A recap of my presentation to Britcham Brazil, 7 October 2025

This morning I had the privilege of presenting to a distinguished audience at the British Chamber of Commerce and Industry in Brazil (Britcham), as part of a webinar titled “Health Data Equity in Latin America in the Age of AI and Genomics.” I was joined by Mr. Adam Patterson (Deputy Director and Honorary Consul in Curitiba), Mr. Luciano Moraes (President of the Technology and Innovation Committee), and Mr. Luiz Felipe Di Sessa (Vice-President of the same committee), who moderated and contextualised the discussion with clarity and insight.

My presentation focused on a simple but powerful idea: genomics and AI can transform healthcare but only if the data that powers them represents us all.

I began by presenting the current landscape: despite Latin America accounting for over 650 million people, it contributes less than 2% of global genomic data. This underrepresentation creates a knock-on effect. AI models trained on Eurocentric datasets inevitably perform worse for populations like Brazil’s, leading to misdiagnoses, overlooked risks, and missed opportunities for precision treatments.

To ground this problem, I shared data from multiple studies (Popejoy & Fullerton 2016; Fatumo et al. 2023; Skantharajah et al. 2023) showing how clinical trials, genomic repositories, and pharmacogenomic datasets disproportionately reflect individuals of European descent: mostly men, mostly from a handful of high-income countries .

Brazil’s population is one of the most genetically diverse in the world, with layered ancestries tracing Indigenous, African, and European roots. I presented evidence of Brazil’s complex Native American admixture, which occurred long before European contact, highlighting the scientific and clinical importance of this diversity.

Yet this diversity is largely invisible in current AI and genomic infrastructures. We risk creating diagnostic tools and therapeutic models that simply don’t work for large segments of Brazil’s population, especially its Indigenous and Afro-descendant communities.

I underlined how biobanks can be powerful enablers of equity, offering researchers large, structured datasets that include genetic, environmental, and social variables. But to serve as global resources, these biobanks must be inclusive from the start. If not, they replicate existing biases at scale .

AI learns from data, and when that data is skewed, so are the outputs. I walked the audience through examples of how AI systems can misclassify risk in underrepresented populations, or entirely miss pathogenic variants because they’re rare in European datasets but common in Latin American ones .

But I also highlighted the positive potential: with the right guardrails, AI can help correct bias. Tools like:

can be deployed to make models more fair and more robust.

I closed by emphasising that Brazil is uniquely positioned to lead in equity-aware AI and genomics. Its national health system (SUS), combined with its genomic richness, creates the conditions for building world-leading, inclusive infrastructure. If Brazil chooses to invest in its own biobanks, equity dashboards, and genomic AI models, it could emerge not just as a regional leader but as a global standard-bearer for responsible innovation.

My message was clear: inclusion drives accuracy. The more representative our data, the more effective our AI. Latin America’s diversity is not a problem to be solved, but a resource to be celebrated. It holds the key to scientific discoveries with global impact.

These weeks I will be putting here the lectures I am currently delivering for Biobanking for Data Science, a module that I lead at the University of Westminster for the MSc AI Digital Health course I also lead. I will be providing a summary and the recordings for these lectures.

The research presented here is available via this preprint, which is currently in revision.

Biobanks have become one of the most powerful infrastructures in modern biomedical research. By systematically collecting, storing, and cataloguing biological materials alongside rich clinical and lifestyle data, they enable discoveries that shape the way we understand disease, predict risk, and develop personalized treatments.

Over the past two decades, biobanking has evolved from static repositories into dynamic digital ecosystems. What began in the early 2000s with standardization efforts has now entered an era of high-throughput data generation, ethical regulation, and most recently artificial intelligence (AI) integration . Today, biobanks are not just about sample storage; they are about data-driven health intelligence.

Biobanks underpin critical advances in both research and healthcare:

From the UK Biobank’s half a million participants to the U.S. All of Us initiative and the Estonian Biobank, these large-scale resources are shaping how science approaches both common and rare diseases .

