A comparison of internal model validation methods for multifactor dimensionality reduction in the case of genetic heterogeneity

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.

A roadmap to multifactor dimensionality reduction methods

Complex diseases are defined to be determined by multiple genetic and environmental factors alone as well as in interactions. To analyze interactions in genetic data, many statistical methods have been suggested, with most of them relying on statistical regression models. Given the known limitations of classical methods, approaches from the machine-learning community have also become attractive. From this latter family, a fast-growing collection of methods emerged that are based on the Multifactor Dimensionality Reduction (MDR) approach. Since its first introduction, MDR has enjoyed great popularity in applications and has been extended and modified multiple times. Based on a literature search, we here provide a systematic and comprehensive overview of these suggested methods. The methods are described in detail, and the availability of implementations is listed. Most recent approaches offer to deal with large-scale data sets and rare variants, which is why we expect these methods to even gain in popularity.

Differential association of RANTES-403 and IL-1B-1464 polymorphisms on histological subtypes in male Korean patients with gastric cancer

The aims of this study were to elucidate the association between RANTES-403 and an increased risk of gastric cancer in Korean males and to investigate the gene-gene interaction between IL-1B and RANTES. In total, 218 male patients with gastric cancer (114 diffuse types, 97 intestinal types, and 7 mixed types) and 377 male controls were included. RANTES-403 was genotyped, and age-adjusted odds ratios (ORs) with 95% confidence intervals (CIs) were estimated by logistic regression. A multifactor dimensionality reduction (MDR) test with three-way split interval validation confirmed by likelihood ratio and permutation analysis was carried out. A significant increase in the risk of gastric cancer for the intestinal-type group was observed for IL-1B-1464G carriers (OR = 2.535; 95% CI = 1.121-5.732; P = 0.02) as well as for those with IL-1B-1464 CG (OR = 2.342; 95% CI = 0.998-5.500; P = 0.05) or IL-1B-1464 GG (OR = 2.819; 95% CI = 1.170-6.793; P = 0.02). For the RANTES-403 genotype, there was no significant difference in the risk of gastric cancer between the overall gastric cancer and the control groups. When further stratified according to histological types, RANTES-403A carriers (OR = 1.743; 95% CI = 1.086-2.798; P = 0.021) or heterozygotes (OR = 1.791; 95% CI = 1.092-2.935; P = 0.021) showed increased risk for developing diffuse-type gastric cancer. MDR revealed a three-way locus-locus interaction between RANTES-403AA, IL-1B-1464GG, and IL-1B-511CT for diffuse-type gastric cancer in Korean males. We demonstrated that RANTES-403 was significantly associated with the risk of developing diffuse-type gastric cancer in men and found a possible gene-gene interaction between RANTES and IL-1B polymorphisms in gastric cancer carcinogenesis.