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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
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
Mountain View, CA 94043-1318
United States
DUNS: 780119710
HUBZone Owned: No
Woman Owned: Yes
Socially and Economically Disadvantaged: No
Principal Investigator
 (901) 848-6668
Business Contact
Phone: (650) 933-9444
Research Institution

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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