Science

Precision Health: Disease Risk Assessment with
Genetic Science

Our groundbreaking research and technology are firmly rooted in advanced genetic science to deliver unparalleled insights into individual disease susceptibility.

Thoroughly Validated Basis of Our Research:

Genome-Wide Association Studies:

 

Our predictions are built on the foundation of GWAS research, which identifies genetic variations associated with specific diseases across large populations. This allows us to pinpoint genetic markers linked to increased disease risk.

Polygenic Risk Scores (PRS)

 

We utilize PRS, a powerful tool in genetic risk assessment. PRS aggregates the effects of many genetic variants to estimate disease risk. Our program calculates PRS for each disease using approximately one million SNPs (Single Nucleotide Polymorphisms).

PGS Catalog Integration:

 

Our system leverages the PGS Catalog, an extensive database containing about 3,900 PGS (Polygenic Score) files. We’ve meticulously selected the most accurate PGS file for each disease through rigorous utility tests, including Likelihood Ratio Test (LRT), perSD Odds Ratio, and Net Reclassification Improvement (NRI).

Risk Group Identification:

 

We convert PRS scores to percentiles, establishing the 41-60 percentile range as the Normal group. Individuals with disease incidence rates more than twice that of the Normal group are categorized as the risk group, and those with rats more than three times higher are classified as high-risk.

How We Applied Science to Our Research

Sample Abstracts of Our Research

Abstract 1: Integration of risk factor Polygenic Risk Score with disease Polygenic Risk Score for disease prediction

Summary:

Traditionally, scientists look at genes directly linked to a disease (disease PRS). This study shows that adding genes related to risk factors (RFPRS) can improve the accuracy of these predictions.

Our researchers analyzed a large dataset and found that combining disease PRS with RFPRS led to better prediction for over 30 diseases. This combined score (RFDiseasemetaPRS) could be used to personalize healthcare and guide preventative strategies. 

For full Abstract

Abstract 2: Identification of asthma-related genes using asthmatic blood eQTLs of Korean patients

Summary:

This study aimed to identify genes involved in asthma by analyzing genetic and gene expression data from Korean Asthma patients. By comparing these data with existing genetic information, researchers identified 15 genes potentially linked to asthma. These findings could contribute to a better understanding of asthma and the development of new treatments.

For full Abstract

Abstract 3: Genetic differences according to onset age and lung function in asthma: A cluster analysis

Summary:

This study aimed to understand the genetic basis of asthma by examining different asthma subtypes based on age of onset and lung function. By analyzing genetic data from a large population, researchers identified distinct genetic patterns associated with each subtype, suggesting that asthma is a complex disease with multiple genetic factors influencing its development and severity.

For full Abstract

For more reference abstracts, click on images below

(coming soon)