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Detection of Clinically-relevant Large-scale Genomic Variation from Short-read Sequencing Data

Wednesday, June 5, 2019

3:00 PM-5:00 PM

BIOMED PhD Research Proposal

Detection of Clinically-relevant Large-scale Genomic Variation from Short-read Sequencing Data

Ramakrishnan Rajagopalan, PhD Candidate
School of Biomedical Engineering, Science and Health Systems
Drexel University

Laura Conlin, PhD, FACMG
Director, Genomics Diagnostics Laboratory
The Children's Hospital of Philadelphia (CHOP)
Assistant Professor, Department of Pathology and Laboratory Medicine
Perelman School of Medicine
University of Pennsylvania

Ming Xiao, PhD
Associate Professor
School of Biomedical Engineering, Science and Health Systems
Drexel University

Copy number variants (CNVs), including deletion or duplications, are an important class of genetic variation that contribute to health and disease. Pathogenic CNVs are identified in ~20% patients with intellectual disability/neurologic disorders, and ~1.5% of healthy individuals are estimated carry at least one pathogenic CNV associated with a recessive disorder. Therefore, detecting CNVs with high sensitivity and specificity is crucial for molecular diagnostics and patient care.

Chromosomal microarrays (CMA) is a first-tier diagnostic test for detection of CNVs associated with many disorders including developmental delay, autism, and congenital anomalies; however, in the last decade exome sequencing (ES) has been increasingly used as a first-tier diagnostic test.  ES is an application of NGS which targets the coding regions of the genome, and it is a standard practice for clinical labs to only detect single nucleotide variants (SNVs) and small insertion deletions (indels) from this data. As the sequencing cost continues to decrease, genome sequencing (GS) is poised to become the single diagnostic test to detect SNVs, indels, and large-scale genomic variation including CNVs and structural variants (SVs) across the entire genome. Detecting all kinds of variation using a single diagnostic test is appealing as it can reduce the cost of serial diagnostic testing, time to achieve a definitive molecular diagnosis, and ultimately improving the patient care. Tools for detecting CNVs from GS data identify thousands of variants per sample, and the sensitivity for those tools are not optimal for clinical use.  While NGS has the potential to detect CNVs, CMA remains the current gold standard for detecting CNVs in clinical laboratories. Unlike SNVs/ indels, CNVs from NGS data do not have well established standards, guidelines, and resources that meet clinical regulatory standards.  Success has been elusive to date to detect both sequence changes and copy-number changes in one diagnostic test. Both ES and GS have their own challenges when it comes to detecting CNVs and it is imminent to address the issues for their utility in clinical settings.

This proposal aims to develop frameworks for better detection CNVs from ES and GS for use in a clinical setting.
In the aim 1 of this proposal, we benchmark the existing software tools for detecting CNVs from ES data and develop a highly sensitive, and specific workflow applicable for use in a clinical setting. We will develop novel annotations and an R-package to implement the reproducible workflow we have developed. In the aim 2, we propose to curate a resource of high-quality benchmark CNVs from GS data, and develop frameworks for better detection of clinically-relevant CNVs from GS data. In the aim 3, we propose to use the knowledge gained from the previous aims, and apply them to analyses of complex genomic rearrangements, such as chromoanasynthesis. Chromoanasynthesis is a class of extremely complex genomic rearrangements involving clustered copy-number changes associated structural rearrangements. Short-read sequencing data alone may not be sufficient in resolving such complex events and in this aim, we propose to use data from multiple genomic platforms to resolve the rearrangements, and gain insight into the mechanisms involved.

In summary, this proposal addresses the current day challenges faced by clinical labs in detecting CNVs from exome data (aim 1), the near future challenge of incorporating CNV detection from genome sequencing data for clinical use (aim 2), and the issue of analyzing complex rearrangements using multiple genomic platforms (aim 3).

Contact Information

Ken Barbee

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