Pathway analysis of genomic data: concepts, methods and prospects for future development. Trends Genet 28(7): 323-332, ISSN:0168-9525
ABSTRACT Genome-wide data sets are increasingly being used to identify biological pathways and networks underlying complex diseases. In particular, analyzing genomic data through sets defined by functional pathways offers the potential of greater power for discovery and natural connections to biological mechanisms. With the burgeoning availability of next-generation sequencing, this is an opportune moment to revisit strategies for pathway-based analysis of genomic data. Here, we synthesize relevant concepts and extant methodologies to guide investigators in study design and execution. We also highlight ongoing challenges and proposed solutions. As relevant analytical strategies mature, pathways and networks will be ideally placed to integrate data from diverse -omics sources to harness the extensive, rich information related to disease and treatment mechanisms.
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- "GSA-SNP (Nam et al., 2010; Ramanan et al., 2012a) was used to identify biological pathways exhibiting enrichment of association in the GWAS. Pathway definitions from three resources (Biocarta, KEGG and Reactome) were downloaded from the Molecular Signatures Database, version 4.0 and analysis was restricted to pathways containing 5–100 genes to limit the potential for size-influenced spurious associations (Ramanan et al., 2012b). Pathways with false discovery rate (FDR)-corrected P 5 0.05 were considered as significant. "
ABSTRACT: Brain amyloid deposition is thought to be a seminal event in Alzheimer's disease. To identify genes influencing Alzheimer's disease pathogenesis, we performed a genome-wide association study of longitudinal change in brain amyloid burden measured by (18)F-florbetapir PET. A novel association with higher rates of amyloid accumulation independent from APOE (apolipoprotein E) ε4 status was identified in IL1RAP (interleukin-1 receptor accessory protein; rs12053868-G; P = 1.38 × 10(-9)) and was validated by deep sequencing. IL1RAP rs12053868-G carriers were more likely to progress from mild cognitive impairment to Alzheimer's disease and exhibited greater longitudinal temporal cortex atrophy on MRI. In independent cohorts rs12053868-G was associated with accelerated cognitive decline and lower cortical (11)C-PBR28 PET signal, a marker of microglial activation. These results suggest a crucial role of activated microglia in limiting amyloid accumulation and nominate the IL-1/IL1RAP pathway as a potential target for modulating this process. © The Author (2015). Published by Oxford University Press on behalf of the Guarantors of Brain. All rights reserved. For Permissions, please email: firstname.lastname@example.org.Brain 08/2015; DOI:10.1093/brain/awv231 · 10.23 Impact Factor
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- "Based on the gene associations inferred by each method, i-GSEA4GWAS and ICSNPathway use rank-based approach and DAVID uses threshold-based approach to identify the related pathways (Ramanan et al., 2012). For the VEGAS analysis, we uploaded the list of SNP ID and P value calculated in the GWAS on to the VEGAS website. "
ABSTRACT: Panic disorder (PD) is an anxiety disorder characterized by panic attacks and anticipatory anxiety. Both genetic and environmental factors are thought to trigger PD onset. Previously, we performed a genome-wide association study (GWAS) for PD and focused on candidate SNPs with the lowest P values. However, there seemed to be a number of polymorphisms which did not reach genome-wide significance threshold due to their low allele frequencies and odds ratios, even though they were truly involved in pathogenesis. Therefore we performed pathway analyses in order to overcome the limitations of conventional single-marker analysis and identify associated SNPs with modest effects. Each pathway analysis indicated that pathways related to immunity showed the strongest association with PD (DAVID, P = 2.08 × 10−6; i-GSEA4GWAS, P < 10−3; ICSNPathway, P < 10−3). Based on the results of pathway analyses and the previously performed GWAS for PD, we focused on and investigated HLA-B and HLA-DRB1 as candidate susceptibility genes for PD. We typed HLA-B and HLA-DRB1 in 744 subjects with PD and 1418 control subjects. Patients with PD were significantly more likely to carry HLA-DRB1∗13:02 (P = 2.50 × 10−4, odds ratio = 1.49). Our study provided initial evidence that HLA-DRB1∗13:02 and genes involved in immune-related pathways are associated with PD. Future studies are necessary to confirm these results and clarify the underlying mechanisms causing PD.Brain Behavior and Immunity 01/2015; 46. DOI:10.1016/j.bbi.2015.01.002 · 6.13 Impact Factor
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- "In contrast, pathway analysis methods are a companion to GWAS studies that consider much larger proportions of the top SNPs and investigate their aggregate associations to known biological groupings or metabolic pathways . Pathway analysis studies have been successful in identifying additional biological insight and finding groupings of genes that represent biological disease processes . These kinds of approaches have shown the value in considering many more of the top GWAS SNPs, even though those hits are more likely to include false-positive associations. "
ABSTRACT: We show here that combining two existing genome wide association studies (GWAS) yields additional biologically relevant information, beyond that obtained by either GWAS separately. We propose Joint GWAS Analysis, a method that compares a pair of GWAS for similarity among the top SNP associations, top genes identified, gene functional clusters, and top biological pathways. We show that Joint GWAS Analysis identifies additional enriched biological pathways that would be missed by traditional Single-GWAS analysis. Furthermore, we examine the similarities of six complex genetic disorders at the SNP-level, gene-level, gene-cluster-level, and pathway-level. We make concrete hypotheses regarding novel pathway associations for several complex disorders considered, based on the results of Joint GWAS Analysis. Together, these results demonstrate that common complex disorders share substantially more genomic architecture than has been previously realized and that the meta-analysis of GWAS needs not be limited to GWAS of the same phenotype to be informative.12/2014; 2:202–211. DOI:10.1016/j.gdata.2014.04.004