Dataset Information

A scan statistic to extract causal gene clusters from case-control genome-wide rare CNV data.

ABSTRACT:

Background

Several statistical tests have been developed for analyzing genome-wide association data by incorporating gene pathway information in terms of gene sets. Using these methods, hundreds of gene sets are typically tested, and the tested gene sets often overlap. This overlapping greatly increases the probability of generating false positives, and the results obtained are difficult to interpret, particularly when many gene sets show statistical significance.

Results

We propose a flexible statistical framework to circumvent these problems. Inspired by spatial scan statistics for detecting clustering of disease occurrence in the field of epidemiology, we developed a scan statistic to extract disease-associated gene clusters from a whole gene pathway. Extracting one or a few significant gene clusters from a global pathway limits the overall false positive probability, which results in increased statistical power, and facilitates the interpretation of test results. In the present study, we applied our method to genome-wide association data for rare copy-number variations, which have been strongly implicated in common diseases. Application of our method to a simulated dataset demonstrated the high accuracy of this method in detecting disease-associated gene clusters in a whole gene pathway.

Conclusions

The scan statistic approach proposed here shows a high level of accuracy in detecting gene clusters in a whole gene pathway. This study has provided a sound statistical framework for analyzing genome-wide rare CNV data by incorporating topological information on the gene pathway.

SUBMITTER: Nishiyama T

PROVIDER: S-EPMC3130692 | biostudies-literature | 2011 May

REPOSITORIES: biostudies-literature

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Publications

A scan statistic to extract causal gene clusters from case-control genome-wide rare CNV data.

Nishiyama Takeshi T Takahashi Kunihiko K Tango Toshiro T Pinto Dalila D Scherer Stephen W SW Takami Satoshi S Kishino Hirohisa H

BMC bioinformatics 20110526

<h4>Background</h4>Several statistical tests have been developed for analyzing genome-wide association data by incorporating gene pathway information in terms of gene sets. Using these methods, hundreds of gene sets are typically tested, and the tested gene sets often overlap. This overlapping greatly increases the probability of generating false positives, and the results obtained are difficult to interpret, particularly when many gene sets show statistical significance.<h4>Results</h4>We propo ...[more]

PMID: 21612662

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Project description:Immunoglobulin A Nephropathy (IgAN) is a complex multifactorial disease whose genetic bases remain unknown. Distinct linkage and genome-wide association studies in both familial and sporadic IgAN suggest that there is a strong genetic component in IgAN. In this context, an intriguing role could be ascribed to copy number variants (CNVs) that have been recognized as an important source of genetic variation in humans. Here, we performed a whole-genome screening of CNVs in IgAN patients, their healthy relatives and healthy subjects (HS). A total of 217 individuals consisting of 51 IgAN cases and 166 healthy relatives were included in the initial screening. The high-throughput analysis of structural genetic variations, to find concordant aberrations across classes of samples, identified 178 IgAN-specific aberrations, specifically 114 loss and 64 gain. Several CNVs overlapped with regions evidenced by previous genome-wide genetic studies. Moreover, we found that IgAN patients characterized by deteriorated renal function carried low copy numbers of a CNV in chromosome 3 (chr3_loss:52031010-52260722). This CNV contained the TLR9 gene whose expression significantly correlated with the loss aberration in patients with progressive renal damage. Conversely, IgAN patients with normal renal function had no chr3_loss:52031010-52260722 and the TLR9 mRNA was expressed at the same level as in HS, still maintaining a strong correlation with the CNV. In conclusion, here we performed the first genome-wide CNV study in IgAN identifying some structural variants specific to IgAN patients and providing a collection of new candidate genes and loci that could help to dissect the complex genomic setting of the disease. Moreover, we identified a specific CNV, spanning the TLR9 gene, which could explain the disease severity in IgAN patients. To perform a genome-wide CNV study in IgAN identifying some structural variants specific to IgAN patients and providing a collection of new candidate genes and loci that could help to dissect the complex genomic setting of the disease.

Dataset Information

A scan statistic to extract causal gene clusters from case-control genome-wide rare CNV data.

Background

Results

Conclusions

Publications

A scan statistic to extract causal gene clusters from case-control genome-wide rare CNV data.

Similar Datasets

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