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Use of wrapper algorithms coupled with a random forests classifier for variable selection in large-scale genomic association studies.

ABSTRACT: Modern large-scale genetic association studies generate increasingly high-dimensional datasets. Therefore, some variable selection procedure should be performed before the application of traditional data analysis methods, for reasons of both computational efficiency and problems related to overfitting. We describe here a "wrapper" strategy (SIZEFIT) for variable selection that uses a Random Forests classifier, coupled with various local search/optimization algorithms. We apply it to a large dataset consisting of 2,425 African-American and non-Hispanic white individuals genotyped for 4,869 single-nucleotide polymorphisms (SNPs) in a coronary heart disease (CHD) case-cohort association study (Atherosclerosis Risk in Communities), using incident CHD and plasma low-density lipoprotein (LDL) cholesterol levels as the dependent variables. We show that most SNPs can be safely removed from the dataset without compromising the predictive (classification) accuracy, with only a small number of SNPs (sometimes less than 100) containing any predictive signal. A statistical (SUMSTAT) approach is also applied to the dataset for comparison purposes. We describe a novel method for refining the subset of signal-containing SNPs (FIXFIT), based on an Extremal Optimization algorithm. Finally, we compare the top SNP rankings obtained by different methods and devise practical guidelines for researchers trying to generate a compact subset of predictive SNPs from genome-wide association datasets. Interestingly, there is a significant amount of overlap between seemingly very heterogeneous rankings. We conclude by constructing compact optimal predictive SNP subsets for CHD (less than 150 SNPs) and LDL (less than 300 SNPs) phenotypes, and by comparing various rankings for two well-known positive control SNPs for LDL in the apolipoprotein E gene.

SUBMITTER: Rodin AS

PROVIDER: S-EPMC2980837 | biostudies-literature |

REPOSITORIES: biostudies-literature

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Project description:Microarray technology is known as one of the most important tools for collecting DNA expression data. This technology allows researchers to investigate and examine types of diseases and their origins. However, microarray data are often associated with a small sample size, a significant number of genes, imbalanced data, etc., making classification models inefficient. Thus, a new hybrid solution based on a multi-filter and adaptive chaotic multi-objective forest optimization algorithm (AC-MOFOA) is presented to solve the gene selection problem and construct the Ensemble Classifier. In the proposed solution, a multi-filter model (i.e., ensemble filter) is proposed as preprocessing step to reduce the dataset's dimensions, using a combination of five filter methods to remove redundant and irrelevant genes. Accordingly, the results of the five filter methods are combined using a voting-based function. Additionally, the results of the proposed multi-filter indicate that it has good capability in reducing the gene subset size and selecting relevant genes. Then, an AC-MOFOA based on the concepts of non-dominated sorting, crowding distance, chaos theory, and adaptive operators is presented. AC-MOFOA as a wrapper method aimed at reducing dataset dimensions, optimizing KELM, and increasing the accuracy of the classification, simultaneously. Next, in this method, an ensemble classifier model is presented using AC-MOFOA results to classify microarray data. The performance of the proposed algorithm was evaluated on nine public microarray datasets, and its results were compared in terms of the number of selected genes, classification efficiency, execution time, time complexity, hypervolume indicator, and spacing metric with five hybrid multi-objective methods, and three hybrid single-objective methods. According to the results, the proposed hybrid method could increase the accuracy of the KELM in most datasets by reducing the dataset's dimensions and achieve similar or superior performance compared to other multi-objective methods. Furthermore, the proposed Ensemble Classifier model could provide better classification accuracy and generalizability in the seven of nine microarray datasets compared to conventional ensemble methods. Moreover, the comparison results of the Ensemble Classifier model with three state-of-the-art ensemble generation methods indicate its competitive performance in which the proposed ensemble model achieved better results in the five of nine datasets.Supplementary informationThe online version contains supplementary material available at 10.1007/s00521-021-06459-9.

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Use of wrapper algorithms coupled with a random forests classifier for variable selection in large-scale genomic association studies.

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