Fine Mapping of Collagen-Induced Arthritis Quantitative Trait Loci in an Advanced Intercross Line¹

Rheumatoid arthritis (RA)⁴ is a chronic inflammatory autoimmune joint disease affecting ∼1% of the general population (1). Like many common autoimmune diseases, RA is influenced by both genetic and environmental factors (2). The genetic contribution to RA variation is estimated to be ∼60%, of which the HLA DRB1 locus is thought to account for 30–50% (3, 4). However, until now, only a small number of potential susceptibility genes have been identified in RA or its animal models, e.g., HLA-DRB1, PADI4, PTPN22, FCRL3, and NCF1 (5, 6, 7, 8, 9). These genes are thought to regulate arthritis development through different mechanisms, including affecting Ag presentation (5), controlling autoantibodies production (6, 9), and regulating T cell activation (7, 8).

Dissecting the genetic basis of RA has been complicated by the small sample size, genetic heterogeneity, environmental differences, and the complex gene-environment interactions thought to contribute to the disease pathogenesis. Thus, well-defined RA experimental models have the potential to markedly accelerate the progress of our understanding of arthritis genetics and its pathogenesis.

Murine collagen-induced arthritis (CIA) is one of the most widely used RA experimental models. It has been used extensively in studying the roles of autoimmunity and inflammation in the pathogenesis of arthritis (10). Susceptibility to CIA is associated with both MHC genes and non-MHC genes. Indeed, the first and strongest arthritis-associated QTL identified, Cia1, is located on chromosome 17 and includes the MHC. Nevertheless, susceptibility and severity of the disease vary considerably among inbred strains sharing the same MHC haplotype. This suggests a strong contribution of polymorphic genes outside the MHC locus. Indeed, a total of 35 CIA QTLs were identified in different crosses between susceptible and resistant inbred strains sharing the same MHC haplotype (11, 12, 13, 14, 15, 16, 17). In a previous study, we conducted linkage analysis in (DBA/1 × FVB/N)F₂ progeny and identified seven QTLs controlling CIA clinical traits and related phenotypes (16).

However, the QTLs identified in F₂ intercrosses have ∼20-cM confidence intervals (CIs) comprising hundreds of genes. Thus, it is necessary to refine the QTLs into regions of 1–2 cM before positional cloning and candidate gene analysis can be performed. Several approaches have been suggested or applied to refine QTLs, including advanced intercross lines (AIL) (18), partial AIL (12), recombination inbred segregation test (19), haplotype analysis (20), heterogeneous stocks (21), Ying Yang crosses (22), and finally congenics and recombinant congenics (19, 23).

An AIL is generated by random intercross breeding of two inbred strains for several generations, resulting in the accumulation of many recombination events and thus high genomic resolution (18). With an appropriate AIL, the CIs of QTLs can be theoretically resolved with a t/2-fold reduction compared with those resulting from same size F₂ progeny, where t is the number of AIL generations. There are at least two advantages in favor of AIL over other QTL fine-mapping approaches. First, an AIL can be used to refine multiple QTLs (18). Second, apart from mapping QTLs into a rather small genomic region, AIL can also separate a QTL that comprises several adjacent QTLs (24, 25, 26).

Despite its advantages, AIL has not been widely used for high-resolution QTL mapping. Since it was proposed in 1995, only eight AIL have been generated, including five mouse lines (24, 25, 27, 28, 29), one rat line (26, 30), one chicken line (31), and one mosquito line (32). The main reason for the reluctance in adopting this approach was the extensive resources that AIL production, phenotyping, and genotyping require. An appropriate AIL could take at least 3 years to generate, and at least 50 random breeding pairs are required for each generation. Eventually, ∼1000–1500 animals should be phenotyped and genotyped with densely spaced markers.

To test the possibility of fine mapping and identification of QTLs with a less resource-demanding AIL, in this study we produced an AIL by random breeding of only 10 couples of males and females in each generation. Thus, 308 F_11/12 (DBA/1 × FVB/N) AIL mice were generated for the experiment. This AIL was applied to refine three CIA QTLs identified in (DBA/1 × FVB/N)F₂ mice, including Cia2 controlling CIA severity, Cia27 controlling anti-collagen II IgG2a Ab, and Trmq3 (T cell ratio modifier QTL 3) controlling lymph nodes (LN) CD4:CD8 T cell ratio. We also performed another QTL fine-mapping approach, haplotype analysis, to refine the QTLs. Furthermore, to select potential candidate genes for each QTL, we performed gene expression profiling to determine mRNA expression differences between the parental strains.

