JBASE: Joint Bayesian Analysis of Subphenotypes and Epistasis

Motivation: Rapid advances in genotyping and genome-wide association studies have enabled the discovery of many new genotype–phenotype associations at the resolution of individual markers. However, these associations explain only a small proportion of theoretically estimated heritability of most diseases. In this work, we propose an integrative mixture model called JBASE: joint Bayesian analysis of subphenotypes and epistasis. JBASE explores two major reasons of missing heritability: interactions between genetic variants, a phenomenon known as epistasis and phenotypic heterogeneity, addressed via subphenotyping. Results: Our extensive simulations in a wide range of scenarios repeatedly demonstrate that JBASE can identify true underlying subphenotypes, including their associated variants and their interactions, with high precision. In the presence of phenotypic heterogeneity, JBASE has higher Power and lower Type 1 Error than five state-of-the-art approaches. We applied our method to a sample of individuals from Mexico with Type 2 diabetes and discovered two novel epistatic modules, including two loci each, that define two subphenotypes characterized by differences in body mass index and waist-to-hip ratio. We successfully replicated these subphenotypes and epistatic modules in an independent dataset from Mexico genotyped with a different platform. Availability and implementation: JBASE is implemented in Cþþ, supported on Linux and is available at http://www.cs.toronto.edu/goldenberg/JBASE/jbase.tar.gz. The genotype data underlying this study are available upon approval by the ethics review board of the Medical Centre Siglo XXI.No sponso

Implementación de algoritmos de inteligencia artificial para la identificación de pacientes diabéticos utilizando los niveles de lípidos en sangre

In recent years the leading cause of death in Mexico is linked to multifactorial diseases, of which diabetes ranks second, only below heart disease, both related to high cholesterol levels and triglycerides in blood. Objective: Classify patients with diabetes using artificial intelligence algorithms previously trained with total cholesterol, HDL, LDL and triglyceride levels. Materials and methods: Descriptors related to blood lipids belong to the Centro Médico Siglo XXI, composed of a sample of 1019. They are considered: Total Cholesterol Levels, HDL, LDH and Triglycerides. The proposed methodology consists of two main stages: training of artificial intelligence algorithms, in which black box models are developed to look for the relationship of the determinants mentioned and the suffering of diabetes in the subjects (presence = 1, absence = 0), and a second stage for the validation of the algorithms, using as a metric the sensitivity and specificity of the algorithms by means of the ROC curve and the area under the curve (AUC). Results: Logistic regression models, decision trees and support vector machine, acquire a value of 0.613 to 0.727 of AUC, being statistically significant for the automatic detection of diabetic patients. Conclusions: The implementation of Artificial Intelligence algorithms, allow the identification of patients with diabetes using blood lipid metrics, for a computer-aided diagnosis.En los últimos años la principal causa de muerte en México está relacionada con enfermedades multifactoriales, de las cuales, la diabetes ocupa el segundo lugar, solo por debajo de enfermedades de corazón, ambas relacionadas con altos niveles de colesterol y triglicéridos en sangre. Objetivo: Clasificar pacientes con diabetes utilizando algoritmos de inteligencia artificial entrenados previamente con los niveles de colesterol total, HDL, LDH y triglicéridos. Materiales y métodos: Los descriptores relacionados con los lípidos en sangre pertenecen el Centro Médico Siglo XXI, compuesta por una muestra de 1019. Se consideran: Niveles de colesterol total, HDL, LDH y triglicéridos. La metodología propuesta consiste en dos etapas principales: entrenamiento de algoritmos de inteligencia artificial, en la cual se desarrollan modelos de caja negra para buscar la relación de los determinantes mencionados y el padecimiento de diabetes en los sujetos (padecimiento = 1, ausencia = 0), y una segunda etapa para la validación de los algoritmos, utilizando como métrica la sensitividad y especificidad de los mismos mediante la curva ROC y el área bajo la curva (AUC). Resultados: los modelos de regresión logística, árboles de decisión y máquina de soporte vectorial, adquieren un valor de 0.613 hasta 0.727 de AUC, siendo estadísticamente significativos para la detección automática de pacientes diabéticos. Conclusiones: La implementación de algoritmos de Inteligencia artificial, permiten la identificación de pacientes con diabetes utilizando las métricas de lípidos en sangre, para un diagnóstico asistido por computadora

Meta-analysis of lipid-traits in Hispanics identifies novel loci, population-specific effects and tissue-specific enrichment of eQTLs

