- Open Access
Pathway analysis for genetic association studies: to do, or not to do? That is the question
© Dufresne et al.; licensee BioMed Central Ltd. 2014
Published: 17 June 2014
In Genetic Analysis Workshop 18 data, we used a 3-stage approach to explore the benefits of pathway analysis in improving a model to predict 2 diastolic blood pressure phenotypes as a function of genetic variation. At stage 1, gene-based tests of association in family data of approximately 800 individuals found over 600 genes associated at p<0.05 for each phenotype. At stage 2, networks and enriched pathways were estimated with Cytoscape for genes from stage 1, separately for the 2 phenotypes, then examining network overlap. This overlap identified 4 enriched pathways, and 3 of these pathways appear to interact, and are likely candidates for playing a role in hypertension. At stage 3, using 157 maximally unrelated individuals, partial least squares regression was used to find associations between diastolic blood pressure and single-nucleotide polymorphisms in genes highlighted by the pathway analyses. However, we saw no improvement in the adjusted cross-validated R2. Although our pathway-motivated regressions did not improve prediction of diastolic blood pressure, merging gene networks did identify several plausible pathways for hypertension.
Pathway analysis of results from genetic association studies has become a very popular approach, and usually the goal is to gain a better understanding of which genes or groups of genes are related to the disease or trait being studied. However, "a better understanding" is not usually well defined. We chose to investigate whether pathway analysis could improve the R2 value in a partial least squares (PLS) regression model predicting phenotype as a function of single-nucleotide polymorphisms (SNPs). Our hypothesis was that pathway analysis would highlight genes containing many SNPs with effects too small to be detected in univariate analyses, but that the PLS components (or latent variables) might benefit from such signals.
Analyses used a multistage approach to analyze associations with diastolic blood pressure (DBP) in the Genetic Analysis Workshop 18 (GAW18) data. At stage 1, we tested for association between sequence variation in each gene and DBP in the families. At stage 2, we built gene networks from the significant genes identified in stage 1, using Cytoscape [1–3] and identified enriched pathways. Stage 3 included the PLS regression models on unrelated individuals using different sets of SNPs motivated by stages 1 and 2.
We used 2 different DBP phenotypes: DBP at the first visit (DBP-1) and a measure of DBP change with age (DBP-C). In the GAW18 dataset, 795 individuals had both a DBP-1 measurement and genotype information; no covariates were used when analysing DBP-1. Slopes of DBP versus age were estimated from only 2 to 4 measurements in each of 855 individuals, and then slopes were adjusted for smoking and antihypertensive medication use, and categorized into 3 levels: none of the visits, some visits, or all visits. The resulting residuals formed our second phenotype, measuring whether an individual's blood pressure changes more or less than the average. There were 611 individuals with DBP-C and genotype information.
Stage 1: ASKAT
Gene-based tests of association between the exome sequencing data and the DBP-1 and DBP-C phenotypes were performed using ASKAT , a method for quantitative phenotype analysis in families developed by our group. ASKAT fits a linear mixed model adjusting for the relationships by using the estimated kinship matrix. Kinship matrices were calculated for each odd-numbered chromosome using the genotype data in the chrx-geno.csv files. SNPs with minor allele frequency less than 0.01 were removed, and the kinship matrices were calculated using GenABEL v1.7-0 . The average of all odd-numbered chromosome-specific kinship matrices was used in the gene-based ASKAT tests. Gene names were retrieved from hg19 build 37 (http://genome.ucsc.edu) , and we tested association between sequence-derived genetic variation (from the chrx-dose.csv files) in the exons of each gene and the DBP-1 and DBP-C phenotypes, using a total of 147,103 genetic variants. To optimize power to detect rare variants, the 201 genes containing more than 50 variants were divided into a series of non-overlapping windows with a maximum of 50 variants per window (based on empirical data from our group, not shown). For such genes, the minimum p value summarized the gene result.
Stage 2: Pathway analysis
For DBP-1 and DBP-C, network analysis was performed using methods in Cytoscape 2.8.2 [7, 8]. Networks were built using "Reactome FI" on genes with p value ≤0.05 from stage 1. The networks for DBP-1 and DBP-C were then compared and merged using "Advanced Network Merge-Intersection." Pathway analysis for genes in common was performed using the "Analyze Module Function."
Stage 3: Regression analysis
PLS regression analysis was used for prediction modeling of our 2 phenotypes as a function of the number of minor alleles in SNPs identified by stages 1 and 2. PLS searches for multidimensional linear combinations of SNPs that explain the maximum variance direction of the phenotype, and can be thought of as constructing latent predictor variables. PLS models, using plsr version 2.3-0 of library pls , were fit to the 157 maximally unrelated individuals, using all variants from the chrx-dose.csv files, located in the genes identified by (a) p value ≤0.05 in gene-based analyses from stage 1, (b) stage 2 enriched pathways for DBP-1 or DBP-C with a false discovery rate (FDR) ≤0.05, or (c) stage 2 enriched pathways in the overlapping gene network. Tenfold cross-validation was used to choose the optimal number of PLS components, and we report the number of PLS components that gave the smallest adjusted cross-validated R2.
