MHC type B15/B15 was clearly more resistant than B2/B2 and B2/B15, but the resistance appeared to be fully recessive, as the heterozygote was virtually identical to the B2/B2 homozygote. Similar results were obtained in the reciprocal backcross study of these founder lines [18
]. This was somewhat unexpected, as a number of studies comparing the influence of various MHC haplotypes on MD in different strains of chickens found that the B2 haplotype is frequently associated with greater resistance [23
]. These data are, however consistent with others that suggest that some genes may interact to complement the MHC haplotype influence [25
The QTL results of this study accord well with those of the previous reciprocal backcross study of these lines [18
]. In the BC case, a total of 15 QTLR were identified. Of these, 3 QTLR were significant for CS only, 12 were significant for ANOVA or Z, of which 8 were also significant for CS. Three-fifths of the QTLR identified in the previous study were also identified in the present study, and the effects of the common QTLR were identical in sign, and highly correlated in magnitude.
Considering the three main MD resistance QTL-mapping studies, the present F6 and the BC [18
] experiments between them identified all four of the significant Yonash et al. [21
] QTL; 8 QTL were common to the F6 and BC [18
] experiments; while 13 and 7 were uniquely identified by the F6 and BC [18
] experiments, respectively. Thus, among them these experiments uncovered a total of 28 QTL affecting MD resistance. Lack of full correspondence between the present F6 and previous BC [18
] experiments, which were carried out in populations derived from the same founder lines, can be attributed to the partial power of the experiments. Assuming a total of 28 QTL, power of the F6 experiment would be 0.77 (21 out of 28) and that of the BC experiments would be 0.54 (15 out of 28). These power estimates seem reasonable considering the size and design of the experiments and magnitude of the QTL effects uncovered, and the difference between the power estimates for the F6 and BC experiments is far from statistical significance. For experiments of comparable size and QTL of comparable effects, F2 and reciprocal backcross designs should provide equivalent statistical power [26
]. Thus the observed difference in power of the two experiments is best attributed to sampling variation.
Overall, Line 2 contributed about twice as many resistance alleles as Line 1, as would be expected from the relative resistance of the two lines. Curiously, in the present study, the pure line controls of Line 1 and Line 2 displayed much higher resistance (28.6 and 7.6% mortality to end of test, respectively), compared to the F6 (52.7% mortality to end of test). Thus, interactions of the background genome appear to have major effects on the expression of resistance [27
]. The fact that the cross of the two lines was more susceptible than each of the individual lines is consistent with other indications that the QTL alleles that confer resistance in these lines are recessive, so that the cross shows negative heterosis for resistance. This stands in some contrast to the results of Stone [28
], who conducted a number of crosses between ADOL resistant and susceptible lines and concluded that MD resistance was dominant, and of Yonash et al. [21
], who found that at a majority of their identified QTL, the resistant alleles were dominant. The results are, however, consistent with the Heifetz et al. [18
] study, in that QTL mapping in the reciprocal backcross populations developed from these founder lines identified different QTL, as would be expected if QTL for resistance are recessive. The hypothesis that resistance alleles are recessive also predicts that the F6 should identify QTL that came to expression in both of the reciprocal BC populations. In this regard, eight of the QTL identified in the BC populations corresponded to QTL identified in the F6. Of these, one was specific to BC1, three were specific to BC2, and four were found in both BC's. Seven of the QTL mapped in the BC, however, did not have corresponding QTLR in the F6. The simplest explanation for these negative results may be the incomplete power of the two experiments, which appreciably reduces the likelihood that the same QTL will be found in the two experiments.
Three of the QTLR identified in the F6 were also identified in the Yonash et al., [21
] study. This is particularly noteworthy, since the challenge strain in the Yonash et al. [21
] study was JM/102W, which is a virulent (v) MDV strain, but less pathogenic than the vv+ MDV strain 648A used in the present study. Thus, this lends some support to the widely held assumption that QTL conferring resistance to one MDV strain will confer resistance to another as well, at least in White Leghorns where genetic diversity is limited.
Considering QTL map locations in the BC as compared to the F6, there is a clear tendency for distinctly narrower QTLR in the F6 than in the BC; average QTLR extent in the F6 was just about half that in the BC, as anticipated from the observed map expansion in the F6. Additional genotyping at higher marker density across the QTLR is clearly warranted to obtain full benefit of the F6 map expansion.
Counting all resistance QTL uncovered in the two BC populations by Heifetz et al. [18
] and in the F6 in the present study, gives a total of 28 QTL; eight common to both series of crosses, 13 uncovered only in the F6, and 7 uncovered only in the BC populations. Of the 8 common QTLR, 5 were significant by Z or by Z and CS, indicating important main effects. These results should provide a strong platform for comparative positional cloning, after confirmation of the associations by individual genotyping of the pools. Comparative functional genomics based on the complete chicken genome sequence could be used to identify candidate genes in the identified chromosomal regions. As a preliminary exercise, we have searched Build 2 of the chicken genome across 5 cM centered at each of three regions which had narrow widths in the present study, and corresponded to regions of significance in [18
] or [21
], namely: QTLR 2-II (supported by [21
]), centered at ADL0270 at about 9.7 Mb; QTLR 8-II (supported by [18
] and [21
]), centered at HYL08003 at about 18.5 Mb; and QTLR 9-II (supported by [18
]), centered at LEI0197 at about 13.0 Mb. The most likely candidate gene in QTLR 2-II is PTPRN2 (protein tyrosine phosphatase, receptor type, N polypeptide 2) at 8.7 Mb (1 Mb downstream of ADL0270). PTP family members are signalling molecules that regulate many cellular processes, including cell growth, and have been implicated in oncogenic transformation. QTLR 8-II contains two interesting candidate genes: CD97 antigen, at 19.2 Mb (~0.7 Mb downstream of HYL08003) and PIGK (phosphatidylinositol glycan, class K), at 19.9 Mb (~1.4 Mb upstream of HYL08003). CD97 antigen is a receptor involved in cell adhesion and signalling, that is present on the surface of most activated leukocytes. MDV is thought to infect and transform only activated CD4+ T cells. Consequently, cell adhesion might assist the virus transmission from one infected cell to another, as MDV is highly cell associated. PIGK is a subunit of the GPI transamidase complex that catalyzes the attachment of GPI (glycosylphosphatidulinositol) to proteins. GPI is a membrane anchor for cell surface proteins. As such it provides for rapid protein release in response to a stimulus, since the protein bound by the GPI anchor can be immediately released without a requirement for RNA or protein synthesis. In this context it is relevant that SCA2 (stem cell antigen 2) has a GPI anchor, and is one of the most strongly documented MD resistance genes [9
]. For QTLR 9-II, using LEI0197 (at 13.0 Mb) as the reference point, we were unable to find any attractive candidate genes. This may in part be because the biology of MD resistance is not well defined. A further contributing factor is the fact that much of the chicken gene annotation is inferred by electronic annotation and not by experimental evidence. Consequently, one can speculate about almost any gene being involved in viral replication and spread, or cellular transformation, especially as the biomedical literature is slanted heavily towards cancer.
These and other candidate genes can be screened further by examining their mRNA expression pattern under MDV challenge, using existing data banks. However, they could also readily be examined for linkage disequilibrium with MD resistance by constructing appropriate resource populations using the existing data and sample banks accumulated at Hy-Line through the routine MD challenge and testing component of their regular commercial breeding program. Such association tests could also be implemented by selective DNA pooling, perhaps using the new fractioned pool designs [29
] to increase power and accuracy.