Is Finding Out Your Baby’s Genetics A New Responsibility of Parenting?
Hours after a baby is born, its heel is pricked with a lancet. Drops of the infant's blood are collected on a porous card, which is then mailed to a state laboratory. The dried blood spots are screened for around thirty conditions, including phenylketonuria (PKU), the metabolic disorder that kick-started this kind of newborn screening over 60 years ago. In the U.S., parents are not asked for permission to screen their child. Newborn screening programs are public health programs, and the assumption is that no good parent would refuse a screening test that could identify a serious yet treatable condition in their baby.
Learning as much as you can about your child's health might seem like a natural obligation of parenting. But it's an assumption that I think needs to be much more closely examined.
Today, with the introduction of genome sequencing into clinical medicine, some are asking whether newborn screening goes far enough. As the cost of sequencing falls, should parents take a more expansive look at their children's health, learning not just whether they have a rare but treatable childhood condition, but also whether they are at risk for untreatable conditions or for diseases that, if they occur at all, will strike only in adulthood? Should genome sequencing be a part of every newborn's care?
It's an idea that appeals to Anne Wojcicki, the founder and CEO of the direct-to-consumer genetic testing company 23andMe, who in a 2016 interview with The Guardian newspaper predicted that having newborns tested would soon be considered standard practice—"as critical as testing your cholesterol"—and a new responsibility of parenting. Wojcicki isn't the only one excited to see everyone's genes examined at birth. Francis Collins, director of the National Institutes of Health and perhaps the most prominent advocate of genomics in the United States, has written that he is "almost certain … that whole-genome sequencing will become part of new-born screening in the next few years." Whether that would happen through state-mandated screening programs, or as part of routine pediatric care—or perhaps as a direct-to-consumer service that parents purchase at birth or receive as a baby-shower gift—is not clear.
Learning as much as you can about your child's health might seem like a natural obligation of parenting. But it's an assumption that I think needs to be much more closely examined, both because the results that genome sequencing can return are more complex and more uncertain than one might expect, and because parents are not actually responsible for their child's lifelong health and well-being.
What is a parent supposed to do about such a risk except worry?
Existing newborn screening tests look for the presence of rare conditions that, if identified early in life, before the child shows any symptoms, can be effectively treated. Sequencing could identify many of these same kinds of conditions (and it might be a good tool if it could be targeted to those conditions alone), but it would also identify gene variants that confer an increased risk rather than a certainty of disease. Occasionally that increased risk will be significant. About 12 percent of women in the general population will develop breast cancer during their lives, while those who have a harmful BRCA1 or BRCA2 gene variant have around a 70 percent chance of developing the disease. But for many—perhaps most—conditions, the increased risk associated with a particular gene variant will be very small. Researchers have identified over 600 genes that appear to be associated with schizophrenia, for example, but any one of those confers only a tiny increase in risk for the disorder. What is a parent supposed to do about such a risk except worry?
Sequencing results are uncertain in other important ways as well. While we now have the ability to map the genome—to create a read-out of the pairs of genetic letters that make up a person's DNA—we are still learning what most of it means for a person's health and well-being. Researchers even have a name for gene variants they think might be associated with a disease or disorder, but for which they don't have enough evidence to be sure. They are called "variants of unknown (or uncertain) significance (VUS), and they pop up in most people's sequencing results. In cancer genetics, where much research has been done, about 1 in 5 gene variants are reclassified over time. Most are downgraded, which means that a good number of VUS are eventually designated benign.
While one parent might reasonably decide to learn about their child's risk for a condition about which nothing can be done medically, a different, yet still thoroughly reasonable, parent might prefer to remain ignorant so that they can enjoy the time before their child is afflicted.
Then there's the puzzle of what to do about results that show increased risk or even certainty for a condition that we have no idea how to prevent. Some genomics advocates argue that even if a result is not "medically actionable," it might have "personal utility" because it allows parents to plan for their child's future needs, to enroll them in research, or to connect with other families whose children carry the same genetic marker.
Finding a certain gene variant in one child might inform parents' decisions about whether to have another—and if they do, about whether to use reproductive technologies or prenatal testing to select against that variant in a future child. I have no doubt that for some parents these personal utility arguments are persuasive, but notice how far we've now strayed from the serious yet treatable conditions that motivated governments to set up newborn screening programs, and to mandate such testing for all.
Which brings me to the other problem with the call for sequencing newborn babies: the idea that even if it's not what the law requires, it's what good parents should do. That idea is very compelling when we're talking about sequencing results that show a serious threat to the child's health, especially when interventions are available to prevent or treat that condition. But as I have shown, many sequencing results are not of this type.
While one parent might reasonably decide to learn about their child's risk for a condition about which nothing can be done medically, a different, yet still thoroughly reasonable, parent might prefer to remain ignorant so that they can enjoy the time before their child is afflicted. This parent might decide that the worry—and the hypervigilence it could inspire in them—is not in their child's best interest, or indeed in their own. This parent might also think that it should be up to the child, when he or she is older, to decide whether to learn about his or her risk for adult-onset conditions, especially given that many adults at high familial risk for conditions like Alzheimer's or Huntington's disease choose never to be tested. This parent will value the child's future autonomy and right not to know more than they value the chance to prepare for a health risk that won't strike the child until 40 or 50 years in the future.
