Exhaustive sliding-window scan strategy for genome-wide association study via PCA-based logistic model

Xiaoshuai Zhang

Exhaustive sliding-window scan strategy for genome-wide association study via PCA-based logistic model

Xiaoshuai Zhang * §

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§ Shanodng university

Exhaustive sliding-window scan strategy for genome-wide association study via PCA-based logistic model

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Abstract

In genome-wide association study (GWAS), various sliding-window scan approaches have been proposed recently. How to determine the optimal window size, which is influenced by the underlying linkage disequilibrium (LD) patterns, minor allele frequency (MAF) of the causal SNP, and others, is crucial for these methods. However, it is difficult to clarify the theoretical relationship between the optimal window size and these factors. In this regard, we proposed exhaustive strategy with ergodic window sizes along the genome matter whatever the relationship is. Simulations are conducted to assess statistical powers under different sample sizes, relative risks, MAF, LD patterns and window sizes, followed by a real data analysis to evaluate its performance. The simulation results suggested that it was difficult to determine the optimal window size because it was influenced by many factors such as MAF and LD pattern. Real data analysis indicated that the p-values with different window sizes were quite different. Furthermore, with the development of multiprocessor computational technique, the proposed exhaustive strategy combined with the cluster computer technique computationally efficient and feasible for analyzing GWAS data.So the exhaustive strategy is a powerful tool for GWAS data analysis regardless of the relationship between the window size and LD.

Funding

No external funding was declared for this work.

Conflict of Interest

The authors declare no conflict of interest.

Ethical Approval

No ethics committee approval was required for this article type.

Data Availability

Not applicable for this article.

How to Cite This Article

Xiaoshuai Zhang. 2012. "Exhaustive sliding-window scan strategy for genome-wide association study via PCA-based logistic model". Global Journal of Science Frontier Research, Global Journal of Science Frontier Research - G: Bio-Tech & Genetics GJSFR-G Volume 12 (GJSFR Volume 12 Issue G4).

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GJSFR Volume 12 Issue G4

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Journal Specifications

Crossref Journal DOI 10.17406/GJSFR

Print ISSN 0975-5896

e-ISSN 2249-4626

Keywords

Causal SNP Cluster computer Minor allele frequency

Classification

GJSFR-G Classification FOR Code: 060407

Submission ReceivedMarch 24, 2012
Peer Review Double Blind
Handling Editor
Accepted April 16, 2012
Published April 26, 2012

Version of record

v1.2

Issue date

August 25, 2012

Language

English

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Article Matrices

Total Score: 187

Country: China

Subject: Global Journal of Science Frontier Research

Authors: Dr. Qingsong Gao, Zhongshang Yuan, Yungang He, Jinghua Zhao, Xiaoshuai Zhang, Fangyu Li, Bingbing Zhang, Fuzhong Xue (PhD/Dr. count: 1)

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Publish Date: 2012 04, Thu

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Total Views: 1.9K

Total Downloads: 102

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