The nature of polymorphism and molecular sequence variation in the genes of the human major histocompatibility complex (MHC) provides strong support for the idea that these genes are under selection. With the understanding that selection shapes MHC variation new questions have become the focus of study. What is the mode of selection that accounts for MHC polymorphism? Is variation maintained by pathogen pressure or by reproductive mechanisms? Discerning between these requires drawing on information from studies on association between HLA genes and infectious diseases, reproductive success and mating preferences relative to HLA genotypes, and theoretical studies that compare the outcomes of different selection regimes. The pattern that has emerged suggests that several types of selection are plausible for the maintenance of HLA polymorphism.

Alpha2-HS glycoprotein (AHSG), which is equivalent to fetuin in other species, is a protein found in human plasma. AHSG is polymorphic with two common alleles and many variants. To examine the intragenic haplotypes and their diversity at this locus, a contiguous genomic DNA sequence (10·3 kb) was analyzed in 20 samples (40 chromosomes), and haplotypes were determined for 309 subjects. Judging from the aligned nucleotide sequences and the conserved amino acid residues comparing human and chimpanzee AHSG, it was concluded that the type 1 allele is probably older and has evolved into four major suballeles. The type 2 allele was generated from one branch of the type 1 allele. AHSG*3 and *5 variants were each found to have a single nucleotide change in exon 7, resulting in the change of an amino acid residue from Arg299 to Cys and from Asp258 to Asn, respectively. It was noted that the AHSG*3 mutation gives rise to an additional cysteine residue, which possibly affects the conformation of the protein. The AHSG gene was found to have a low mutation rate and no apparent recombination events. Furthermore, the detected substitutions were nonhomogeneously distributed at this locus. In particular, four nonsynonymous substitutions were concentrated in the carboxyl-terminal domain.

Coeliac disease (CD) is a malabsorptive disorder of the small intestine resulting from ingestion of gluten. The HLA risk factors involved in CD are well known but do not explain the whole genetic susceptibility. Several regions of potential linkage on chromosomes 3q, 5q, 10q, 11q, 15q and 19q have already been reported in the literature. These six regions were analyzed with the Maximum Lod Score method on a dense set of markers. A new sample of 89 Italian sibpairs was available for study. There was no evidence for linkage for any of the regions tested, except for chromosome 5q. For this region, our data, as well as a sample of 93 sibpairs from our first genome screen (Greco et al. 1998), are compatible with the presence of a risk factor for CD with a moderate effect.

Although molecular genetic evidence continues to accumulate that is consistent with a recent common African ancestry of modern humans, its ability to illuminate regional histories remains incomplete. A set of unique event polymorphisms associated with the non-recombining portion of the Y-chromosome (NRY) addresses this issue by providing evidence concerning successful migrations originating from Africa, which can be interpreted as subsequent colonizations, differentiations and migrations overlaid upon previous population ranges. A total of 205 markers identified by denaturing high performance liquid chromatography (DHPLC), together with 13 taken from the literature, were used to construct a parsimonious genealogy. Ancestral allelic states were deduced from orthologous great ape sequences. A total of 131 unique haplotypes were defined which trace the microevolutionary trajectory of global modern human genetic diversification. The genealogy provides a detailed phylogeographic portrait of contemporary global population structure that is emblematic of human origins, divergence and population history that is consistent with climatic, paleoanthropological and other genetic knowledge.

In order to investigate the origin of the Eastern Slavs, mitochondrial DNA (mtDNA) sequence variation was examined in Russians and Ukrainians by hypervariable segment I (HVS I) sequencing and restriction analysis of the haplogroup-specific sites. No significant differences were found for Russians and Ukrainians when compared to other Europeans – in fact, they fall within the range of gene diversity seen throughout Europe and exhibit the unimodal pattern of pairwise sequence differences. Moreover, HVS I sequences in the Russians and Ukrainians are similar or identical to those found in eastern and western European populations. Despite the small genetic distances between Europeans, phylogenetic analysis reveals a considerable heterogeneity of Eastern Slavonic populations – they do not cluster together onto a phylogenetic tree. Analysis of distribution of rare HVS I types shared between populations of Eastern Slavs and other West Eurasians has shown that Russians share rare haplotypes mainly with Germans and Finno–Ugric populations. Of these, subhaplogroup H1 sequence types, which are defined by different combinations of nucleotides 16192T, 16294T, 16304C, 16311C and 16320T, are found predominantly in common between Russians and German-speaking populations. The data obtained allow us to conclude that the Slavonic migrations in early Middle Ages from their putative homeland in central Europe to the east of Europe were accompanied mostly by the same mtDNA types characteristic for the pre-Slavonic populations of eastern Europe.

Positive crossover interference refers to the phenomenon that the occurrence of a crossover reduces the probability of another crossover in its vicinity. There have been studies reporting the presence of positive interference in humans. Some studies have also found evidence suggesting within and between chromosomal interference heterogeneity on some of the chromosomes. However, there has been no systematic study of interference and interference heterogeneity in the whole human genome, using pedigree data without first inferring crossovers. In this paper, we studied the Chi-square interference model and other models extensively to compare the relative performance of each of these models for accounting for interference and measuring strength of interference. Our results showed that the Chi-square model consistently fitted the data well and provided easily interpretable estimates of interference strength. The Chi-square model was then used to study interference and interference heterogeneity within and between chromosomes. We found strong evidence of positive interference in the whole human genome. Our results also indicated that the level of interference was fairly constant in most parts of the genome, but there was some evidence suggesting that the levels of interference for two of the chromosomes were different from the rest. We also found evidence of within chromosomal interference heterogeneity for several of the chromosomes.

Single nucleotide polymorphisms (SNPs) are very common throughout the genome and hence are potentially valuable for mapping disease susceptibility loci by detecting association between SNP markers and disease. However as SNPs are biallelic they may have relatively little power in association studies compared with the information that would be obtainable if marker haplotypes were available and could be used efficiently. Modelling the evolutionary events leading to linkage disequilibrium is very complex and many methods that seek to use information from multiple markers simultaneously need to make simplifying assumptions and may only be applicable when marker haplotypes, rather than genotypes, are available for analysis. We explore the properties of a simple application of a standard artificial neural network to this problem. The pattern-recognition properties of the network are used in the hope that marker haplotypes implicit in the genotypes will differ between cases and controls in a way which will lead to the network being able to classify the subjects correctly, according to their marker genotype. This method makes no assumptions at all regarding population history or the marker map, and can be applied to genotypes, as would be available from a simple case-control sample, without any need to determine haplotypes. Through application to data simulated under a very wide range of assumptions we show that such an analysis produces a useful augmentation in power above that which would be achieved by testing each marker individually, in particular when more than one mutation has occurred in a disease gene at different points in evolution. The application of neural networks to such problems shows considerable promise and further work could usefully be directed towards optimising the design and implementation of such networks.