The AIL was established from CIA-susceptible DBA/1J and CIA-resistant FVB/N mouse strains that share the H-2q MHC haplotype allowing identification of non-MHC genes. Both were originally obtained from The Jackson Laboratory and were housed at the animal facility of the University of Rostock. Ten pairs of F₂ mice were used to generate F₃ mice; sibling pairing was avoided, but otherwise pairing was at random. From generation 2, random breeding of 10 males and 20 females, consistently avoiding brother-sister mating, produced all subsequent generations until generation 10. Thereafter, 29 breeding pairs were used to produce F₁₁ mice, and 32 breeding pairs were used to produce F₁₂ mice. Seventy-six C5-deficient mice were excluded to increase the CIA incidence and decrease the mask effect of C5^−/−. Eventually, 308 F_11/12 AIL mice were used for the experiment. All procedures and assays were preapproved by the competent state’s animal care committee.

CIA was induced according to established protocols (16). In brief, 308 AIL progeny were immunized at 8–12 wk of age at the base of the tail with 125 μg of bovine collagen II (Chondrex) dissolved in 50 μl of 0.1 M acetic acid and mixed with an equal volume (50 μl) of CFA (IFA with 4 mg/ml Mycobacterium tuberculosis; Difco Laboratories). Mice were followed for 14 wk postimmunization.

The clinical scoring of arthritis was commenced from 21 days after immunization, and animals were monitored three times weekly for signs of CIA. An arthritic index was assigned to each mouse by a scoring system described previously (13). This scoring system is based on the number of inflamed joints, ranging from 1 to 15 for each affected paw. Each affected ankle/wrist was scored as 5, and each inflamed knuckle and toe was given 1 point. The scores of the four paws were added, yielding a maximum total score of 60 for each mouse. The severity trait is the maximal score observed in each individual mouse. The onset and area under the curve (AUC) were calculated as described previously (12, 16). Mice that did not develop CIA were given a score of 0 for the traits of severity, onset, and AUC.

IgG2a Abs to bovine collagen II were measured in the sera prepared from blood, which was taken from 308 AIL mice on the 35th day after immunization. CD4⁺ and CD8⁺ T cells were determined in LN from 97 AIL mice on the 95th day after immunization by FACS, and the percentage of CD4⁺ T cells was divided by the percentage of CD8⁺ T cells to calculate the CD4:CD8 ratio. The detailed methods of measurement of both Abs and T cell subsets were described previously (16).

Two chromosomes of 308 AIL mice were genotyped with 59 informative markers (53 microsatellite markers and 6 single nucleotide polymorphism (SNP) markers) covering chromosome 2 (31 markers) and chromosome 5 (28 markers). Primers for informative markers were ordered from Metabion. Genotyping of 308 AIL mice was performed on DNA extracted from tail tips by using PCR amplification as described earlier (16) or by a PCR-restriction fragment length polymorphism method. The PCR products were resolved on denaturating polyacrylamide gels, and were detected by using a LI-COR Model 4200L automated DNA sequencer (LI-COR). The genotypes were evaluated manually and double-checked.

All linkage analyses have been made with QTX Map manager software and the imputation model in R/qtl software package. The physical position order of the loci was obtained from Ensembl (〈www.ensembl.org〉). Arthritis severity, onset, collagen-specific Abs, and T cell subsets were taken as phenotypes. The significance linkage threshold values were determined by permutation tests (n = 1000). In accordance with the suggestion of Lander and Botstein (33), the CI was defined for the location of each quantitative trait locus (QTL) as the distance between points on each side of the peak at which the logarithm of the likelihood ratios (LOD) score falls by 1.

Haplotype analysis was performed on QTLs identified in different crosses. The SNP haplotype block structure of mice strains was constructed by ∼14,000 SNPs covering the whole genome. The SNP genotypes information were retrieved from the Wellcome Trust Centre for Human Genetics (〈http://zeon.well.ox.ac.uk/rmott-bin/strains.cgi〉). The haplotype block that distinguishes the susceptible strains and resistant strains is taken as the support interval of the QTL.

