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Venous thromboembolism (VTE) is a frequent disease that has a major genetic component of risk. However, known identified genetic risk factors account for <30% of idiopathic (without any environmental origin) VTE cases. This article aims to review the lessons learnt during recent decades in the field of the genetics of VTE, describe the present state-of-art methods and discuss promising themes for finding new susceptibility loci.

The mechanisms underlying the variability of factor VIII (FVIII) levels are still poorly understood. The only receptor of FVIII identified so far is the lipoprotein receptor-related protein (LRP), which is thought to be involved in FVIII degradation. We aimed to characterize biological and genetic factors related to FVIII variability, focusing on coding polymorphisms of the LRP gene and polymorphisms potentially detected by molecular screening of the LRP-binding domains of the FVIII gene. Plasma FVIII coagulant activity (FVIII:C) and von Willebrand factor (VWF:Ag) antigen levels were measured in a sample of 100 healthy nuclear families (200 parents and 224 offspring). The ABO blood group and the three coding polymorphisms of the LRP gene (A217V, D2080N and C766T) were genotyped. Lipids and anthropometric factors poorly contributed to the variability of FVIII:C (<5%). A strong effect of ABO blood groups on FVIII:C levels was observed that remained significant after adjustment for VWF:Ag levels (P = 0.02). These two factors explained more than 50% of FVIII:C variability. After adjustment for VWF:Ag and ABO blood groups, a residual resemblance for FVIII:C persisted between biological relatives (rho = 0.13 +/- 0.06 between parents and offspring, rho = 0.24 +/- 0.09 between siblings) compatible with an additional genetic influence. The N allele of the LRP/D2080N polymorphism was associated with decreased levels of FVIII:C (90.4 +/- 8.7 vs. 102.2 +/- 3.5 IU/dl, P = 0.03) and VWF:Ag levels (109.1 +/- 11.2 vs. 125.4 +/- 4.4 IU/dl, P = 0.02). No polymorphism was detected in the LRP-binding domains of the FVIII gene. This study reinforces the hypothesis of a genetic influence of FVIII levels beyond the influence of VWF:Ag and ABO blood groups. The D2080N polymorphism of the LRP gene weakly contributed to the variability of FVIII:C levels in this healthy population.

A detailed exploration of all the polymorphisms in candidate genes is required to better characterize the relationship between gene variability and complex traits. We propose a novel strategy for investigating the association between a highly polymorphic gene and a phenotype, by combining a multilocus genotype analysis and an haplotype analysis. For the multilocus genotype analysis, a data mining tool--termed DICE (Detection of Informative Combined Effects)--was developed to identify the best subset of polymorphisms that are associated--individually or in combination--with the phenotype. For the haplotype analysis, we used our recently developed method of haplotype-phenotype association to determine the most informative and parsimonious haplotype model fitting the data. We illustrate this strategy by investigating the association between twelve polymorphisms of the APOB gene and plasma apoB levels in 1442 European subjects. After exploring all main effects and interactions between polymorphisms, DICE identified the N4311S polymorphism as the most informative polymorphism in relation to apoB levels. Haplotype analysis led to the same conclusion. Additionally, DICE identified the E4154K (EcoRI) and the T2488T (XbaI) polymorphisms as potentially interesting. This selection was not modified by inclusion of the common APOE polymorphism in the analysis.

It is now widely accepted that haplotypic information can be of great interest for investigating the role of a candidate gene in the etiology of complex diseases. In the absence of family data, haplotypes cannot be deduced from genotypes, except for individuals who are homozygous at all loci or heterozygous at only one site. Statistical methodologies are therefore required for inferring haplotypes from genotypic data and testing their association with a phenotype of interest. Two maximum likelihood algorithms are often used in the context of haplotype-based association studies, the Newton-Raphson (NR) and the Expectation-Maximisation (EM) algorithms. In order to circumvent the limitations of both algorithms, including convergence to local minima and saddle points, we here described how a stochastic version of the EM algorithm, referred to as SEM, could be used for testing haplotype-phenotype association. Statistical properties of the SEM algorithm were investigated through a simulation study for a large range of practical situations, including small/large samples and rare/frequent haplotypes, and results were compared to those obtained by use of the standard NR algorithm. Our simulation study indicated that the SEM algorithm provides results similar to those of the NR algorithm, making the SEM algorithm of great interest for haplotype-based association analysis, especially when the number of polymorphisms is quite large.

P-selectin and P-selectin glycoprotein ligand (SELPLG, selectin P ligand) constitute a receptor/ligand complex that is likely to be involved in the development of atherosclerosis and its complications. While the genetic variability of P-selectin has already been investigated in depth, that of the SELPLG gene has not yet been extensively explored. The coding and regulatory sequences of the SELPLG were screened and nine polymorphisms were identified. The identified polymorphisms were genotyped in the AtheroGene study, a case-control study of coronary artery disease (CAD). Haplotype analysis revealed that two polymorphisms of SELPLG, the M62I and the VNTR, independently influenced plasma SELPLG levels. Conversely, haplotypes of SELPLG were not associated with CAD risk.

Transmission Disequilibrium Test (TDT)-based methods have been advocated by several authors for testing that a marker-phenotype association is actually due to linkage and not to uncontrolled stratification. As a pre-requisite of TDT-type methods is the presence of an association between marker and phenotype, one may wish to first investigate the association using a classical association study, and then to check by a TDT approach whether this association is actually due to linkage. We propose an estimating equation (EE) procedure, to compute analytically the minimum sample size of sibship data required to detect the association between a marker and a quantitative phenotype, and that required to confirm it by two TDT methods. We show that, when the marker allele frequency is low or high, the number of informative sibs needed in TDT-type methods can be lower than the number required in an association analysis, and even more so when the familial clustering is strong. However, in all cases, the number of sibs that need to be sampled to get the appropriate number of informative sibs for analysis is always larger for TDT methods than for an association study. In a phenotype-first strategy, this number may be critical when investigating costly phenotypes.

Unlike monogenic diseases for which considerable progress has been made in past years, the identification of susceptibility genes involved in multifactorial diseases still poses numerous challenges, including the development of new statistical methodologies. Recently, several authors have advocated the use of the estimating equations (EE) approach as an alternative to standard maximum likelihood methods for analysing correlated data. Since most genetic studies rely on family data, the EE found a natural field of application in genetic epidemiology. The objective of this review is to give a brief description of the EE principles, and to outline its applications in the main areas of genetic epidemiology, including familial aggregation analysis, segregation analysis, linkage analysis and association studies.