Following Significant Gene Discovery, Dr. Buhm Han, Ph.D., and Team Published in The American Journal of Human Genetics for Disease Detecting Algorithm

    SEOUL, KOREA, February 09, 2021 -- Genealogy Inc. and Seoul National University announced that the research team led by Dr. Buhm Han, Ph.D. of the College of Medicine, had developed an algorithm dubbed "PLEIO" to identify key genes that cause various chronic diseases. The research was published in the American Journal of Human Genetics by Cell.

The algorithm works by identifying pleiotropic genes, which can simultaneously affect the expression of several diseases. Pinpointing and interpreting pleiotropic loci is essential to understanding the shared causes of diseases and complex traits. Using their algorithm, the team discovered 13 essential genes that play a role in developing cardiovascular diseases.

Many chronic diseases, such as heart disease, high blood pressure, and diabetes, are significantly linked to family history and genetic inheritance. However, exactly which genes play primary roles in disease development is not yet fully known. One reason is that researchers often study each disease separately, which can limit the number of observable samples.

Additionally, each gene is incredibly complex. Its impact could be obfuscated when looking at the "big picture" results. Therefore, researchers have established a network analysis approach that combines all the data from various disease studies into one source.

By focusing on the genes of a pleiotropic locus, researchers could predict the expression of several diseases at once. For example, a deletion of the gene located on the 22nd chromosome is associated with both autism and schizophrenia expression.

Identifying pleiotropic expression genes helps to understand human physiological mechanisms and can provide important clinical information by presenting joint treatment targets for multiple diseases. However, there was no effective methodology for finding these core genes previously.

Professor Han and his team at Seoul National University College of Medicine have developed the PLEIO algorithm to identify critical genes with a high level of accuracy by analyzing diseases within a network of known disorders.

PLEIO (Pleiotropic Locus Exploration and Interpretation using Optimal test) integrates and analyzes genetic studies of several diseases into one source and effectively identifies key genes by examining their relationship. In other words, it creates a multi-sided gene map on the genomic level.

Using PLEIO, the researchers analyzed big data from 18 characteristics related to cardiovascular disease (heart disease, diabetes, high blood pressure, hyperlipidemia, obesity, etc.). As a result, 13 key genes that simultaneously affect the expression of these diseases were discovered. These genes have never before been linked to or reported in cardiovascular disease studies.

Professor Buhm Han, co-founder and CTO of Genealogy Inc., said, "We expect that the discovery of these key genes will play an important role in understanding the mechanisms of developing chronic diseases."

The study was conducted with the Korea Research Foundation and the Asan Social Welfare Foundation. The findings were recently published in the American Journal of Human Genetics which features a detailed explanation of the technology behind the discovery.

The PLEIO algorithm will be a core technology used by Genealogy Inc. to identify risk factors for chronic diseases. The Genealogy Care platform will launch in March 2021 to provide consumers with more accurate and affordable in-home testing options to screen for and prevent fatal diseases using genomic data.

Genealogy is a Bioinformatics and Computational Genetics startup with locations in the U.S. and South Korea. They are focused on developing novel machine learning and deep learning algorithms to better analyze DNA, identify potential genetic disorders, and provide users with valuable health insights. They were recently recognized as an "Impact Maker" by the 2020 United Nations' UNDP.

# # #