Stage 1: ASKAT analysis
Ten most significant genes from stage 1, ASKAT, with p values
6.31 × 10−5
7.21 × 10−5
6.08 × 10−6
1.11 × 10−5
4.71 × 10−5
Stage 2: Pathway analysis
Pathways enriched in overlapping networks of genes associated with DBP-1 and DBP-C
Cadherin Signalling Pathway (P)
Wnt Signalling Pathway (P)
Integrin Signalling Pathway (P)
G2/M Transition (R)
Stage 3-Regression analysis
Results from PLS regression analysis
C-V # PLS components*
R2 with 1 PLS component†
Average R2 per SNP‡
SNPs from genes with ASKAT p value ≤ 0.05
7.1 × 10−5
6.2 × 10−5
SNPs from genes in Cytoscape pathway with FDR ≤ 0.05
4.4 × 10−4
5.2 × 10−4
SNPs from genes in Cytoscape common network
1.6 × 10−3
1.3 × 10−3
Our pathway analysis was performed with Cytoscape, which is one among many possible pathway analysis tools. We also tried Gene Set Enrichment Analysis (GSEA) using the JAVA applet GSEA software v2.07 from the Broad Institute . GSEA takes into account the gene rank, and tests whether a pathway is enriched by looking for an overrepresentation of genes at the top or bottom of a ranked list of p values. Using this method, no enriched pathway had FDR ≤0.05. However, a study comparing gene-set enrichment tests reported that GSEA was more consistent in finding enriched pathways .
Pathway analysis is often heralded as a solution for better understanding genetic effects, but how to best benefit from it is unclear. We selected a definition, improvement in R2 of a multivariate (PLS) regression model, which is one of many possible definitions, and we explored this in GAW18 DBP data. We selected PLS regression in order to optimize construction of latent predictors, but the optimal number of PLS components identified through cross-validation was often zero for either phenotype and for any of the 3 gene and pathway selection strategies. We did not, in fact, find evidence for improved prediction of DBP using our strategy. However, PLS was not designed for categorical data and may not be the best choice for capturing associations between numerous SNPs and phenotype. When we forced a PLS fit with 1 component, the per-SNP contributions to R 2 from the overlapping genes were larger than when we used other approaches. It must be noted that although stage 1 used family data and stage 3 used unrelated individuals data, there is some overlap between these sets of individuals, and hence the per-SNP improvement in Table 3 may be partially explained by model over-fitting.
We chose to work with 2 DBP phenotypes, DBP at first visit, and a measure of DBP change with age. In most individuals, blood pressure increases with age. Our DBP-C phenotype, the residuals from a regression on age, measures each individual's sensitivity to the factors that lead to this general population trend of increasing DBP with age. We adjusted for medication use and smoking in a second model because with only 2 to 4 measurements per person, full longitudinal modeling was not possible. In this work, we have not adjusted for the variable precision of the slope estimates or age for DBP-1, and we recognize that this is a limitation.
Because our 2 phenotypes (DBP-1 and DBP-C) are closely related, we decided to focus our pathway analysis on gene networks present for both phenotypes. This strategy led to identification of 4 pathways significantly associated with DBP-1 and DBP-C. The first 3 pathways (see Table 2) interact together and play a role in pathogenesis of hypertension [12, 13]. The G2/M transition pathway is known to be affected by leptin , a protein associated with hypertension .
Only odd-numbered chromosomes were included in GAW18, thus our identified pathways and networks will be incomplete. An analysis of the entire genome might validate our findings and provide additional significant associations. Furthermore, Cytoscape only assigned pathways to 120 genes out of the approximately 600 selected for either phenotype, thus providing further motivation to continue our analysis of a larger curated set of genes in pathways. Despite all the caveats in this work, it is interesting that our approach of examining overlapping pathways identified 3 pathways that are plausibly related to hypertension.
For DBP or DBP changes in GAW18 data, we examined whether using pathway analysis results improved R2 in multivariate regression models. Although we did not find evidence for improved model fits, 3 enriched pathways contained plausible hypertension-related genes.
This work is supported by CIHR grant MOP-115110 to C. Greenwood. 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.
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.
