- Deposited research article
The genetic structure of recombinant inbred mice: High-resolution consensus maps for complex trait analysis
Genome Biology volume 2, Article number: preprint0007.1 (2001)
Recombinant inbred (RI) strains of mice are an important resource used to map and analyze complex traits. They have proved particularly effective in multidisciplinary genetic studies. Widespread use of RI strains has been hampered by their modest numbers and by the difficulty of combining results derived from different RI sets.
We have increased the density of typed microsatellite markers 2- to 5-fold in each of several major RI sets that share C57BL/6 as a parental strain (AXB, BXA, BXD, BXH, and CXB). A common set of 490 markers was genotyped in just over 100 RI strains. Genotypes of another ~1100 microsatellites were generated, collected, and error checked in one or more RI sets. Consensus RI maps that integrate genotypes of ~1600 microsatellite loci were assembled. The genomes of individual strains typically incorporate 45-55 recombination breakpoints. The collected RI set - termed the BXN set - contains approximately 5000 breakpoints. The distribution of recombinations approximates a Poisson distribution and distances between breakpoints average about 0.5 cM. Locations of most breakpoints have been defined with a precision of < 2 cM. Genotypes deviate from Hardy-Weinberg equilibrium in only a small number of intervals.
Consensus maps derived from RI strains conform almost precisely with theoretical expectation and are close to the length predicted by the Haldane-Waddington equation (X3.6 for a 2-3 cM interval between markers). Non-syntenic associations among different chromosomes introduce predictable distortions in QTL data sets that can be partly corrected using two-locus correlation matrices.
Recombinant inbred (RI) strains have been used extensively to map a wide range of Mendelian and quantitative traits . They offer compelling advantages for mapping complex genetic traits, particularly those that have modest heritabilities. Each recombinant genome is replicated in the form of an entire isogenic line [2,3,4,5,6] and variance associated with environmental factors and technical errors can be suppressed to low levels. This elevates heritability and improves the prospects of mapping underlying quantitative trait loci (QTLs). Recently, we have used RI strains to map QTLs that generate variation in the architecture of the mouse CNS [7,8,9,10,11,12,13,14]. The main advantage in this context is that the complex genetic and epigenetic correlations between interconnected parts of the brain can be explored using complementary molecular, developmental, structural, pharmacological, and behavioral techniques. Gene effects can also be tested under a spectrum of environmental perturbations and experimental conditions. RI strains can be exploited to expose gene-environment interactions and gene pleiotropy. These important facets of genetics can only be explored with difficulty using conventional mapping populations in which each genome is unique.
A third advantage of RI strains is that genotypes generated by different groups using a variety of methods can be pooled to generate high-density linkage maps. As a result, loci that segregate in RI sets can often be mapped with impressive precision without genotyping. This attribute was a significant advantage before the advent of efficient and easy PCR genotyping methods . Unfortunately, over the last decade databases of RI genotypes have accumulated many typing errors. Each error expands distances between marker loci and degrades linkage, inevitably blurring associations between genotypes and phenotypes and making it difficult to map traits, whether they are Mendelian or quantitative in nature. The accumulation of false recombinations has become extreme in common RI sets. For example, the map of Chr 1 in the complete BXD data set (Mouse Genome Informatics Release 2.5: www.informatics.jax.org/searches/riset_form.shtml) is based on 160 linked marker loci and is an astonishing 1305 cM long. This map is approximately 12 times the length of an F2 map of Chr 1, and just over 3 times the length expected of an RI map of Chr 1. The accumulation of typing errors has led to efforts to reconstitute maps using curated subsets of markers for which genotypes can be adequately and independently verified. Sampson and colleagues  assembled maps for the AXB and BXA recombinant inbred strains that improved the utility of this set. Similarly, Taylor and colleagues  assembled comparable high quality maps for the complete set of 36 BXD strains that are based almost entirely on easily typed and verified microsatellite markers.
Our study's aims complement this previous work. Our first aim has been to generate reliable high-resolution genetic maps for each of five widely used sets of RI strains: AXB, BXA, BXD, BXH, and CXB. These RI sets all share C57BL/6 alleles, and they can be assembled into a BXN superset consisting of just over 100 lines. The introduction of the RIX cross by Threadgill and colleagues (Threadgill DW, Manly KF, Williams, RW, personal comm) provides an impetus to precisely define recombination breakpoints in RI strains. RIX progeny are isogenic F1 hybrids made between pairs of RI strains and 5050 unique isogenic but non-inbred RIX genometypes can be constructed from 101 RI strains. Selected subsets of this huge pool of recombinant F1 genomes can be made by crossing those RI strains with breakpoints in intervals thought to harbor QTLs. These interval-specific RIX progeny are phenotyped and used to refine the genetic analysis of complex traits. Knowing the precise location of breakpoints in RI lines also makes it possible to map modifier loci of mutations by simply making a series of F1 crosses between inbred carrier stock (for example a knockout carried on a C57BL/6 background) and fully typed RI lines. These F1 crosses have a genetic structure similar to a conventional N2 backcross, but they will not need to be genotyped and they have the major advantage that groups of isogenic backcross progeny can be typed to obtain much more reliable scores.
