Group additive regression models for genomic data analysis
Abstract
One important problem in genomic research is to identify genomic features such as gene expression data or DNA single nucleotide polymorphisms (SNPs) that are related to clinical phenotypes. Often these genomic data can be naturally divided into biologically meaningful groups such as genes belonging to the same pathways or SNPs within genes. In this paper, we propose group additive regression models and a group gradient descent boosting procedure for identifying groups of genomic features that are related to clinical phenotypes. Our simulation results show that by dividing the variables into appropriate groups, we can obtain better identification of the group features that are related to the phenotypes. In addition, the prediction mean square errors are also smaller than the component-wise boosting procedure. We demonstrate the application of the methods to pathway-based analysis of microarray gene expression data of breast cancer. Results from analysis of a breast cancer microarray gene expression data set indicate that the pathways of metalloendopeptidases (MMPs) and MMP inhibitors, as well as cell proliferation, cell growth, and maintenance are important to breast cancer-specific survival.
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Ridge regression and LASSO have been considered in genetic association analysis contexts, and other methods have been proposed.
“Two such techniques, ridge regression74 and the LASSO75,76 have been considered in genetic association analysis contexts, and other methods have also been proposed as well.77–80, 81”
From Statistical analysis strategies for association studies involving rare variants (2010) · cites this paper as [79]
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Statistical analysis strategies for association studies involving rare variants
Bansal, V., Libiger, O., Torkamani, A., & Schork, N. J. (2010) · Nature Reviews Genetics · doi:10.1038/nrg2867 · Cited by 455 · see citing works
- Authors
- Luan, Y., & Li, H.
- Affiliations
- Shandong University (CN)
- University of Pennsylvania (US)
- Corresponding author
- Li, H.
- Venue
- Biostatistics
- Publisher
- Oxford University Press (OUP)
- Published
- 2007-05-18
- Volume
- 9
- Issue
- 1
- Pages
- 100-113
- ISSN
- 1465-4644 (ISSN-L)
- 1468-4357
- OpenAlex
- W2149776317
- Cited by
- 48
- References
- 36
- Citations by year
- 2023: 1 · 2022: 2 · 2021: 1 · 2020: 3 · 2019: 3 · 2018: 2 · 2017: 1 · 2016: 2 · 2015: 4 · 2014: 2 · 2013: 2 · 2012: 3
- FWCI
- 1.98
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Biostatistics (publishedVersion)
- PubMed (publishedVersion)
- Collection of Biostatistics Research Archive (submittedVersion)
- Funders
- National Science Foundation
- University of Pennsylvania
- National Institutes of Health
- National Institute of Environmental Health Sciences (R01ES009911, ES009911)
- Topics
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Gene expression and cancer classification
- Physical Sciences › Mathematics › Statistics and Probability › Statistical Methods and Inference
- Life Sciences › Biochemistry, Genetics and Molecular Biology › Molecular Biology › Bioinformatics and Genomic Networks
- Keywords
- gene expression data, single nucleotide polymorphisms, pathway analysis, breast cancer, microarray gene expression, metalloendopeptidase, matrix metalloproteinase inhibitors, cell proliferation, clinical phenotype prediction
- MeSH terms
- Breast Neoplasms, Data Interpretation, Statistical, Female, Humans, Models, Genetic, Regression Analysis, Genome, Human, Survival Analysis, Polymorphism, Single Nucleotide, Matrix Metalloproteinases, Gene Expression Profiling, Matrix Metalloproteinase Inhibitors
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