Parents are not obligated to learn about their children's risk for a condition that cannot be prevented, has a small risk of occurring, or that would appear only in adulthood.
Contemporary understandings of parenting are famously demanding. We are asked to do everything within our power to advance our children's health and well-being—to act always in our children's best interests. Against that backdrop, the need to sequence every newborn baby's genome might seem obvious. But we should be skeptical. Many sequencing results are complex and uncertain. Parents are not obligated to learn about their children's risk for a condition that cannot be prevented, has a small risk of occurring, or that would appear only in adulthood. To suggest otherwise is to stretch parental responsibilities beyond the realm of childhood and beyond factors that parents can control.
Two years, six million deaths and still counting, scientists are searching for answers to prevent another COVID-19-like tragedy from ever occurring again. And it’s a gargantuan task.
Our disturbed ecosystems are creating more favorable conditions for the spread of infectious disease. Global warming, deforestation, rising sea levels and flooding have contributed to a rise in mosquito-borne infections and longer tick seasons. Disease-carrying animals are in closer range to other species and humans as they migrate to escape the heat. Bats are thought to have carried the SARS-CoV-2 virus to Wuhan, either directly or through another host animal, but thousands of novel viruses are lurking within other wild creatures.
Understanding how climate change contributes to the spread of disease is critical in predicting and thwarting future calamities. But the problem is that predictive models aren’t yet where they need to be for forecasting with certainty beyond the next year, as we could for weather, for instance.
The association between climate and infectious disease is poorly understood, says Irina Tezaur, a computational scientist at Sandia National Laboratories. “Correlations have been observed but it’s not known if these correlations translate to causal relationships.”
To make accurate longer-term predictions, scientists need more empirical data, multiple datasets specific to locations and diseases, and the ability to calculate risks that depend on unpredictable nature and human behavior. Another obstacle is that climate scientists and epidemiologists are not collaborating effectively, so some researchers are calling for a multidisciplinary approach, a new field called Outbreak Science.
Climate scientists are far ahead of epidemiologists in gathering essential data.
Earth System Models—combining the interactions of atmosphere, ocean, land, ice and biosphere—have been in place for two decades to monitor the effects of global climate change. These models must be combined with epidemiological and human model research, areas that are easily skewed by unpredictable elements, from extreme weather events to public environmental policy shifts.
“There is never just one driver in tracking the impact of climate on infectious disease,” says Joacim Rocklöv, a professor at the Heidelberg Institute of Global Health & Heidelberg Interdisciplinary Centre for Scientific Computing in Germany. Rocklöv has studied how climate affects vector-borne diseases—those transmitted to humans by mosquitoes, ticks or fleas. “You need to disentangle the variables to find out how much difference climate makes to the outcome and how much is other factors.” Determinants from deforestation to population density to lack of healthcare access influence the spread of disease.
Even though climate change is not the primary driver of infectious disease today, it poses a major threat to public health in the future, says Rocklöv.
The promise of predictive modeling
“Models are simplifications of a system we’re trying to understand,” says Jeremy Hess, who directs the Center for Health and the Global Environment at University of Washington in Seattle. “They’re tools for learning that improve over time with new observations.”
Accurate predictions depend on high-quality, long-term observational data but models must start with assumptions. “It’s not possible to apply an evidence-based approach for the next 40 years,” says Rocklöv. “Using models to experiment and learn is the only way to figure out what climate means for infectious disease. We collect data and analyze what already happened. What we do today will not make a difference for several decades.”
To improve accuracy, scientists develop and draw on thousands of models to cover as many scenarios as possible. One model may capture the dynamics of disease transmission while another focuses on immunity data or ocean influences or seasonal components of a virus. Further, each model needs to be disease-specific and often location-specific to be useful.
“All models have biases so it’s important to use a suite of models,” Tezaur stresses.
The modeling scientist chooses the drivers of change and parameters based on the question explored. The drivers could be increased precipitation, poverty or mosquito prevalence, for instance. Later, the scientist may need to isolate the effect of one driver so that will require another model.
There have been some related successes, such as the latest models for mosquito-borne diseases like Dengue, Zika and malaria as well as those for flu and tick-borne diseases, says Hess.
Rocklöv was part of a research team that used test data from 2018 and 2019 to identify regions at risk for West Nile virus outbreaks. Using AI, scientists were able to forecast outbreaks of the virus for the entire transmission season in Europe. “In the end, we want data-driven models; that’s what AI can accomplish,” says Rocklöv. Other researchers are making an important headway in creating a framework to predict novel host–parasite interactions.
Modeling studies can run months, years or decades. “The scientist is working with layers of data. The challenge is how to transform and couple different models together on a planetary scale,” says Jeanne Fair, a scientist at Los Alamos National Laboratory, Biosecurity and Public Health, in New Mexico.
