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| Content Provider | Springer Nature : BioMed Central |
|---|---|
| Author | Xu, Jing Yuan, Zhongshang Ji, Jiadong Zhang, Xiaoshuai Li, Hongkai Wu, Xuesen Xue, Fuzhong Liu, Yanxun |
| Abstract | Background The genetic variants identified by Genome-wide association study (GWAS) can only account for a small proportion of the total heritability for complex disease. The existence of gene-gene joint effects which contains the main effects and their co-association is one of the possible explanations for the “missing heritability” problems. Gene-gene co-association refers to the extent to which the joint effects of two genes differ from the main effects, not only due to the traditional interaction under nearly independent condition but the correlation between genes. Generally, genes tend to work collaboratively within specific pathway or network contributing to the disease and the specific disease-associated locus will often be highly correlated (e.g. single nucleotide polymorphisms (SNPs) in linkage disequilibrium). Therefore, we proposed a novel score-based statistic (SBS) as a gene-based method for detecting gene-gene co-association. Results Various simulations illustrate that, under different sample sizes, marginal effects of causal SNPs and co-association levels, the proposed SBS has the better performance than other existed methods including single SNP-based and principle component analysis (PCA)-based logistic regression model, the statistics based on canonical correlations (CCU), kernel canonical correlation analysis (KCCU), partial least squares path modeling (PLSPM) and delta-square (δ 2) statistic. The real data analysis of rheumatoid arthritis (RA) further confirmed its advantages in practice. Conclusions SBS is a powerful and efficient gene-based method for detecting gene-gene co-association. |
| Related Links | https://bmcgenomdata.biomedcentral.com/counter/pdf/10.1186/s12863-016-0331-3.pdf |
| Ending Page | 8 |
| Page Count | 8 |
| Starting Page | 1 |
| File Format | HTM / HTML |
| ISSN | 27306844 |
| DOI | 10.1186/s12863-016-0331-3 |
| Journal | BMC Genomic Data |
| Issue Number | 1 |
| Volume Number | 17 |
| Language | English |
| Publisher | BioMed Central |
| Publisher Date | 2016-01-29 |
| Access Restriction | Open |
| Subject Keyword | Life Sciences Animal Genetics and Genomics Microbial Genetics and Genomics Plant Genetics and Genomics Genetics and Population Dynamics Gene-gene co-association Score-based Gene-based |
| Content Type | Text |
| Resource Type | Article |
| Subject | Health Informatics Genetics |
| Journal Impact Factor | 1.9/2023 |
| 5-Year Journal Impact Factor | 1.9/2023 |
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