Our second aim has been to describe the recombination characteristics of 1 typical RI strains and their chromosomes in a more theoretical context. We empirically tested the Haldane-Waddington equation of map expansion in sib-mated RI strains. We also tested relatedness among RI lines, and measured deviations from Hardy-Weinberg equilibrium associated with 10-30 years of inbreeding, genetic drift, mutation, and selection.
Our third aim has been to help resolve a serious but unrecognized problem in QTL mapping that arises from non-syntenic genetic correlations within mapping panels. Genetic correlations between intervals on different chromosomes can be high in RI sets and this can result in spurious results and false positive QTLs. We provide detailed correlation matrices that can be used to detect and control for non-syntenic association.
The results are divided into two sections. The first summarizes the RI consensus map and genotypes of individual strains. The second section considers the structure of the multiple generation meiotic recombination maps of RI strains. We highlight the problem of non-syntenic association that is a feature of these maps and we outline a solution to minimize the risk of type I and type II error in QTL mapping studies.
RI consensus maps of mouse chromosomes
Mapping complex genetic traits involves matching strain distribution patterns (SDPs) of genotypes with those of phenotypes. The utility of an RI set and the probability of successfully mapping any heritable quantitative trait or novel Mendelian trait is therefore a function of the number of well defined and correctly positioned SDPs of marker loci. We therefore concentrated genotyping efforts on those intervals with comparatively low densities of fully typed microsatellite markers or those intervals that harbored large numbers of recombinations between neighboring markers. One goal in generating dense maps for each chromosome was to discover and verify as many recombination breakpoints and SDPs as possible using available microsatellite primer pairs. Ideally, in high density genetic maps the number of markers should exceed the number of SDPs, and all recombination breakpoints in an set RI would be defined with subcentimorgan precision. We have worked with more than 1600 microsatellite markers, a number that is still insufficient to reach a subcentimorgan goal. However, the density of markers on most chromosomes is sufficient to locate the majority of recombination breakpoints within ± 2 cM.
Fewer than 25 common microsatellite markers had been typed on all major RI sets when we began this work. This number has been increased to 490 common makers (Table 1). These markers were used to assemble the consensus BXN maps - B for the C57BL/6 allele that all sets have in common and N for the not-B6 parental allele that differ among the four RI sets (A/J in AXB-BXA, DBA/2J in BXD, C3H/HeJ in BXH, and BALB/cByJ in CXB). The set of 490 shared markers are supported by an additional 1089 MIT markers that we or other groups have typed in at least one RI set (Table 2). In the BXN database summarized in Table 1 any pair of RI sets shares between 500 and 600 fully genotyped markers. The two largest RI sets, AXB-BXA and BXD, have been typed at 591 common markers. The composite BXN maps are based on a total of just under 1600 microsatellite makers and just over 100 RI strains (Tables 1 and 2).
Undiscovered recombinations and SDPs
The number of recombinations in RI sets still significantly exceeds the number of SDPs that have been unequivocally defined. Based on current marker density we estimate that we have defined from 37% (AXB/BXA) to 59% (CXB) of the total set of SDPs (Table 3). The entire BXN set contains approximately 4800 known recombination breakpoints (Tables 3 and 4). There are likely to be another 400 breakpoints that we have not yet detected. To discover 623 (41%) of the 1492 SDPs in the BXD set required 936 selected markers. Recovering the majority of the remaining SDPs could require an additional 1000 to 1500 well placed marker loci. The density of informative microsatellite markers is not yet dense enough to define many more SDPs in the BXN set, but once SNP and microsatellite maps have been fully integrated into chromosome sequence databases, it will be straightforward to generate additional markers and use these to define all 5000-6000 SDPs in the BXN set.
To minimize genotyping errors we retyped many markers, particularly those that were associated with unusually large numbers of recombination events. We were particularly interested in minimizing the number of genotypes that appeared to be associated with two closely located recombination events - what are sometime referred to as double recombinant haplotypes. These haplotypes appear to be the result of two separate crossover events, one of which is just proximal to a particular marker and the other of which is just distal to the same marker. For example, the haplotype of a short chromosome interval, -B-B-B-N-B-B-B-, is associated with two recombinations that flank the central marker with the N genotype. Because of interference, the occurrence of two recombinations within 10 cM is highly improbable in an F2 intercross, and consequently, double recombinants are often used as a measure of genotyping error or incorrect marker order. However, in RI strains recombination events accumulate over many generations, and two or more recombinations can therefore be extremely close to each other and can produce true double recombinant haplotypes. It is therefore necessary to verify, rather than discard, all apparent double recombinants in RI strains. We checked our own marker genotypes and the majority of microsatellite markers typed by other investigators genotypes they were associated with double recombination events in one of more RI strain. When two or more strains contributed to double recombinants we usually retyped all strains. Approximately 150 double recombinant haplotypes (and 300 false recombinations) were eliminated in the process of error checking. Our genotypes therefore differ from those of many microsatellites reported in original publications and listed in the MGI release 2.5 (www.jax.org). In a few instances, our revisions have generated new (but verified) double recombinant haplotypes.
We discovered unexpected polymorphisms at several loci in a few lines and all were scored as unknown (U) (Table 5). The clustering of aberrant products in AXB13 and AXB14 is consistent with the common origin of these strains from a partly inbred progenitor line. However the genotypes of the other three sets of strains (e.g., AXB1 and AXB3) are generally completely independent.
