Multiple Group Testing Procedures for Analysis of High-Dimensional Genomic Data
In genetic association studies with high-dimensional genomic data, multiple group testing procedures are often required in order to identify disease/trait-related genes or genetic regions, where multiple genetic sites or variants are located within the same gene or genetic region. However, statistic...
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BioMed Central
2016-12-01
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| Series: | Genomics & Informatics |
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| Online Access: | http://genominfo.org/upload/pdf/gni-14-187.pdf |
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| author | Hyoseok Ko Kipoong Kim Hokeun Sun |
| author_facet | Hyoseok Ko Kipoong Kim Hokeun Sun |
| author_sort | Hyoseok Ko |
| collection | DOAJ |
| description | In genetic association studies with high-dimensional genomic data, multiple group testing procedures are often required in order to identify disease/trait-related genes or genetic regions, where multiple genetic sites or variants are located within the same gene or genetic region. However, statistical testing procedures based on an individual test suffer from multiple testing issues such as the control of family-wise error rate and dependent tests. Moreover, detecting only a few of genes associated with a phenotype outcome among tens of thousands of genes is of main interest in genetic association studies. In this reason regularization procedures, where a phenotype outcome regresses on all genomic markers and then regression coefficients are estimated based on a penalized likelihood, have been considered as a good alternative approach to analysis of high-dimensional genomic data. But, selection performance of regularization procedures has been rarely compared with that of statistical group testing procedures. In this article, we performed extensive simulation studies where commonly used group testing procedures such as principal component analysis, Hotelling's T2 test, and permutation test are compared with group lasso (least absolute selection and shrinkage operator) in terms of true positive selection. Also, we applied all methods considered in simulation studies to identify genes associated with ovarian cancer from over 20,000 genetic sites generated from Illumina Infinium HumanMethylation27K Beadchip. We found a big discrepancy of selected genes between multiple group testing procedures and group lasso. |
| format | Article |
| id | doaj-art-828aa5cb78dc4baf91e7c31538a3c82f |
| institution | Kabale University |
| issn | 1598-866X 2234-0742 |
| language | English |
| publishDate | 2016-12-01 |
| publisher | BioMed Central |
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| series | Genomics & Informatics |
| spelling | doaj-art-828aa5cb78dc4baf91e7c31538a3c82f2025-02-02T21:04:22ZengBioMed CentralGenomics & Informatics1598-866X2234-07422016-12-0114418719510.5808/GI.2016.14.4.187175Multiple Group Testing Procedures for Analysis of High-Dimensional Genomic DataHyoseok Ko0Kipoong Kim1Hokeun Sun2Department of Statistics, Pusan National University, Busan 46241, Korea.Department of Statistics, Pusan National University, Busan 46241, Korea.Department of Statistics, Pusan National University, Busan 46241, Korea.In genetic association studies with high-dimensional genomic data, multiple group testing procedures are often required in order to identify disease/trait-related genes or genetic regions, where multiple genetic sites or variants are located within the same gene or genetic region. However, statistical testing procedures based on an individual test suffer from multiple testing issues such as the control of family-wise error rate and dependent tests. Moreover, detecting only a few of genes associated with a phenotype outcome among tens of thousands of genes is of main interest in genetic association studies. In this reason regularization procedures, where a phenotype outcome regresses on all genomic markers and then regression coefficients are estimated based on a penalized likelihood, have been considered as a good alternative approach to analysis of high-dimensional genomic data. But, selection performance of regularization procedures has been rarely compared with that of statistical group testing procedures. In this article, we performed extensive simulation studies where commonly used group testing procedures such as principal component analysis, Hotelling's T2 test, and permutation test are compared with group lasso (least absolute selection and shrinkage operator) in terms of true positive selection. Also, we applied all methods considered in simulation studies to identify genes associated with ovarian cancer from over 20,000 genetic sites generated from Illumina Infinium HumanMethylation27K Beadchip. We found a big discrepancy of selected genes between multiple group testing procedures and group lasso.http://genominfo.org/upload/pdf/gni-14-187.pdfgenetic association studiesgenetic selectiongenetic testingprincipal component analysis |
| spellingShingle | Hyoseok Ko Kipoong Kim Hokeun Sun Multiple Group Testing Procedures for Analysis of High-Dimensional Genomic Data Genomics & Informatics genetic association studies genetic selection genetic testing principal component analysis |
| title | Multiple Group Testing Procedures for Analysis of High-Dimensional Genomic Data |
| title_full | Multiple Group Testing Procedures for Analysis of High-Dimensional Genomic Data |
| title_fullStr | Multiple Group Testing Procedures for Analysis of High-Dimensional Genomic Data |
| title_full_unstemmed | Multiple Group Testing Procedures for Analysis of High-Dimensional Genomic Data |
| title_short | Multiple Group Testing Procedures for Analysis of High-Dimensional Genomic Data |
| title_sort | multiple group testing procedures for analysis of high dimensional genomic data |
| topic | genetic association studies genetic selection genetic testing principal component analysis |
| url | http://genominfo.org/upload/pdf/gni-14-187.pdf |
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