Assistant Professor Xiaoli Gao Studies DNA Copy Number Variation

We normally think that of the human genome as a collection of DNA containing many genes, each of which contains the information required for the cell to make a particular protein. Interestingly, our genome contains multiple copies of some genes, and the number of copies can vary between individuals. This copy number variation may play a role in many diseases, such as Crohn's disease and psoriasis. Analysis of copy number variation is essentially a statistical issue, and represents one of those topics in “quantitative biology” that can benefit from the collaboration between biologists and mathematicians.

Assistant Professor Xiaoli Gao, of the Department of Mathematics and Statistics, recently published a paper in BMC Genomics that analyzed copy number variation. Gao and her collaborator Jian Huang from the University of Iowa developed A Robust Penalized Method for the Analysis of Noisy DNA Copy Number Data (Volume 11, Article Number 517, doi:10.1186/1471-2164-11-517). The abstract from their paper is given below.

"Deletions and amplifications of the human genomic DNA copy number are the causes of numerous diseases, such as, various forms of cancer. Therefore, the detection of DNA copy number variations (CNV) is important in understanding the genetic basis of many diseases. Various techniques and platforms have been developed for genome-wide analysis of DNA copy number, such as, array-based comparative genomic hybridization (aCGH) and high-resolution mapping with high-density tiling oligonucleotide arrays. Since complicated biological and experimental processes are often associated with these platforms, data can be potentially contaminated by outliers. We propose a penalized LAD regression model with the adaptive fused lasso penalty for detecting CNV. This method contains robust properties and incorporates both the spatial dependence and sparsity of CNV into the analysis. Our simulation studies and real data analysis indicate that the proposed method can correctly detect the numbers and locations of the true breakpoints while appropriately controlling the false positives. The proposed method has three advantages for detecting CNV change points: it contains robustness properties; incorporates both spatial dependence and sparsity; and estimates the true values at each marker accurately."