The 2020s have brought a decisive shift: the convergence of biobanking with AI and digital health. Machine learning tools now allow us to sift through immense, heterogeneous datasets—genomes, imaging, electronic health records—to generate insights that were previously inaccessible .

This is where Large Language Models (LLMs) enter the stage. Trained on massive text corpora, LLMs like GPT and Claude are proving their value in mining biobank-related literature, summarising biomedical findings, and even benchmarking patterns across thousands of studies .

In recent work, I explored how LLMs perform when applied to UK Biobank research outputs. The findings are illuminating:

Yet challenges remain—coverage does not guarantee accuracy, and LLMs still struggle with clinical reasoning, multimodal integration, and hypothesis generation .

The promise of AI-enabled biobanking is vast, but so are the challenges:

If addressed properly, the integration of biobanking with LLM-driven analytics could revolutionise our ability to link genetics, lifestyle, environment, and health outcomes.

The future of biobanking lies in its digital transformation. AI and LLMs are not replacing the scientific process but augmenting it, helping us navigate complexity, accelerate discovery, and bring equitable precision medicine closer to reality.

The question now is not whether we will use these tools, but how responsibly and effectively we can embed them into global health research infrastructures.

When I’m asked how I became a scientist, I usually smile, because the truth is, I’ve been aspiring to be one for over 30 years. My career didn’t follow the straight path I once imagined. I failed to get into medical school, so I turned to biomedical sciences instead. During my undergraduate studies, something unexpected happened, I caught the “research bug.” I became obsessed with understanding nature, driven by curiosity and the thrill of discovery.

At first, I was far from a stellar biologist. I contaminated more samples than I care to admit. But I found myself fascinated by data, and this was the dawn of the internet era, when the Human Genome Project and the first waves of digital transformation in health were reshaping science. That curiosity led me to a PhD in bioinformatics at the University of Manchester. Suddenly, I was at the forefront of marrying computer science with biology, a field still in its infancy, and I’ve been working at that intersection for 25 years.

My postdoctoral years at the Wellcome Sanger Institute in Cambridge immersed me in large-scale genomic data. I worked on DECIPHER, a database that became the backbone of genomic sequencing across UK and Irish paediatric centres. But I realised genomics wasn’t just for rare childhood conditions: it was for everyone.

In a move that would shape my career, my family and I sequenced our genomes and made them freely available online. At the time, we were likely the first family to do so. It sparked a community of citizen scientists and crowdsourced analyses, culminating in a widely cited 2015 paper.

But something caught my attention: when I compiled thousands of voluntarily shared genetic datasets from around the world, 95% were of European ancestry. This was 2017, before health data equity became a mainstream discussion, and it was a wake-up call. Who had access to genomic testing? Who was being represented in the data? And crucially, who was being left out?

The underrepresentation of global populations in genomics isn’t just about cost, though tests remain prohibitively expensive in much of the world. Many companies don’t even ship to the Global South. In countries like Peru, where I work frequently, lab reagents cost twice as much, and specialist equipment is scarce. Often, samples must be shipped abroad for analysis, stripping local scientists of agency and missing opportunities to train local talent and strengthen local economies.

For me, the solution is clear: whenever possible, genomic analyses should be performed locally. It’s about capacity-building, empowerment, and ensuring that scientific progress benefits the communities it studies.

Based in the UK, I saw a unique opportunity. The UK is a leader in genomic technology and implementation, but it has limited ties with Latin America, my cultural and linguistic home. That’s why I launched the first Spanish-speaking congress dedicated to genomic medicine, creating a bridge between cutting-edge UK science and the Spanish-speaking world.

The challenges are stark. Many genetic tests are built on European genetic markers, limiting their accuracy for non-European populations. In pharmacogenomics, this can be dangerous. For example:

These are not abstract disparities, they are real, life-and-death consequences of inequitable data.

