TPQCI: A topology potential-based method to quantify functional influence of copy number variations

Yusong Liu; Xiufen Ye; Xiaohui Zhan; Christina Y Yu; Jie Zhang; Kun Huang

doi:10.1016/j.ymeth.2021.04.015

TPQCI: A topology potential-based method to quantify functional influence of copy number variations

Methods. 2021 Aug:192:46-56. doi: 10.1016/j.ymeth.2021.04.015. Epub 2021 Apr 22.

Authors

Yusong Liu¹, Xiufen Ye², Xiaohui Zhan³, Christina Y Yu⁴, Jie Zhang⁵, Kun Huang⁶

Affiliations

¹ Collage of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, Heilongjiang 150001, China; Indiana University School of Medicine, Indianapolis, IN 46202, USA.
² Collage of Intelligent Systems Science and Engineering, Harbin Engineering University, Harbin, Heilongjiang 150001, China.
³ Indiana University School of Medicine, Indianapolis, IN 46202, USA; National-Regional Key Technology Engineering Laboratory for Medical Ultrasound, Guangdong Key Laboratory for Biomedical Measurements and Ultrasound Imaging, School of Biomedical Engineering, Health Science Center, Shenzhen University, Shenzhen, Guangdong 518037, China; Department of Bioinformatics, School of Basic Medicine, Chongqing Medical University, Chongqing 400016, China.
⁴ Indiana University School of Medicine, Indianapolis, IN 46202, USA; Department of Biomedical Informatics, The Ohio State University, Columbus, OH 43210, USA.
⁵ Indiana University School of Medicine, Indianapolis, IN 46202, USA.
⁶ Indiana University School of Medicine, Indianapolis, IN 46202, USA; Regenstrief Institute, Indianapolis, IN 46202, USA. Electronic address: kunhuang@iu.edu.

PMID: 33894380
DOI: 10.1016/j.ymeth.2021.04.015

Abstract

Copy number variation (CNV) is a major type of chromosomal structural variation that play important roles in many diseases including cancers. Due to genome instability, a large number of CNV events can be detected in diseases such as cancer. Therefore, it is important to identify the functionally important CNVs in diseases, which currently still poses a challenge in genomics. One of the critical steps to solve the problem is to define the influence of CNV. In this paper, we provide a topology potential based method, TPQCI, to quantify this kind of influence by integrating statistics, gene regulatory associations, and biological function information. We used this metric to detect functionally enriched genes on genomic segments with CNV in breast cancer and multiple myeloma and discovered biological functions influenced by CNV. Our results demonstrate that, by using our proposed TPQCI metric, we can detect disease-specific genes that are influenced by CNVs. Source codes of TPQCI are provided in Github (https://github.com/usos/TPQCI).

Keywords: Copy number variation; Functional influence of CNV; Gene module detection; Multi-omics data integration; Protein-protein interaction network; Topology potential.

Publication types

Research Support, Non-U.S. Gov't

MeSH terms

Breast Neoplasms
DNA Copy Number Variations* / genetics
Female
Gene Expression Regulation
Genomics
Humans