RNA was prepared from LN of DBA/1 and FVB/N mice on day 0 (before immunization), day 10, day 35 (onset phase), as well as day 95 (chronic phase) after CIA induction with the Qiagen RNeasy Mini kit according to the supplied instructions. Each group contained three mice. Analysis of gene expression was conducted using MOE 430A array (Affymetrix), interrogating >22,000 genes. RNA probes were labeled according to the Affymetrix’s instruction. Samples from individual mice were hybridized onto individual arrays. Hybridization and washing of gene chips was done as described previously (34). Fluorescent signals were collected by laser scan (Hewlett-Parkard Gene Scanner). The normalized expression values were imported to and analyzed by dCHIP software (35). Differentially expressed genes were identified by defining the following filtering criteria in the dCHIP software: 1) the fold change between the group means exceeded 2-fold; 2) the absolute difference between the two groups exceeded 100; 3) the p value threshold of the unpaired t test was 0.05. The false discovery rate was established with permutation test for each pairwise comparison to estimate the proportion of false-positive genes.

Table I summarizes the phenotypic characteristics in AIL progeny. A total of 308 F_11/12(DBA/1J × FVB/N) AIL mice were immunized with collagen II and examined for clinical signs of CIA. An incidence of 33.4% (105 of 308) was observed. Affected animals displayed an average maximum arthritis score of 19.4 and a mean onset score of 31.9. Because 203 mice did not develop CIA and were given a score of 0 for the phenotypes of severity and onset score as well as AUC, these phenotypes were not normally distributed. Some immune response phenotypes were determined such as the anti-collagen II IgG2a Ab as well as CD4:CD8 T cell ratio in LN. These phenotypes were normally distributed.

A significant sex effect on CIA incidence was observed, with males having higher incidence than females (34.4 to 20.6%), but there was no sex effect on arthritis onset and severity phenotypes. We also observed a sex effect on CD4:CD8 ratios in LN, with higher ratio in males than females (1.39 to 1.28).

In a previous study, we identified Cia2, Cia27, and Trmq3 in F₂ mice controlling disease severity, anti-collagen II IgG2a Ab, and CD4:CD8 T cell ratio, respectively. These three QTLs were confirmed and refined in this AIL. The first QTL, Cia2, displayed a maximum LOD score of 3.35 for the CIA severity phenotype, and the DBA/1 allele at the peak marker (Hc) is the disease-enhancing allele in an additive manner. Fig. 1,A plots the LOD score curves for Cia2 in both F₂ and AIL mice, and the CI of Cia2 was refined from 40 Mb (F₂) to 12 Mb (AIL). The second QTL, Cia27, displays a maximum LOD score of 3.5 for the IgG2a phenotype. The DBA/1 allele at the peak marker (rs3653889) is the IgG2a-enhancing allele. Fig. 1,B plots the LOD score curves of Cia27 in both F₂ and AIL mice; the CI of the QTL was refined from 43 Mb (F₂) into 4.1 Mb (AIL). Finally, Trmq3, the QTL controlling the CD4:CD8 ratio was also confirmed and the peak marker identified was D2Mit394 (LOD score of 3.5) with FVB/N allele as the ratio enhancing allele in an additive manner. Fig. 1 C plots the LOD score curves of Trmq3 in both F₂ and AIL mice; the CI of the QTL was refined from 48 Mb (F₂) to 12 Mb (AIL).

Cia2 was identified in three independent crosses, DBA/1 × SWR (14), B10.Q × NOD.Q (13), and DBA/1 × FVB/N (16), and it was not found in the cross of DBA/1 × B10.Q (15). The results indicate that DBA/1 and B10.Q mice share the susceptible allele of Cia2, whereas the other three strains share the resistance allele. Therefore, it is possible to perform the haplotype analysis by comparing the SNP haplotypes between susceptible strains and resistant strains and searching for the SNP haplotype blocks distinguishing the two strain groups. The haplotype block structure of six mouse strains, five strains involved Cia2 and C57BL/6 strain as a reference, derived from ∼14,000 SNPs were used for the analysis. The 12-Mb CI of Cia2 from AIL of this study was analyzed by the SNP haplotype blocks derived from 57 SNPs (data not shown). Only one SNP haplotype block spanning a ∼2-Mb (33.3- to 35.3-Mb) genomic region within the 12-Mb AIL CI showed differences between the two groups (Fig. 2). The 2-Mb support interval contains 24 genes, including the important candidate gene Hc. Therefore, the support interval of Cia2 was further refined to 2 Mb by haplotype analysis. Unfortunately, the other two QTLs could not be further refined by this approach, because these QTLs were not identified in previous crosses.

