Using Information Interaction to Discover Epistatic Effects in Complex Diseases
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- TL;DR
- Complex diseases are typically caused by the joint effects of multiple genetic variations rather than a single one, a phenomenon known as epistasis.
- Problem
- Traditional approaches, such as standard classification or greedy feature selection methods like the Fleuret method, struggle to effectively identify these multilocus interactions.
- Method
- This research explores an information interaction method for discovering pairwise epistatic effects, evaluating it against competitors like BEAM and SNPHarvester using artificial datasets.
- Results
- When applied to the WTCCC breast cancer dataset and validated using permutation tests, the method identified 89 statistically significant pairwise interactions with a p-value below 10(-3).
- Contributions
- Not specified in the abstract.
- Limitations
- Not specified in the abstract.
- Takeaways
- While many recent algorithms target epistasis with low marginals, almost all SNPs found in these significant interactions had moderate or high marginals, and the interactions were not present in the STRING gene-gene interaction network.
- Applications
- Not specified in the abstract.
- Topics
- Epistasis, complex diseases, information interaction, genetic variations, bioinformatics
- For industry
- Not specified in the abstract.
- Why it matters
- Not specified in the abstract.
Abstract
It is widely agreed that complex diseases are typically caused by the joint effects of multiple instead of a single genetic variation. These genetic variations may show stronger effects when considered together than when considered individually, a phenomenon known as epistasis or multilocus interaction. In this work, we explore the applicability of information interaction to discover pairwise epistatic effects related with complex diseases. We start by showing that traditional approaches such as classification methods or greedy feature selection methods (such as the Fleuret method) do not perform well on this problem. We then compare our information interaction method with BEAM and SNPHarvester in artificial datasets simulating epistatic interactions and show that our method is more powerful to detect pairwise epistatic interactions than its competitors. We show results of the application of information interaction method to the WTCCC breast cancer dataset. Our results are validated using permutation tests. We were able to find 89 statistically significant pairwise interactions with a p-value lower than 10(-3). Even though many recent algorithms have been designed to find epistasis with low marginals, we observed that all (except one) of the SNPs involved in statistically significant interactions have moderate or high marginals. We also report that the interactions found in this work were not present in gene-gene interaction network STRING.