Most methods for genome-wide association studies (GWAS) focus on discovering a single genetic variant, but the pathogenesis of complex diseases is thought to arise from the joint effect of multiple genetic variants. Information about pathway structure, such as the interactions and distances between gene products within pathways, can help us learn more about the functions and joint effect of genes associated with disease risk. We developed a novel sub-pathway based approach to study the joint effect of multiple genetic variants that are modestly associated with disease. The approach prioritized sub-pathways based on the significance values of single nucleotide polymorphisms (SNPs) and the interactions and distances between gene products within pathways. We applied the method to seven complex diseases. The result showed that our method can efficiently identify statistically significant sub-pathways associated with the pathogenesis of complex diseases. The approach identified sub-pathways that may inform the interpretation of GWAS data.
[Show abstract][Hide abstract] ABSTRACT: Identification of pathway effects responsible for specific diseases has been one of the essential tasks in systems epidemiology. Despite some advance in procedures for distinguishing specific pathway (or network) topology between different disease status, statistical inference at a population level remains unsolved and further development is still needed. To identify the specific pathways contributing to diseases, we attempt to develop powerful statistics which can capture the complex relationship among risk factors.
Acute myeloid leukaemia (AML) data obtained from 133 adults (98 patients and 35 controls; 47% female).
Simulation studies indicated that the proposed Pathway Effect Measures (PEM) were stable; bootstrap-based methods outperformed the others, with bias-corrected bootstrap CI method having the highest power. Application to real data of AML successfully identified the specific pathway (Treg→TGFβ→Th17) effect contributing to AML with p values less than 0.05 under various methods and the bias-corrected bootstrap CI (-0.214 to -0.020). It demonstrated that Th17-Treg correlation balance was impaired in patients with AML, suggesting that Th17-Treg imbalance potentially plays a role in the pathogenesis of AML.
The proposed bootstrap-based PEM are valid and powerful for detecting the specific pathway effect contributing to disease, thus potentially providing new insight into the underlying mechanisms and ways to study the disease effects of specific pathways more comprehensively.
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BMJ Open 01/2015; 5(1):e006721. DOI:10.1136/bmjopen-2014-006721 · 2.27 Impact Factor
[Show abstract][Hide abstract] ABSTRACT: By the year 2000, a draft of the human genome sequence was completed. Millions of single-nucleotide polymorphisms (SNPs) had been deposited into public databases, and high throughput technologies were under development for SNP genotyping. At that time, it was predicted that large case control association studies would provide far better resolution and power than genome-wide linkage studies. Type 1 diabetes was one of the first phenotypes to be examined by genome-wide association studies (GWAS), and to date over 50 genomic regions have been associated with the disease. In general, the great majority of these loci individually contribute a relatively small degree of risk, and most loci lie outside of coding sequences. The identification of molecular mechanisms from these genomic data therefore remains a significant challenge. Here, we summarize genetic candidate, linkage, and association studies of type 1 diabetes and discuss a potential strategy to identify mechanisms of disease from genomic data.
The Review of Diabetic Studies 01/2012; 9(4):201-223. DOI:10.1900/RDS.2012.9.201
[Show abstract][Hide abstract] ABSTRACT: In the last few years', research has focused on Single Nucleotide Polymorphisms (SNPs) in the search for underlying genetic aetiology of complex disorders. This has been afforded by the rapid technological advancement to enable the interrogation of hundreds of thousands of SNPs in one assay via microarrays. However SNPs are only one form of genetic variation and in the midst of the Genome-Wide Association Study (GWAS) explosion Variable Number Tandem Repeat (VNTR) polymorphism exploration has seemingly been left behind. This review will argue that VNTR investigations still hold substantial potential for a role in complex disorders via possible functional properties.
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