Scientists, clinicians, and even many pharmaceutical companies don’t want these inequities. The real barrier is the business case: the financial return for tailoring interventions to small or underserved populations is often unclear. But there are models to draw from, like those used in rare disease research, where “n=1” cases have inspired novel, scalable approaches.

The truth is, diversity is no longer optional. Global migration means that every country now hosts a rich mix of populations. Medicine must adapt not just for fairness, but for accuracy.

I envision a future where it doesn’t matter where you’re born, what you look like, or your socioeconomic status, everyone can benefit from the best that science offers.

That means:

The COVID-19 vaccine rollout showed what’s possible when the world aligns around a single goal. If we can do that for a pandemic, we can do it for health equity.

As a technologist, I believe we spend too much time on tools and too little on their human and ethical context. Innovation is essential, but so is remembering that every data point has a face, a story, and a community behind it. True progress in genomics and in medicine comes when technology serves people, not the other way around.

Final Thought: Health equity in genomics isn’t a niche goal. It’s essential for the validity, safety, and universality of medical science. We’ve identified the problem. We have the tools. Now we need the will, the business models, and the partnerships to make equitable precision medicine a reality.

by Dr. Manuel Corpas

Despite breathtaking advances in genomics over the past two decades, we are failing to answer a fundamental question: who benefits from this progress?

The sobering truth is that the benefits of precision medicine are overwhelmingly skewed toward people of European ancestry. In 2016, 81% of all genome-wide association studies (GWAS) were conducted in Europeans. Shockingly, by 2021 that number had increased to 86% . Rather than diversifying, the field has become even more homogenous.

This imbalance is not just academic, it has real consequences for global health. The lack of diversity in genomic datasets means we’re missing key genetic variants that influence disease risk, drug response, and treatment efficacy in underrepresented populations. In pharmacogenomics, for example, African populations suffer disproportionately high rates of adverse drug reactions, in part due to the near absence of population-specific variants in prescribing algorithms .

Take CYP2D6, a gene that affects how the body metabolizes more than 25% of all prescribed drugs. In Ethiopia, the high prevalence of a specific duplication variant means that standard codeine use can be life-threatening, yet this information is largely invisible to current clinical guidelines . Similarly, warfarin dosing algorithms (routinely used for stroke prevention) perform well in Europeans, but fail to account for genetic variation in African and mixed ancestry populations .

Underrepresentation isn’t limited to GWAS or pharmacogenomics. Across clinical trials and biobanks, individuals from Latin America, Africa, Asia, and Indigenous communities remain largely excluded. This lack of inclusion leads to medical practices that work well for some, but leave many behind.

We must do better.

We must ensure that every person, regardless of their ancestry, has an equal chance to benefit from genomic insights. That means investing in globally inclusive research, building equitable reference databases, and rethinking the scientific standards that currently reward sample size over representation.

Health equity in the genomic era isn’t a luxury, it’s a necessity.

Only by confronting the diversity gap head-on can we unlock the full potential of precision medicine for all.

Genomics holds the promise to revolutionize healthcare—offering tailored diagnoses, treatments, and prevention strategies. But as I shared in my recent talk, this vision remains dangerously incomplete without one critical ingredient: diversity.

At the heart of my presentation, delivered on 26 June 2025, was a stark reminder from Popejoy and Fullerton’s landmark 2016 Nature paper: over 80% of genomic data used in research comes from individuals of European ancestry . Nearly a decade later, while awareness has improved, the numbers have barely shifted. This lack of representation is not just a scientific flaw—it’s a humanitarian one.

Genomic diversity matters because it directly influences the accuracy and efficacy of medical interventions. When reference panels are built on narrow population groups, they fail to capture the genetic variants that are common—or even critical—in other ancestries . This can lead to misdiagnoses, ineffective treatment plans, and serious adverse drug reactions.

