Data preparing and analysis for personalized medicine; methods and challenges

Preparing a comprehensive solution tool along with recent advances in high-throughput technologies has been dramatically demanded so that many scientists believe that the emergence of system biology as a result of the advances is causing to come personalized medicine in the near future, whereas achieving the goal of personalized medicine is required to tackle big data and integrate omics in a multi-stage platform. However, assemble a useful setting dependents on some factors including generation of cost-effective high-throughput data; multidisciplinary education and making multitasking teams; data storage and processing; data integration and interpretation; and cryptic relatedness especially among large healthcare population-genomic studies. In this way, there is a growing interest in the development of statistical methods especially for deriving optimal individualized treatment rules. The developments have to observe some conditions which were not met in normal situations such as intense cleaning algorithms, correlation between individuals, correlation between genotypes, outlier observations detection, big data challenges, developing integrated software, and likely prevention of inflation of statistical errors. A deep whole genome sequencing data includes 60 million genetic markers on more than 15000 Iranian individuals with a family-based structure has been generated by Tehran Cardio-Metabolic Genetic Study (TCGS). This study originated from a bigger ongoing Iranian cohort study, Tehran Lipid and Colugos Study (TLGS). The TCGS always has been tackling the challenges and nowadays, this dataset has changed as a powerful infrastructure for implementing scientific researches for personalized medicine in Iranian population.