However, markers cannot be mapped any place in the genome where there is no polymorphism between Mo17 and B73. It is useful to keep in mind that the units on the IBM maps are not really centimorgans. The IBM population is described in more detail by Lee et al, Plant Mole Bio 48:453-63. After genotyping these IRILs with 2,046 markers, the Maize Mapping Project (MMP) constructed a genetic map (IBM2) that contains 2,026 markers (COE et al. 2002; CONE et al. 2002). Core markers that where put on the map with strong statistical support are now called "FRAME" markers. "NEIGHBORS" are markers that are added to the map by calculation, not by actual mapping.


IBM Neighbors: This description is taken directly from Cone et al, Plant Phys: 130:1598- "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." "The key distinction between the IBM and IBM Neighbors maps lies in the confidence level of locus order; the IBM has fewer well-ordered loci and IBM Neighbors has more loci, but confidence in the order is lower."


ISU-IBM Map4 was prepared by the Fu et al, (Genetics 174:1671-1683) using a panel of 91 IBM lines. They mapped 1,329 new gene-based insertion-deletion polymorphism markers (called IDPs or indels) and 2,029 previously developed markers on the IBM map. This groups calls markers "skeleton" if there is excellent statistical support to place them accurately on the map, and "muscle" if support is not as good.


LHRF Gnp2004 and IBM GNP2004: Falque et al (Genetics 170:1957-1966) generated a IRIL population from the inbreds F2 x F252 to map loci monomorphic on IBM. In their paper, they state: "We built framework maps of 237 loci from the IBM panel and 271 loci from the LHRF panel. Both maps were used to place 1454 loci (1056 on map IBM_Gnp2004 and 398 on map LHRF_Gnp2004) that corresponded to 954 cDNA probes previously unmapped.


>Nested Association Mapping (NAM): The NAM populations are like RILs on steriods. Regarding the IBM map, recall that markers can NOT be genetically mapped in any region that is not polymorphic between B73 and Mo17. To overcome this, Yu et al (Genetics 178:539-551) crossed B73 to 25 diverse maize lines (called "founders"). Twentyfive F1s where generated, from which 200 RILs where made from each F1. The F1's were never intermated, so these are RILs, not IRILs.While each mapping set from an individual F1 has less resolution than the IBM map, together the 25 sets (5000RILs) are much more powerful. The large number of "founders" greatly increases the liklihood that most regions in the genome will be polymorphic in at least one mapping set, allowing markers to be mapped in those genomic regions. Excellent figures describing the process of generating the NAM populations are in the Yu et al paper above. The Diversity Group (panzea.org) is genetically mapping literally millions of SNP markers using these lines. This will lead to a very high resolution genetic map.




Figure1. Generation of IRILs, a special mapping population (for a ppt, see the tutorials).