Take, for instance, CYP2D6, a gene involved in the metabolism of over a quarter of all registered drugs. A specific duplication variant of this gene affects nearly 30% of Ethiopians, leading to toxic outcomes with codeine—a commonly prescribed painkiller. Similarly, certain alleles impact how women with African ancestry respond to tamoxifen, a key breast cancer drug . Without this knowledge, clinicians are left in the dark—and patients bear the consequences.

The issue is compounded by sex bias in pharmacogenomics. Women, especially older women, experience adverse drug reactions almost twice as often as men. Yet gender and sex differences are often sidelined in drug trials and genomic datasets . With the rise of polypharmacy in aging populations, this oversight poses a growing threat to public health.

My presentation also touched on the right to science. If genomic medicine is to fulfill its promise, then all populations must have equal access—not only to its benefits but also to participation in its construction.

The takeaway is clear: we must challenge the systemic biases in genomic research. We need broader, deeper, and more inclusive data. And we need to do it now—not later. Because when only a fraction of humanity is represented in the data, only a fraction can truly benefit from the science.

In this episode of Personal Genomics Zone, I sit down with Professor Yves Moreau from KU Leuven for a deep and thought-provoking conversation on the intersections of artificial intelligence, genomics, and society.

Our connection goes back to our shared contributions to the DECIPHER database — a foundational project in rare disease research that has shaped how genetic data is shared across borders. With over 2,000 citations, it’s a landmark we’re both proud of.

But this chat goes far beyond our technical roots. Yves shares his journey from engineering and neural networks to bioinformatics and drug discovery, and his growing preoccupation with the social consequences of technology. From his work with Carta Academica — a Belgian academic network engaged in public debate — to his activism against the misuse of forensic genetics, Yves offers a compelling vision of what it means to be a scientist with conscience.

Whether you’re a researcher, policymaker, or just curious about the ethical frontiers of genomic science, this conversation is not to be missed.

Subscribe for more interviews at the cutting edge of genomics and society.

#Genomics #AI #Bioethics #PersonalGenomics #YvesMoreau #DECIPHER #ForensicGenetics #TechnologyAndSociety #Bioinformatics #PrecisionMedicine

Imagine two people walk into a hospital. Same symptoms. Same diagnosis. One receives a treatment that works like magic. The other? Nothing.

Why? Because what’s written in our DNA is as unique as our fingerprint. And the hard truth is: the science that informs healthcare—its research, data, and trials—has been built on just a handful of fingerprints, while billions of others remain invisible.

As genomics rapidly becomes the foundation of medicine—from diagnosing diseases to developing life-saving drugs—we must confront an urgent question: who is being left out?

When I first analyzed open-access genetic datasets back in 2017, I stumbled upon a troubling fact: 95% of those datasets came from individuals of European ancestry. That was a wake-up call.

It wasn’t just a statistical imbalance—it was a systemic flaw. Populations across Africa, Latin America, Asia, and Oceania are missing from the very data that’s driving our genomic breakthroughs. That absence leads to real-world consequences: treatments that don’t work, adverse drug reactions, and diagnostic tools that fail.

One example: warfarin, the world’s most prescribed anticoagulant. In people of African ancestry, one in four experience adverse drug reactions. Why? Because dosing algorithms are trained predominantly on European data. That’s not just a data problem. It’s a healthcare injustice.

My journey began at the Sanger Institute in Cambridge, where I worked on rare developmental diseases. Back then, we lacked the maps and tools to diagnose many of these conditions. But sequencing the human genome changed that. Suddenly, we could read the blueprint of life.

That experience convinced me of genomics’ potential—not just for the few, but for everyone. Every human carries mutations. Every genome holds clues about disease susceptibility, drug response, and more. Genomics should be a universal right. But that can only happen if our science includes everyone.

We talk a lot about “precision medicine.” But how precise can it really be if entire populations are absent from the data? Precision for some is not precision at all.

