The promise of predicting future health and even traits like intelligence through genetic analysis has moved rapidly from research labs into the hands of consumers. But are these technologies delivering on their hype, or are they amplifying existing societal inequalities under the guise of scientific progress? That’s the central question explored by Sam Trejo, assistant professor of sociology at Princeton University, and Daphne Martschenko, assistant professor of biomedical ethics at Stanford University, in their new book, “What We Inherit.” Their work isn’t a condemnation of genomic research, but a crucial intervention in a conversation often dominated by either breathless enthusiasm or outright rejection, highlighting a critical gap between what these technologies can tell us and what they’re being marketed to tell us. The urgency of this discussion stems from the fact that we’re in a relatively early “post-genomic” era – only within the last decade have databases become large enough to allow for rigorous, though still imperfect, genetic discovery.
The core of Trejo and Martschenko’s concern lies in what they term the “destiny myth” – the pervasive, and often commercially exploited, idea that our DNA dictates our traits and life outcomes in a simple, immutable way. This isn’t a new concept, but the increasing sophistication of genomic technologies, particularly polygenic scoring, lends it a dangerous veneer of scientific legitimacy. Polygenic scores attempt to predict the likelihood of a trait or disease based on an individual’s genome, and are now being offered to consumers through direct-to-consumer genetic tests and, more controversially, in the context of polygenic embryo selection during IVF. While the ability to summarize genetic predisposition has improved, the authors emphasize that correlation doesn’t equal causation. Identifying genetic regions associated with traits like educational attainment or depression doesn’t explain why those associations exist, or the complex interplay of environmental and social factors. This lack of mechanistic understanding is a fundamental limitation often glossed over in marketing materials.
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The book meticulously unpacks the historical context of these misconceptions, tracing how claims of genetic differences have been used to justify social harm, from laws prohibiting interracial marriage to forced sterilization programs. This history isn’t merely a cautionary tale; it’s a direct warning about the potential for these technologies to exacerbate existing biases. Martschenko points to a greater public acceptance of polygenic scores for medical conditions like heart disease or type 2 diabetes, contrasting it with the heightened controversy surrounding their use for traits like intelligence. This difference, she argues, stems from the long and troubling history of using genetic arguments to justify social stratification. The potential for “application genetic screening” – using polygenic scores to stratify access to medical interventions or even educational opportunities – is a particularly fraught area, raising questions about fairness and equity. While a cardiologist might use a polygenic score to assess heart disease risk and tailor treatment, the prospect of a private school using the same score for admissions is understandably unsettling.
However, the authors aren’t advocating for a complete halt to genomic research or its applications. Trejo highlights the potential benefits of polygenic scoring in clinical settings, arguing that it could allow for more targeted resource allocation and potentially reduce health disparities. If a polygenic score identifies an individual at high risk for heart disease, preventative interventions could be prioritized. But this potential benefit is contingent on careful implementation and a clear understanding of the limitations of the technology. A significant limitation is the lack of transparency from companies offering direct-to-consumer genetic tests. Martschenko and Trejo note that these companies often overstate the predictive power of their tests, and provide limited information about the datasets and analytical methods used to generate their reports. For most traits, these tests may be looking at only a tiny fraction of the relevant genetic variants, rendering their results inaccurate or even meaningless.
Furthermore, the accuracy of polygenic scores is heavily influenced by ancestry. Current scores are largely trained on data from individuals of European descent, meaning their predictive power declines significantly for individuals of other ancestries – Hispanic Americans, Asian Americans, and Black Americans. This disparity isn’t simply a technical challenge; it reflects a broader issue of representation in genomic research and the potential for these technologies to perpetuate existing health inequities. Martschenko is careful to distinguish between genetic ancestry, a complex and fluid concept, and the social construct of race, emphasizing that race is a system of power and discrimination, not a biological reality. The authors call for greater regulation of these technologies, and a more nuanced public conversation about their ethical and social implications.
The next crucial step isn’t simply more research, but a deliberate effort to address the systemic biases embedded within genomic data and to develop regulatory frameworks that prioritize equity and transparency. We should be asking not just can we predict these traits, but should we, and what safeguards are necessary to prevent these technologies from reinforcing existing social inequalities. Specifically, as polygenic scores become increasingly integrated into healthcare, will we see a widening gap in access to preventative care based on genetic predisposition, or will these tools be used to proactively address health disparities? The answer to that question will define whether this new era of genomic technology truly benefits all of humanity.
