[DNA. Photo Credit to Pixabay]

In their new book, What We Inherit, bioethicist Daphne Martschenko and sociologist Sam Trejo warn that the “wild west” of consumer genetics could worsen social inequalities unless the industry faces urgent regulation, an interview with Live Science reveals.

Their analysis spans a range of emerging technologies, from at-home DNA kits to polygenic embryo selection. 

Historically, gene analysis and editing relied on relatively limited tools.

Early genetic testing focused on identifying single-gene disorders such as cystic fibrosis or Huntington’s disease, where one mutation is directly responsible for causing the disease.

Technologies like SNP-chip genotyping allowed scientists to scan for specific genetic variants, but these methods only captured small portions of the genome and often missed rare mutations or complex interactions.

Later, more advanced techniques such as whole-genome sequencing enabled scientists to read nearly the entire genetic code.

Meanwhile, gene-editing technologies like CRISPR-Cas9 revolutionized biology by allowing precise changes to DNA sequences.

However, even with these breakthroughs, understanding how genes influence complex traits—such as intelligence or behavior—remained extremely difficult due to the involvement of thousands of interacting genetic variants.

Today, a new wave of genomic technologies is emerging, particularly in the consumer market.

According to a recent Live Science report, tools such as direct-to-consumer genetic tests and polygenic scoring systems are gaining increasing popularity.

These technologies aim to predict a person’s likelihood of developing diseases or even estimate traits such as height, educational attainment, or athletic ability.

One of the most controversial applications is polygenic embryo selection, where embryos created through IVF are analyzed and ranked based on predicted genetic traits prior to implantation.

Experts cited in the article acknowledge that many of these genetic predictions are based on scientific models that are still incomplete.

According to expert commentary in the article, some trait predictions produced by these tests currently show accuracy levels that they characterize as ‘close to zero.’

Despite these limitations, researchers also identify several potential benefits associated with emerging genomic technologies.

In medicine, polygenic risk scores could help identify individuals at higher risk for diseases such as heart disease or diabetes, enabling earlier interventions and more personalized treatments.

These tools could also improve resource allocation in healthcare by focusing preventative care to those who need it most.

Furthermore, advances in genetic analysis could accelerate research into complex diseases, leading to new therapies and improved public health outcomes.

However, these technologies also raise serious ethical concerns.

One major issue is the risk of misleading consumers.

Many companies market genetic tests as highly predictive, despite limited scientific evidence and lack of transparency about how results are generated.

There is also a growing concern about the revival of “genetic determinism”—the false belief that DNA alone determines a person’s future.

Experts Daphne Martshenko and Sam Trejo warn that this mindset could reinforce social inequalities, especially if genetic data is used in fields like education, employment, or insurance.

Additionally, polygenic embryo selection raises moral questions about ”designer babies” and the potential commodification of human life, as embryos may be selected based on subjective or socially valued traits.

Looking ahead, the implications of these technologies extend far beyond healthcare.

Genomic data could influence fields such as education, sports, and public policy.

For example, genetic screening might one day be used to tailor learning strategies or predict athletic potential — though such applications remain highly controversial and scientifically uncertain.

At the same time, continued improvements in data quality, diversity, and computational models may increase the accuracy and reliability of genetic predictions, making these tools more useful in clinical settings.

Researchers aim to improve the accuracy of genetic predictions by expanding diverse genomic datasets and refining their statistical models.

As Sam Trejo explains, such efforts are essential because current tools cannot reliably predict many complex traits, and improving their foundations is necessary before broader applications can be responsibly pursued.