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SMART Genomics Advisor Tutorial

Hello and welcome to a demonstration of the SMART Genomics Advisor. We will

demonstrate, among other things, merging a patients genomic and non-genomic

clinical information into an integrated clinical informatics display. This video addresses

diabetes mellitus type 2.

While we can run the SMART Genomics Advisor App standalone, we will first show it

integrated into a version of the SMART Diabetes Monograph (see Figure S1, S2). The

standard SMART Diabetes Monograph app presents blood pressure, vitals,

cholesterol, other key labs and other information germane to diabetes type 2. Sample

data come from the SMART Reference system using the normal SMART application

programming interface. The SMART Genomics Advisor App integration brings in

patient genetic data from a secondary database here an instance of openSNP and

genomics analytics.

In our integrated SMART Diabetes Monograph, we present genomic information

directly on the main screen, leaving a deeper dive into SNP-level detail to a pop-up

screen

Looking at the top right, we see the genomics risk graph for selected diabetes related

conditions: diabetes type 1, diabetes type 2, and two serious co-morbidities

hypertension, and coronary heart disease. These are presented because a patients

genetic disposition for having them is important to clinical decision-making even if the

problems have not developed.

The risk is a numeric factor, that is, a multiplier, relative to the population norm. A risk

factor of one (one-X) represents the overall normal population risk. In the graph, for

example, a bar which reaches 1X for diabetes mellitus type 1, or DM1, means the

patients genetic risk for being or becoming DM1 is 1, exactly the population average.

A bar reaching 2X would be twice, a bar reaching 1/2X, would mean half the population

average.

If the risk factor is greater than 1 (blue is baseline) the section above the half way linewould be colored red; if less, it is colored green.

Looking lower, we see the bar heights reported numerically, colored-coded them green

for low and red for high risk.

The last piece of integrated genomic information is medication advisories. These

represent gene-drug interactions relative to the patients current medication list: how

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our patients genome affects a medications impact in terms of effectiveness, sensitivity,

morbidity, or mortality.

Genomic risk on the main screen is calculated from individual SNP-details in the

patients genome. To see this detail that, we click the Genomics Advisor link.

To the left (see Figure S2), we show a list of specific SNPs, patient genotype and

associated genetic information group by each of the four conditions. Individual SNP-level risk is used to calculate the summary genomic risk, color coded exactly as we

saw on the main screen. The numbers are color-coded here, too: green for low, orange

for medium, and red for high genomic risk.

To the right, we show these data in one radar graph per risk condition. Each graph has

as many axes as there are contributing SNPs that contribute to the aggregate risk per

risk condition. The relative risk for each SNP is its distance from its graphs origin. The

summary genomic risk is shown on the border of each graph. The Diabetes Type 2

graph region is larger because it is the focus of the SMART Diabetes Monograph app.

On the pop-up, we add something new, namely, a disease information summary,

based on risk for each of the SNPs and all conditions, including the comorbidity

conditions as they are relevant for recommending treatment and/or further testing. We

also repeat the medication advisories information exactly as seen on the main

monograph screen.

Because any disease that has known genetic factors can be captured to a good first

approximation in a single SMART Genomics Advisor screen, we have also created astandalone for the SMART Genomics Advisor App (see Figure S3). Launching this

app, we can see how, unlike the integrated version, the summary and detail

information are consolidated into a single screen. Certain sizing tweaks are also

applied to the radar graphs. The SMART API brings in relevant patient data since, as

before, they can help trim results to current problems, e.g., issuing gene drug

interaction advisories only for the current medication list. The genomic data come, as

before, from a complementary source.

Of course, we intend to enhance the SMART Genomics Advisor App in many waysincluding for dynamic diseases, which have evolving genomic risk factors. For

example, cancer will involve connecting the progression of the actual disease with

changing gene expression.

In summation, by engineering a sophisticated clinical app mash up of data, calculation,

and visualization, our SMART Genomics Advisor-augmented SMART Diabetes

Monograph App incorporates genomics for everyday clinical care of diabetes.

Importantly, we hope our demonstration hints at how this informatics approach can be

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generalized for many diseases. Our demonstration of our standalone SMART

Genomics Advisor App is but a first step toward more complex diseases and broader

omics integration.

We welcome comments and questions at genomics@smartplatforms.org

Figure S1.SMART Diabetes Monograph main screen and integrated Genomics Advisor summary

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Figure S2. SMART Diabetes Monograph integrated Genomics Advisor SNP-detail overlay

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Figure S3.StandaloneSMART Genomics Advisor App