PCR primer pairs in several intervals gave two bands consistent with a genuine heterozygous haplotype. Heterozygous loci are rare among the fully inbred RI strains but they are fairly common among new BXH strains that were genotyped at the tenth to 16 generation of inbreeding. In scoring recombination frequency we treated all heterozygous loci and intervals as if they had not been typed. Mutations in microsatellite loci may be responsible for some heterozygosity .
Changed locus order
The order of loci of the BXN consensus maps generally conforms to that of the chromosome committee reports (CCR) and the MIT-Whitehead genetic maps (Table 2). In about 130 instances we have changed the order of loci over short intervals. For example, D1Mit276 and D1Mit231 on proximal Chr 1 do not recombine in the MIT F2 cross, but in the BXN set there is a single recombination between these markers in BXA11 that is most consistent with a reversal of order relative to the CCR (compare the columns labeled CCRcM, MITcM, and BXNcM in Table 2). The only non-trivial discrepancy was on proximal Chr 15. We reordered approximately 32 loci on Chr 15 to improve linkage statistics. We have not attempted to integrate the BXN data with numerous other mapping panels, and it is likely that original CCR order will often be well supported by other large mapping panels or rapidly improving physical maps. Full sequence data will soon resolve these minor inconsistencies.
Reassigned microsatellite loci
A number of microsatellite loci were reassigned to locations on chromosomes other than those expected on the basis of their original assignments (Table 6). Mapping data in one or more of the RI sets is consistent with a reassignment of 16 microsatellite loci to different chromosomes. All of these reassignments are provisional, particularly those with LOD scores of less than 10. In several cases, (e.g., D10Nds10) we have reassigned microsatellite loci typed by other investigators that now are linked to new and firmly mapped markers. All primers used to amplify these microsatellites (except D10Nds10) were resynthesizing to confirm that they are identical to those originally specified by Dietrich and colleagues .
Individual maps are based on genotypes of as few as 37 markers (Chr X) to as many as 129 makers (Chr 1) per chromosome (Table 1). The mean separation between markers is approximately 1 cM (0.95 cM using CCR maps as a reference and 0.87 cM using the RI maps themselves). When the 577 markers that do not have unique SDPs are excluded from the analysis, the average separation increases to 1.2 cM using CCR maps and 1.4 cM using the RI data. Typical resolution of the BXN set for mapping a Mendelian trait is 1-2 cM. Approximately 90% the mouse genome is currently less than 2 cM from a typed microsatellite marker in the RI set. The asymptotic resolution of the set of BXN strains given infinitely dense maps in which every possible SDP has been characterized would average about 0.3-0.4 cM. There are currently 14 poorly typed regions. These regions are operationally defined as intervals of 5 to 12 cM between adjacent markers (Fig. 1). The largest is on proximal Chr 2 between 9 and 21 cM (Table 2).
Several RI strains share common haplotypes and recombination breakpoints. This non-independence of RI lines will distort genetic maps. To systematically search for and eliminate partial duplicate RI lines we constructed a genotype similarity matrix for all strains using the QTL analysis program Qgene . An example of a small part of this matrix is illustrated in Table 7 for the CXB set.
As already noted by Sampson et al. , three sets of AXB and BXA strains show high genetic similarity, and genotypes of four strains should be excluded from most genome-wide mapping panels. Phenotype data obtained from members of the three groups listed below should often be collapsed and treated as a single strain.
BXA8 and BXA17: 99.8% genetic identity. Only two markers are known to be polymorphic, D3Mit392 and D6Mit108. The polymorphism at D6Mit108 has been verified using independent DNA samples from these two strains. BXA17 is actually a direct derivative of BXA8 separated in 1996-1997 . Any divergence in genotypes or phenotypes is due to the recent generation and fixation of new mutations in these two separately maintained lines.
AXB18, AXB19, and AXB20: 97% to 99% identity among any of the three pairs.
AXB13 and AXB14: 92% identity.
These three sets of strains were treated as three single strains when analyzing recombination frequencies.
The mean allele similarity of the remaining strains averages almost precisely 50%. The distribution of values is symmetrical about the mean (Fig. 2) with the great majority of strain pairs falling in the range of 30% to 70% similarity. The highest remaining similarities within RI sets are between BXD13 and BXD41 (74%), AXB6 and AXB17 (73%), BXHB2 and BXH9 (71%), AXB6 and AXB12 (70%), BXD28 and BXD33 (69%), BXD19 and BXD29 (68%), and AXB11 and AXB14 (67%). These values are not significantly higher than the similarity scores typically noted across RI sets.
In theory a set of 75,000 genotypes generated across the genome of 100 RI strains should detect only a single residual heterozygous loci at generation F55 of inbreeding (Fig. 2, fine line; the inbreeding coefficient at F55 is 0.99998812). DNA from most lines was extracted in the 1990s at F generations between F20 and F70 (see Methods and Materials). We detected a total of 13 strains that were still heterozygous (BXA20 from D1Mit77 to D1Mit490; AXB21 from D2Mit102 to D2Mit420, AXB24 at D3Mit62, BXA23 at D5Mit95, AXB3 and BXA16 at D12Mit167, BXA20 from D13Mit224 to D13Mit254; BXD31 at D9Mit243, BXD34 at D7Mit281, BXD37 at D1Mit83; BXH12 at D1Mit417, BXH10 at D12Mit167; CXB8 from D1Mit361 to D1Mit291). DNA samples were taken from single animals of each strain and for this reason these estimates of residual heterozygosity underestimate the total heterozygosity about twofold.
