Speaker 1: The Healthy Nevada Project is a population genomics study. What that means is that we try to aggregate information for research purposes on a large portion of our population, which for the purposes of Healthy Nevada is in northern Nevada, and study things like the relationship between genetics and health outcomes, genetics and traits like height, weight, and then integrate all of the factors that make someone healthy or sick, so lifestyle factors, behavior factors. So population genomics integrates genomics into the population health equation, and that's what we do here at Healthy Nevada Project.

Speaker 2: Looking at the demographics of the participants in the Healthy Nevada Project is an ongoing effort. We're continually looking at census tract block group data to look at demographics so we can understand down to a very fine level where the participants are coming from and where we might recruit.

Speaker 1: A big question in population genomics studies is to overcome the lack of diversity that is normally present in particularly medical studies.

Speaker 2: This is an ongoing work for us in that we're never satisfied that we're able to represent everybody as much as we would like to. We're also looking at all different educational levels, all different kinds of job levels. We don't stop and we're always using SAS to go in and extract and just take a very simple look at who are the people that are enrolling in the study.

Speaker 1: We are very analytically driven. We use SAS tools all the time to help understand the portrait that we're creating of the population and how it mimics the data that the hospital system, for example, has.

Speaker 2: So one of the things that this study does, and this is a really important part, is that people do the genetic testing and then we examine their genome for what are called CDC Tier 1 conditions. Another CDC Tier 1 condition that we check for are for BRCA1 and BRCA2 mutations for breast cancer and ovarian cancer. So we have a couple of cases where women have gotten back to us and told us, Thank you so much. You saved my life. Those are the things that we live for in this project is being able to proactively get in and look at people's genes and then be able to tell them this is something that you might want to keep an eye out for.

Speaker 1: Examples of those diseases of interest in population health studies, you want obviously more than just one or two or even 10 or 100 individuals with certain diseases, particularly if you're doing genetic analysis. So we really focus on highly prevalent conditions, cardiovascular disease, metabolic disease, cancer, etc.

Speaker 2: The SAS platform is used to extract the information that we need for the phenotype. I say phenotype, this in comparison to genotype. Phenotype is the physical manifestation of your genes. So your blood pressure, your lab results, your diseases. We extract the phenotype from the electronic health care records and then we marry that with the genetics, the genotype. The amount of data that we work with, with the SAS platform, again being the underpinning of all of this, is approximately 10 terabytes every four months. We've done it approximately 20 times now. So we're talking about 200 terabytes of data that we use the SAS platform to comb through and manipulate and extract. And I will say that in the nearly seven years we've been working on this project, the SAS platform has been rock solid.

Speaker 1: I think the entire team is proud of the work that they've delivered and reported on and its impact, if not instantaneously, but that it will impact individuals as we continue to understand what makes people sick and what makes them well.