With nearly eight billion people on our planet, it should come as no surprise that humanity is as complex as it is diverse, especially when it comes to our genetic information. Unfortunately, this diversity can lead to disparities in health care that can be difficult to account for.
Researchers in the budding field of pharmacogenomics have made extensive efforts to understand these disparities. While many cultural and lifestyle causes have been identified, genetic variation seems to offer the most effective explanation. So, what happens when pharmacogenomic studies do not accurately represent the scope of global genetic diversity?
Pharmacogenomics is the study of how genes affect an individual’s response to pharmaceutical drugs, combining the fields of pharmacology and genomics to develop medical practices that are safe, effective, and uniquely tailored to an individual—known as precision medicine. From finding the right pharmaceutical to determining the proper dose, our genetic information is the key to understanding how medicines function within us—a patient may break down a drug too slowly, too quickly, be non-responsive, or experience potentially fatal side-effects. Understanding the genetic background of a patient can help determine if a drug is safe and effective before the first dose is even administered.
In theory, the most effective way to understand the pharmacogenomics of an individual would be to sequence and compare the genomes of the entire population on Earth to look for genetic variants that are associated with various diseases. Unfortunately, sequencing every genome on the planet would be cost-prohibitive and logistically impossible. Scientists have overcome this hurdle by using techniques that can analyze many individuals’ genomes and compare them across different cultural, racial, or ancestral populations.
Genome-wide association studies (GWAS) are one of the most prevalent techniques used to conduct genomic medical research, as they scan the genome of many individuals for genetic variants that may be associated with certain diseases. A typical GWAS utilizes single-nucleotide polymorphism (SNP) arrays to identify possible variants. However, there has been growing pushback against using SNP arrays in favor of whole-genome sequencing (WGS) as it provides more complete genomic data.
For example, SNP arrays can only associate diseases with known variants, making them particularly susceptible to association bias. An SNP array looks only at previously identified SNP loci within a genome to find variation, meaning these arrays are not effective for discovering novel or rare variants. However, as WGS creates a complete set of genetic data, it has the potential to discover all genetic variants—especially rare variants—making WGS a more powerful and unbiased tool than SNP arrays for the identification of genetic risk factors. Even still, SNP arrays are widely used due to their reliability, low cost, and maturity of the data processing technology—the opposite of which is true for WGS. Fortunately, it is expected that as the cost of WGS reduces, the prevalence will increase.
Regardless of the techniques used, GWAS come with some limitations. GWAS are inherently geared toward establishing an association and not a causal link between a variant and disease. To establish a causal link, post-GWAS techniques such as statistical fine-mapping are often required. Fine-mapping attempts to take a trait-associated region or SNP from a GWAS and analyze it to identify candidate genetic variants that may have a causal link to disease. These candidates can then be further studied through laboratory-based functional experimentation to confirm any causal links.
As with any genomic studies, GWAS can only analyze a limited slice of humanity, so it is imperative that they adequately represent the global genetic diversity to obtain unbiased extrapolations of the genome’s effect on disease. Unfortunately, most research within genomic study has not reflected this diversity.
Pharmacogenomics lacks diversity
The bulk of research in pharmacogenomics displays an alarmingly disproportionate amount of people of European descent, despite the overwhelming majority of genetic variation stemming from people of non-European descent, particularly African.
This Euro-centric bias can have dire consequences for clinical health care, resulting in medical practice that is incomplete, ineffective, or even mistaken. If a study only focuses on the European population and disregards the majority of global genetic variance, the data may only be relevant for those of European descent. This misrepresentation of diversity harms individuals of under-represented ancestries by weakening or destroying any clinical utility that pharmacogenomic findings may offer through sub-optimal disease prediction, diagnoses, or treatment.
Due to this, there has been concern regarding the replicability of the genetic variant associations across different genetic populations found in many GWAS.
In a recent interview, Minoli Perera, an associate professor of pharmacology at Northwestern University, highlights the dangers that a lack of diversity in pharmacogenomic research poses to medical care, particularly in people of African descent. In her example, she highlights two gene variants that are heavily associated with the pharmaceutical warfarin. While she acknowledges their clinical utility in diagnoses, her research team discovered that these variants are far less sensitive when determining the dose-response of warfarin in people of African descent—a fact that was obscured due to a historically woeful misrepresentation of African Americans in these GWAS. Through the use of a more representative GWAS, her team identified additional gene variants that accounted for this variability in dose-response. Minoli’s research group was just one of numerous other groups to identify the genetic disparity between warfarin-associated variants that were only present in people of African descent.
Warfarin is not the only example of adverse drug reactions due to genetic variability associated with geographic ancestry. Evidence has shown that more inclusive research often identifies novel or rare genetic variants that are more clinically relevant for precision medicine. Examples such as these highlight the need for an accurate and complete understanding of a patient’s ancestry and ethnicity to better anticipate what genetic variants may be present when creating a treatment plan.
A hopeful future
Though fast-growing, the field of pharmacogenomics is relatively new. As such, there have only been a handful of pharmaceutical successes tied to genomic study, but as knowledge of genomic diversity grows, so too will pharmacogenomics and precision medicine. In short, inclusion is the name of the game for ensuring effective and clinically relevant medicine, even if this may be easier said than done.
Alice Popejoy, a researcher at Stanford University School of Medicine, noted that the solutions to the lack of diversity in genomic studies involve “ideological, analytic, cultural, demographic, and systemic elements,” and adds that “researchers must commit to broader inclusion of diverse study participants and mentorship of underrepresented trainees from the bottom-up, and institutions must facilitate the development of a diverse knowledge base and workforce from the top-down."
Researchers like Popejoy make it clear that continued efforts must be made that account for global genomic diversity if research is to obtain more complete data for more effective treatments in underrepresented populations. Logistic elements such as funding, training, and clinical recruitment must increase while systemic practices that perpetuate a Euro-centric bias in genomics research must be quelled.
To this end, researchers can look toward the efforts of research initiatives such as the Population Architecture using Genomics and Epidemiology, Phase II (PAGE II), which developed the Multi-Ethnic Genotyping Array (MEGA) to increase variant coverage across multiple ethnicities. Another option is the Human Heredity and Health in Africa (H3Africa), a consortium that developed a pan-African genotyping array for the express purpose of building a greater capacity for genetic research in Africa and driving novel research in genetic variance.
Unfortunately, genome-wide arrays such as these are used infrequently due to high costs and the relative novelty of the technology; instead relying on targeted genotyping arrays that use SNPs discovered using subjects of European ancestry.
If researchers are to combat the flagrant Euro-centric bias and increase the clinical utility of pharmacogenomics for people across the globe, genome-wide arrays like MEGA or H3Africa must be increasingly prevalent.
The birth of pharmacogenomics in the early 2000s promised a hopeful future, yet almost two decades later it is clear that this hopeful future has been marred by a fundamental bias. Fortunately, this bias has been recognized and efforts are being made to correct the misguided course. Researchers are beginning to understand that our genetic differences must be accounted for if pharmacogenomics is to guide the future of precision medicine.