The central part of Chr 1 is interesting because it is heterozygous in three strains (BXD37, BXH12, and BXA20). There is also an interval that is approximately 2.5-cM-long that is apparently maintained in heterozygosity in AXB21 on Chr 2. Such maintenance should be accompanied by reduced fecundity in this line if homozygotes are lethal or sublethal. This would account for poor breeding performance. It is also possible the heterozygosity is the result of a mutation, but if this were the case we would expect novel length polymorphisms, and the two alleles were usually the expected parental lengths.
Structure of RI genomes
RI mean map lengths
The mean frequency of recombinations, CRI,between two linked markers in an RI strain generated by breeding siblings is approximately 4c/(1+6c) where c is the recombination fraction per meiosis [21, 22]. An infinitely dense RI map should average four times the length of the conventional one-generation F2 map. Most expansion is achieved in the first few generations, and by F7 the genetic map is approximately three times the length of an F2 map (Fig. 3). The expectation is that a map based on loci that are spaced at intervals of 1 cM (c = 0.01 in an intercross) will be expanded approximately 3.66-fold. Similarly, a low-density map based on markers that are spaced at 16 cM intervals will be expanded 2-fold. F2 and N2 maps generated using uniform typing procedures typically have a cumulative length of 1300 to 1400 cM. Five conventional crosses that we generated (four F2s and one N2, each genotyped at 91 to 148 loci) average 1320 ± 50 (SEM) cM in length. In comparison the fully error-checked native BXN map is approximately 3.6- to 3.7-fold longer, or a total of 4786 cM. The expansion averages approximately 3.4-fold when the comparison is made to the CCR consensus maps (Fig. 4, Table 4). The expansion between common proximal and distal markers ranges from 2.8 in Chr 5 to 3.8 in Chr 12. In general, the expansion estimate of 3.6-fold agrees well with the Haldane-Waddington expectation given a mean spacing between neighboring markers of 2-3 cM. The X chromosome only recombines with half the frequency of the autosomes, and for this reason its expansion is only 1.8 fold.
Comparison to other maps
The summed length of all chromosomes is approximately 1413 cM when values are converted from RI recombination frequencies to those expected of typical single-generation meiotic maps. The corresponding CCR maps have a cumulative length of 1494 cM between the same markers. The MIT-Whitehead microsatellite maps have a cumulative length of approximately 1384 cM. The agreement is excellent.
Recombination density per RI strain
Individual RI strains contain an average of 47 recombinations with a range that typically lies between 40 and 60 (Fig. 4). The 13 CXB strains are associated with a total of 671 recombinations, an average of 52 per strain. The BXD strains are associated with approximately 1500 recombinations, an average of about 42 per strain, and approximately one recombination per centimorgan on a standard genetic map (Tables 3 and 4). There is considerable variation in the total load of recombinations and map expansion per strain: from a low expansion of 2.24 in BXD40 (the RI strain with the fewest recombinations) to a high of about 6 in BXH6 (Fig. 4). These estimates are systematically deflated by a failure to discover recombinations in sparsely mapped regions (regions where the recombination fraction c is as high as 0.1) but are inflated by residual typing errors and errors of marker order.
Recombination density per chromosome
Single chromosomes in RI strains accumulate as many as 12 recombinations, but across the whole set the recombination density averages about 2.4 recombinations per chromosome. The mean extends from 3.47 recombinations for Chr 1 to 1.88 for Chr 9. A Poisson model fits the distribution of recombination events per chromosome reasonably well and most chromosomes have insignificant Χ2 values. High Χ2 for individual chromosomes are generally due to a small number of apparently highly recombinant chromosomes in particular strains. These highly recombinant chromosomes are probably associated with residual typing errors or incorrect marker order.
Segregation distortion and Hardy-Weinberg equilibrium expectation of allele fixation in RI sets
In the absence of selection, approximately 50% of the strains should have inherited B alleles at each marker. A chi-square statistic can be used to assess whether the segregation ratio of a particular marker differs significantly from expectation. Only the 11 intervals listed in Table 8 have chi-squared values that are significant at the 0.01 level. Eight of 11 intervals are biased in favor of B alleles. This is most extreme on chromosomes 1, 15, and X, where there are about twice as many strains with B alleles as N alleles. The opposite pattern is seen on chromosomes 9, 11, and 12. Given the large number of comparisons, many instances of segregation distortion may be type I statistical errors. In collaboration with the Mammalian Genotyping Service (http://research.marshfieldclinic.org/genetics/Genotyping_Service/mgsver2.htm),we recently genotyped a tenth-generation advanced intercross between C57BL/6J and DBA/2J (genotype data for this cross are available at www.nervenet.org. It is therefore possible to test whether similar segregation distortion patterns are present in this related multigeneration cross. The short answer is that the segregation distortions noted in the BXN RI strains are replicated in 6 of 11 intervals. The correlation between ratios of alleles (logarithm of B:N) in these intervals was positive (r = 0.41). It is therefore likely that several of the intervals marked in Table 8 with asterisks represent regions that harbor loci that affect fitness.
