Identification of Type 2 Diabetes-associated combination of SNPs using Support Vector Machine

Abstract

BackgroundType 2 diabetes mellitus (T2D), a metabolic disorder characterized by insulin resistance and relative insulin deficiency, is a complex disease of major public health importance. Its incidence is rapidly increasing in the developed countries. Complex diseases are caused by interactions between multiple genes and environmental factors. Most association studies aim to identify individual susceptibility single markers using a simple disease model. Recent studies are trying to estimate the effects of multiple genes and multi-locus in genome-wide association. However, estimating the effects of association is very difficult. We aim to assess the rules for classifying diseased and normal subjects by evaluating potential gene-gene interactions in the same or distinct biological pathways.ResultsWe analyzed the importance of gene-gene interactions in T2D susceptibility by investigating 408 single nucleotide polymorphisms (SNPs) in 87 genes involved in major T2D-related pathways in 462 T2D patients and 456 healthy controls from the Korean cohort studies. We evaluated the support vector machine (SVM) method to differentiate between cases and controls using SNP information in a 10-fold cross-validation test. We achieved a 65.3% prediction rate with a combination of 14 SNPs in 12 genes by using the radial basis function (RBF)-kernel SVM. Similarly, we investigated subpopulation data sets of men and women and identified different SNP combinations with the prediction rates of 70.9% and 70.6%, respectively. As the high-throughput technology for genome-wide SNPs improves, it is likely that a much higher prediction rate with biologically more interesting combination of SNPs can be acquired by using this method.ConclusionsSupport Vector Machine based feature selection method in this research found novel association between combinations of SNPs and T2D in a Korean population.

Finding type 2 diabetes causal single nucleotide polymorphism combinations and functional modules from genome-wide association data

“…RF with variable selection effectively selects T2D causal SNP combinations consisting of 1-161 SNPs (average 76.55 SNPs) from 1-500 SNPs, and has low error rates. As shown in Figure 1, the number of T2D causal SNPs for the T2D disease risk prediction could be more than that predicted by previous studies, which used 10-20 diabetes-related SNPs [7,17]. The proposed method shows that SNP combinations of more than 100 SNPs have lower error rates than SNP combinations of the top 10-20 SNPs.…”

“…To rank SNPs and find SNP combinations, various methods are applied: Bayes factors [3], logistic regression [4,5], Hidden Markov Model (HMM) [6], Support Vector Machine (SVM), [7,8] and Random Forests (RF) [8-12]. Among the applied standard statistical methods and the machine learning-based methods, RF effectively ranks causal SNPs to detect SNP interactions [13,14].…”

“…From p-values that were less than 0.6, a SNP combination with 14 SNPs was selected using a p-value-based filtering method, and the prediction accuracy was 0.653 [7]. Ban et al successfully suggested the possibility of T2D causal SNP combinations, but the size of the dataset was small and the accuracy was not significantly improved.…”

Prognosis of muscular dystrophy with extrinsic and intrinsic descriptors through ensemble learning

“…In medical applications, the capability of machine learning is well-suited to analyzing complex diseases such as diabetes [8], hepatitis [9], rheumatoid arthritis [10], and schizophrenia [11]. However, not many studies have been carried out on variation in muscular dystrophy using machine-learning algorithms.…”

Study on distribution of Pro12Ala SNP of PPAR gamma 2 gene in randomly sampled diabetic population from Guwahati city

“…According to the WHO projections, the 30 million to 33 million diabetic patients in India will shoot up to 40 million by 2010 and 74 million by 2025. 1 WHO has declared a forewarning in 2012 that India will be the diabetes capital of the world by 2025. 2 In both, type 1 and type 2 diabetes, blood sugar levels, blood pressure and blood fats must be well monitored to prevent possible development of blindness, renal failure, and cardiac complications.…”