Journal article
Identification of non-Hodgkin's lymphoma prognosis signatures using the CTGDR method
Bioinformatics (Oxford, England), Vol.26(1), pp.15-21
10/22/2009
DOI: 10.1093/bioinformatics/btp604
PMCID: PMC2796812
PMID: 19850755
Abstract
Motivation:
Although NHL (non-Hodgkin's lymphoma) is the fifth leading cause of cancer incidence and mortality in the USA, it remains poorly understood and is largely incurable. Biomedical studies have shown that genomic variations, measured with SNPs (single nucleotide polymorphisms) in genes, may have independent predictive power for disease-free survival in NHL patients beyond clinical measurements.
Results:
We apply the CTGDR (clustering threshold gradient directed regularization) method to genetic association studies using SNPs, analyze data from an association study of NHL and identify prognosis signatures to diffuse large B cell lymphoma (DLBCL) and follicular lymphoma (FL), the two most common subtypes of NHL. With the CTGDR method, we are able to account for the
joint effects of multiple genes/SNPs
, whereas most existing studies are single-marker based. In addition, we are able to account for the ‘gene and SNP-within-gene’ hierarchical structure and identify
not only predictive genes but also predictive SNPs within identified genes
. In contrast, existing studies are limited to either gene or SNP identification, but not both. We propose using resampling methods to evaluate the predictive power and reproducibility of identified genes and SNPs. Simulation study and data analysis suggest satisfactory performance of the CTGDR method.
Contact:
shuangge.ma@yale.edu
Supplementary information:
Supplementary data
are available at
Bioinformatics
online.
Details
- Title: Subtitle
- Identification of non-Hodgkin's lymphoma prognosis signatures using the CTGDR method
- Creators
- Shuangge Ma - Yale UniversityYawei Zhang - University of IowaJian Huang - University of IowaXuesong Han - University of IowaTheodore Holford - University of IowaQing Lan - University of IowaNathaniel Rothman - University of IowaPeter Boyle - University of IowaTongzhang Zheng - University of Iowa
- Resource Type
- Journal article
- Publication Details
- Bioinformatics (Oxford, England), Vol.26(1), pp.15-21
- Publisher
- Oxford University Press
- DOI
- 10.1093/bioinformatics/btp604
- PMID
- 19850755
- PMCID
- PMC2796812
- ISSN
- 1367-4803
- eISSN
- 1367-4811
- Language
- English
- Date published
- 10/22/2009
- Academic Unit
- Statistics and Actuarial Science
- Record Identifier
- 9984257610702771
Metrics
21 Record Views