Statistical genetics and polygenic risk score for precision medicine
Abstract The prediction of disease risks is an essential part of personalized medicine, which includes early disease detection, prevention, and intervention. The polygenic risk score (PRS) has become the standard for quantifying genetic liability in predicting disease risks. PRS utilizes single-nucl...
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| Format: | Article |
| Language: | English |
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BMC
2021-06-01
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| Series: | Inflammation and Regeneration |
| Subjects: | |
| Online Access: | https://doi.org/10.1186/s41232-021-00172-9 |
| _version_ | 1819105913426935808 |
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| author | Takahiro Konuma Yukinori Okada |
| author_facet | Takahiro Konuma Yukinori Okada |
| author_sort | Takahiro Konuma |
| collection | DOAJ |
| description | Abstract The prediction of disease risks is an essential part of personalized medicine, which includes early disease detection, prevention, and intervention. The polygenic risk score (PRS) has become the standard for quantifying genetic liability in predicting disease risks. PRS utilizes single-nucleotide polymorphisms (SNPs) with genetic risks elucidated by genome-wide association studies (GWASs) and is calculated as weighted sum scores of these SNPs with genetic risks using their effect sizes from GWASs as their weights. The utilities of PRS have been explored in many common diseases, such as cancer, coronary artery disease, obesity, and diabetes, and in various non-disease traits, such as clinical biomarkers. These applications demonstrated that PRS could identify a high-risk subgroup of these diseases as a predictive biomarker and provide information on modifiable risk factors driving health outcomes. On the other hand, there are several limitations to implementing PRSs in clinical practice, such as biased sensitivity for the ethnic background of PRS calculation and geographical differences even in the same population groups. Also, it remains unclear which method is the most suitable for the prediction with high accuracy among numerous PRS methods developed so far. Although further improvements of its comprehensiveness and generalizability will be needed for its clinical implementation in the future, PRS will be a powerful tool for therapeutic interventions and lifestyle recommendations in a wide range of diseases. Thus, it may ultimately improve the health of an entire population in the future. |
| first_indexed | 2024-12-22T02:29:48Z |
| format | Article |
| id | doaj.art-85a3a8e4799b4e869325456cf82bb17a |
| institution | Directory Open Access Journal |
| issn | 1880-8190 |
| language | English |
| last_indexed | 2024-12-22T02:29:48Z |
| publishDate | 2021-06-01 |
| publisher | BMC |
| record_format | Article |
| series | Inflammation and Regeneration |
| spelling | doaj.art-85a3a8e4799b4e869325456cf82bb17a2022-12-21T18:41:54ZengBMCInflammation and Regeneration1880-81902021-06-014111510.1186/s41232-021-00172-9Statistical genetics and polygenic risk score for precision medicineTakahiro Konuma0Yukinori Okada1Department of Statistical Genetics, Osaka University Graduate School of MedicineDepartment of Statistical Genetics, Osaka University Graduate School of MedicineAbstract The prediction of disease risks is an essential part of personalized medicine, which includes early disease detection, prevention, and intervention. The polygenic risk score (PRS) has become the standard for quantifying genetic liability in predicting disease risks. PRS utilizes single-nucleotide polymorphisms (SNPs) with genetic risks elucidated by genome-wide association studies (GWASs) and is calculated as weighted sum scores of these SNPs with genetic risks using their effect sizes from GWASs as their weights. The utilities of PRS have been explored in many common diseases, such as cancer, coronary artery disease, obesity, and diabetes, and in various non-disease traits, such as clinical biomarkers. These applications demonstrated that PRS could identify a high-risk subgroup of these diseases as a predictive biomarker and provide information on modifiable risk factors driving health outcomes. On the other hand, there are several limitations to implementing PRSs in clinical practice, such as biased sensitivity for the ethnic background of PRS calculation and geographical differences even in the same population groups. Also, it remains unclear which method is the most suitable for the prediction with high accuracy among numerous PRS methods developed so far. Although further improvements of its comprehensiveness and generalizability will be needed for its clinical implementation in the future, PRS will be a powerful tool for therapeutic interventions and lifestyle recommendations in a wide range of diseases. Thus, it may ultimately improve the health of an entire population in the future.https://doi.org/10.1186/s41232-021-00172-9Statistical genomicsGenome-wide association studyPolygenic risk scorePrecision medicine |
| spellingShingle | Takahiro Konuma Yukinori Okada Statistical genetics and polygenic risk score for precision medicine Inflammation and Regeneration Statistical genomics Genome-wide association study Polygenic risk score Precision medicine |
| title | Statistical genetics and polygenic risk score for precision medicine |
| title_full | Statistical genetics and polygenic risk score for precision medicine |
| title_fullStr | Statistical genetics and polygenic risk score for precision medicine |
| title_full_unstemmed | Statistical genetics and polygenic risk score for precision medicine |
| title_short | Statistical genetics and polygenic risk score for precision medicine |
| title_sort | statistical genetics and polygenic risk score for precision medicine |
| topic | Statistical genomics Genome-wide association study Polygenic risk score Precision medicine |
| url | https://doi.org/10.1186/s41232-021-00172-9 |
| work_keys_str_mv | AT takahirokonuma statisticalgeneticsandpolygenicriskscoreforprecisionmedicine AT yukinoriokada statisticalgeneticsandpolygenicriskscoreforprecisionmedicine |