Pathway-based analysis of rare and common variants to test for association with blood pressure
© Alsulami et al.; licensee BioMed Central Ltd. 2014
Published: 17 June 2014
Our goal is to test the effect of both rare and common variants in a blood pressure study. We use a pathway-based approach, gene-set enrichment analysis, to search for related genes affecting 4 phenotypes: systolic blood pressure, diastolic blood pressure, the difference between each of them and mean arterial pressure, which is a weighted linear combination of systolic and diastolic blood pressure. Using the real Genetic Analysis Workshop 18 data, we consider both rare and common variants in our analysis and incorporate other covariates by using a recently proposed test statistic.
Our study identified a commonly enriched gene set/pathway for the two derived phenotypes we analyzed: the difference between systolic and diastolic blood pressure and mean arterial pressure, but none is identified with the individual blood pressure phenotypes. The gene CD47, in the enriched gene pathway/set, was reported in previous studies to be related to blood pressure.
The findings are not surprising because the sample size we use in our analysis is small, and hence power to detect small but important effects is likely inadequate.
Worldwide, hypertension contributes to more than 10 million deaths and it affects one-third of the adult population per year . It was predicted that the incidence of hypertension among adults in 2025 will reach 1.56 billion and contribute to approximately 54% of stroke and 47% of ischemic heart disease. Furthermore, it is a major risk factor for cardiovascular disease . Several factors, including genetic, environmental, and demographic factors, play a major role in the development of hypertension. However, it is believed that 30% to 60% of the variability in blood pressure (BP) is inherited .
Many genome-wide association studies (GWAS) have been conducted to identify single-nucleotide polymorphism (SNPs) that are significantly associated with systolic blood pressure (SBP), diastolic blood pressure (DBP), and/or hypertension.
Meta-analysis findings of the Global BPgen (Global Blood Pressure Genetics) consortium (n = 34,433) and CHARGE (The Cohorts for Heart and Aging Research in Genome Epidemiology) consortium (n = 29,136) based on populations of European ancestry identified 4 loci significantly associated with SBP (ATP2B1, CYP17A1, PLEKHA7, SH2B3), 6 associated with DBP (ATP2B1, CACNB2, CSK-ULK3, SH2B3, TBX3-TBX5, ULK4), and 1 associated with hypertension (ATP2B1) . However, a genome-wide association study by Adeyemo et al  based on a population of African Americans (n = 1017) identified significant loci for SBP in or near the genes PMS1, SLC24A4, YWHA7, IPO7, and CACANA1H, while no significant loci were discovered to be associated with DBP or hypertension.
Unlike single-gene analysis, pathway-based approaches consider multiple genes that are related together within gene sets/pathways; these pathways are predefined gene sets from biological databases. The aim of pathway-based approaches is to assess the significance of these sets/pathways by evaluating the enrichment of genes within a pathway at the top of a list of ranked genes [4-6]. Pathway-based analysis was originally applied to gene expression data; however, it has also been applied to GWAS data . In this paper, we use a pathway-based approach based on Gene Set Enrichment Analysis (GSEA) . We consider both rare and common variants and incorporate other covariates, including age, gender, use of antihypertensive medications, and smoking status. Our main focus is to test the effects of both rare and common variants on SBP, DBP, the difference between them (SBP-DBP), and mean arterial pressure (defined as MAP = [2/3 DBP] + [1/3 SBP]) by applying GSEA.
Phenotype and covariate data description
Descriptive statistics for phenotypes and covariates at baseline for 129 unrelated individuals
128.4 ± 21.8
71.8 ± 9.2
90.7 ± 11.6
56.6 ± 19
Hypertension (Yes, No)
52.9 ± 15.6
Medications use (Yes, No)
Smoking status (Yes, No)
Genotype data description
Genotype data were provided only for odd-numbered autosomal chromosomes. In this paper, we focus on variants on chromosome 3 (as suggested by the GAW18 organizers to allow comparisons of findings with other GAW18 contributions).
Step 1: Mapping SNPs to genes
Among the 1,215,296 SNPs on chromosome three, 523,147 SNPs were mapped to 1224 known genes using NBCI2R.
Step 2: Obtaining test statistics for genes
where are the optimal weights.
where is the p value of the test and . To evaluate the p value of , we used the permutation test.
Step 3: Pathway analysis
To estimate the significance level of NES for each set/pathway, we used the gene-based permutation approach to obtain the empirical p values of the NES. We used 1000 gene-set permutations and then we considered the set/pathway to be significantly enriched if its false discovery rate (FDR) q value is less than 0.05. We implemented the analysis using the GSEApreranked tool included in the GSEA software [6, 7].
The top 10 gene sets/pathways from c2 curated gene sets ranked by FDR q values for MAP
The top 10 gene sets/pathways from c2 curated gene sets ranked by FDR q-values for the difference between SBP and DBP
The top 10 gene sets/pathways from c2 curated gene sets ranked by FDR q values for SBP
The top 10 gene sets/pathways from c2 curated gene sets ranked by FDR q values for DBP
Because our pathway-based analysis is restricted to genes on chromosome 3, the number of pathways used for analysis exceeded the number of genes, which can have important implications in interpreting our findings. The results from our analyses should be interpreted cautiously.
Gene-set enrichment analysis considers multiple genes that are related biologically. In our data, we identified 1 identical enriched gene set/pathway with the MAP and the difference between SBP and DBP. The gene CD47 in this pathway was reported previously to be related to BP.
Our analysis included only 129 unrelated individuals. Sample size plays a major role in identifying enriched gene sets/pathways, which could explain the lack of significant pathways in our analysis. Future studies can be done by applying GSEA on large family-based data where incorporating both rare and common variants, taking into account the correlations between individuals and increasing the sample size, may lead to new discoveries.
JB would like to acknowledge Discovery Grant funding from the Natural Sciences and Engineering Research Council of Canada (NSERC) (grant number 293295-2009) and Canadian Institutes of Health Research (CIHR) (grant number 84392). JB holds the John D. Cameron Endowed Chair in the Genetic Determinants of Chronic Diseases, Department of Clinical Epide- miology and Biostatistics, McMaster University. The GAW18 whole genome sequence data were provided by the T2D-GENES Consortium, which is supported by NIH grants U01 DK085524, U01 DK085584, U01 DK085501, U01 DK085526, and U01 DK085545. The other genetic and phenotypic data for GAW18 were provided by the San Antonio Family Heart Study and San Antonio Family Diabetes/Gallbladder Study, which are supported by NIH grants P01 HL045222, R01 DK047482, and R01 DK053889. The Genetic Analysis Workshop is supported by NIH grant R01 GM031575. We would like to thank two anonymous reviewers and the editor for insightful comments that improved the presentation and clarity of our manuscript.
This article has been published as part of BMC Proceedings Volume 8 Supplement 1, 2014: Genetic Analysis Workshop 18. The full contents of the supplement are available online at http://www.biomedcentral.com/bmcproc/supplements/8/S1. Publication charges for this supplement were funded by the Texas Biomedical Research Institute.
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