Dataset Information

Discovering Genetic Factors for psoriasis through exhaustively searching for significant second order SNP-SNP interactions.

ABSTRACT: In this paper, we aim at discovering genetic factors of psoriasis through searching for statistically significant SNP-SNP interactions exhaustively from two real psoriasis genome-wide association study datasets (phs000019.v1.p1 and phs000982.v1.p1) downloaded from the database of Genotypes and Phenotypes. To deal with the enormous search space, our search algorithm is accelerated with eight biological plausible interaction patterns and a pre-computed look-up table. After our search, we have discovered several SNPs having a stronger association to psoriasis when they are in combination with another SNP and these combinations may be non-linear interactions. Among the top 20 SNP-SNP interactions being found in terms of pairwise p-value and improvement metric value, we have discovered 27 novel potential psoriasis-associated SNPs where most of them are reported to be eQTLs of a number of known psoriasis-associated genes. On the other hand, we have inferred a gene network after selecting the top 10000 SNP-SNP interactions in terms of improvement metric value and we have discovered a novel long distance interaction between XXbac-BPG154L12.4 and RNU6-283P which is not a long distance haplotype and may be a new discovery. Finally, our experiments with the synthetic datasets have shown that our pre-computed look-up table technique can significantly speed up the search process.

SUBMITTER: Lee KY

PROVIDER: S-EPMC6185942 | biostudies-literature | 2018 Oct

REPOSITORIES: biostudies-literature

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Discovering Genetic Factors for psoriasis through exhaustively searching for significant second order SNP-SNP interactions.

Lee Kwan-Yeung KY Leung Kwong-Sak KS Tang Nelson L S NLS Wong Man-Hon MH

Scientific reports 20181012 1

In this paper, we aim at discovering genetic factors of psoriasis through searching for statistically significant SNP-SNP interactions exhaustively from two real psoriasis genome-wide association study datasets (phs000019.v1.p1 and phs000982.v1.p1) downloaded from the database of Genotypes and Phenotypes. To deal with the enormous search space, our search algorithm is accelerated with eight biological plausible interaction patterns and a pre-computed look-up table. After our search, we have disc ...[more]

PMID: 30315195

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Project description:BackgroundStudy of biological networks is an essential first step to understand the complex functions they govern in different organisms. The topology of interactions that define how biological networks operate is often determined through high-throughput experiments. Noisy nature of high-throughput experiments, however, can result in multiple alternative network topologies that explain this data equally well. One key step to resolve the differences is to identify the subnetworks which appear significantly more frequently in a biological network data set than expected.MethodWe present a method named SiS (Significant Subnetworks) to find subnetworks with the largest probability to appear in a collection of biological networks. We define these subnetworks as the most probable subnetworks. SiS summarizes the interactions in the given collection of networks in a special template network. It uses the template network to guide the search for most probable subnetworks. It computes the lower and upper bound scores on how good the potential solutions are (i.e., the number of input networks that contain the subnetwork). As the search continues, it tightens the bound dynamically and prunes a massive number of unpromising solutions in that process.Results and conclusionsExperiments on comprehensive data sets depict that the most probable subnetworks found by SiS in a large collection of networks are also very frequent as well. In metabolic network data set, we found that subnetworks in eukaryote are more conserved than those of prokaryote. SiS also scales well to large data sets and subnetworks and runs orders of magnitude faster than an existing method, MULE. Depending on the size of the subnetwork in the same data set, the running time of SiS ranges from a few seconds to minutes; MULE, on the other hand, runs either for hours or does not even finish in days. In human transcription regulatory network data set, SiS finds a large backbone subnetwork that appears frequently regardless of diverse cell types.

Dataset Information

Discovering Genetic Factors for psoriasis through exhaustively searching for significant second order SNP-SNP interactions.

Publications

Discovering Genetic Factors for psoriasis through exhaustively searching for significant second order SNP-SNP interactions.

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