As discussed above, even well powered GWAS cannot detect associations with rare variants. In 2012, our group reported the results of a smaller meta-analysis of VWF levels in two young healthy cohorts, N = 3462. We analysed a sibling cohort in order to perform both GWAS and linkage studies. Although much less powerful than association studies, linkage studies are not blind to rare variants clustered into loci with high allelic heterogeneity. We hypothesized that these types of loci, wherein single nucleotide variants are individually rare but in aggregate common, would accrue linkage signals across affected families and may explain a portion of the missing heritability for complex traits . In our GWAS, common variants in ABO and VWF explained about 18% of the variation in VWF levels. Linkage analysis detected a strong signal near the centromere of chromosome 2 that collectively explained about 19% of the variance in VWF levels. The chromosome 2 linkage interval had not been detected in a previous larger pedigree-based study , but was identified in a linkage study for FVIII levels in a French-Canadian VTE-enriched pedigree . This linkage interval was too large (>20 megabases) to usefully pick out candidate genes, although the interval included several genes involved in posttranslational modifications that could affect clearance of VWF and SNARE complex proteins that may modify the secretion rate of VWF. Follow-up linkage studies to narrow the linkage interval and DNA sequencing of top candidate genes will be required to better understand the functional mutations driving this linkage signal and their connection to VWF and VTE phenotypes.
Several groups have investigated the genetic determinants of proteins in the fibrinolytic system. Huang et al.[31,32] have reported GWAS for both tissue plasminogen activator (t-PA) and plasminogen activator inhibitor 1 (PAI-1) in nearly 20 000 individuals. Variants in two SNARE complex proteins, STX2 and STXBP5, were associated with t-PA levels. These two loci were also associated with VWF levels consistent with genetic regulation of a shared secretory pathway. The most recent fibrinogen meta-analysis in nearly 90 000 individuals reported significant associations with 23 loci . The strongest association was in the FGB (fibrinogen beta chain) locus itself. However, the total amount of variance explained by these 23 loci was only 3.7%, suggesting lower overall fibrinogen heritability, difficulty with accurate fibrinogen measurement and variance due to unmeasured factors. Our group reported an analysis of plasminogen levels in nearly 3000 people finding significant associations with variants in the PLG (plasminogen) locus as well as LPA [apolipoprotein (a)] and SIGLEC14 (sialic acid binding Ig-like lectin 14) that explained a total of 6.8% of the variation in plasminogen levels [34▪].
In the anticoagulant pathway, several groups have reported GWAS of protein C levels [35,36]. Interestingly, in a study of nearly 3000 individuals, the strongest association signal for protein C level was in PROCR (protein C receptor) explaining nearly 11% of the variance in protein C levels. In a larger study of nearly 9000 people, variants in PROC itself were also detected explaining nearly 2% of the variance. The later study also made associations with variants in EDEM2 (ER degradation enhancer, mannosidase alpha-like), GCKR (glucokinase regulatory protein) and BAZ1B (bromodomain adjacent to zinc finger domain 1B), genes whose function in protein C regulation has yet to be described. More recently, a study of the African-American subset of the ARIC cohort (N = 2701) reported associations in PROCR, PROC and an intergenic region near PROC that collectively explained nearly 14% of the variance in protein C levels [37▪].
In order to move closer to the goal of using an individual's genetic information to inform disease risk, individualize care and create targeted therapies, the genetic determinants of complex disease traits need to be more thoroughly defined. Towards this end, studies are underway employing next-generation sequencing technology to examine the DNA sequence from the 2% of the genome that encodes proteins (exome) as well as the whole genome to understand the role of rare mutations and regulatory variants in complex disease . Aggregate tests of mutation burden are being performed to identify genes with significant differences in mutation load between cases and controls [44▪▪]. In these tests, the mutations in a specific gene are, in a way, added together and analysed in aggregate to increase their apparent frequency so that associations can be made with rare or extremely rare variants if they are accumulating in a single locus. More complex computational methods and complementary functional studies will be required to improve the power of mutation burden tests and differentiate mutations causing loss of function from mutations causing gain of function in the same locus.
A large number of GWAS have discovered strong signals in intergenic areas. These areas may harbour regulatory variants such as enhancers that change disease risk through altered gene expression patterns. Understanding the genetic signature of these regulatory elements remains a research priority for groups studying complex genetic traits . Likewise, a better understanding of gene–gene interactions (epistasis) may shed light on how specific gene variants function in a gene network. For example, most individuals with factor V Leiden mutation never suffer from VTE. This could be due to the presence of other protective genetic variants in these individuals or the requirement of specific genetic variants in people with factor V Leiden who develop VTE. To detect these interactions, a large case–control cohort of individuals with factor V Leiden would be required. Unfortunately, the search for epistatic interactions is computationally intensive and will likely require larger studies in order to detect significant interactions in VTE .
More complete knowledge of the genetic determinants of the hemostatic and thrombotic systems will require further studies into the common, rare and regulatory variants in the genome. These studies will provide a context for the impact of a given variant in a population and aid in the discovery of new genes playing a role in thrombosis and hemostasis. Ultimately, the results from these studies should inform clinicians about an individual's risk for thrombotic disease and help guide therapeutic decisions.
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