Identifying disease-associated SNP clusters via contiguous outlier detection

Identifying association model for single-nucleotide polymorphisms of ORAI1 gene for breast cancer

Background

ORAI1 channels play an important role for breast cancer progression and metastasis. Previous studies indicated the strong correlation between breast cancer and individual single nucleotide polymorphisms (SNPs) of ORAI1 gene. However, the possible SNP-SNP interaction of ORAI1 gene was not investigated.

Results

To develop the complex analyses of SNP-SNP interaction, we propose a genetic algorithm (GA) to detect the model of breast cancer association between five SNPs (rs12320939, rs12313273, rs7135617, rs6486795 and rs712853) of ORAI1 gene. For individual SNPs, the differences between case and control groups in five SNPs of ORAI1 gene were not significant. In contrast, GA-generated SNP models show that 2-SNP (rs12320939-GT/rs6486795-CT), 3-SNP (rs12320939-GT/rs12313273-TT/rs6486795-TC), 5-SNP (rs12320939-GG/rs12313273-TC/rs7135617-TT/rs6486795-TT/rs712853-TT) have higher risks for breast cancer in terms of odds ratio analysis (1.357, 1.689, and 13.148, respectively).

Conclusion

Taken together, the cumulative effects of SNPs of ORAI1 gene in breast cancer association study were well demonstrated in terms of GA-generated SNP models.

SITDEM: a simulation tool for disease/endpoint models of association studies based on single nucleotide polymorphism genotypes

The association analysis between single nucleotide polymorphisms (SNPs) and disease or endpoint in genome-wide association studies (GWAS) has been considered as a powerful strategy for investigating genetic susceptibility and for identifying significant biomarkers. The statistical analysis approaches with simulated data have been widely used to review experimental designs and performance measurements. In recent years, a number of authors have proposed methods for the simulation of biological data in the genomic field. However, these methods use large-scale genomic data as a reference to simulate experiments, which may limit the use of the methods in the case where the data in specific studies are not available. Few methods use experimental results or observed parameters for simulation. The goal of this study is to develop a Web application called SITDEM to simulate disease/endpoint models in three different approaches based on only parameters observed in GWAS. In our simulation, a key task is to compute the probability of genotypes. Based on that, we randomly sample simulation data. Simulation results are shown as a function of p-value against odds ratio or relative risk of a SNP in dominant and recessive models. Our simulation results show the potential of SITDEM for simulating genotype data. SITDEM could be particularly useful for investigating the relationship among observed parameters for target SNPs and for estimating the number of variables (SNPs) required to result in significant p-values in multiple comparisons. The proposed simulation tool is freely available at http://www.snpmodel.com.

Insights on personalized medicine from Ayurveda

The "omics" era of research has provided vital information on the genetic and biochemical diversity of individuals. This has lead to the emergence of "personalized medicine," wherein one aims to design specific drugs for individual patients or subtypes of patients. Indeed, the ongoing patent wars on this matter, suggest that personalized medicine represents a major goal for today's pharmaceutical industries. Although the concept of personalized medicine is new to modern medicine, it is a well-established concept in Ayurveda, the traditional system of Indian medicine that is still being practiced. Therefore, this article discusses topics that are crucial for the advancement of modern personalized medicine. These topics include disease susceptibility, disease subtypes, and Ayurvedic therapeutics. First, we explain how Ayurveda, Traditional Chinese Medicine, and Traditional Korean medicine or Sasang Constitutional medicine; conceptualize disease susceptibility and disease subtypes. Next, we focus on conceptual similarities between molecular medicine and Ayurvedic concepts of disease susceptibility and disease subtypes. For each topic, we explain the relevant experimental evidence reported in the literature. We also propose new hypotheses and suggest experimental approaches for their testing and validation.