When you use Facebook, advertisements related to products that you have searched or seen often appear one after another, and acquaintances are sometimes recommended as friends. After searching for a product on the Internet, you will be faced with a screen where related products are advertised when you do other work. It’s a set of rules that precisely define a sequence of operations.
Algorithms handle a lot of work not only in everyday life but also in genetic research. What genes cause high blood pressure? What are the genes associated with diabetes? What genes are all involved in cardiovascular disease?
An algorithm for finding answers to these questions was developed by a research team led by Han Beom, a professor at Seoul National University’s medical school. PLEIO, developed by the research team, is an algorithm that discovers “core genes” that cause various chronic diseases at the same time. The key gene, in other words, is a gene that affects the expression of various diseases, not a single disease. For example, the deletions of a gene on chromosome 22 of the human gene are known to be related to both autism and schizophrenia expression.
Using the PLEIO algorithm, the research team discovered 13 key genes that simultaneously affect cardiovascular diseases such as heart disease, diabetes, high blood pressure, hyperlipidemia, and obesity. The PLEIO algorithm can effectively detect key genes by integrating and analyzing genetic studies of various diseases through inter-disease network analysis. In this study, the research team analyzed the impact of genes related to 18 diseases with big data, and found 13 new genes that have not been reported in cardiovascular disease studies so far.
There are several key gene discovery algorithms, but the research team says “PLEIO” showed the highest sensitivity among them. The ‘core gene’ research associated with a number of diseases, not just one disease, helps us understand the physiological activity in the human body in a large overall framework. The study of “core genes” that allow us to see forests, not trees, and the study of genes in various angles using big data will help us learn more about various disease mechanisms of disease.
Cue Hyunkyu Lee, Huwenbo Shi, Bogdan Pasaniuc, Eleazar Eskin, Buhm Han,
PLEIO: a method to map and interpret pleiotropic loci with GWAS summary statistics,
The American Journal of Human Genetics,
Volume 108, Issue 1,