Disease forecasting will require a significant investment into the infrastructure needed to collect data about the environment, vectors, and hosts a tall spatial and temporal resolutions.
And it’s a constantly changing picture. A modeling study in an April 2022 issue of Nature predicted that thousands of animals will migrate to cooler locales as temperatures rise. This means that various species will come into closer contact with people and other mammals for the first time. This is likely to increase the risk of emerging infectious disease transmitted from animals to humans, especially in Africa and Asia.
Other things can happen too. Global warming could precipitate viral mutations or new infectious diseases that don’t respond to antimicrobial treatments. Insecticide-resistant mosquitoes could evolve. Weather-related food insecurity could increase malnutrition and weaken people’s immune systems. And the impact of an epidemic will be worse if it co-occurs during a heatwave, flood, or drought, says Hess.
The devil is in the climate variables
Solid predictions about the future of climate and disease are not possible with so many uncertainties. Difficult-to-measure drivers must be added to the empirical model mix, such as land and water use, ecosystem changes or the public’s willingness to accept a vaccine or practice social distancing. Nor is there any precedent for calculating the effect of climate changes that are accelerating at a faster speed than ever before.
The most critical climate variables thought to influence disease spread are temperature, precipitation, humidity, sunshine and wind, according to Tezaur’s research. And then there are variables within variables. Influenza scientists, for example, found that warm winters were predictors of the most severe flu seasons in the following year.
The human factor may be the most challenging determinant. To what degree will people curtail greenhouse gas emissions, if at all? The swift development of effective COVID-19 vaccines was a game-changer, but will scientists be able to repeat it during the next pandemic? Plus, no model could predict the amount of internet-fueled COVID-19 misinformation, Fair noted. To tackle this issue, infectious disease teams are looking to include more sociologists and political scientists in their modeling.
Addressing the gaps
Currently, researchers are focusing on the near future, predicting for next year, says Fair. “When it comes to long-term, that’s where we have the most work to do.” While scientists cannot foresee how political influences and misinformation spread will affect models, they are positioned to make headway in collecting and assessing new data streams that have never been merged.
Disease forecasting will require a significant investment into the infrastructure needed to collect data about the environment, vectors, and hosts at all spatial and temporal resolutions, Fair and her co-authors stated in their recent study. For example real-time data on mosquito prevalence and diversity in various settings and times is limited or non-existent. Fair also would like to see standards set in mosquito data collection in every country. “Standardizing across the US would be a huge accomplishment,” she says.
Understanding how climate change contributes to the spread of disease is critical for thwarting future calamities.
Jeanne Fair
Hess points to a dearth of data in local and regional datasets about how extreme weather events play out in different geographic locations. His research indicates that Africa and the Middle East experienced substantial climate shifts, for example, but are unrepresented in the evidentiary database, which limits conclusions. “A model for dengue may be good in Singapore but not necessarily in Port-au-Prince,” Hess explains. And, he adds, scientists need a way of evaluating models for how effective they are.
The hope, Rocklöv says, is that in the future we will have data-driven models rather than theoretical ones. In turn, sharper statistical analyses can inform resource allocation and intervention strategies to prevent outbreaks.
Most of all, experts emphasize that epidemiologists and climate scientists must stop working in silos. If scientists can successfully merge epidemiological data with climatic, biological, environmental, ecological and demographic data, they will make better predictions about complex disease patterns. Modeling “cross talk” and among disciplines and, in some cases, refusal to release data between countries is hindering discovery and advances.
It’s time for bold transdisciplinary action, says Hess. He points to initiatives that need funding in disease surveillance and control; developing and testing interventions; community education and social mobilization; decision-support analytics to predict when and where infections will emerge; advanced methodologies to improve modeling; training scientists in data management and integrated surveillance.
Establishing a new field of Outbreak Science to coordinate collaboration would accelerate progress. Investment in decision-support modeling tools for public health teams, policy makers, and other long-term planning stakeholders is imperative, too. We need to invest in programs that encourage people from climate modeling and epidemiology to work together in a cohesive fashion, says Tezaur. Joining forces is the only way to solve the formidable challenges ahead.
This article originally appeared in One Health/One Planet, a single-issue magazine that explores how climate change and other environmental shifts are increasing vulnerabilities to infectious diseases by land and by sea. The magazine probes how scientists are making progress with leaders in other fields toward solutions that embrace diverse perspectives and the interconnectedness of all lifeforms and the planet.
Scientists use AI to predict how hospital stays will go
The Friday Five covers five stories in research that you may have missed this week. There are plenty of controversies and troubling ethical issues in science – and we get into many of them in our online magazine – but this news roundup focuses on scientific creativity and progress to give you a therapeutic dose of inspiration headed into the weekend.
Here are the promising studies covered in this week's Friday Five:
- The problem with bedtime munching
- Scientists use AI to predict how stays in hospitals will go
- How to armor the shields of our livers against cancer
- One big step to save the world: turn one kind of plastic into another
- The perfect recipe for tiny brains
And an honorable mention this week: Bigger is better when it comes to super neurons in super agers