The declining cost of whole exome sequencing (WES) is nearing the point at which the spread of WES into clinical practice will be limited largely by the cost of interpreting the results and comparing them to the patient's clinical findings. This project builds on our demonstrated capability to reduce this interpretation cost by pairing our diagnostic software, in wide use for clinical diagnosis, with automated genomic sequencing. The clinical diagnostic software compares patients to "phenotypes" of findings in known diseases, so the combination with genome analysis, developed under an SBIR Phase 1 grant, is referred to as automated genome-phenome analysis. This award-winning capability is valued because of its ability to analyze genomes in seconds, and its hypothesis-independent nature. Here we propose to advance the genome-phenome analysis as follows:
Aim 1 is to generalize the analysis beyond the trio (affected individual plus parents) in order to support a wider variety of family structures. These include nuclear families with more than one sibling, families that extend beyond the nuclear family and unrelated affected individuals. These capabilities will be useful in both clinical diagnosis and discovery of new connections between genes and diseases. These capabilities will be added in a way that preserves the speed and hypothesis-independent nature of the analysis.
Aim 2 is to detect copy number variation (CNV) using exomes and analyze that genomic data in the clinical context. Using WES for CNV analysis will lower the cost of diagnosis by reducing the need to order a microarray before exome analysis, and will facilitate the automated analysis of DNA deletions and duplications in clinical care.
Aim 3 is to improve the core analysis by taking into account which genes were well-read but normal, information that is important in excluding other diagnoses. The analysis will also deal with situations of ambiguity over whether an affected individual is homozygous or heterozygous, and do so in a way that only adds possibilities for diagnosis but doesn't reduce possibilities considered by the original analysis.
Aim 4 is to improve output by reporting on incidental findings and exporting information in ways that facilitate interactions with referring physicians and reporting of genome variants to public databases. The overall goal is to improve accuracy and reduce the time and cost of analysis, making WES more robust as a clinical tool, as well as a tool for gene discovery. Today, interpretation costs exceed reimbursement rates, and interviews with labs suggest that the major reason for high costs is the manual nature of the clinical correlation, which we automate. As the phenotype becomes known for a greater fraction of genetic abnormalities, the applicability of our automated genome-phenome analysis and the market for it will grow.

Public Health Relevance

Advancing automated genome-phenome analysis With the declining cost of whole exome and genome sequencing, the main cost of such testing is becoming the cost of interpreting the huge amount of data that is generated. This project combines the power of diagnostic software that examines all known diagnoses (the phenome) with the power of whole exome sequencing to examine the genome, and uses advanced capabilities such as compatibility with all family structures and the ability to take into account DNA deletions and duplications. In automating the genome-phenome analysis, this project brings the power of genome analysis to clinical practice - lowering costs while increasing accuracy.

National Institute of Health (NIH)
National Human Genome Research Institute (NHGRI)
Small Business Technology Transfer (STTR) Grants - Phase II (R42)
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Special Emphasis Panel (ZRG1-IMST-J (15))
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Ramos, Erin
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Simulconsult, Inc.
Chestnut Hill
United States
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