Table II summarizes the QTLs refined in this study. The support intervals of the three QTLs ranged from 4 to 12 Mb, and Cia2 was further refined with haplotype analysis into a 2-Mb genomic region.

The causal polymorphisms in the susceptibility genes could be divided into two categories: polymorphisms in regulatory region regulating the transcription or mRNA stability and those in coding regions causing qualitative changes (36). To aid in search for the susceptibility genes that control the phenotype through regulating the genes expression, we performed a gene expression profile of the LN of the parental DBA/1J and FVB/N strains, at four different phases of the CIA, i.e., day 0, day 10, day 35, and day 95 after immunization. Since we examined the gene expression in LN, the gene expression difference could be accounted for by differences in cellularity and/or differences in the amounts of mRNA per cell. Being aware of this, we detected the cell composition in LN for both strains at the four phases of CIA. Three main cell types, CD4 and CD8 single-positive T cell as well as B cell, were examined. Although the cell composition in LN changed significantly during CIA, the change patterns were very similar in both strains. Except for day 35 postimmunization, when the FVB/N strain had higher percentage of B cell than DBA/1 strain, the differences in cellularity were quite small (data not shown). Therefore, the gene expression difference between the two strains is likely reflecting the difference in the amount of mRNA per cell.

The pairwise comparisons of DBA/1 and FVB/N in the four phases of CIA, respectively, were performed to search for the strain-specific differentially expressed genes. Of the ∼22,000 genes, 533, 314, 431, and 180 genes were differentially expressed between DBA/1 and FVB/N strains on days 0, 10, 35, and 95, respectively. The merged set contained 1396 strain-specific differentially expressed genes (supplemental Table I).⁵

Within the three refined QTL support intervals, 18 genes were differentially expressed between DBA/1 and FVB/N at one or more phases of CIA. Three genes within Cia2, stomatin (Stom), glyoxalase 1 (Glo1), and glycoprotein galactosyltransferase α1 (Ggta1), were differentially expressed between the two strains. Glo1 consistently had higher expression in the DBA/1 strain than in the FVB/N strain during CIA. Stom and Ggta1, although defined as differentially expressed genes, could be false positives because their expression in both strains was very low. Three strain-specific differentially expressed genes were identified within Cia27, including two pore channel 1 (Tpcn1) with higher expression and 2′,5′-oligoadenylate synthetase 1A (Oas1a) with lower expression in the DBA/1 strain on day 0 as well as protein tyrosine phosphatase nonreceptor type 11 (Ptpn11) with higher expression in the DBA/1 strain on day 35. Twelve genes within Trmq3 were differentially expressed at one or more phases of CIA. They were RAS guanyl releasing protein 1 (Rasgrp1), thrombospondin 1 (Thbs1), 1810008K03Rik, 1500003O03Rik, leukocyte tyrosine kinase (Ltk), MAX gene associated (Mga), ubiquitin protein ligase E3 component n-recognin 1 (Ubr1), sorbitol dehydrogenase 1(Sdh1), pallidin (Pldn), semaphorin 6D (Sema6d), solute carrier family 27, member 2 (Scl27a2), and histidine decarboxylase (Hdc) (Table III).

