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Open Access Highly Accessed Research

Whole genome sequencing in support of wellness and health maintenance

Chirag J Patel12, Ambily Sivadas3, Rubina Tabassum3, Thanawadee Preeprem3, Jing Zhao3, Dalia Arafat3, Rong Chen124, Alexander A Morgan12, Gregory S Martin5, Kenneth L Brigham5, Atul J Butte12 and Greg Gibson35*

Author Affiliations

1 Division of Systems Medicine, Department of Pediatrics, Stanford University School of Medicine, 251 Campus Drive, Palo Alto, CA 94304, USA

2 Lucille Packard Children's Hospital, 725 Welch Rd, Palo Alto, CA 94304, USA

3 School of Biology, Georgia Institute of Technology, 310 Ferst Drive, Atlanta GA 30332, USA

4 Personalis, Inc., 1350 Willow Rd Suite 202, Menlo Park, CA 94025, USA

5 Center for Health Discovery and Well Being, and School of Medicine, Emory University Midtown Hospital, 550 Peachtree St, Atlanta GA 30308, USA

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Genome Medicine 2013, 5:58  doi:10.1186/gm462

Published: 27 June 2013

Abstract

Background

Whole genome sequencing is poised to revolutionize personalized medicine, providing the capacity to classify individuals into risk categories for a wide range of diseases. Here we begin to explore how whole genome sequencing (WGS) might be incorporated alongside traditional clinical evaluation as a part of preventive medicine. The present study illustrates novel approaches for integrating genotypic and clinical information for assessment of generalized health risks and to assist individuals in the promotion of wellness and maintenance of good health.

Methods

Whole genome sequences and longitudinal clinical profiles are described for eight middle-aged Caucasian participants (four men and four women) from the Center for Health Discovery and Well Being (CHDWB) at Emory University in Atlanta. We report multivariate genotypic risk assessments derived from common variants reported by genome-wide association studies (GWAS), as well as clinical measures in the domains of immune, metabolic, cardiovascular, musculoskeletal, respiratory, and mental health.

Results

Polygenic risk is assessed for each participant for over 100 diseases and reported relative to baseline population prevalence. Two approaches for combining clinical and genetic profiles for the purposes of health assessment are then presented. First we propose conditioning individual disease risk assessments on observed clinical status for type 2 diabetes, coronary artery disease, hypertriglyceridemia and hypertension, and obesity. An approximate 2:1 ratio of concordance between genetic prediction and observed sub-clinical disease is observed. Subsequently, we show how more holistic combination of genetic, clinical and family history data can be achieved by visualizing risk in eight sub-classes of disease. Having identified where their profiles are broadly concordant or discordant, an individual can focus on individual clinical results or genotypes as they develop personalized health action plans in consultation with a health partner or coach.

Conclusion

The CHDWB will facilitate longitudinal evaluation of wellness-focused medical care based on comprehensive self-knowledge of medical risks.

Keywords:
genetic prediction; risk assessment; preventive medicine; clinical profiling