Research Topic

Statistical method for CNV detection and disease association

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Although the single nucleotide polymorphism (SNP) has served as major genetic marker in human genetic studies, copy number variation (CNV) has been found to be associated with various complex diseases including cancer, mental disorders, and eye diseases. ...

Although the single nucleotide polymorphism (SNP) has served as major genetic marker in human genetic studies, copy number variation (CNV) has been found to be associated with various complex diseases including cancer, mental disorders, and eye diseases.
Copy number of variation (CNV) is a type of structure variation in human genome, where a segment of base pairs are dislocated, duplicated, deleted, or reversed. CNV has been found to be associated with various complex diseases including cancer, mental disorders, HIV, and eye diseases. CNVs vary in length ranging from a few base pairs to millions of base pairs, and in types of how a segment of DNA is modified. Detecting CNVs and testing their roles in diseases are challenging problems in genetic studies that are quite different from those of single nucleotide polymorphism (SNP) where extensive databases and established methods exist. With the availability of high throughput SNP and next generation sequencing technologies, detecting CNVs has become more feasible and reliable providing another area to look for disease mechanism. The related topics have gained more attention recently and motivated rapid developments of new statistical methods and applications. They are, however, often scattered in different journals, which triggers the need for a focused theme to facilitate scientific communications. So we propose a research focus on statistical method for CNV detection and disease association.
The important topics in this area include but not limited to:
(1) Method development: hidden Markov models, change-point models, scan statistics, variable selection in high dimensional data; methods for analysis of DNA sequence, RNA-Seq, CNV-Seq, etc.; statistical models for data integration, global transcription population genetic model.
(2) Method evaluation: using public or simulated data to evaluate the performance of existing or novel methods.
(3) Algorithm and tool development: proper calibration of data, mapping algorithms, computational and visualization tool;
(4) Real data application: genome-wide or targeted-region association studies.


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