We performed genome-wide meta-analysis of lipid traits on three samples of Mexican and Mexican American ancestry comprising 4,383 individuals and followed up significant and highly suggestive associations in three additional Hispanic samples comprising 7,876 individuals. Genome-wide significant signals were observed in or near CELSR2, ZNF259/APOA5, KANK2/DOCK6 and NCAN/MAU2 for total cholesterol, LPL, ABCA1, ZNF259/APOA5, LIPC and CETP for HDL cholesterol, CELSR2, APOB and NCAN/MAU2 for LDL cholesterol and GCKR, TRIB1, ZNF259/APOA5 and NCAN/MAU2 for triglycerides. Linkage disequilibrium and conditional analyses indicate that signals observed at ABCA1 and LIPC for HDL cholesterol and NCAN/MAU2 for triglycerides are independent of previously reported lead SNP associations. Analyses of lead SNPs from the European Global Lipids Genetics Consortium (GLGC) dataset in our Hispanic samples show remarkable concordance of direction of effects as well as strong correlation in effect sizes. A meta-analysis of the European GLGC and our Hispanic datasets identified five novel regions reaching genome-wide significance: two for total cholesterol (FN1 and SAMM50), two for HDL cholesterol (LOC100996634 and COPB1) and one for LDL cholesterol (LINC00324/CTC1/PFAS). The top meta-analysis signals were found to be enriched for SNPs associated with gene expression in a tissue-specific fashion, suggesting an enrichment of tissue-specific function in lipid-associated loci

Identification of Diabetic Patients through Clinical and Para-Clinical Features in Mexico: An Approach Using Deep Neural Networks

Diabetes is a chronic and noncommunicable but preventable disease that is affecting the Mexican population at worrying levels, being the first place in prevalence worldwide. Early diabetes detection has become important to prevent other health conditions that involve low organ yield until the patient death. Based on this problem, this work proposes the architecture of an Artificial Neural Network (ANN) for the automated classification of healthy patients from diabetics patients. The analysis was performed used a set of 19 para-clinical features to determine the health status of the patients. The developed model was evaluated through a statistical analysis based on the calculation of the loss function, accuracy, area under the curve (AUC) and receiving operating characteristics (ROC) curve. The results obtained present statistically significant values, with accuracy of 0.94 and AUC values of 0.98. Based on these results, it is possible to conclude that the ANN implemented in this work can classify patients with presence of diabetes from controls with significant accuracy, presenting preliminary results for the development of a diagnostic tool that can be supportive for health specialists

Prevention of Diabetes Mellitus Through the Use of Mobile Technology (mHealth): Case Study

Currently advances in technology have allowed the development of tools focused on the field of medicine, such as mobile technology applied to health or mHealth, through which it seeks to improve the health and quality of life of people. In 2017 there were more than 200 million downloads in mHealth apps from online app stores, however, likewise, the dropout rate was high due to the problems faced by the users when using those apps. On the other hand, in the field of health, one of the main causes of death in Mexico is diabetes mellitus. Derived from the above, this article presents the design of a mobile application prototype as a support tool in the prevention of this disease, taking as reference the Risk Factors Questionnaire (RFQ). For the development of the prototype, the stages of the User Centered Design (UCD) process were implemented in accordance with the ISO 9241-210: 2010. The purpose of the final application, is to provide an easy-to-use tool that provides the user with information about the possible risk of developing diabetes, based on user-provided data and analyzed with artificial intelligence algorithms, also to provide recommendations that impact on the people’s lifestyles, as well as providing a list of doctors an

Distal Symmetric Polyneuropathy Identification in Type 2 Diabetes Subjects: A Random Forest Approach

The prevalence of diabetes mellitus is increasing worldwide, causing health and economic implications. One of the principal microvascular complications of type 2 diabetes is Distal Symmetric Polyneuropathy (DSPN), affecting 42.6% of the population in Mexico. Therefore, the purpose of this study was to find out the predictors of this complication. The dataset contained a total number of 140 subjects, including clinical and paraclinical features. A multivariate analysis was constructed using Boruta as a feature selection method and Random Forest as a classification algorithm applying the strategy of K-Folds Cross Validation and Leave One Out Cross Validation. Then, the models were evaluated through a statistical analysis based on sensitivity, specificity, area under the curve (AUC) and receiving operating characteristic (ROC) curve. The results present significant values obtained by the model with this approach, presenting 67% of AUC with only three features as predictors. It is possible to conclude that this proposed methodology can classify patients with DSPN, obtaining a preliminary computer-aided diagnosis tool for the clinical area in helping to identify the diagnosis of DSPN