To detect whether Cia27 and Trmp3, controlling anti-collagen II IgG2a and CD4:CD8 T cell ratio, respectively, affect the CIA clinical phenotypes, and to reveal the potential pathway in which the susceptibility genes inside the Cia2 are involved, we examined the three QTLs through investigating the relationship between them and the CIA clinical phenotypes such as severity, onset score, susceptibility, and AUC as well as anti-collagen II IgG2a Ab and CD4:CD8 T cell ratio in LN. Apart from controlling CIA severity, Cia2 was also linked to CIA susceptibility, onset, and AUC, with LOD scores of 1.93, 2.32, and 3.10, respectively. In addition, the linkages between Cia2 and anti-collagen IgG2a Ab and CD4:CD8 T cell ratio were weak, with LOD scores of only ∼1, indicating, that the susceptibility gene(s) inside Cia2 affected the CIA clinical phenotypes through other mechanisms than regulating Abs response and T cell subset in LN (Fig. 3 A).

Cia27 was previously identified as a QTL controlling anti-collagen IgG2a Abs in (DBA/1 × FVB/N)F₂ progeny. As expected, this QTL was confirmed in AIL to be linked to anti-collagen IgG2a Ab, with a LOD score of 3.5. Interestingly, it was also linked to CIA clinical phenotypes, including susceptibility, onset, severity, and AUC, with LOD scores of 2.9, 2.5, 2.3, and 1.8, respectively. DBA/1 allele of Cia27 enhancing anti-collagen IgG2a Ab increased the score of clinical phenotypes. However, it was not linked to CD4:CD8 T cell ratio (LOD score of 1) (Fig. 3,B). This suggests that the susceptibility gene inside Cia27 controlled anti-collagen IgG2a and thus affected CIA clinical phenotypes. The LOD scores of the linkage between Cia27 and susceptibility and onset were higher than those between Cia27 and severity and AUC, indicating that Cia27 might be involved in stages of CIA priming. Weak linkages between Trmq3 and CIA clinical phenotypes were also observed. The marker D2Mit395 was linked to CIA severity, onset, susceptibility, and AUC, with LOD scores of 1.45, 1.49, 1.28, and 1.84, respectively (Fig. 3 C). FVB/N allele that increased the CD4:CD8 T cell ratio was correlated with higher scores of CIA clinical phenotypes.

AIL is a powerful approach for high-resolution mapping of QTLs. However, producing an appropriate AIL requires at least 50 matings in each generation with considerable resources required to maintain the crosses and the resulting large numbers of mice to be screened. The total number of animals required for such a cross is ∼5000. In this study, we produced an AIL that requires limited resources by reducing the number of breeding pairs/cages of each generation, and eventually characterized only 300 mice, thus using only ∼1000 mice. Hence, four-fifths of animals and space were saved in this experimental setup. Some genomic regions were expected to be fixed and to show lower recombination frequency in this current cross (data not shown). Surprisingly, that was rather limited and did not affect those genomic regions in which the previously F₂-identified QTLs were located. Indeed, three QTLs identified in the F₂ progeny were confirmed and refined with support intervals of 4–12 Mb, suggesting that the AIL was successfully applied for fine mapping of QTLs. More importantly, it suggests that it is the genetic quality in QTL intervals rather than those in the whole genome that are necessary for fine mapping of most QTLs.

It is worthy to learn valuable lessons for fine mapping QTL in AIL from this study. As Fig. 1 shows, all three refined QTLs had lower LOD scores than their counterparts from F₂ progeny, and the CIs were also slightly larger than those expected. One reason for this is because of the AIL approach itself. When compared with F₂, AIL accumulates more recombination events and thus needs more genotypes, which would reduce the quantitative effect associated with a particular marker (18). Another reason could be the limitation of the strategy of generating AIL in the present study. In this study, AIL was produced through only 10 random crosses in each generation, and thus it was characterized by relatively lower heterogeneity and low recombination frequencies. The latter resulted in larger QTL CIs, and the lower heterogeneity resulted in low LOD scores of QTLs. The question is then whether it is possible to overcome these two limitations while using limited resources for AIL. One solution is to produce a “selective” AIL by keeping good genetic characteristics only in the QTL intervals. To do this, only mice heterozygous at the QTL peak markers will be used for random breeding to generate AIL progeny. Because our study showed that recombination frequencies and heterogeneity for AIL matter only in QTL loci intervals, rather than in the whole genome, a “selective” AIL could be used for QTL fine mapping.

