A novel gene THSD7A is associated with obesity

Body mass index (BMI) is a non-invasive measurement of obesity. It is commonly used for assessing adiposity and obesity-related risk prediction. Genetic differences between ethnic groups are important factors, which contribute to the variation in phenotypic effects. India inhabited by the first out-of-Africa human population and the contemporary Indian populations are admixture of two ancestral populations; ancestral north Indians (ANI) and ancestral south Indians (ASI). Although ANI are related to Europeans, ASI are not related to any group outside Indian-subcontinent. Hence, we expect novel genetic loci associated with BMI. In association analysis, we found eight genic SNPs in extreme of distribution (P <= 3.75 x 10(-5)), of which WWOX has already been reported to be associated with obesity-related traits hence excluded from further study. Interestingly, we observed rs1526538, an intronic SNP of THSD7A; a novel gene significantly associated with obesity (P = 2.88 x 10(-5), 8.922 x 10(-6) and 2.504 x 10(-9) in discovery, replication and combined stages, respectively). THSD7A is neural N-glycoprotein, which promotes angiogenesis and it is well known that angiogenesis modulates obesity, adipose metabolism and insulin sensitivity, hence our result find a correlation. This information can be used for drug target, early diagnosis of obesity and treatment.

OCR Prediction Using Support Vector Machine Based on Piezocone Data

The determination of the overconsolidation ratio (OCR) of clay deposits is an important task in geotechnical engineering practice. This paper examines the potential of a support vector machine (SVM) for predicting the OCR of clays from piezocone penetration test data. SVM is a statistical learning theory based on a structural risk minimization principle that minimizes both error and weight terms. The five input variables used for the SVM model for prediction of OCR are the corrected cone resistance (qt), vertical total stress (sigmav), hydrostatic pore pressure (u0), pore pressure at the cone tip (u1), and the pore pressure just above the cone base (u2). Sensitivity analysis has been performed to investigate the relative importance of each of the input parameters. From the sensitivity analysis, it is clear that qt=primary in situ data influenced by OCR followed by sigmav, u0, u2, and u1. Comparison between SVM and some of the traditional interpretation methods is also presented. The results of this study have shown that the SVM approach has the potential to be a practical tool for determination of OCR.