Name
Abstract | ||
|---|---|---|
Genetic risk score (GRS, also known as polygenic risk score) analysis is an increasingly popular method for exploring genetic architectures and relationships of complex diseases. However, complex diseases are usually measured by multiple correlated phenotypes. Analyzing each disease phenotype individually is likely to reduce statistical power due to multiple testing correction. In order to conquer the disadvantage, we proposed a principal component analysis (PCA)-based GRS analysis approach. Extensive simulation studies were conducted to compare the performance of PCA-based GRS analysis and traditional GRS analysis approach. Simulation results observed significantly improved performance of PCA-based GRS analysis compared to traditional GRS analysis under various scenarios. For the sake of verification, we also applied both PCA-based GRS analysis and traditional GRS analysis to a real Caucasian genome-wide association study (GWAS) data of bone geometry. Real data analysis results further confirmed the improved performance of PCA-based GRS analysis. Given that GWAS have flourished in the past decades, our approach may help researchers to explore the genetic architectures and relationships of complex diseases or traits. |
Year | DOI | Venue |
|---|---|---|
2019 | 10.1093/bib/bby075 | BRIEFINGS IN BIOINFORMATICS |
Keywords | Field | DocType |
bioinformatics,principal component analysis,genetic risk score,correlation analysis,complex diseases | Data mining,Genetic correlation,Biology | Journal |
Volume | Issue | ISSN |
20 | 6 | 1467-5463 |
Citations | PageRank | References |
0 | 0.34 | 3 |
Authors | ||
18 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Yan Zhao | 1 | 0 | 0.68 |
| Yujie Ning | 2 | 0 | 0.68 |
| Feng Zhang | 3 | 0 | 0.34 |
| Miao Ding | 4 | 0 | 0.34 |
| Yan Wen | 5 | 1 | 2.12 |
| Liang Shi | 6 | 0 | 0.34 |
| Kunpeng Wang | 7 | 1 | 1.03 |
| Mengnan Lu | 8 | 0 | 0.34 |
| Jingyan Sun | 9 | 0 | 0.34 |
| Menglu Wu | 10 | 0 | 0.34 |
| Bolun Cheng | 11 | 0 | 0.34 |
| Mei Ma | 12 | 0 | 1.01 |
| Lu Zhang | 13 | 0 | 1.01 |
| Shiqiang Cheng | 14 | 0 | 0.34 |
| Hui Shen | 15 | 23 | 3.79 |
| Qing Tian | 16 | 0 | 1.01 |
| Xiong Guo | 17 | 0 | 1.35 |
| Hong-Wen Deng | 18 | 3 | 2.41 |