Haplotype analysis could be one of the fastest methods for QTL fine mapping. When a QTL is identified in different inbred-strain crosses, the strain distribution pattern can be combined with mapping data to refine the region that contains the functional variant (23). In this study, we refined Cia2, a QTL identified in three different studies, from a 12-Mb support interval into a 2-Mb genomic region using haplotype analysis. Although it is a fast and powerful QTL fine-mapping approach, wider application of haplotype analysis is based on two conditions: First, the QTL refined by this approach should be identified in different inbred-strain crosses. There is some progress in this respect: since the first QTL was identified in mouse in 1990 (37), >2600 QTLs have been identified in the mouse (〈www.jax.org〉), and the pace of novel QTL identification is still accelerating. Second, enough SNPs should be collected to construct a credible SNP haplotype structure. It is difficult to decide how dense the SNPs should be for a reliable SNP haplotype structure because the haplotype SNP density is dependent on the variation distribution complexities of mice strains. Yalcin et al. (38) genotyped eight inbred mice strains with 700 SNPs inside a 2-Mb sequence region. The results showed that the variance is more complex than expected, although the unexpected complexity does not exist in the whole region. This result indicates that more SNPs should be used to define the haplotype block when haplotype analysis is applied for QTL fine mapping.

Despite using a relatively small number of SNPs to define the haplotype structure of the Cia2, there is strong evidence that we successfully refined that QTL. Complement C5 (Hc) is believed to be the main susceptibility gene for Cia2 and Cia4 for several reasons. Firstly, a 2-bp deletion in one exon of Hc leads to its deficiency. The mutation exists in several mouse strains (39), all of which are resistant to both CIA and serum/Ab-transferred arthritis (11, 14, 40). Second, Hc-deficient DBA/1 mice are resistant to the induction of CIA (41), and anti-C5 mAb therapy can prevent CIA and ameliorate established disease (42). Finally, Hc has been identified as the susceptibility gene for experimental allergic asthma, an inflammatory disease (43), confirming the role of Hc in inflammation. Using haplotype analysis, we refined Cia2 into a 2-Mb genomic region containing Hc gene, supporting Hc as the main susceptibility gene for Cia2. Moreover, it also suggested that the haplotype analysis was successfully performed for the purpose of QTL fine mapping.

In contrast, there is some evidence suggesting the existence of an additional susceptibility gene(s) within the Cia2. First, in F₂(DBA/1 × FVB/N) progeny, the peak markers of Cia4 and Cia2 are different. The peak marker of Cia4 is D2Mit367, close to Hc, whereas D2Mit81, the peak marker of Cia2, is located at an 11-Mb distance from Hc gene. This difference was confirmed in AIL mice. In AIL mice, Cia4 had only Hc as peak marker, whereas Cia2 has two peaks; apart from the same peak as Cia4, another peak is located at 22-Mb distance from the centromere of chromosome 2 with an LOD score of 2.0 (Fig. 3 A). Although the significance level of the additional peak is not even suggestive, it still hints that there might be a small-effect susceptibility gene controlling CIA severity. Second, evidence comes from the three studies identifying Cia2, SWR × DBA/1J (14), B10.Q × NOD.Q (11), and DBA/1 × FVB/N (16). Although Cia2 was identified in all three studies, the peak positions of the QTLs were different among crosses. In the SWR × DBA/1J cross, the peak marker is D2Mit6 located at 61 Mb from the centromere of chromosome 2. In B10.Q × NOD.Q cross, the peak marker is D2Mit7 located at 38.6 Mb from the centromere of chromosome 2, whereas D2Mit81, the peak marker from the DBA/1 × FVB/N cross, is located at 25 Mb from the centromere of chromosome 2. Apart from D2Mit7, the other two peak markers are >10 Mb away from the Hc gene. This suggests that, besides C5, there could be other candidate susceptibility genes for Cia2.

