A deep hybrid model to detect multi-locus interacting SNPs in the presence of noise.

Identifying genetic variants associated with complex diseases is a central focus of genome-wide association studies. These studies extensively adopt univariate analysis by ignoring interaction effects. It is widely accepted that the etiology of most complex diseases depends on interactions between genetic variants and / or environmental factors. Several machine learning and data mining methods have been consistently successful in exposing these interaction effects. However, there has been no major breakthrough due to various biological complexities, and statistical computational challenges facing in the field of genetic epidemiology, despite of many efforts. Deep learning is emerging machine learning approach that promises to reveal the hidden patterns of big data for accurate predictions. In this study, a deep neural network is unified with a random forest by forming hybrid architecture, for achieving reliable detection of multi-locus interactions between single nucleotide polymorphisms. The proposed hybrid method is evaluated on various simulated scenarios in the absence of main effect for six epistasis models. The best model with optimal hyper-parameters (grid and random grid search) is chosen to enhance the power of the method by maximising the model's prediction accuracy. The performance metrics of each model is analysed for both training and validation. Further, the performance of the method in the presence of noise due to missing data, genotyping errors, genetic heterogeneity, and phenocopy, and their combined effects are evaluated. The power of the method in detecting two-locus interactions is compared with the previous methods in the presence and absence of noise. On an average, the power of the proposed method is much higher than the previous methods for all simulated scenarios. Finally, findings are confirmed on a chronical dialysis patient's data, obtained from the published study performed at the Kaohsiung Chang Gung Memorial Hospital. It is observed that the interaction between SNP 21 (2) and SNP 28 (2) in the mitochondrial D-loop has the highest risk for the disease manifestation.

A Review on Methods for Detecting SNP Interactions in High-Dimensional Genomic Data.

In this era of genome-wide association studies (GWAS), the quest for understanding the genetic architecture of complex diseases is rapidly increasing more than ever before. The development of high throughput genotyping and next generation sequencing technologies enables genetic epidemiological analysis of large scale data. These advances have led to the identification of a number of single nucleotide polymorphisms (SNPs) responsible for disease susceptibility. The interactions between SNPs associated with complex diseases are increasingly being explored in the current literature. These interaction studies are mathematically challenging and computationally complex. These challenges have been addressed by a number of data mining and machine learning approaches. This paper reviews the current methods and the related software packages to detect the SNP interactions that contribute to diseases. The issues that need to be considered when developing these models are addressed in this review. The paper also reviews the achievements in data simulation to evaluate the performance of these models. Further, it discusses the future of SNP interaction analysis.