The effects of common variants of cholesteryl ester transfer protein (CETP) (TaqIB), hepatic lipase (HL) (−514C>T), lipoprotein lipase (LPL) (S447X) and lecithin cholesterol acyl transferase (LCAT) (S208T) on the determination of high density lipoprotein cholesterol (HDL-C) and apolipoprotein AI (apoAI) levels were examined in 2773 healthy middle-aged men participating in the second Northwick Park Heart Study. The extent of gene:gene, gene:smoking and gene:alcohol interactions were determined. For HDL-C levels, only CETP genotype was associated with significant effects (p<0·0001), with the B2 allele being associated with higher levels in both smokers and non-smokers. This interaction was significant at the lowest tertile of TG, suggesting that TG levels were rate limiting. As previously reported, CETP, LPL and HL genotypes were all associated with significant effects on apoAI levels (all p<0·01), with carriers of the rare alleles having higher levels and with no evidence of heterogeneity of effects in smokers and non-smokers. LCAT genotype was not associated with significant effects on either trait. There was no significant interaction between any of the genotypes and alcohol consumption on either HDL-C or apoAI levels. All genotypic effects were additive for HDL-C and apoAI. Environmental and TG levels explained more than 20% and 5·5% of the variance in HDL-C and apoAI, respectively. The novel aspect of this finding is that genetic variation at these loci explained in total only 2·5% of the variance in HDL-C and 1·89% of the variance in apoAI levels. Thus despite the key roles played by these enzymes in HDL metabolism, variation at these loci, at least as detected by these common genotypes, contributes minimally to the variance in HDL-C and apoAI levels in healthy men, highlighting the polygenic and multifactorial control of HDL-C.

Susceptibility to coeliac disease involves HLA and non-HLA-linked genes. The CTLA4/CD28 gene region encodes immune regulatory T-cell surface molecules and is a strong candidate as a susceptibility locus. We evaluated CTLA4/CD28 in coeliac disease by genetic linkage and association and combined our findings with published studies through a meta-analysis. 116 multiplex families were genotyped across CTLA4/CD28 using eight markers. The contribution of CTLA4/CD28 to coeliac disease was assessed by non-parametric linkage and association analyses. Seven studies were identified that had evaluated the relationship between CTLA4/CD28 and coeliac disease and a pooled analysis of data undertaken. In our study there was evidence for a relationship between variation in the CTLA4/CD28 region and coeliac disease by linkage and association analyses. However, the findings did not attain formal statistical significance (p = 0·004 and 0·039, respectively). Pooling findings with published results showed significant evidence for linkage (504 families) and association (940 families): p values, 0·0001 and 0·0014 at D2S2214, respectively, and 0·0008 and 0·0006 at D2S116, respectively. These findings suggest that variation in the CD28/CTLA4 gene region is a determinant of coeliac disease susceptibility. Dissecting the sequence variation underlying this relationship will depend on further analyses utilising denser sets of markers.

We performed phenotyping of human phosphoglucomutase 3 (PGM3) and screening for mutations in the human N-acetylglucosamine-phosphate mutase gene (AGM1) to identify PGM3 as AGM1. By sequencing the coding region of AGM1, two alleles containing a G or A base at nucleotide 1396, that can respectively encode aspartic acid or asparagine at codon 466, were identified. Cell extracts of COS7 cells after transfection with the pcDNA 3·1(+) plasmid containing an AGM1 allele with 1396G or 1396A showed similar electrophoretic patterns to the PGM3 1 or PGM3 2 protein, respectively, with the isozyme detection method used for PGM3 phenotyping. The genotypes determined by the two alleles of AGM1 coincided exactly with the PGM3 phenotypes in 20 individuals. We also investigated the allele frequency of the AGM1 nucleotide polymorphism in a Japanese population by DNA sequencing and found that the frequencies of alleles 1396G and 1396A were similar to previously reported PGM3*1 and PGM3*2 frequencies. Overall, the facts that the AGM1 gene product shows PGM activity, AGM1 is polymorphic, the electrophoretic mobility is similar between AGM1 allele-specific products and PGM3 1 and 2 proteins, PGM3 phenotypes and AGM1 genotypes completely coincide in 20 individuals, and AGM1 allele frequencies are similar to those of PGM3*1 and PGM3*2 in Japanese populations, suggest that PGM3 is identical to AGM1.

In studies of association between genetic markers and a disease, the transmission disequilibrium test (TDT) has become a standard procedure. It was introduced originally as a test for linkage in the presence of association and can be used as a test for association under appropriate assumptions. The power of the TDT test for association between a candidate gene and disease depends on the underlying genetic model and the TDT is the optimal test if the additive model holds. Related methods have been obtained for a given mode of inheritance (e.g. dominant or recessive). Quite often, however, the true model is unknown and selection of a single method of analysis is problematic, since use of a test optimal for one genetic model usually leads to a substantial loss of power if another genetic model is the true one. The general approach of efficiency robustness has suggested two types of robust procedures, which we apply to TDT-type association tests. When the plausible range of alternative models is wide (e.g. dominant through recessive) our results indicate that the maximum (MAX) of several test statistics, each of which is optimal for quite different models, has good power under all genetic models. In situations where the set of possible models can be narrowed (e.g. dominant through additive) a simple linear combination also performs well. In general, the MAX has better power properties than the TDT for the study of candidate genes when the mode of inheritance is unknown.

We have previously described extending our method of ‘model-free’ linkage analysis, implemented in the MFLINK program, in order to deal with liability classes. This allows a new form of conditional two-locus linkage analysis, meaning that the genotypes of a known risk locus can be used to define liability classes so that their effects can be incorporated in tests for linkage at additional loci. In this method, relationships between transmission models for different liability classes were constrained so that there was a constant multiplicative effect on penetrance values. Here we present further extensions to the method to allow for different relationships. In particular, rather than only having a multiplicative effect on risk of affection we now allow specification of a multiplicative effect on risk of non-affection, or a combination of both relationships, across liability classes. We now also allow specification of an additive effect on penetrance. By way of example, we apply these methods to genome scan data for Alzheimer's disease using apolipoprotein E genotype to define liability classes. We show that, although in general the different methods produce results which tend to be quite highly correlated, certain markers can produce quite different results according to the method applied and that these could well lead to differences of interpretation. Without knowing a priori which relationship is likely to be most appropriate to describe the overall combined effect of the two loci one might be obliged to apply a number of different methods. This in turn may lead to the familiar difficulties associated with multiple testing. Nevertheless, the new method allows researchers greater flexibility in analysing linkage data for diseases in which one or more risk polymorphisms have already been identified.

Sibship methods have been shown to be more powerful than traditional sib-pair methods in mapping quantative trait loci. We propose a statistical procedure which integrates data on sibships into a so-called ‘contrast function’, a natural extension of the classical squared sib-pair trait difference proposed by Haseman & Elston (1972). We also develop a combined mean and contrast function which provides more information on linkage compared to the contrast function. Our method is extended to multiple, epistatically interacting trait loci. Monte-Carlo simulations are included to compare the efficiencies of the proposed procedures with some currently used methods. An application of our proposed method is presented using data on alcohol dependence.