One important issue in using RI strains for mapping complex traits is that intervals on different chromosomes can become tightly associated in a statistical sense. This non-syntenic association can arise either as a result of random fixation of alleles on different chromosomes during the production of RI strains or can arise as a result of selection for particular combinations of alleles on different chromosomes. Similar patterns of non-syntenic disequilibrium are common in recently admixed human populations and often lead to false positive signals when mapping complex traits. In mice even a modest selection coefficient expressed over 10 generations of inbreeding can generate positive and negative non-syntenic disequilibrium throughout the genome. For example, if the combination of B alleles on distal Chr 1 and B alleles on proximal Chr 19 is favorable for fitness, then these two intervals will effectively be in linkage disequilibrium in the final RI set. Disequilibrium can also take on the form of strong negative correlations and B alleles may be associated strongly with the group of N alleles.
We searched for marked deviations from the expected Hardy-Weinberg two-locus equilibrium by making a series of large correlation matrices of SDPs of marker pairs. This was done for the entire BXN set and for the constituent RI sets. Table 9 summarizes the most extreme positive and negative correlations among the composite set of 102 independent BXN RI strains. Whether due to chance fixation, selection and epistasis, non-syntenic associations of the sort illustrated in Table 9 are a major source of both false positive and negative results in using RI sets for mapping. It is helpful to examine the correlation matrix once a set of QTLs has been provisionally mapped to see how summed effects of single or multiple QTLs might produce spurious QTLs in regions not actually associated with trait variance.
Controlling for non-syntenic association
Non-syntenic associations among loci and intervals can be computed in advance of QTL mapping. It is therefore possible to statistically control for genetic correlations. For example, in Table 9 the genotypes at marker D1Mit83 can be partly predicted by genotypes at markers on Chr 7 and Chr 10. If the genotype at D1Mit83 is treated statistically as a dependent variable and markers on Chr 7 and 10 are used as predictors, then one can compute the residual genotype, or independent contribution of D1Mit83 and any other marker or interval to the quantitative trait. Unlike composite interval mapping, the set of controlled loci will vary for each marker and interval. This procedure will reduce Type I error, but there will be a regional loss of power. The correction will introduce blind spots in a genome scan. In extreme cases (usually small RI sets), intervals that can be perfectly predicted by small numbers of other non-syntenic intervals will effectively be eliminated from a mapping study and QTLs in those intervals will be missed. For this reason, it is essential to perform each genome-wide scan both with and without control for non-syntenic association. Single QTLs may occasionally be assigned to two or more physically unlinked intervals.
Recombinant inbred strains are currently one of the best genetic resources for exploring phenotypic variance modulated by complex mixtures of genetic and environmental factors. Having a renewable resource of genetically defined genomes is a tremendous advantage in exploring gene pleiotropy, genetic correlation, epistasic interactions, and reaction norms. However, their modest numbers have impeded the widespread adoption of RI strains by mammalian geneticists. To improve the utility and power of complex trait analysis and to provide a better basis for community-based QTL mapping we have increased marker density in several of the major sets of RI lines and have merged data from over 100 mouse RI strains using a framework based on 490 shared markers. Approximately 1000 unique strain distribution patterns (SDPs) - an average of about one per 1.5 cM were defined and mapped in the collected set. Three to four times as many SDPs remain to be discovered in the BXN set. At the current marker density the cumulative RI map is about 5000 cM in length, roughly 3.6 times the length of standard intercross or backcross maps. When corrected using the Haldane-Waddington equation, the RI maps have a cumulative length of 1400 cM, perfectly consistent with those of chromosome committee reports.
Making better RI resources
The usefulness of RI strains for mapping is largely a function of the number of known recombination breakpoints that they harbor. By genotyping and selectively breeding the most highly recombinant F2 animals it should be possible to generate RI strain sets that significantly exceed the map expansion predicted by the Haldane-Waddington equation; an equation that assumes random mating of sibs. A 6x to 8x map should be attainable, particularly if recombinations are tracked during the inbreeding process (Fig 3). Recombination density could be further increased by starting RI strains using either advanced intercross progeny or heterogeneous stock (Fig. 3).
Use of the BXN set
Most mapping software applications used by mouse geneticists are adapted for diallele crosses of various types. The BXN data set has therefore been formatted in a way that collapses all non-B6 alleles into a single N class so that the collected set of just over 100 strains can be used without complication with software such a Map Manger QTX . There are obvious limitations that follow from the collapse of all non-B alleles (A/J, DBA/2J, C3H/HeJ, and BALB/cByJ) into a single category. Geneticists using the BXN set should begin virtually all studies of non-Mendelian traits by mapping with the individual component RI sets (AXB-BXA, BXD, BXH, and CXB) to maximize power and to detect possible levels of allele effects. Because the BXN set includes 490 common marker loci and a consistent alignment and integration of the component RI maps, it is now much easier to combine linkage likelihood ratios from the component RI sets. A simple method based on Fisher's method is described by Williams and colleagues  in a study that pooled data from BXD and BXH sets. More sophisticated methods to automatically extract and combine linkage statistics from the multi-allele BXN sets will require modification of mapping application programs. Pooling data will require judicious and well justified statistical procedures. Combining data across the BXN sets can easily degrade a linkage analysis. The statistical exploration of different combinations of RI sets provides new degrees of freedom that may generate false positive results, but that may also generate interesting hypotheses regarding QTL action.
