In biomedical research, understanding causes and physical characteristics, known as phenotypes, is crucial for correcting abnormalities like diseases. Scientists use genetic techniques to identify disease-associated locations within the human genome, a process known as Genome-wide association research (GWAS). This research helps predict disease risk and develop prevention or treatment strategies.
However, a significant issue with GWAS is the lack of diversity in the data, primarily comprising individuals of European descent. This limits the application of results to other ancestries like Asia or Africa. Previous studies on rheumatoid arthritis have highlighted this limitation.
Using GWAS analysis, scientists generate statistics to predict an individual’s likelihood of developing traits or diseases based on their genetics, resembling a polygenic score report card. This analysis also shows how genes are inherited and their impact on traits like height, weight, and blood pressure.
To address this diversity gap, researchers from Australia, Japan, Taiwan, and South Korea integrated European polygenic scores into genetic studies of various ancestries.
They utilized data from biobanks like UK Biobank, Biobank Japan, Taiwan Biobank, and Korea Genome Epidemiology Study, analyzing traits such as height, BMI, blood pressure, and diabetes. Statistical models helped calculate polygenic scores and evaluate GWAS results alongside European scores.
Their method aimed to enhance medical discoveries for underrepresented populations by analyzing genome segments unique to certain traits. They found that adjusting GWAS with polygenic scores improved the detection of rare genetic differences and trait relationships.
While primarily focusing on East Asian data, the authors suggested applying this method to other ancestries using polygenic scores. Although computationally intensive, this method shows promise in improving genetic data analysis for future GWAS studies.
In conclusion, the authors believe that their method will enhance genetic data exploration and can be easily integrated with existing GWAS software tools. They encourage researchers to utilize this method, particularly with underrepresented population data, to study genetic interactions and their effects on traits and diseases.
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Source: sciworthy.com
