Oakland, CA, United States
Oakland, CA, United States
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Grant
Agency: GTR | Branch: EPSRC | Program: | Phase: Research Grant | Award Amount: 2.06M | Year: 2016

Medicine is undergoing a simultaneous shift at the levels of the individual and the population: we have unprecedented tools for precision monitoring and intervention in individual health and we also have high-resolution behavioural and social data. Precision medicine seeks to deploy therapies that are sensitive to the particular genetic, lifestyle and environmental circumstances of each patient: understanding how best to use these numerous features about each patient is a profound mathematical challenge. We propose to build upon the mathematical, computational and biomedical strengths at Imperial to create a Centre for the Mathematics of Precision Healthcare revolving around the theme of multiscale networks for data-rich precision healthcare and public health. Our Centre proposes to use mathematics to unify individual-level precision medicine with public health by placing high-dimensional individual data and refined interventions in their social network context. Individual health cannot be separated from its behavioural and social context; for instance, highly targeted interventions against a cancer can be undermined by metabolic diseases caused by a dietary behaviour which co-varies with social network structure. Whether we want to tackle chronic disease or the diseases of later life, we must simultaneously consider the joint substrates of the individual together with their social network for transmission of behaviour and disease. We propose to tackle the associated mathematical challenges under the proposed Centre bringing to bear particular strengths of Imperials mathematical research in networks and dynamics, stochastic processes and analysis, control and optimisation, inference and data representation, to the formulation and analysis of mathematical questions at the interface of individual-level personalised medicine and public health, and specifically to the data-rich characterisation of disease progression and transmission, controlled intervention and healthcare provision, placing precision interventions in their wider context. The programme will be initiated and sustained on core research projects and will expand its portfolio of themes and researchers through open calls for co-funded projects and pump-priming initiatives. Our initial set of projects will engage healthcare and clinical resources at Imperial including: (i) patient journeys for disease states in cancer and their successive hospital admissions; multi-omics data and imaging characterisations of (ii) cardiomyopathies and (iii) dementia and co-morbidities; (iv) national population dynamics for epidemiological and epidemics simulation data from Public Health; social networks and (v) health beliefs and (vi) health policy debate. The initial core projects will build upon embedded computational capabilities and data expertise, and will thus concentrate on the development of mathematical methodologies including: sparse state-space methods for the characterisation of disease progression in high-dimensional data using transition graphs in discrete spaces; time-varying networks and control for epidemics data; geometrical similarity graphs to link imaging and omics data for disease progression; stochastic processes and community detection from NHS patient data wedding behavioural and social network data with personal health indicators; statistical learning for the analysis of stratified medicine. The mathematical techniques used to address these requirements will need to combine, among others, ingredients from dynamical and stochastic systems with graph-theoretical notions, sparse statistical learning, inference and optimisation. The Centre will be led by Mathematics but researchers in the Centre span mathematical, biomedical, clinical and computational expertise.


Patent
Omicia Inc. and University of Utah | Date: 2015-10-07

The present disclosure provides methods and systems for prioritizing phenotype-causing genomic variants. The methods include using variant prioritization analyses and in combination with biomedical ontologies using a sophisticated re-ranking methodology to re-rank these variants based on phenotype information. The methods can be useful in any genomics study and diagnostics; for example, rare and common disease gene discovery, tumor growth mutation detection, drug responder studies, metabolic studies, personalized medicine, agricultural analysis, and centennial analysis.


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase II | Award Amount: 1.58M | Year: 2012

DESCRIPTION (provided by applicant): Taking sequence to bedside is the stated and primary focus of the NHGRI for the next 5 years. Ironically, cheap genome sequencing is now producing analysis bottlenecks, especially as regards the clinical interpretationof genetic variants. The purpose of this SBIR FastTrack proposal is to obtain funding for the integration of two innovative analysis tools, developed by the PIs in parts by other NIH sponsored projects, to produce an integrated system called CGIS for sequenced-based clinical diagnostics using personal genome sequences. CGIS will greatly speed the clinical decision making process, and hence has enormous potential for commercial impact. Early adopters and collaborators include ARUP, one of the nation's top tier diagnostic, CLIA regulated, reference laboratories.


Disclosed is a method for determining whether an individual has an enhanced, diminished, or average probability of exhibiting one or more phenotypic attributes and related methods of selecting a set of genetic markers; for providing relevant genetic information to an individual; of evaluating the probability that progeny of two individuals of the opposite sex will exhibit one or more phenotypic attributes; and for determining the genomic ethnicity of an individual.


Disclosed is a method for determining whether an individual has an enhanced, diminished, or average probability of exhibiting one or more phenotypic attributes and related methods of selecting a set of genetic markers; for providing relevant genetic information to an individual; of evaluating the probability that progeny of two individuals of the opposite sex will exhibit one or more phenotypic attributes; and for determining the genomic ethnicity of an individual.


Disclosed is a method for determining whether an individual has an enhanced, diminished, or average probability of exhibiting one or more phenotypic attributes and related methods of selecting a set of genetic markers; for providing relevant genetic information to an individual; of evaluating the probability that progeny of two individuals of the opposite sex will exhibit one or more phenotypic attributes; and for determining the genomic ethnicity of an individual.