The BXN map could be refined further by interpolating genotypes of other markers and genes that have been mapped independently by many investigators in single RI sets. For example, our BXD database includes only microsatellite loci and excludes hundreds of potentially informative polymorphic loci, many in interesting genes. We regret having to employ this procrustean approach, but because of the difficulty of verifying genotypes and because numerous loci introduce improbable double-recombinant haplotypes, we have used exclusive criteria to ensure high quality maps. Those investigators interested in recovering some of these lost data should certainly refer to the comprehensive lists of genotypes maintained by the Mouse Genome Database (www.informatics.jax.org/searches/riset_form.shtml). However, genotypes of any marker or gene that introduce new double-recombinants into the BXN map should be regarded with a high level of suspicion.
Maximizing RI resources by the RIX method
The most common criticisms leveled at QTL mapping using RI strains is that the small number of lines limits both precision and power and that only those QTLs with very large effects can be detected reliably. The BXN set provides a partial solution to this problem by expanding the set of RI strains that can be treated statistically as a complex cross. A second objection to using RI strains to map traits is that fully inbred strains may provide unrepresentative trait values precisely because they are inbred. The abnormal genetic architecture of inbred strains and the fixation of multiple alleles that affect fitness will almost inevitably produce unusual pleiotropic and epistatic effects on a range of complex traits.
There is a surprisingly simple solution to these problems; namely to map QTLs using a set of F1 intercrosses between RI strains (, Threadgill DW, Williams RW and Manly KF personal comm). QTLs mapped using RI sets can be quickly verified and positionally refined by generating sets of RI F1 intercrosses (RIX) and RI backcrosses among individual RI lines with recombinations in critical QTL intervals. The RIX method has already proved to be a highly effective way to extract QTLs from the tiny set of 13 CXB strains . The 13 inbred lines can be converted to as many as156 F1 lines. This greatly increases the power to detect QTLs in the presence of strong genetic, parental, and developmental background noise and simultaneously exposed gene dominance deviations to help refine QTL effect and position. The BXN opens up a huge RIX domain for analysis. Approximately 88 of the BXN RI strains are now available from the Jackson Laboratory, and these strains can be crossed to generate about 88x87/2 (3828) genetically unique recombinant inbred intercross progeny (RIX progeny) with breakpoints in precisely defined intervals. Each one of these F1s can be made in reciprocal pairs to assess the role of parental effects (e.g., a BXD1 mother crossed to a AXB2 father, or vice versa). Like RI strains many isogenic individuals can be typed to reduce the non-genetic variance.
F1 and F2 crosses among any of the RI strains can also be used to verify the original assignment. Once QTLs have been mapped to candidate intervals, the subset of strains with recombinations within those intervals become an important resource for confirming and refining QTL location . This is especially the case if one exploits the RIX method. For example, if a QTL maps between 10 and 25 cM on Chr 1 in the BXD set (that is between D1Mit430 and D1Mit375), and if B alleles in this interval are associated with high phenotypes, then the cross of BXD15 by BXD20 may be particularly informative because the F1 hybrid is an obligatory B homozygote on a short interval between 15 cM and 17 cM and is also an obligatory D homozygote proximal to 13 cM and distal to 18 cM. A set of isogenic F1 RIX progeny made by crossing several RI lines with recombinations in a critical interval can be used to refine the probable position of a QTL. Map Manger QTX has now been updated to automatically generate the genotypes of the RIX progeny produced by a one-generation cross of RI parents (http://mapmgr.roswellpark.org/mmQTX.html). Given this huge sample of unique F1 genomes, even modest quantitative differences between C57BL/6 and other strains should be readily mapped (or confirmed) using the BXN and RIX mapping.
Information content of RI strain sets
Despite the accumulation of genotypes in RI strains, these genetic resources have often not been typed with sufficient density to accurately define the frequency and positions of recombination breakpoints. For example, in the venerable set of 13 CXB strains only 11 unique SDPs had been assigned to Chr 1 prior to our work. With a more dense map of Chr 1 that is now based on approximately 60 markers we have recovered at total of 38 recombinations on Chr 1 - approximately 3 recombinations per strain. The positions of these recombinations has been defined with a precision that ranges from 0.5 to 6.0 cM intervals (2.3 cM average) as referenced to standard CCR maps. Twenty-one of the 38 SDPs are represented by one or more of the marker genotype, but at least 17 SDPs remain to be defined and these SDPs unfortunately cannot be predicted unambiguously. For example, if two adjacent markers P and D have genotypes BBCCC and CCCCC, then there must be at least one unrecovered SDP between P and D. Unfortunately, until we actually type markers in the P-D interval, we do not know whether the intercalated SDP is BCCCC or CBCCC. To discover the missing SDP may require considerable effort especially if available polymorphic markers on the P-D interval have been exhausted. All unrecovered SDPs lower the information content of an RI set. Their absence can significantly reduce linkage of both Mendelian and quantitative traits that are unlucky enough to be controlled by loci in the intervals with ambiguous SDPs.
