What if a DNA test could help predict what diseases you might be at risk for — years before they appear? This is one of the dreams of genome-enabled personalized medicine, with DNA tests and screenings opening an unprecedented window into a person’s unique genetic risk of developing certain illnesses.
New research from Georgia Tech shows that the contexts in which genes act — lifestyle and environmental factors — are also important in understanding how genes express themselves, and how genetic risk may lead to disease.
In a new study published in Nature Genetics, Pervasive Interactions Between Exposures and Polygenic Risk Can Inform More Effective Clinical and Behavioral Interventions, researchers analyzed the genetic data, health outcomes, and lifestyle factors of over 400,000 individuals.
“Genetic risk is often seen as something that can’t be controlled, but that approach assumes a determinism that does not reflect reality,” says the study’s corresponding author Greg Gibson, Regents’ Professor, Tom and Marie Patton Chair in the School of Biological Sciences, and director of the Center for Integrative Genomics at Georgia Tech. “Our research shows that people with low genetic risk who live in disadvantaged environments face disease risks similar to those with high genetic risk living in more resourced circumstances.”
The findings suggest that people at highest genetic risk may also be the individuals who benefit the most from targeted interventions — paving the way for more informed genetic counseling and social policy.
Science at Scale
Gibson began his career studying the genetics of Drosophila fruit flies before turning his attention to human genetics when he joined Georgia Tech in the late 2000s, soon after the Human Genome Project successfully delivered the first complete sequence of the human genome. “It transformed the way we do this type of research,” he recalls. “Suddenly it was possible to analyze genetics across large groups of people.”
Because the effects of genetics can be small in an individual, large datasets are key to understanding genetics and disease at scale. “If we need to study 200,000 people to see a genetic effect, it takes even more than that to see how those genes interact with the environment,” Gibson says.
The team, which included first author Sini Nagpal, formerly a postdoctoral fellow at the Georgia Tech Center for Integrative Genomics, leveraged the UK Biobank, a program that collects genetic information from half a million volunteers in the United Kingdom to understand who falls ill and why. Started in 2006, the UK Biobank is home to the world’s largest single set of genetic sequencing data — along with the world’s largest whole-body imaging and long-term health tracking. This type of data is critical, Gibson says, to the advancement of genetic research, and is in large part what has made this new research possible.
“Our research shows that we do have a way to capture situations where a person’s lifestyle and environment interacts with genetics in interesting ways,” he explains. “These genotype-environment interactions matter. They are important, but they can't be captured one variant at a time. An integrated approach — utilizing information in the thousands of genes influencing each disease — is much more powerful.”
Improving Health Outcomes — For Everyone
The team is now interested in expanding their research outside of the British database. Nagpal is now an assistant professor at the Koita Centre for Digital Health at IIT Bombay, where she is investigating translational genetics in India, including research into diabetes, cardiovascular disease and asthma. Gibson is interested in translating the research to more broadly apply to the United States, with a focus on rural and disadvantaged communities.
“Ultimately, if we can quantify which people are at highest risk, we believe we can suggest therapeutic and environmental interventions that could help mitigate their risk, reducing the prevalence of disease in the first place,” he says.
“We want to dig deeply in ways that reveal disease pathways and begin to unearth the mechanisms behind disease,” Gibson says. “It's not just our ancestry that determines health; it's also culture and environment. We want to incorporate those factors into treatment of individuals and at scale.”
These themes are also present in Gibson’s newly published book, Natural Virtue: Genetics, Anti-Eugenics, and Moral Philosophy, which explores how nature and nurture impact social issues like educational and gender disparities.
Gibson adds that the message is hopeful. “The genetic differences between different socioeconomic groups aren’t meaningful,” he explains. “Our genetics do not predetermine our health outcomes. If we can create more equitable environments, we can improve outcomes across the board.”
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Dr. Williamson will discuss her research on birds that make extreme seasonal shifts in elevation during migration and how these movements affect ecology, evolution, and physiology. She will highlight her lab’s work on giant hummingbirds (Patagona spp.) in the Andes, where her team has combined movement tracking, genomics, and field physiological experiments to uncover extreme daily movements, a spectacular long-distance migratory journey involving an extreme elevational ascent, and cryptic speciation between the world’s largest hummingbirds.
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Experimental evolution in vivo is a powerful approach to identify the selective pressures shaping bacterial adaptation during infection, yet how host immunity modulates these evolutionary trajectories remains a fundamental gap in infection biology. Applying whole-population genomic sequencing to replicate bacterial populations serially passaged through murine or porcine infection models, we demonstrate that niche-specific selective pressures produce convergent genetic adaptations, including parallel mutations modulating pneumococcal surface charge during colonization and prophage-mediated disruption of global regulators during chronic Pseudomonas aeruginosa wound infection. Host immune status critically shapes these trajectories, as neutropenic hosts broaden mutational pathways to fluoroquinolone resistance in Acinetobacter baumannii while functional immunity constrains the outgrowth of resistant variants. Most recently, we show that Streptococcus pneumoniae subjected to combined antibiotic and immune pressure evolves convergent mutations altering the RNA degradosome that confer broad-spectrum antibiotic tolerance through a bet-hedging transcriptional strategy influenced by host immune state. Together, these studies establish that in vivo experimental evolution provides a mechanistic roadmap for predicting genetic pathways to treatment failure and informing strategies to constrain the evolution of antimicrobial resistance.
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I will present work spanning three areas. First, a simulation-based framework establishing best practices for local ancestry inference across reference panel compositions, admixture demographics, and genotype discovery approaches. Second, our recently published method Tractor-Mix, a local ancestry-informed mixed model that extends our earlier Tractor framework to enable well-calibrated GWAS in admixed cohorts with relatedness. We demonstrate Tractor-Mix’s performance across multiple large datasets including the UK Biobank, Yale-Penn cohort, and Mexico City Prospective Study, finding novel loci missed by traditional methods and better determining the ancestry driving unique signals. Third, I will describe our application of local ancestry inference across more than 140,000 genomes in gnomAD, which substantially refines ancestry-specific allele frequency estimates with direct implications for clinical variant interpretation. Together, this work illustrates how ancestry-aware statistical methods can improve genetic discovery, risk prediction, and variant interpretation across the full range of human genetic diversity.
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