The lack of diversity isn’t always intentional. It’s often the result of where research happens: in the Global North, among predominantly white populations, with infrastructures that don’t reach remote or under-resourced regions. But the outcome is the same—a system skewed toward a narrow slice of humanity.

One of the most humbling parts of my work has been engaging directly with indigenous communities in Latin America. These encounters changed me. They reminded me that science isn’t just about data—it’s about people.

It’s one thing to run algorithms. It’s another to sit in a village deep in the Amazon and hear a mother say, “Please help us. We want access to medicine. We want our children to be healthy.”

Science must begin with listening. If we want to build trust, we have to show up—not as experts, but as humans. Ethical genomic research must be culturally sensitive, community-driven, and grounded in genuine relationships.

We also need to address how ancestry and sex biases creep into research. Women are underrepresented in trials. Pregnant women are often excluded altogether. Africa, the most genetically diverse continent, is still treated monolithically in many studies.

Even our definitions of “diversity” vary wildly across datasets—from FDA classifications to pharmacogenomics to clinical genomics. There’s no consensus on what counts as representative, and that makes equity nearly impossible to measure.

We need standards. We need metrics. And we need leadership willing to prioritize inclusion—not just as a checkbox, but as a scientific imperative.

I often say that genomics is waiting for its “ChatGPT moment.” That moment when a powerful, complex technology becomes accessible and impactful for everyone—not just specialists.

We’re not there yet. Biology is complex. The genome is only one layer. Add the epigenome, transcriptome, proteome, and microbiome, and the picture becomes exponentially more intricate. But I believe AI can help us untangle that complexity. If—and only if—we include the full diversity of human biology in the training data.

If we do nothing, the future of medicine will be one of stark inequality. Those with wealth and access will benefit from personalized treatments and longer lives. Those without will be left behind.

But that’s not inevitable. There is hope.

The UK, for example, through initiatives like Genomics England and Our Future Health, has made real strides in democratizing access to genomic data—even if its datasets still reflect a predominantly white population. The openness to global researchers is a step forward.

More than technology, what gives me hope is the spirit of collaboration, integrity, and shared purpose I see in communities around the world. The real future of healthcare will be shaped not by algorithms alone, but by people—scientists, clinicians, advocates, and listeners—committed to equity.

To every young researcher, policymaker, or citizen reading this: don’t chase prestige. Chase truth. Chase justice. Your work matters, not just for citations or promotions, but for the lives it can improve.

Science that leaves people behind isn’t science at all. It’s time to build something better.

Because the future of healthcare belongs to all of us. And it starts with listening.

Welcome back to the Personal Genomics Zone podcast, the show where we dive deep into the stories shaping the future of genomics and beyond. I’m your host, Manuel Corpas, and today we have a very special guest: Sean O’Donoghue, Organizer for VizBi—the VISUALIZING BIOLOGICAL DATA conference—coming to Cambridge University’s Magdalene College next week. Sean is at the forefront of bringing together scientists, designers, and technologists to reimagine how we see and interpret biological data.

In this episode, we’ll explore what first sparked Sean’s passion for both science and art, uncover what motivates him to organize an international conference like VizBi, and learn about his vision for the future of biological visualization. We’ll also touch on why “vision” itself—both literal and metaphorical—matters so deeply in biology, influencing everything from groundbreaking research to the ways we collaborate across fields. Strap in for an inspiring conversation about the power of clear, compelling visuals in fueling scientific discovery—and the drive to help others see biology in a completely new light. Let’s get started!

By Dr. Manuel Corpas

Good afternoon, everyone! Welcome to this exploration of genomics, big data, and how they’re reshaping the future of healthcare. As I record this on the last day of January 2025, it strikes me that today—this moment—will never come again. It’s a reminder that science and technology, like time, keep moving forward. My hope is that revisiting some fundamentals of genomics will help reinforce our collective understanding and passion for this ever-evolving field.

