186 J. A. Traherne characterized the LD relationships between the highly polymorphic HLA genes and background variation by typing the classical HLA genes and > 7500 common SNPs across the extended MHC region of 7.5 Mb in four population samples (African, European, Chinese and Japanese ancestry). The data add signiﬁcantly to our knowledge of the underlying LD block structure. The analysis also provided informative tag SNPs that capture much of the common variation in the region to facilitate efﬁcient ﬁne-mapping of MHC-associated disease (de Bakker et al., 2006a). However, it remains to be assessed how efﬁciently less common variants, haplotypes or recombinants can be captured via the tagging approach. With continued advancement in sequencing technology, re-sequencing will probably continue to be a preferential method for detecting rare variants outside HLA genes that inﬂuence complex traits. There is currently no knowledge of tag SNPs that can serve as surrogates of the RCCX CNVs. However, analysis has shown that SNPs outside the HLA genes can be informative about HLA types (de Bakker et al., 2006b). Hence, tags could potentially provide a simpler, complementary approach to classical HLA typing for matching transplant donors and patients or for prescreening subjects for further analysis. However, several factors make HLA tagging more challenging than tagging of SNPs. First, balancing selection has resulted in a multitude of HLA alleles in populations, the majority of which are not common but collectively they account for a signiﬁcant proportion of the genetic diversity. Furthermore, certain forms of balancing selection, such as pathogen-driven, generate new combinations of alleles across multiple HLA genes. Consequently, a given HLA allele might be found on a number of different haplotype backgrounds. These effects, in addition to demography and genetic drift, mean that although tagging of certain common HLA alleles in some populations may be relatively straightforward, selection of tags and the boundaries of haplotype blocks are likely to differ between populations and HLA haplotypes. However, once deﬁned, differences in LD structure between populations can be helpful in differen- tiating speciﬁc gene effects (Oksenberg et al., 2004). Epistasis In the MHC, a region of the genome where at least 30% of genes are immune related and many share interrelated functions, it would be surprising if extensive epistasis were not active. This is an important consideration in disease-mapping studies within the MHC since epistasis can hinder the search for susceptibility loci; inherently the effect of one locus is altered or masked by the effects of another. However, genetic interactions can be informative by revealing not only the nature of the mutations but also gene function, functional redundancy and protein interactions. A range of statistical methods can identify or control for the presence of epistasis, although the degree to which statistical modelling can elucidate the underlying biological mechanism is limited. Consequently, the identi- ﬁcation of true interactions can sometimes be better achieved via molecular, rather than statistical approaches. A recent study highlights the value of molecular studies in characterizing epistatic interactions. The major candi- date region for multiple sclerosis susceptibility comprises alleles of two genes located 85 kb apart that show almost complete LD in all ethnic groups studied to date, namely HLA-DRB1*1501 and HLA-DRB5*0101. The tight linkage between these two DR alleles might relate to the persistence of a founder haplotype, although its occurrence in diverse ethnic groups opposes this theory. A study using functional analysis in humanized mice indicates that the LD between the two alleles may be due to epistasis, whereby one allele (DRB5*0101) modulates the severity of the immune response activated by the second allele (DRB1*1501) through peripheral T-cell deletion (Gregersen et al., 2006). This particular epistatic interaction was found to be associated with a milder form of multiple sclerosis-like disease in mice. Interactions of this type might prove to be a general regulatory mechanism for modifying immune responses and could be widespread throughout the MHC, contribut- ing signiﬁcantly to patterns of LD. For instance, LD in the HLA-DQB1–HLA-DQA1 region and HLA-DRB1 alle- les could reﬂect the effects of persistent or strong selec- tion for maintenance of preferred combinations of HLA- DQB1, HLA-DQA1 and HLA-DRB1 alleles. Interest- ingly, speciﬁc allele combinations of these three loci are major determinants of predisposition to type 1 diabetes (Koeleman et al., 2004). In addition to epistasis internal to the MHC, there is mounting evidence for interactions between MHC genes and unlinked genes outside the complex resulting in susceptibility to disease. Of prominence are HLA class I interactions with the killer immunoglobulin receptor (KIR) genes encoded in the leucocyte receptor complex (LRC) on chromosome 19q13. KIRs are expressed by natural killer (NK) cells as well as subsets of T cells and interaction with their ligands, HLA class I molecules, modulates both innate and adaptive immune responses. Combinations of HLA class I and KIR variants have been associated with pathologies as diverse as autoimmunity, viral infections, pregnancy-related disorders and cancer (Parham, 2005; Khakoo & Carrington, 2006). Thus, interactions between KIR and MHC class I polymor- phisms have probably been involved in human survival during incidences of epidemic infections and have affected reproduction and population expansion. These types of selection pressures might explain the functional coevolu- tion of KIR with diverging HLA class I molecules and why KIR sequences, like the HLA loci, are highly polymorphic and rapidly evolving (Guethlein et al., 2007; Martin et al., 2007; Single et al., 2007). Multiple factors complicate the interpretation of KIR- HLA disease association, including (i) the extensive polymorphism of the KIR and HLA class I ligands, (ii) the LD between variants within each gene family, (iii) incomplete knowledge of KIR ligands and oversimpliﬁcation of the structural complexities of their interactions, and (iv) © 2008 The Author Journal compilation © 2008 Blackwell Publishing Ltd, International Journal of Immunogenetics 35, 179–192