Gene set analysis of SNP data: benefits, challenges, and future directions

Pathway analysis of genome-wide association studies on uric acid concentrations

OBJECTIVE

The aims of this study were to identify the candidate causal single nucleotide polymorphisms (SNPs) and candidate causal mechanisms influencing uric acid level and to generate hypotheses for the SNP to gene to pathways that influence uric acid concentrations.

METHODS

Meta-analysis data of 954 SNPs with genome-wide significance in 14 genome-wide association studies (GWASs) comprising 28,141 individuals of European ancestry was subjected to ICSNPathway (Identify candidate Causal SNPs and Pathways) analysis to establish associations between pathways and uric acid concentrations.

RESULTS

ICSNPathway analysis identified 14 candidate causal SNPs, five genes, and two candidate causal pathways, which provided two hypothetical biologic mechanisms: (1) rs2728121 (regulatory region) to polycystic kidney disease 2 (PKD2) to ion transmembrane transporter activity; (2) rs942377, rs3799346, rs3799344, rs2762353, rs13197601, rs3757131, rs1165215, rs1165196 to SLC17A1 to ion transmembrane transporter activity and secondary active transmembrane transporter activity. SLC17A1, SLC17A3, SLC17A4, SLC22A11, and SLC2A9 were involved in both pathways, and PKD2 and SLC16A9 in one pathway.

CONCLUSION

By applying ICSNPathway analysis to GWAS data on uric acid levels, 14 SNPs, five genes, and two pathways involving the PKD2, SLC17A1, SLC17A3, SLC17A4, and SLC2A9 genes were identified that might contribute to the condition in Europeans.

Gene set analysis of survival following ovarian cancer implicates macrolide binding and intracellular signaling genes

BACKGROUND

Genome-wide association studies (GWAS) for epithelial ovarian cancer (EOC), the most lethal gynecologic malignancy, have identified novel susceptibility loci. GWAS for survival after EOC have had more limited success. The association of each single-nucleotide polymorphism (SNP) individually may not be well suited to detect small effects of multiple SNPs, such as those operating within the same biologic pathway. Gene set analysis (GSA) overcomes this limitation by assessing overall evidence for association of a phenotype with all measured variation in a set of genes.

METHODS

To determine gene sets associated with EOC overall survival, we conducted GSA using data from two large GWAS (N cases = 2,813, N deaths = 1,116), with a novel Principal Component-Gamma GSA method. Analysis was completed for all cases and then separately for high-grade serous histologic subtype.

RESULTS

Analysis of the high-grade serous subjects resulted in 43 gene sets with P < 0.005 (1.7%); of these, 21 gene sets had P < 0.10 in both GWAS, including intracellular signaling pathway (P = 7.3 × 10(-5)) and macrolide binding (P = 6.2 × 10(-4)) gene sets. The top gene sets in analysis of all cases were meiotic mismatch repair (P = 6.3 × 10(-4)) and macrolide binding (P = 1.0 × 10(-3)). Of 18 gene sets with P < 0.005 (0.7%), eight had P < 0.10 in both GWAS.

CONCLUSION

This research detected novel gene sets associated with EOC survival.

IMPACT

Novel gene sets associated with EOC survival might lead to new insights and avenues for development of novel therapies for EOC and pharmacogenomic studies.

Fast identification of biological pathways associated with a quantitative trait using group lasso with overlaps

Where causal SNPs (single nucleotide polymorphisms) tend to accumulate within biological pathways, the incorporation of prior pathways information into a statistical model is expected to increase the power to detect true associations in a genetic association study. Most existing pathways-based methods rely on marginal SNP statistics and do not fully exploit the dependence patterns among SNPs within pathways.We use a sparse regression model, with SNPs grouped into pathways, to identify causal pathways associated with a quantitative trait. Notable features of our "pathways group lasso with adaptive weights" (P-GLAW) algorithm include the incorporation of all pathways in a single regression model, an adaptive pathway weighting procedure that accounts for factors biasing pathway selection, and the use of a bootstrap sampling procedure for the ranking of important pathways. P-GLAW takes account of the presence of overlapping pathways and uses a novel combination of techniques to optimise model estimation, making it fast to run, even on whole genome datasets.In a comparison study with an alternative pathways method based on univariate SNP statistics, our method demonstrates high sensitivity and specificity for the detection of important pathways, showing the greatest relative gains in performance where marginal SNP effect sizes are small.

Genome-wide pathway analysis of a genome-wide association study on psoriasis and Behcet's disease

The aim of this study was to identify candidate causal single nucleotide polymorphisms (SNPs) and candidate causal mechanisms of psoriasis and Behcets's disease (BD) and to generate an SNP → gene → pathway hypothesis. A psoriasis genome-wide association study (GWAS) dataset that included 436,192 SNPs in 1,409 psoriasis cases and 1,436 controls of European descent and a BD GWAS dataset that contained 310,324 SNPs in 1,215 BD cases and 1,278 controls were used in this study. Identify candidate causal SNPs and pathways (ICSNPathway) analysis was applied to the GWAS datasets. ICSNPathway analysis identified 15 candidate causal SNPs and 28 candidate causal pathways. The top five candidate causal SNPs were rs1063478 (P = 1.45E-10), rs8084 (P = 2.20E-08), rs7192 (P = 5.18E-08), rs20541 (P = 5.30E-06), and rs1130838 (P = 5.65E-06), which with the exception of rs20541 [interleukin (IL)-13] are at human leukocyte antigen (HLA) loci. These candidate causal SNPs and pathways provided ten hypothetical biological mechanisms. The most strongly associated pathway concerned HLA. When HLA loci were excluded, ICSNPathway analysis provided one hypothetical biological mechanism. rs20541 (non_synonymous_coding) → IL-13 → dendritic cell involvement in the regulation of Th1 and Th2 development, and the GATA3 pathway. ICSNPathway analysis identified four candidate causal SNPs, eleven candidate causal pathways, and three hypothetical biological mechanisms. One of them was as follows: rs2072895 (non_synonymous_coding & splice-site) and rs2735059 (non_synonymous_coding) → HLA-F → type I diabetes mellitus, antigen processing and presentation, and autoimmune thyroid disease. The application of ICSNPathway analysis to GWAS dataset of psoriasis and BD resulted in the identification of candidate causal SNPs and candidate pathways that might contribute to psoriasis susceptibility.

Use of the gamma method for self-contained gene-set analysis of SNP data

Gene-set analysis (GSA) evaluates the overall evidence of association between a phenotype and all genotyped single nucleotide polymorphisms (SNPs) in a set of genes, as opposed to testing for association between a phenotype and each SNP individually. We propose using the Gamma Method (GM) to combine gene-level P-values for assessing the significance of GS association. We performed simulations to compare the GM with several other self-contained GSA strategies, including both one-step and two-step GSA approaches, in a variety of scenarios. We denote a 'one-step' GSA approach to be one in which all SNPs in a GS are used to derive a test of GS association without consideration of gene-level effects, and a 'two-step' approach to be one in which all genotyped SNPs in a gene are first used to evaluate association of the phenotype with all measured variation in the gene and then the gene-level tests of association are aggregated to assess the GS association with the phenotype. The simulations suggest that, overall, two-step methods provide higher power than one-step approaches and that combining gene-level P-values using the GM with a soft truncation threshold between 0.05 and 0.20 is a powerful approach for conducting GSA, relative to the competing approaches assessed. We also applied all of the considered GSA methods to data from a pharmacogenomic study of cisplatin, and obtained evidence suggesting that the glutathione metabolism GS is associated with cisplatin drug response.