Whole genome sequencing in support of wellness and health maintenance

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

Figure 1

Temporal change in clinical measures in the eight Center for Health Discovery and Well Being (CHDWB) participants: (A) body mass index (BMI), (b) serum triglycerides, (c) systolic blood pressure (SBP), and (d) bone mineral density (BMD). Each plot shows the temporal sequence from first to fourth visit over 3 years (left to right, dark to pale shading) for the four men (blue) and four women (purple) in the study. Data were only available for the first three visits for CHD-1. Adjusted R² and significance measures refer to ANOVA of the individual differences, providing an estimate of the proportion of variance between individuals for each measure. Background color coding shows very high (pink), high (green), intermediate (white), low (yellow), and very low (blue) risk score categories defined by 1 or 2 standard deviation units from the CHDWB mean. These categories are also used in Figure 3 (see text for specific results). In order to protect participant privacy, actual trait values are not shown, but ranges in the full cohort are 16 to 61 for BMI, 30 to 708 mg/dL for serum triglycerides, 78 to 187 for SBP, and -3 to +3 for BMD z-scores.

Figure 2

(A,C) Risk-o-gram plots for common diseases in standard format for two individuals, (A) CHD-5 and (B) CHD-8. (C,D) Risk-o-grams adjusted according to observed clinical risk for (C) CHD-5 and (D) CHD-8. Gender and age-specific disease prevalence for Caucasians are indicated by the black triangles (or hash marks in lower panels). Genotypic effects are predicted to increase (right point) or decrease (left point) overall risk by the indicated magnitude (orange or purple, respectively), resulting in the indicated rank-ordered overall risk. The number of single-nucleotide polymorphism (SNPs) used in the computation for each disease is indicated to the right. (C,D) The adjusted risk-o-grams according to observed clinical risk either increase (red triangle) or decrease (green triangle) the baseline without affecting the genotypic component, but result in adjustment of overall risk and rank order. Note that CHD-5 is a woman, and CHD-8 a man, so breast and prostate cancer are indicated for each as appropriate.

Figure 3

Joint representation of clinical and genetic risk assessment. (A) Metabolic risk showing joint genotypic likelihood ratios for obesity (OB), Type 2 diabetes (T2D), and hypertriglyceridemia (HTG) as bars, and related clinical measures as the indicated points, for each of the 8 individuals in the study. (B) Similar cardiovascular risk assessments for hypertension (HTN), myocardial infarction (MI), and coronary artery disease (CAD) as bars, along with related clinical measures and/or Framingham Risk Score. All measures were averaged over the first three visits to the Center for Health Discovery and Well Being (CHDWB). (C) Proposal for 'gridiron plot' representation of clinical risk (y-axis) against genotypic risk (z-axis) in eight disease domains described in the text, for individual CHD-5. The plot gives a glimpse of where the two types of assessment are concordant (for example, cardiovascular disease (CVD), cardiovascular)or discordant (that is, immunological (IMM)), and more refined analyses such as in (A) and (B) provide further clues as to the genetic basis of overall risk.