Using a hypothesis-generating approach, we have identified that the possession of common genetic variation in genes associated with vascular growth and development and estrogen action and signaling was associated with HPS in this case-control study. In contrast, we did not find any association between HPS and vasoregulatory genes such as nitric oxide, heme oxygenase, and the endothelin-B receptor, which have been specifically implicated in HPS.28–31
Our findings are in line with recent experimental results that demonstrate an important role for pulmonary angiogenesis in HPS.13
We have identified a number of genetic risk factors for HPS that modulate angiogenesis or vascular development. For example, endostatin, the proteolytic fragment of the C-terminus COL18A1
, inhibits angiogenesis.32,33
In addition to the genetic association reported here, we have recently demonstrated that overexpression of endostatin in an animal model of HPS blocks the expansion of pulmonary microvessels as well as the oxygen diffusion impairment characteristic of that model.13
Endoglin is a transmembrane auxillary receptor for transforming growth factor (TGF)-β
that is predominantly expressed on proliferating endothelial cells. Mutations in endoglin and activin receptor-like kinase 1 (ALK1
), an endothelial specific TGF-β
type I receptor, have been linked to hereditary hemorrhagic telangiectasia, an autosomal dominant vascular dysplasia characterized by telangiectasias and arteriovenous malformations.34,35
Interestingly, among patients with hereditary hemorrhagic telangiectasia, pulmonary arteriovenous malformations are significantly more likely in subjects with endoglin mutations.36
, an endothelial specific receptor tyrosine kinase, is essential for the activation of TIE2
by vascular endothelial growth factor (VEGF
), thus modulating vascular remodeling and blood vessel development.37
Low oxygen tension (hypoxia) is a potent stimulator of vascular growth and remodeling, and, in the pulmonary vasculature, oxygen sensing is critical for maintenance of normal gas exchange via adjustments in vascular tone. Four of the genes implicated here–HIFA1
–play central roles in oxygen-dependant vascular phenotypes. HIF1A
stimulates endothelial cell angiogenesis under hypoxic conditions by activating the transcription of numerous transcription and growth factors38
and is regulated by SAT2
Variation in both genes was associated with HPS case status. RUNX1
is a hematopoetic transcription factor that contributes to the angio- and vasculogenic phenotype via its interaction with other transcription factors such as HIF1A
and insulin growth factor binding protein 3.40–42
is one of the enzymes responsible for generation of reactive oxygen species in endothelial cells that modulate angiogenesis and has been implicated in hypoxia-induced proliferation.43
These results identify variation in specific genes that may contribute to susceptibility in HPS and be candidates for future studies.
Three specific signaling pathways–carbon monoxide, nitric oxide, and endothelin–have been implicated in pulmonary vasodilatation in experimental and human HPS. Increased production of the gaseous vasodilators nitric oxide and carbon monoxide has also been associated with vascular dilatation in HPS,30,44,45
and, thus, we tested variants in the inducible and endothelial forms of nitric oxide synthase (NOS) as well as heme oxygenase 1 (HMOX1
), the rate-limiting enzyme in the production of carbon monoxide. A recent report found that the Glu298Asp (rs1799983) variant in NOS3
was associated with risk of HPS in 20 subjects with pediatric (predominately anatomic or metabolic) liver disease. We did not replicate this observation in our cohort (OR, 0.75; 95% CI: 0.43–1.31, P
=.31). Altered endothelin signaling has been implicated in experimental HPS, with the liver producing increased circulating ET-1, which signals through up-regulated ET-B receptors on pulmonary endothelial cells.46
We analyzed SNPs in endothelin converting enzyme as well as both endothelin A and B receptors. Germ-line variation in none of these genes was associated with risk of HPS in our study population.
In addition to our single SNP analyses, we undertook gene- and pathway-based approaches to provide additional insight into the relationship between genotype and disease phenotype. Two genes with single SNP associations–CAV3 and RUNX1–were also identified in these analyses. CAV3 gene had an overall association with HPS using PC analysis, and SNPs from CAV3 were found in the CART and Random Forests approaches. A SNP from RUNX1 was identified as the most discriminating polymorphism (first split) in the CART tree, and this was confirmed by the Random Forests algorithm. Because these 2 genes were shown to be important using multiple methodologies, this provides stronger evidence that CAV3 and RUNX1 are associated with HPS. In addition to supporting these associations, these analyses also indicated 3 genes not found in the single SNP analysis—TGFB1, TNC, and TRPC6–may actually be associated with the disease.
There are several limitations to this study. First, the sample size was small, limiting our ability to find genetic alleles associated with HPS that were rare, had small effect sizes, or whose effect depended on gene-gene or gene-environment interaction. However, this is the largest reported epidemiologic study of HPS with strict case and control phenotypes and the first in HPS to employ high-throughput genotyping.
A fundamental challenge in high-throughput genetic analyses is the control of type I error. Given that we analyzed multiple SNPs for each of more than 90 genes, we can reasonably expect a certain number of statistically significant associations because of chance alone. We attempted to minimize the chance of “false-positives” by using a curated candidate gene list, thusly increasing the prior probability that one or more of these genes has mechanistic importance in HPS. There are commonly utilized frequentist methods to adjust for multiple comparisons in high-throughput studies, such as the Bonferroni correction and false discovery rate.47
Both methodologies assume that the association of each individual SNP with case status is entirely independent of those of the other SNPs. We have documented patterns of linkage disequilibrium between genotyped SNPs (data not shown). Because most accepted methods to account for multiple comparisons do not consider such relatedness, they are overly conservative for this purpose. We have therefore presented the results without adjustment and consider these results to be hypothesis generating. Whereas replication would be important, the biologic plausibility of our findings, the multiple gene “hits” in certain pathways, and the demonstration of association via both single loci and gene-based approaches is reassuring that type I error does not explain the findings.
In conclusion, our results implicate common genetic variation in the pathogenesis of HPS. Future studies should focus on replication in other populations and the mechanisms that explain the associations between the SNPs of interest and HPS.