Interestingly, Cia27, a QTL controlling anti-collagen IgG2a Ab, also regulated the CIA clinical phenotypes, and it had a stronger effect on CIA susceptibility and onset than on CIA severity and AUC, suggesting that IgG2a could play a role in priming of the disease. This is in accordance with a previous observation showing that IgG2a is one of the important isotypes contributing to arthritis (44). Additionally, Trmq3, a QTL controlling CD4:CD8 T cell ratio in LN, also showed weak linkages to the CIA clinical phenotypes. The DBA/1 allele enhancing CD8⁺ T cell proportion and thus decreasing CD4:CD8 T cell ratio was correlated with lower CIA clinical scores than the FVB/N allele. Although the mechanism behind this correlation is still unknown, our observation supports a previous suggestion that CD8⁺ T cell could play a protective effect on CIA (45). These observations suggest that genes controlling such disease-related traits could have effects on clinical phenotypes, pointing to a strategy to identify small-effect QTLs of complex diseases. In (DBA/1 × FVB/N)F₂ progeny, only the genomic region of Cia2 was significantly linked to the CIA clinical phenotypes, indicating that there should be some small-effect QTLs whose contributions were not big enough to reach the significance level of linkage analysis (16). The present study revealed Cia27 and Trmq3 could be such small-effect QTLs. Therefore, a strategy to identify the small-effect QTL for complex diseases could be detecting the QTL controlling disease-related phenotypes.

Cia27 was refined with AIL into a 4.1-Mb genomic region containing 37 genes. Interestingly, 10 of the 37 genes encode subunits of 2′,5′-oligoadenylate synthetase, including Oas1a, which was differentially expressed between DBA/1 and FVB/N strains. 2′-5′-Oligoadenylate synthetase was discovered as the first IFN-induced antiviral enzyme but might also be involved in other cellular processes (46). Previous studies showed that the 2′,5′-oligoadenylate synthetase is expressed in several lymphoid tissues in mouse without stimulation of IFN (47), and the activity of the enzyme is associated with T cell subsets (48). Another strain-specific differentially expressed gene, PTPN11, plays a critical role in the Noonan syndrome (49). Recently, Uhlen et al. (50) showed that PTPN11 regulates the calcium oscillations and NFAT signaling. Ca²⁺/calcineurin-NFAT-mediated signaling pathways are crucial for both active immune responses and T cell anergy (51, 52). One non-strain-specific differentially expressed gene within Cia27, tescalcin (Tesc), also effects the Ca²⁺/calcineurin-NFAT-mediated signaling pathways. Tesc encodes a putative EF-hand Ca²⁺-binding protein. It shares sequence and functional homology with calcineurin-B homologous protein (CHP), which can inhibit the phosphatase activity of calcineurin A, which regulates the NFAT nuclear translocation and activation (53, 54).

Trmq3, a QTL controlling the LN CD4:CD8 T cell ratio, was confirmed and refined into a 12-Mb genomic region in AIL. FVB/N allele in this locus had a lower CD8⁺ T cell proportion and thus had a higher CD4:CD8 T cell ratio, suggesting that the gene affects the LN CD4:CD8 T cell ratio by regulating CD8⁺ T cell proportion. Two genes in this genomic region were previously reported to be involved in the development of the CD8⁺ T cell. The first one is Rasgrp1, which was differentially expressed between strains, with higher gene expression in the DBA/1 strain. RasGRP1 is a guanine nucleotide exchange factor for Ras that is required for the efficient production of both CD4 and CD8 single-positive thymocytes (55). A transgenic mouse model showed that overexpression of RasGRP1 induced the maturation of double-negative thymocytes and enhanced the production of CD8 single-positive thymocytes (56). This was in accordance with our result: the DBA/1 strain has higher expression of Rasgrp1 and higher CD8 single-positive T cell than the FVB/N strain. Another candidate gene could be β₂-microglobulin precursor (B2m). B2m encodes the β-chain of the MHC class I molecules, which is essential for CD8⁺ T cell subset development (57).

In conclusion, we used AIL to refine three CIA QTLs with CIs spanning genomic regions from 4 to 12 Mb. Thus, we have demonstrated that fine mapping QTLs with a less resource-demanding AIL is possible. Additionally, we also performed haplotype analysis and gene expression profiling as complementary approaches for QTL fine mapping and candidate gene identification. Several putative candidate genes have been identified according to both expression profiles and potential functions. Our future studies will be conducted using two approaches. The first is generating congenic animals with the refined QTL genomic for further fine mapping and consequent positional cloning. The second approach is functional analysis of potential polymorphic candidate genes both in vivo and in vitro.

We thank Ilona Klamfuss for animal care and Dr. Lena Wester-Rosenlöf for critical reading of the manuscript and helpful discussion.

The authors have no financial conflict of interest.