Disclosed is a method for determining whether an individual has an enhanced, diminished, or average probability of exhibiting one or more phenotypic attributes and related methods of selecting a set of genetic markers; for providing relevant genetic information to an individual; of evaluating the probability that progeny of two individuals of the opposite sex will exhibit one or more phenotypic attributes; and for determining the genomic ethnicity of an individual.


Patent
Omicia Inc. | Date: 2011-09-09

Disclosed are methods for detecting and/or prioritizing phenotype-causing genomic variants and related software tools. The methods include genomic feature based analysis and can combine variant frequency information with sequence characteristics such as amino acid substation. The methods disclosed are useful in any genomics study; for example, rare and common disease gene discovery, tumor growth mutation detection, personalized medicine, agricultural analysis, and centennial analysis.


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 174.36K | Year: 2012

DESCRIPTION (provided by applicant): Taking sequence to bedside is the stated and primary focus of the NHGRI for the next 5 years. Ironically, cheap genome sequencing is now producing analysis bottlenecks, especially as regards the clinical interpretationof genetic variants. The purpose of this SBIR FastTrack proposal is to obtain funding for the integration of two innovative analysis tools, developed by the PIs in parts by other NIH sponsored projects, to produce an integrated system called CGIS for sequenced-based clinical diagnostics using personal genome sequences. CGIS will greatly speed the clinical decision making process, and hence has enormous potential for commercial impact. Early adopters and collaborators include ARUP, one of the nation's top tier diagnostic, CLIA regulated, reference laboratories. PUBLIC HEALTH RELEVANCE: The purpose of this application is to build an integrated system that will reduce the time and costs for personal genome analysis by at least a factor of 100, while atthe same time improve analysis quality. Specifically we are requesting funding for the integration of two innovative analysis tools, both developed by the PIs in part with NIH support, in order to produce an integrated system for sequence variant prioritization for purposes of clinical diagnostics.


Grant
Agency: Department of Health and Human Services | Branch: | Program: SBIR | Phase: Phase I | Award Amount: 150.69K | Year: 2010

DESCRIPTION (provided by applicant): Genetic Hotspots: The goal of this Phase 1 SBIR project is to develop and apply new methods to identify genes that harbor genetic variants that affect disease risk. The long-term objective of this work is to identify genes and gene variants that can improve human health by providing greater information about individual susceptibility to disease risk. The hypothesis driving this work is that multiple variants within a single gene may contribute independently to the risk of a single disease. This genetic heterogeneity is known to exist for familial diseases, and is anticipated for complex disorders. When individuals in a study may have one of a number of risk-enhancing alleles, this genetic heterogeneity decreases the power to detect such variants. The proposed work aims to increase the power of genome wide association studies (GWAS) to detect such genetic hotspots by developing new types of gene-based tests of association. Specific Aim 1 is to develop Bayesian regularization strategies that can reliably identify the correct model for a gene: the number of independent disease-linked variants it contains (0 for most genes), and the genotyped marker most highly correlated with each effect. The power of these methods to detect real associations will be compared to traditional SNP-based tests. Specific Aim 2 is to generalize these methods for application to actual, published genotype data sets where personal information has been censored for privacy concerns (such as the SHARe dataset from the Framingham Heart Study), leaving only summary p-values or regression statistics available to the public domain for analysis. Specific Aim 3 is to test the proposed gene-based methods in real data sets. If the proposed work in Phase 1 is successful, the Phase 2 aims will be to increase the computational efficiency, to develop related methods for genetic studies using ultra-high- throughput sequencing (also called Nextgen-sequencing methods) to analyze genetic variation, and to integrate these gene-based tests with pathway-based tests that require gene-specific p- values as input. The proposed methods have the potential to increase the ability to link specific genetic variants with disease risk, a critical step in predicting individual disease risk especially for new complete genome sequence data. In Phase 2 the developed methods will be integrated into the Genome Interpretation System, a commercial workflow software suite developed at Omicia. As such, it will serve as licensable commercial technology for the company by helping other biotechnology companies to develop their genetic biomarkers for diagnostic and therapeutic developments (theranostics). In addition, any novel variants drawn from this Phase 1 study will be licensable intellectual property, useful both as the basis for future products in our internal pipeline, as well as potentially valuable additions to our patent portfolio. PUBLIC HEALTH RELEVANCE: Genetic Hotspots: Project Narrative A single gene can have multiple independent variants that all contribute to risk for cardiovascular disease, cancer, or other complex disorders. Current genetic analysis methods focus on individual markers, usually single-nucleotide polymorphisms (SNPs), and are not designed to detect gene-based patterns. This proposal will develop new methods that are able to detect the presence of multiple independent risk-enhancing alleles within a gene, increasing the ability to predict individual risk for disease susceptibility. In Aim3 we will be testing the methods with respect to performance in known datasets with the focus in the area of cardiovascular disease (CVD). The improved methods will be used as part of the Omicia/s Genome Interpretation System (GIS) product pipeline, and can be licensed to third parties. In addition, any novel genetic markers identified as part of the Aim3 study will themselves be valuable additions to the Omicia product and IP portfolio. Omicia's goal is to provide content and analysis tools for molecular diagnostic tests for cardiovascular conditions, with the promise of identifying patients at high risk to enable them to begin preventive care before symptoms appear. Given the prevalence of CVD in the developed world, these products are potentially a great boon to public health, as well as being significant commercial opportunities.

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