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Pattern-Selection Based Power Analysis and Discrimination of Low- and High-Grade Myelodysplastic Syndromes Study Using SNP Arrays

By Xiaorong Yang, Xiaobo Zhou, Wan-Ting Huang, Lingyun Wu, Federico A. Monzon, Chung-Che Chang and Stephen T. C. Wong

Abstract

Copy Number Aberration (CNA) in myelodysplastic syndromes (MDS) study using single nucleotide polymorphism (SNP) arrays have been received increasingly attentions in the recent years. In the current study, a new Constraint Moving Average (CMA) algorithm is adopted to determine the regions of CNA regions first. In addition to large regions of CNA, using the proposed CMA algorithm, small regions of CNA can also be detected. Real-time Polymerase Chain Reaction (qPCR) results prove that the CMA algorithm presents an insightful discovery of both large and subtle regions. Based on the results of CMA, two independent applications are studied. The first one is power analysis for sample estimation. An accurate estimation of sample size needed for the desired purpose of an experiment will be important for effort-efficiency and cost-effectiveness. The power analysis is performed to determine the minimum sample size required for ensuring at least () detected regions statistically different from normal references. As expected, power increase with increasing sample size for a fixed significance level. The second application is the distinguishment of high-grade MDS patients from low-grade ones. We propose to calculate the General Variant Level (GVL) score to integrate the general information of each patient at genotype level, and use it as the unified measurement for the classification. Traditional MDS classifications usually refer to cell morphology and The International Prognostic Scoring System (IPSS), which belongs to the classification at the phenotype level. The proposed GVL score integrates the information of CNA region, the number of abnormal chromosomes and the total number of the altered SNPs at the genotype level. Statistical tests indicate that the high and low grade MDS patients can be well separated by GVL score, which appears to correlate better with clinical outcome than the traditional classification approaches using morphology and IPSS sore at the phenotype level

Topics: Research Article
Publisher: Public Library of Science
OAI identifier: oai:pubmedcentral.nih.gov:2662412
Provided by: PubMed Central
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    Citations

    1. (1992). A power primer.
    2. (2005). A robust algorithm for copy number detection using high-density oligonucleotide single nucleotide polymorphism genotyping arrays.
    3. (2001). An efficient and robust statistical modeling approach to discover differentially expressed genes using genomic expression profiles.
    4. (2004). An integrated view of copy number and allelic alterations in the cancer genome using single nucleotide polymorphism arrays.
    5. (1992). Applied Multivariate Statistical Analysis.
    6. (2004). Circular binary segmentation for the analysis of array-based DNA copy number data.
    7. (2002). Common fragile sites associated with the breakpoints of chromosomal aberrations in hematologic neoplasms.
    8. (1995). Cytogenetic findings in 179 patients with myelodysplastic syndromes.
    9. (2007). Detection of cryptic chromosomal lesions including acquired segmental uniparental disomy in advanced and low-risk myelodysplastic syndromes.
    10. (2005). Detection of DNA copy number alterations using penalized least squares regression.
    11. (2004). Hidden Markow models approach to the analysis of CGH data.
    12. (2005). Identification of novel cytogenetic markers with prognostic significance in a series of 968 patients with primary myelodysplastic syndromes.
    13. (1997). International scoring system for evaluating prognosis in myelodysplastic syndromes.
    14. (2001). Model-based analysis of oligonucleotide arrays: expression index computation and outlier detection.
    15. (2001). Model-based analysis of oligonucleotide arrays: model validation, design issues and standard error application.
    16. (1999). Molecular classification of cancer: Class discovery and class prediction by gene expression monitoring.
    17. (2009). Multiple distinct clones may co-exist in different lineage in myelodysplastic syndromes. Leukemia Research;
    18. (2004). Myelodysplastic syndromes.
    19. (2007). New insights into the prognostic impact of the karyotype in MDS and correlation with subtypes: evidence from a core dataset of 2124 patients.
    20. (2007). Prevalence and prognostic significance of allelic imbalance by single nucleotide polymorphism analysis in low risk myelodysplastic syndromes.
    21. (2001). Significance analysis of microarrays applied to the ionizing radiation response.
    22. (1988). Statistical power analysis for the behavioral sciences (2nd Ed.).
    23. (2003). Understanding the pathogenesis of myelodysplastic syndromes.

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