How dense should a marker map be to define more than 90% of the total number of SDPs? With 862 markers, we were able to define approximately 60% of all likely SDPs among the 13 CXB strains. However, in the collected set of BXN RI strains, only 23% of the estimated 5000 possible SDP have be confidently defined with MIT microsatellites. We can estimate the density of the marker map that would be necessary to define 95% of the SDPs. For example, for the BXD set if one assumes a random and independent distribution of breakpoints across strains and a random distribution of markers, it would take a map with about 2,700 markers to define 95% of the 1,536 SDPs.
Power and precision of 100 RI strains
A set of 100 conventional RI strains will have twice the genetic variance as a matched set of 100 F2 progeny and four times that of 100 backcross progeny. This increased genetic variance comes at some cost: 100 F2 animals represent 200 meioses and contain almost 200 unique haplotypes per chromosome (the non-recombinant chromosomes reduce this number somewhat). RI strains are fully inbred and 100 lines represent almost 100 unique haplotypes per chromosome. A set of 100 RI strains therefore has approximately twice the load of recombinations as 100 F2s. For a semidominant Mendelian trait or marker, 100 RI strains therefore provide twice the precision of 100 F2 progeny and four times the precision of 100 N2 progeny. When both genetic variance and recombination load are considered together, a set of 100 RI strains should be approximately four times as effective (precise) for mapping complex traits as an F2, and 8 times as effective as a backcross. This estimate assumes that only a single RI animal is sampled per line; a strategy that is appropriate for mapping marker loci and other Mendelian loci. The gain for mapping quantitative trait will be greater and will depend strongly on the heritability and to a lesser extent on the degree of dominance at each locus. Belknap  has compared the relative power of RI strains and F2 intercrosses under several models and assuming different levels of heritability. For morphometric traits such as brain weight, with narrow sense heritabilities of around 0.5, 100 RI strains will provide a level of precision and power that is conservatively equivalent to that of 600-1000 F2 intercross progeny. The advantage shifts further in favor of RI strains for traits with lower heritability.
BXN and sequencing efforts
Five of the widely used sets of RI strains that we have typed and analyzed share C57BL/6 as a parental strain. The genome of C57BL/6J is currently being sequenced as part of a public effort  and for this reason, the utility of the BXN set for converting QTLs to strong candidate genes will increase significantly in the next few years . It will become far easier to generate complete lists of positional candidate genes and then to obtain data on gene and protein expression patterns. The two other major strains incorporated into the BXN set - A/J and DBA/2J - are also being sequence by Celera Genomics, and in principle, it will be possible to compare sequences of these three major strains to generate lists of possible allelic variants in positional candidate genes. The recent cloning of the Sac QTL, a locus controlling sugar and saccharin preference on distal Chr 4, provides a fine example of the increased power of QTL analysis. This QTL was initially mapped using 20 BXD stains [28, 29]. In the absence of high-resolution mapping, but with astute analysis of human and mouse sequence data, Sac has been identified almost simultaneously by several groups as the T1R3 receptor gene [30,31,32,33,34]. In a few years, the cloning of Sac will probably be no more of a special exception than the cloning of huntingtin was in the early 1990s .
Materials and Methods
Strains and DNA
Genomic DNA from most recombinant inbred and parental strains was purchased from the Jackson Laboratory (www.jax.org). DNA was obtained from 40 of 41 AXB and BXA strains and 35 of 36 BXD strains, 13 CXB strains, and 12 BXH strains - 100 strains total. For visual clarity in this paper we have dropped hyphens and substrain designations from RI strain names. For example, strain BXD-1/Ty is referred to as BXD1. Databases and web-accessible data tables at www.nervenet.org also use this simplified nomenclature.
All DNA from the Jackson Laboratory Mouse DNA Resource was extracted from individual male mice. The RI animals that we genotyped were, with a few exceptions, the progeny of more than 20 serial matings between siblings. Data on the particular generation that we used for genotyping and the current generation of RI animals are listed in one of several web accessible tables that accompany this publication (www.nervenet.org/papers/bxn.html). DNA from seven new BXH strains generated by Dr. Linda Siracusa (Thomas Jefferson Medical College, Philadelphia) was extracted from the spleen using a high salt procedure . The new BXH strains were generated by crossing C57BL/6J-c2J/c2J albino males with C3H/HeJ females and their production and genotyping will be described in detail elsewhere (L Siracusa and RW Williams, personal comm). Three of the new BXH albino strains are no longer available (C2, D1, and E2). We genotyped 107 of RI strains. Several sets of strains share haplotypes (Table 10). We deleted redundant strains (AXB18, ABX20 and BXA17).
Strains BXHD1, BXHE1, BXHE2 were backcrossed to C57BL/6J for one generation before sib matings were begun. There is therefore a pronounced increase in the number chromosomal segments inherited from C57BL/6J. These N2-derived RI strains were dropped from most aspects of the analysis of RI genome structure. BXD41 has been extinct for several years and was never complete inbred. Although we have DNA for this strain our sample is from a F12 generation male. We did not genotype BXD41 in this study.
We refer to the collected RI set as the BXN set because each of the strains includes C57BL/6 (B6 or B) as one of the parental strains - the common substrain C57BL/6J in the case of AXB, BXA, BXD, and BXH; and the substrain C57BL/6By in the case of CXB. The other parental strain in the BXN set is not B6-derived: A/J in both AXB and BXA sets, DBA/2J in BXD, C3H/HeJ in BXH, and BALB/cBy in CXB.