I’m Dr. Manuel Corpas, a senior lecturer in genomics with over 20 years of experience. I hold a PhD from the University of Manchester and spent four years as a postdoctoral researcher at the University of Cambridge. For the past three and a half years, I’ve been part of the University of Westminster, where I delve into big data and genomics—both as a lecturer and through my own company, which assists clinics in leveraging genetic data to understand diseases and predispositions. If you’re keen on the intersection of data science and biology, feel free to get in touch; I’d be delighted to discuss projects or career options with you.

Genomics is transforming medicine by bringing us closer to precision medicine, sometimes referred to as personalized medicine. Rather than relying on a one-size-fits-all approach, clinicians can increasingly tailor treatments to an individual’s genetic makeup. The ultimate aim is to prescribe the right drug, in the right dose, at the right time—something that becomes more feasible as we learn how genetic variants affect metabolism, drug response, and disease risk.

However, there’s a critical challenge: much of our understanding of genomics—and many commonly used treatments—derive from studies primarily focused on Northern European ancestries. This leaves other populations underrepresented in genomic research. It’s essential to include diverse genetic backgrounds if we want equitable healthcare for everyone, regardless of geographic location or socioeconomic status.

My own work in the field began in 2008, analyzing next-generation sequencing data to diagnose rare genomic disorders. For instance, I’ve been part of a project that sequenced 13,000 children with rare disorders (and their parents), creating an informatics infrastructure that remains in use to this day. Although rare disorders (often called Mendelian or monogenic diseases) can be linked to a single gene, their cumulative impact is significant—and identifying the exact genetic cause offers immense value to families, clinicians, and researchers.

Yet the real population-level burden comes from complex (polygenic) disorders, such as Type 2 diabetes, coronary artery disease, and many cancers. These conditions involve numerous genes and are also shaped by environmental factors like diet, exercise, and exposure to toxins. Genome-wide association studies (GWAS) help us identify which genetic variants (or SNPs, single nucleotide polymorphisms) correlate with higher risk. The result is a deeper understanding of how certain people develop certain diseases—and why individuals in the same environment can have dramatically different outcomes.

If you’ve ever used Google Maps, you’ll appreciate the power of having a reliable reference map. The Human Genome Project gave us an initial “map” of our DNA, but that was just the beginning. Sequencing a single human genome today generates about 200 gigabytes of data—exponentially more than we can manage in a simple spreadsheet. With large-scale projects now sequencing tens or hundreds of thousands of people, data storage and interpretation have become gargantuan tasks, often measured in petabytes.

The good news is that the cost of genome sequencing has dropped even faster than computer processor power has increased under Moore’s Law. What cost billions of dollars 20 years ago can now be done for under $1,000, and it continues to get cheaper. This is ushering in a whole new era for precision medicine, but it also means we need more data scientists, bioinformaticians, and computational biologists to interpret the data flood.

One of my biggest concerns—and greatest passions—is making sure the benefits of genomic medicine reach everybody. Cutting-edge tools and treatments need the right infrastructure, supply chains, and trained professionals. In many parts of the world, even basic healthcare resources are limited. How do we bring complex genomic diagnostics and personalized treatments to remote communities or regions with fewer technological resources?

Despite these hurdles, progress continues. In the UK, we’ve seen initiatives like the 100,000 Genomes Project, followed by mass-scale efforts involving half a million genomes—some of the largest in the world. Soon, it’s likely that every newborn will have their genome sequenced. We’ll enter an era where everyone’s DNA is on record from day one, offering unprecedented opportunities for preventive healthcare and personalized treatment. The challenge then becomes interpreting all that data: distinguishing benign variants from dangerous ones, predicting how genetic backgrounds affect drug metabolism, and using AI-driven models to provide actionable insights.

