Big Data Solutions For Mapping Genetic Markers Associated With Lifestyle Diseases

Abstract

Lifestyle diseases are present in multitudes and strike indiscriminately, leading to chronic illnesses. There are no precise answers to many of the questions that perplex the general populace. Identifying individuals who need additional surveillance to prevent the onset of these diseases is increasingly becoming a major priority. The big data era and the advent of high-throughput technologies have led to a significant reduction in the cost of genotyping thousands of individuals to identify genetic markers associated with complex diseases. Searching for tiny genetic signals amidst millions of DNA variants means analyzing tediously large data sets. We discuss big data analytics as a solution for the identification of genetic markers associated with lifestyle diseases. Given the limitations of predictive modeling of complex diseases despite having powerful predictive tools, we propose a new matrix-based gene-gene variant association test called Mod-Log. Using this approach, we identify thousands of genetic markers that are predictive of health-related traits in a large data set of 15,000 aging men, showing a strong association between gene-gene interactions and complex diseases.