Nutritional habits, lifestyle, and genetic predisposition in cardiovascular and metabolic traits in Turkish population.

OBJECTIVES: Cardiovascular and metabolic traits (CMT) are influenced by complex interactive processes including diet, lifestyle, and genetic predisposition. The present study investigated the interactions of these risk factors in relation to CMTs in the Turkish population. METHODS: We applied bootstrap agglomerative hierarchical clustering and Bayesian network learning algorithms to identify the causative relationships among genes involved in different biological mechanisms (i.e., lipid metabolism, hormone metabolism, cellular detoxification, aging, and energy metabolism), lifestyle (i.e., physical activity, smoking behavior, and metropolitan residency), anthropometric traits (i.e., body mass index, body fat ratio, and waist-to-hip ratio), and dietary habits (i.e., daily intakes of macro- and micronutrients) in relation to CMTs (i.e., health conditions and blood parameters). RESULTS: We identified significant correlations between dietary habits (soybean and vitamin B12 intakes) and different cardiometabolic diseases that were confirmed by the Bayesian network-learning algorithm. Genetic factors contributed to these disease risks also through the pleiotropy of some genetic variants (i.e., F5 rs6025 and MTR rs180508). However, we also observed that certain genetic associations are indirect since they are due to the causative relationships among the CMTs (e.g., APOC3 rs5128 is associated with low-density lipoproteins cholesterol and, by extension, total cholesterol). CONCLUSIONS: Our study applied a novel approach to integrate various sources of information and dissect the complex interactive processes related to CMTs. Our data indicated that complex causative networks are present: causative relationships exist among CMTs and are affected by genetic factors (with pleiotropic and non-pleiotropic effects) and dietary habits.

Genetic diversity of disease-associated loci in Turkish population.

Many consortia and international projects have investigated the human genetic variation of a large number of ethno-geographic groups. However, populations with peculiar genetic features, such as the Turkish population, are still absent in publically available datasets. To explore the genetic predisposition to health-related traits of the Turkish population, we analyzed 34 genes associated with different health-related traits (for example, lipid metabolism, cardio-vascular diseases, hormone metabolism, cellular detoxification, aging and energy metabolism). We observed relevant differences between the Turkish population and populations with non-European ancestries (that is, Africa and East Asia) in some of the investigated genes (that is, AGT, APOE, CYP1B1, GNB3, IL10, IL6, LIPC and PON1). As most complex traits are highly polygenic, we developed polygenic scores associated with different health-related traits to explore the genetic diversity of the Turkish population with respect to other human groups. This approach showed significant differences between the Turkish population and populations with non-European ancestries, as well as between Turkish and Northern European individuals. This last finding is in agreement with the genetic structure of European and Middle East populations, and may also agree with epidemiological evidences about the health disparities of Turkish communities in Northern European countries.

GSTM1, GSTP1, and GSTT1 genetic variability in Turkish and worldwide populations.

OBJECTIVE: Glutathione S-transferase (GST) variants have been widely investigated to better understand their role in several pathologic conditions. To our knowledge, no data about these genetic polymorphisms within the Turkish population are currently available. The aim of this study was to analyze GSTM1 positive/null, GSTT1 positive/null, GSTP1*I105V (rs1695), and GSTP1*A114V (rs1138272) variants in the general Turkish population, to provide information about its genetic diversity, and predisposition to GST-related diseases. METHODS: Genotyping was performed in 500 Turkish individuals using the Sequenom MassARRAY platform. A comparative analysis was executed using the data from the HapMap and Human Genome Diversity Projects (HGDP). Sequence variation was deeply explored using the Phase 1 data of the 1,000 Genomes Project. RESULTS: The variability of GSTM1, GSTT1, and GSTP1 polymorphisms in the Turkish population was similar to that observed in Central Asian, European, and Middle Eastern populations. The high linkage disequilibrium between GSTP1*I105V and GSTP1*A114V in these populations may have a confounding effect on GSTP1 genetic association studies. In analyzing GSTM1, GSTT1, and GSTP1 sequence variation, we observed other common functional variants that may be candidates for associated studies of diseases related to GST genes (e.g., cancer, cardiovascular disease, and allergy). CONCLUSIONS: This study provides novel data about GSTM1 positive/null, GSTT1 positive/null, GSTP1*I105V, and GSTP1*A114V variants in the Turkish population, and other functional variants that may affect GSTM1, GSTT1, and GSTP1 functions among worldwide populations. This information can assist in the design of future genetic association studies investigating oxidative stress-related diseases.

A practice model for personalized healthcare with a public health genomics perspective.

Epidemiological and demographic transition has brought populations to an extended life expectancy in the 21st Century. The diseases of this century are complex, which stem mainly from the complex interactions of the human genome with lifestyle and environmental factors. These diseases are common, chronic and costly. Currently, the best-known prevention for complex diseases is adopting a healthy lifestyle. However, this is not achieved in many places in the world. Effective intervention models, including lifestyle changes for the prevention of these diseases, is urgently needed. In this report, we introduce a preventive healthcare model based on personalized healthcare. It is based on the application of public health genomics tools and concepts on an individual level, in order to stratify individuals according to risk groups, prevent diseases and detect them early.