Estimating disease risk using genetic data

Award Information
Agency: Department of Health and Human Services
Branch: National Institutes of Health
Contract: 1R43HG009089-01A1
Agency Tracking Number: R43HG009089
Amount: $241,905.00
Phase: Phase I
Program: SBIR
Solicitation Topic Code: 172
Solicitation Number: PA15-269
Timeline
Solicitation Year: 2015
Award Year: 2017
Award Start Date (Proposal Award Date): 2017-04-14
Award End Date (Contract End Date): 2019-03-31
Small Business Information
1390 SHOREBIRD WAY, Mountain View, CA, 94043-1318
DUNS: 780119710
HUBZone Owned: N
Woman Owned: Y
Socially and Economically Disadvantaged: N
Principal Investigator
 NICHOLAS FURLOTTE
 (901) 848-6668
 nick.furlotte@23andme.com
Business Contact
 KATHY HIBBS
Phone: (650) 933-9444
Email: khibbs@23andme.com
Research Institution
N/A
Abstract
Project Summary In the past years over genetic variants have been linked to complex human traits through genome wide association studies GWAS However the predictive power of many of these variants remains limited and it is still unclear how best to use the wealth of information generated by GWAS to impact personal health and clinical practice For nearly years andMe has been not only a driving force in direct to consumer genetic testing but also has established an innovative crowd sourced genetics research platform This platform has yielded a compelling data resource and many genetic discoveries In this proposal we will address the next phase of andMe human genetics research the development of highly scalable and accurate disease risk estimation Two of the key challenges in human genetics research are to determine how to use results of GWAS to paint an accurate picture of an individualandapos s disease risk and to determine how these estimates can provide information of personal and clinical utility These challenges are difficult due to many factors including the wide spectrum of disease classes the paucity of genetic and phenotypic data and significant methodological and computational challenges In this proposal we present a plan to utilize the genetic and phenotypic data stores at andMe to develop validated risk estimation algorithms In Phase I we will build a computational pipeline that will be used to develop predictive algorithms for estimating disease risk Aim and use this pipeline to evaluate predictive ability of different estimation approaches in a broad class of human complex traits Aim In Phase II we will validate these algorithms in external cohorts and build customer facing reports that we will test for user comprehension We believe that the development of accurate risk estimation capability will have a major impact on both consumer genetics and clinical genetics markets Project Narrative The promise of genetics based estimation of disease risk has yet to be realized In this project andMe will use its database of genetic and phenotypic information from over research participants who have contributed more than phenotypic data points on a wide spectrum of disease to build risk estimation algorithms This project will enable andMe to produce the first validated risk estimation algorithms that provide both personal and clinical utility

* Information listed above is at the time of submission. *

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