SBIR-STTR Award

Automated genome-phenome analysis 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 the to the patient's clinical findings. This project tests the feasibility of reducing this interpretaton cost by pairing automated genome sequencing with an automated comparison of the patient's findings to the "phenotype" of findings of known diseases. This uses the SimulConsult diagnostic tool to provide "phenome analysis", and then integrate with genome analysis results to provide an automated genome-phenome analysis. Aim 1 is to compute unified severity scores for genome-phenome analysis so as to replace the current methods, which use iterative manual modifications of Boolean filtering of variants. The new approach is a one- pass method based on quantitative severity scores that are then processed by comparison to the phenome. This approach combines many assessments of gene variants provided by SeattleSeq, including conservation scores, read quality scores and variant frequency in the population, to automatically construct quantitative severity scores. To refine the quantitative severity score input weightings, 10 patients will be analyzed for whom SimulConsult has already been used to assist in diagnosis. This builds upon the ability added in 2012 to SimulConsult to import and process the "variant table" of WES results that includes the HGNC gene name, severity score, and zygosity. Also, the ability will be added to import more than one variant table and compute with the intersection (i.e., variants present in both) so familial genetic information can be incorporated. Aim 2 is to assess the effectiveness of automated genome-phenome analysis to identify known disease- causing genes in patients by retrospectively analyzing 20 patient cases in which WES was already performed on a family with two or more affected members and a known disease-causing mutation was found. The diagnostic accuracy will be assessed by (1) the rank of the correct diagnosis and (2) the probability assigned by the software. This will compare the genome alone, phenome alone, and genome + phenome approaches, as well as other situations involving incidence and onset ages. Aim 3 is to determine the need for having genomes from others in the family, by assessing differences between examining only the proband versus utilizing information on a second affected family member. The overall goal is to create and test the capability for making WES more practical to analyze and more accurate by integrating phenome information with the genome information, combining two independent assessments of the diagnosis. Today, interpretation costs exceed reimbursement rates, and interviews with relevant labs suggest need for lower costs. As the phenotype becomes known for a greater fraction of genetic abnormalities, the applicability of the automated genome-phenome analysis and the market for it will grow.

Public Health Relevance Statement:


Public Health Relevance:
Automated genome-phenome analysis. With the declining cost of whole 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 using diagnostic software to examine all known diagnoses (the "phenome") with the power of whole exome sequencing to examine the genome. In automating the genome-phenome analysis, this project brings the power of genome analysis to clinical practice - lowering costs while increasing accuracy.

Project Terms:
Affect; Age of Onset; base; Candidate Disease Gene; Clinical; clinical practice; Code; commercialization; Computer software; cost; Data; Databases; Diagnosis; Diagnostic; diagnostic accuracy; Disease; disease-causing mutation; Effectiveness; exome sequencing; experience; Family; Family member; Fees; Frequencies (time pattern); Genes; Genetic; Genetic Counseling; Genome; genome analysis; genome sequencing; Goals; improved; Incidence; Individual; Interview; Manuals; Marketing; member; Methods; Metric; Modification; Molecular Abnormality; Names; neurogenetics; novel strategies; Pathogenicity; Patients; Phase; phenome; Phenotype; Physicians; Population; Positioning Attribute; Probability; proband; Process; programs; prototype; public health relevance; Reading; Severities; Small Business Innovation Research Grant; Speed (motion); TACSTD1 gene; Testing; text searching; Time; tool; Variant; Variation (Genetics); Weight; Work

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.

Thesaurus Terms:
Accounting;Affect;Award;Clinical;Clinical Care;Clinical Diagnosis;Clinical Practice;Code;Computer Software;Copy Number Polymorphism;Cost;Cost Analysis;Data;Databases;Diagnosis;Diagnostic;Diagnostic Accuracy;Disease;Dna;Dna Analysis;Efficacy Testing;Exome;Exome Sequencing;Extended Family;Family;Family Structure;Fathers;Foundations;Funding;Gene Abnormality;Gene Discovery;Genes;Genome;Genome Analysis;Genome Sequencing;Genomics;Goals;Grant;Health;Human;Improved;Incidental Findings;Individual;Interview;Manuals;Marketing;Modeling;Molecular Abnormality;Mothers;National Library Of Medicine (U.S.);Nature;Notification;Nuclear Family;Online Mendelian Inheritance In Man;Output;Parents;Patients;Persons;Pharmacogenetics;Phase;Phenome;Phenotype;Physicians;Predisposition;Probability;Proband;Rare Diseases;Reading;Reporting;Siblings;Simulate;Small Business Innovation Research Grant;Specific Qualifier Value;Speed (Motion);Structure;Success;System;Systematized Nomenclature Of Medicine;Terminology;Testing;Time;Tool;Variant;Work;