- Smoot ME, Ono K, Ruscheinski J, Wang P, Ideker T: Cytoscape 2.8: new features for data integration and network. Bioinformatics. 2011, 27: 431-432. 10.1093/bioinformatics/btq675.PubMed CentralView ArticlePubMedGoogle Scholar
- Cline MS, Smoot M, Cerami E, Kuchinsky A, Landys N, Workman C, Christmas R, Avila-Campilo I, Creech M, Gross B, et al: Integration of biological networks and gene expression data using Cytoscape. Nat Protoc. 2007, 2: 2366-2382. 10.1038/nprot.2007.324.PubMed CentralView ArticlePubMedGoogle Scholar
- Shannon P, Markiel A, Ozier O, Baliga NS, Wang JT, Ramage D, Amin N, Schwikowski B, Ideker T: Cytoscape: a software environment for integrated models of biomolecular interaction networks. Genome Res. 2003, 13: 2498-2504. 10.1101/gr.1239303.PubMed CentralView ArticlePubMedGoogle Scholar
- Oualkacha K, Dastani Z, Li R, Cingolani P, Spector T, Hammond C, Richards JB, Ciampi A, Greenwood CMT: Adjusted sequence kernel association test for rare variants controlling for cryptic and family relatedness. Genet Epidemiol. 2013, 37: 366-376. 10.1002/gepi.21725.View ArticlePubMedGoogle Scholar
- Aulchenko YS, Ripke S, Isaacs A, van Duijn CM: GenABEL:an R package for genome-wide association analysis. Bioinformatics. 2007, 23: 1294-1296. 10.1093/bioinformatics/btm108.View ArticlePubMedGoogle Scholar
- Kent WJ, Sugnet CW, Furey TS, Roskin KM, Pringle TH, Zahler AM, Haussler D: The human genome browser at UCSC. Genome Res. 2002, 12: 996-1006. 10.1101/gr.229102. Article published online before print in May 2002.PubMed CentralView ArticlePubMedGoogle Scholar
- Matthews L, D'Eustachio P, Gillespie M, Croft D, de Bono B, Gopinath G, Jassal B, Lewis S, Schmidt E, Vastrik I, et al: An Introduction to the Reactome Knowledgebase of Human Biological Pathways and Processes. Bioinformatics Primer, NCI/Nature Pathway Interaction Database. 2007Google Scholar
- Joshi-Tope G, Vastrik I, Gopinathrao G, Matthews L, Schmidt E, Gillespie M, D'Eustachio P, Jassal B, Lewis S, Wu G, Birney E, Stein L: The Genome Knowledgebase: A resource for biologists and bioinformaticists. Cold Spring Harb Symp Quant Biol. 2003, 68: 237-243. 10.1101/sqb.2003.68.237.View ArticlePubMedGoogle Scholar
- Björn-Helge M, Wehrens R: The pls package: principal component and partial least squares regression in R. J Stat Softw. 2007, 18 (2): 1-24.Google Scholar
- Subramanian A, Tamayo P, Mootha VK, Mukherjee S, Ebert BL, Gillette MA, Paulovich A, Pomerov SL, Golub TR, Lander ES, Mesirov JP: GSEA: a knowledge-based approach for interpreting genome-wide expression profile. Proc Natl Acad Sci U S A. 2005, 102: 15545-15550. 10.1073/pnas.0506580102.PubMed CentralView ArticlePubMedGoogle Scholar
- Abatangelo L, Maglietta R, Distaso A, D'Addabbo A, Creanza TM, Mukherjee S, Ancona N: Comparative study of gene set enrichment methods. BMC Bioinformatics. 2009, 10: 275-10.1186/1471-2105-10-275.PubMed CentralView ArticlePubMedGoogle Scholar
- Chao JT, Davis MJ: The roles of integrins in mediating the effects of mechanical force and growth factors on blood vessels in hypertension. Curr Hypertens Rep. 2011, 13: 421-429. 10.1007/s11906-011-0227-6.View ArticlePubMedGoogle Scholar
- Weber GF, Bjerke MA, DeSimone : Integrins and cadherins join forces to form adhesive networks. J Cell Sci. 2011, 124: 1183-1193. 10.1242/jcs.064618.PubMed CentralView ArticlePubMedGoogle Scholar
- Huang F, Xiong X, Wang H, You S, Zeng H: Leptin-induced vascular smooth muscle cell proliferation via regulating cell cycle, activating ERK1/2 and NF-kB. Acta Biochim Biophys Sin (Shanghai). 2010, 42: 325-331. 10.1093/abbs/gmq025.View ArticleGoogle Scholar
- Schäfer K, Halle M, Goeschen C, Dellas C, Pynn M, Loskutoff DJ, Konstantinides S: Leptin promotes vascular remodeling and neointimal growth in mice. Arterioscler Thromb Vasc Biol. 2004, 24: 112-117. 10.1161/01.ATV.0000105904.02142.e7.View ArticlePubMedGoogle Scholar
This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.