Microsatellite loci distributed across all autosomes and the X chromosome were typed using a modified version of the protocol of Love and colleagues  and Dietrich and colleagues  described in detail at www.nervenet.org/papers/pcr.html. A total of 1773 primer pairs (MapPairs) that selectively amplify polymorphic MIT microsatellite loci were purchased from Research Genetics (www.resgen.com). Each 10 μl PCR reaction mixture contained 1X PCR buffer, 1.92 mM MgCl2, 0.25 units of Taq DNA polymerase, 0.2 mM of each deoxynucleotide, 132 nM of the primers, and 50 ng of genomic DNA. Reactions were set up using a 96-channel pipetting station. A loading dye (60% sucrose, 1.0 mM cresol red) was added to the reaction before the PCR . PCRs were carried out in 96-well microtiter plates. We used a high-stringency touchdown protocol in which the annealing temperature was lowered progressively from 60°C to 50°C in 2°C steps over the first 6 cycles . After 30 cycles, PCR products were run on cooled 2.5% Metaphor agarose gels (FMC Inc., Rockland ME), stained with ethidium bromide, and photographed. Gel photographs were scored and directly entered into relational database files. Eighteen primer pairs were resynthesized at our request by Research Genetics using the original sequence data (Whitehead/MIT SSLP Release 8) to verify that our chromosome reassignments of microsatellite loci were not due to the use of incorrect primer sequences.
When we began this work fewer than 25 MIT markers had been typed on each of the four major RI sets. We were able to increase to 489 markers. We relied on these loci to assemble consensus RI maps. The additional 986 MIT markers were typed by us and other groups in at least one set of RI strains. The BXN genotype database includes 1578 markers. Any pair of RI sets share between 500 and 600 fully genotyped markers. For example, the two largest RI sets - AXB/BXA and BXD - have been typed at 591 common microsatellite markers.
Relational database files were assembled from the 1998-2000 chromosome committee reports, the Portable Dictionary of the Mouse Genome  and the MIT/Whitehead SSLP database Release 8. These files contain a summary of information on chromosomal positions of 6332 MIT microsatellite markers and information on an additional 15000 genes and markers. We have included Nuffield Department of Surgery (Nds) microsatellite markers for which primer sequences are available. Additional databases devoted to each RI set were assembled from text files downloaded from the Mouse Genome Database (www.jax.org). New and corrected genotypes were entered directly into these files.
Additional data files available with the online version of this article include Excel, FileMaker Pro, Map Manager QTX, and text files (also available from Informatics Center fore Mouse Neurogenetics [http://www.nervenet.org/papers/bxn.html]). Two types of key data are included in the list below in various formats. Items 1 through 4 are all various versions of the BXN genetic maps and microsatellite marker genotypes. Item 5 includes several different files that present the two-locus correlation matrices of genotypes. These correlations matrices are used to detect unsuspected associations between loci on different chromosomes (see main text for an explanation of non-syntenic association and the use of the matrices).
BXN Database in Map Manager text format (inferred genotypes. Genotype codes: B, N, U, and H, 108 strains)
BXN Database in Excel format (original data. Genotype codes: 1 = B, N = 0, Unknown = 2, Het = 0.5)
Inferred BXN Database in Excel format (inferred genotypes. Genotype codes: 1 = B, N = 0, Unknown = 2, Het = 0.5). This database is more useful for mapping than the original genotype files.
BXN Consensus Maps in GIF image format (this 9-meter-long file may require increased RAM to download)
Correlation Matrices of Genotypes
All BXN Genotypes: GIF image format
All BXN Genotypes (102 strains): text format
All BXN Genotypes (102 strains, 1.1 MB): Excel format
BXD Genotypes (34 living strains):text format
BXD Genotypes (34 living strains, 1.1 MB): Excel format
BXD Genotypes (first 26 strains 1 to 32): text format
BXD Genotypes (first 26 strains 1 to 32, 1.1 MB): Excel format
CXB Genotypes (13 strains): text format
CXB Genotypes (13 strains, 1.1 MB): Excel format
AXB Genotypes (24 living and independent strains, 1.1 MB): text format
AXB Genotypes (24 living and independent strains, 1.1 MB): Excel format
BXH Genotypes: text format
BXH Genotypes (1.1 MB): Excel format
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This research project was support by a Human Brain Project/Neuroinformatics program project (Informatics Center for Mouse Neurogenetics) funded jointly by the National Institute of Mental Health, National Institute on Drug Abuse, and the National Science Foundation (P20-MH 62009). The authors thank Dr. Xiyun Peng for her assistance in genotyping CXB and BXH mice. The authors thank Research Genetics Inc. (Invitrogen) and Ms. Felisha Scruggs for resynthesizing 18 MapPairs for us. We thank Susan Deveau of the Jackson Laboratory DNA Resource for information on the generation numbers of RI DNA samples. We thank Drs. David Threadgill, Gary Churchill, and Kenneth Manly for comments on this paper.
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Williams, R.W., Gu, J., Qi, S. et al. The genetic structure of recombinant inbred mice: High-resolution consensus maps for complex trait analysis. Genome Biol 2, preprint0007.1 (2001). https://doi.org/10.1186/gb-2001-2-8-preprint0007