Genetic control of susceptibility to UV-induced immunosuppression by interacting quantitative trait loci

Abstract

Ultraviolet B radiation (290-320 nm) initiates a dose and wavelength dependent down-regulation of cell-mediated immunity which is critical in experimental ultraviolet radiation (UV) carcinogenesis, preventing immune attack on highly antigenic UV-induced tumors. UV-induced immunosuppression has been demonstrated in humans and may be a risk factor for skin cancer. In this study, we have investigated genetic linkage of the autosomal loci controlling this trait. Previously, we had derived a model describing control of susceptibility to UV-induced immunosuppression in inbred mice by unlinked interacting autosomal and X-linked loci. A genome-wide scan using MIT microsatellite markers was carried out on 100 backcross {BALB/c × (BALB/c × C57BL/6) F 1 } mice derived from the inbred strains BALB/c (low susceptibility) and C57BL/6 (high susceptibility) and tested for systemic UV-induced immunosuppression of a contact hypersensitivity response. The values for % suppression for each animal and the genotype data were used to investigate genetic linkage by multiple regression analysis. Significance was assessed using the permutation test. Both main effects and interactive effects were investigated, first with each genotype marker singly, and secondly, in a novel approach using markers pairwise. A joint model was derived in which all loci and pairs of loci identified were included simultaneously in a multiple regression model. This model indicates four quantitative trait loci (QTLs) with significant main effects, one on chromosome 10 which decreased susceptibility to UV-induced immunosuppression and QTLs on chromosomes 6, 17 and 1 which increased susceptibility. Additionally, loci on chromosomes 14 and 19 showed significant interaction with the locus on chromosome 1. Further investigation indicated a potential three-way interaction involving the loci on chromosomes 1,14 and 19. Genes and Immunity (2000) 1, 251-259.

Age-related thymic involution in C57BL/6J × DBA/2J recombinant-inbred mice maps to mouse chromosomes 9 and 10

“…We have previously identified that the thymocytes proliferative response to anti-CD3 stimulation was significantly lower at young and old age in parental D2 mice compared to B6 mice. 6 Consistent with this, previous mapped QTLs close to our mapped Chr 10 region were mostly associated with immune regulation 30 and development of autoimmune diseases, 31,32 with two recent reports indicating that proximal to this region at Chr 10, 35 cM contains a QTL that is associated with agerelated tumor suppression and the regulation of lifespan in mice. 33,34 Thus, although we do not rule out the possibility that regulation of the value of thymic involution can be affected by the hormonal effects, at least in the B6, D2, and B Â D RI strains of mice, the development and regulation of T-cell response may be more important in influencing the thymic involution rate.…”

A new strategy for estimating two-locus recombination fractions under some natural inequality restrictions

“…Repeating the similar procedure for case of equal prior probabilities; we can obtain the restricted MLEs of the two-locus recombination fractions correspondingly. An example Zhou et al (2008) analysed a real data set that comprises of 134 individuals from a backcross of mice (Clemens et al 2000). Here we use the proposed RPA to revisit that data set and estimate three two-locus recombination fractions among marker loci D2Mit365, D2Mit272 and D2Mit456 on the linkage map of chromosome 2.…”

Analysis of genotype effects for the immunosuppression via two-step method

“…After computation with the DS method, we obtain the estimates of QTL effects, many of which are zero in fact, and some significant main effects are same with those given in the literature [10]. Owning to this fact, we choose the most significant six main effects and the possible interacting loci to build a new statistical model to further detect the significant interacting loci.…”

“…The data is from the literature [10]. Clemens et al [10] collected the genotype data on 64 SNPs and immunosuppression data of 134 backcross individuals, and they detected some main effects and interactions among these loci. Here we proposed a new strategy and found different genotype effects for the immunosuppression.…”