For those of you pursuing careers in life sciences, I can’t emphasize enough the importance of data literacy. Being comfortable with big data tools and computational methods gives you a tremendous advantage in this field. The future belongs to those who can navigate datasets too large for any single human to analyze by eye. Whether you’re interested in rare diseases, cancer genomics, or population-level studies, computational skills will open more doors than ever.

Genomics isn’t a magic crystal ball predicting who will inevitably develop a particular disease. But it offers powerful insights into probabilities, risk stratification, and targeted therapies. It helps us see how each patient’s DNA interacts with their environment. Most importantly, it pushes medicine to become more proactive and patient-centered.

As the last day of January 2025 fades into history, I’m reminded that time stands still for no one—just as scientific progress keeps accelerating. By embracing genomics, data science, and a commitment to equitable healthcare, we can chart a path toward a future where “precision medicine” truly means medicine for everyone.

Thank you for reading, and I hope this glimpse into the world of genomics inspires you to explore new questions and possibilities. If you’re passionate about big data, genetics, or the future of personalized healthcare, let’s keep the conversation going.

Dr. Manuel Corpas is a senior lecturer in genomics with over two decades of experience. He earned a PhD at the University of Manchester and served as a postdoctoral researcher at the University of Cambridge. Currently at the University of Westminster, he also leads a company dedicated to helping clinics harness genetic data for diagnosing rare diseases and assessing predispositions to more common health conditions. Dr. Corpas is committed to expanding equitable access to genomic medicine worldwide.

Genomic research has made tremendous strides over the past decades, paving the way for breakthroughs in precision medicine. However, these advances have not benefited everyone equally. A glaring issue in this field is the systemic underrepresentation of certain populations, particularly indigenous groups from Latin America. In a recent talk at Queen Mary University of London’s PHURI, I had the opportunity to highlight the unique challenges and opportunities in addressing this disparity.

The global genomic research landscape has been dominated by studies focusing on populations of European ancestry. Leading repositories like PharmGKB—dedicated to gene-drug association data—exemplify this imbalance. While European and, to a lesser extent, Asian populations are reasonably represented, native populations from the Americas account for a minuscule fraction of the data. This disparity leaves indigenous communities underserved and their unique genetic profiles largely unexplored.

To understand why this underrepresentation matters, it’s essential to delve into the historical and geographical context of Latin America.

Our research seeks to bridge the gap in genomic representation by focusing on Peru’s indigenous communities. Through the analysis of 150 genomes from seven populations, we uncovered exciting findings:

One of the biggest challenges in advancing genomic research in Latin America is the lack of a coordinated framework. Unlike Africa, which has the H3Africa initiative—a Pan-African genomics consortium that secures international funding and fosters local research—Latin America lacks a similar structure. This limits the region’s ability to scale research efforts and fully capture its rich genetic diversity.

What could such an initiative achieve?

Collaboration with pharmaceutical companies can play a critical role in achieving these goals. In some regions, such partnerships are structured to ensure mutual benefit: companies gain access to valuable genetic data while investing in local research infrastructure. A similar model could be applied in Latin America, ensuring that profits from drug discoveries benefit the communities contributing their data.

The underrepresentation of indigenous populations in genomic research isn’t just an academic issue—it’s a question of equity and global health. Precision medicine aims to tailor treatments to the genetic makeup of individuals, but this cannot be achieved if vast swathes of humanity are left out of the equation. Indigenous communities in Latin America face unique health challenges, and understanding their genetic profiles could lead to breakthroughs in addressing these needs.

The genomic research community is at a pivotal moment. Awareness of the need for diversity in research has grown, creating a window of opportunity to correct historical imbalances. Latin America, with its rich and underexplored genetic heritage, is uniquely positioned to contribute to this effort.

By building a federated, inclusive approach to genomics, we can unlock the potential of indigenous populations—not only for their benefit but for the advancement of global health. The journey ahead is challenging, but the rewards promise to transform our understanding of humanity’s shared genetic legacy.