Hierarchical structural component model for pathway analysis of common variants
Abstract Background Genome-wide association studies (GWAS) have been widely used to identify phenotype-related genetic variants using many statistical methods, such as logistic and linear regression. However, GWAS-identified SNPs, as identified with stringent statistical significance, explain just a...
| Main Authors: | , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
BMC
2020-02-01
|
| Series: | BMC Medical Genomics |
| Subjects: | |
| Online Access: | http://link.springer.com/article/10.1186/s12920-019-0650-0 |
| _version_ | 1818726209360494592 |
|---|---|
| author | Nan Jiang Sungyoung Lee Taesung Park |
| author_facet | Nan Jiang Sungyoung Lee Taesung Park |
| author_sort | Nan Jiang |
| collection | DOAJ |
| description | Abstract Background Genome-wide association studies (GWAS) have been widely used to identify phenotype-related genetic variants using many statistical methods, such as logistic and linear regression. However, GWAS-identified SNPs, as identified with stringent statistical significance, explain just a small portion of the overall estimated genetic heritability. To address this ‘missing heritability’ issue, gene- and pathway-based analysis, and biological mechanisms, have been used for many GWAS studies. However, many of these methods often neglect the correlation between genes and between pathways. Methods We constructed a hierarchical component model that considers correlations both between genes and between pathways. Based on this model, we propose a novel pathway analysis method for GWAS datasets, Hierarchical structural Component Model for Pathway analysis of Common vAriants (HisCoM-PCA). HisCoM-PCA first summarizes the common variants of each gene, first at the gene-level, and then analyzes all pathways simultaneously by ridge-type penalization of both the gene and pathway effects on the phenotype. Statistical significance of the gene and pathway coefficients can be examined by permutation tests. Results Using the simulation data set of Genetic Analysis Workshop 17 (GAW17), for both binary and continuous phenotypes, we showed that HisCoM-PCA well-controlled type I error, and had a higher empirical power compared to several other methods. In addition, we applied our method to a SNP chip dataset of KARE for four human physiologic traits: (1) type 2 diabetes; (2) hypertension; (3) systolic blood pressure; and (4) diastolic blood pressure. Those results showed that HisCoM-PCA could successfully identify signal pathways with superior statistical and biological significance. Conclusions Our approach has the advantage of providing an intuitive biological interpretation for associations between common variants and phenotypes, via pathway information, potentially addressing the missing heritability conundrum. |
| first_indexed | 2024-12-17T21:54:34Z |
| format | Article |
| id | doaj.art-d7397c04325640939338c57ee049f2b0 |
| institution | Directory Open Access Journal |
| issn | 1755-8794 |
| language | English |
| last_indexed | 2024-12-17T21:54:34Z |
| publishDate | 2020-02-01 |
| publisher | BMC |
| record_format | Article |
| series | BMC Medical Genomics |
| spelling | doaj.art-d7397c04325640939338c57ee049f2b02022-12-21T21:31:09ZengBMCBMC Medical Genomics1755-87942020-02-0113S311010.1186/s12920-019-0650-0Hierarchical structural component model for pathway analysis of common variantsNan Jiang0Sungyoung Lee1Taesung Park2Interdisciplinary Program in Bioinformatics, Seoul National UniversityCenter for Precision Medicine, Seoul National University HospitalInterdisciplinary Program in Bioinformatics, Seoul National UniversityAbstract Background Genome-wide association studies (GWAS) have been widely used to identify phenotype-related genetic variants using many statistical methods, such as logistic and linear regression. However, GWAS-identified SNPs, as identified with stringent statistical significance, explain just a small portion of the overall estimated genetic heritability. To address this ‘missing heritability’ issue, gene- and pathway-based analysis, and biological mechanisms, have been used for many GWAS studies. However, many of these methods often neglect the correlation between genes and between pathways. Methods We constructed a hierarchical component model that considers correlations both between genes and between pathways. Based on this model, we propose a novel pathway analysis method for GWAS datasets, Hierarchical structural Component Model for Pathway analysis of Common vAriants (HisCoM-PCA). HisCoM-PCA first summarizes the common variants of each gene, first at the gene-level, and then analyzes all pathways simultaneously by ridge-type penalization of both the gene and pathway effects on the phenotype. Statistical significance of the gene and pathway coefficients can be examined by permutation tests. Results Using the simulation data set of Genetic Analysis Workshop 17 (GAW17), for both binary and continuous phenotypes, we showed that HisCoM-PCA well-controlled type I error, and had a higher empirical power compared to several other methods. In addition, we applied our method to a SNP chip dataset of KARE for four human physiologic traits: (1) type 2 diabetes; (2) hypertension; (3) systolic blood pressure; and (4) diastolic blood pressure. Those results showed that HisCoM-PCA could successfully identify signal pathways with superior statistical and biological significance. Conclusions Our approach has the advantage of providing an intuitive biological interpretation for associations between common variants and phenotypes, via pathway information, potentially addressing the missing heritability conundrum.http://link.springer.com/article/10.1186/s12920-019-0650-0Common variantsGenome-wide association studyHierarchical componentsPathway analysis |
| spellingShingle | Nan Jiang Sungyoung Lee Taesung Park Hierarchical structural component model for pathway analysis of common variants BMC Medical Genomics Common variants Genome-wide association study Hierarchical components Pathway analysis |
| title | Hierarchical structural component model for pathway analysis of common variants |
| title_full | Hierarchical structural component model for pathway analysis of common variants |
| title_fullStr | Hierarchical structural component model for pathway analysis of common variants |
| title_full_unstemmed | Hierarchical structural component model for pathway analysis of common variants |
| title_short | Hierarchical structural component model for pathway analysis of common variants |
| title_sort | hierarchical structural component model for pathway analysis of common variants |
| topic | Common variants Genome-wide association study Hierarchical components Pathway analysis |
| url | http://link.springer.com/article/10.1186/s12920-019-0650-0 |
| work_keys_str_mv | AT nanjiang hierarchicalstructuralcomponentmodelforpathwayanalysisofcommonvariants AT sungyounglee hierarchicalstructuralcomponentmodelforpathwayanalysisofcommonvariants AT taesungpark hierarchicalstructuralcomponentmodelforpathwayanalysisofcommonvariants |