A random forest approach to the detection of epistatic interactions in case-control studies

By Rui Jiang, Wanwan Tang, Xuebing Wu, Wenhui Fu. BMC Bioinformatics, 2009

305 citations recorded by the source corpus.

BackgroundThe key roles of epistatic interactions between multiple genetic variants in the pathogenesis of complex diseases notwithstanding, the detection of such interactions remains a great challenge in genome-wide association studies. Although some existing multi-locus approaches have shown their successes in small-scale case-control data, the "combination explosion" course prohibits their applications to genome-wide analysis. It is therefore indispensable to develop new methods that are able to reduce the search space for epistatic interactions from an astronomic number of all possible combinations of genetic variants to a manageable set of candidates.ResultsWe studied case-control data from the viewpoint of binary classification. More precisely, we treated single nucleotide polymorphism (SNP) markers as categorical features and adopted the random forest to discriminate cases against controls. On the basis of the gini importance given by the random forest, we designed a sliding window sequential forward feature selection (SWSFS) algorithm to select a small set of candidate SNPs that could minimize the classification error and then statistically tested up to three-way interactions of the candidates. We compared this approach with three existing methods on three simulated disease models and showed that our approach is comparable to, sometimes more powerful than, the other methods. We applied our approach to a genome-wide case-control dataset for Age-related Macular Degeneration (AMD) and successfully identified two SNPs that were reported to be associated with this disease.ConclusionBesides existing pure statistical approaches, we demonstrated the feasibility of incorporating machine learning methods into genome-wide case-control studies. The gini importance offers yet another measure for the associations between SNPs and complex diseases, thereby complementing existing statistical measures to facilitate the identification of epistatic interactions and the understanding of epistasis in the pathogenesis of complex diseases.

Subfields: Genetics, Ophthalmology, Radiology, Nuclear Medicine and Imaging

View Paper: https://www.semanticscholar.org/paper/2591e8480ebf5a3c87e2466f41780a15d9482d47

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Citations in the corpus

Detecting gene-gene interactions using a permutation-based random forest method (2016, background): “Other than that, Jiang et al. developed a random forest approach, sliding window sequential forward feature selection (SWSFS) algorithm, to detect epistatic interactions in case-control studies according to gini importance [23].”
A Review of Ensemble Methods in Bioinformatics (2010, methodology): “Meng et al. [63] modified random forests to take into account the linkage disequilibrium (LD) information when measuring the importance of SNPs. Jiang et al. [64] developed a sequential forward feature selection procedure to improve random forests in epistatic interaction identification.”
A comparison of random forest and its Gini importance with standard chemometric methods for the feature selection and classification of spectral data (2009, methodology): “Most studies using the Gini importance [22,29] and the related permutation-based feature importance of random forests [16,18,20,21,23] together with random forests in a recursive feature elimination scheme, also showed an increases in prediction performance.”
Overview of random forest methodology and practical guidance with emphasis on computational biology and bioinformatics (2012, methodology): “Finally, some authors apply a two-stage approach [40, 50].”
Ecological genomics meets community-level modelling of biodiversity: mapping the genomic landscape of current and future environmental adaptation. (2015, methodology): “For very large numbers of SNPs, GF may be better suited to a strategy where either portions of the genome are analysed in a sliding window approach (Jiang et al. 2009) or a reduced set of candidate SNPs are mapped after testing for their outlier status using other approaches (FST or genetic…”
Explainable AI for Trees: From Local Explanations to Global Understanding (2019, methodology): “While previous work has used global measures of feature interactions [43, 28], to the best of our knowledge SHAP interaction values represent the first local approach to feature interactions beyond simply listing decision paths.”
Random Forests for Genomic Data Analysis (2012, methodology): “Jiang et al. [49] proposed a two-stage analysis method to identify interactions.”