A slide from J. Craig Venter's recent study on facial prediction presented at the Summit Conference in Los Angeles on Nov. 3, 2017.
Renowned genetics pioneer Dr. J Craig Venter is no stranger to controversy.
Back in 2000, he famously raced the public Human Genome Project to decode all three billion letters of the human genome for the first time. A decade later, he ignited a new debate when his team created a bacterial cell with a synthesized genome.
Most recently, he's jumped back into the fray with a study in the September issue of the Proceedings of the National Academy of Sciences about the predictive potential of genomic data to identify individual traits such as voice, facial structure and skin color.
The new study raises significant questions about the privacy of genetic data.
His study applied whole-genome sequencing and statistical modeling to predict traits in 1,061 people of diverse ancestry. His approach aimed to reconstruct a person's physical characteristics based on DNA, and 74 percent of the time, his algorithm could correctly identify the individual in a random lineup of 10 people from his company's database.
While critics have been quick to cast doubt on the plausibility of his claims, the ability to discern people's observable traits, or phenotypes, from their genomes may grow more precise as technology improves, raising significant questions about the privacy and usage of genetic information in the long term.
J. Craig Venter showing slides from his recent study on facial prediction at the Summit Conference in Los Angeles on Nov. 3, 2017.
(Courtesy of Kira Peikoff)
Critics: Study Was Incomplete, Problematic
Before even redressing these potential legal and ethical considerations, some scientists simply said the study's main result was invalid. They pointed out that the methodology worked much better in distinguishing between people of different ethnicities than those of the same ethnicity. One of the most outspoken critics, Yaniv Erlich, a geneticist at Columbia University, said, "The method doesn't work. The results were like, 'If you have a lineup of ten people, you can predict eight."
Erlich, who reviewed Venter's paper for Science, where it was rejected, said that he came up with the same results—correctly predicting eight of ten people—by just looking at demographic factors such as age, gender and ethnicity. He added that Venter's recent rebuttal to his criticism was that 'Once we have thousands of phenotypes, it might work better.' But that, Erlich argued, would be "a major breach of privacy. Nobody has thousands of phenotypes for people."
Other critics suggested that the study's results discourage the sharing of genetic data, which is becoming increasingly important for medical research. They go one step further and imply that people's possible hesitation to share their genetic information in public databases may actually play into Venter's hands.
Venter's own company, Human Longevity Inc., aims to build the world's most comprehensive private database on human genotypes and phenotypes. The vastness of this information stands to improve the accuracy of whole genome and microbiome sequencing for individuals—analyses that come at a hefty price tag. Today, Human Longevity Inc. will sequence your genome and perform a battery of other health-related tests at an entry cost of $4900, going up to $25,000. Venter initially agreed to comment for this article, but then could not be reached.
"The bigger issue is how do we understand and use genetic information and avoid harming people."
Opens Up Pandora's Box of Ethical Issues
Whether Venter's study is valid may not be as important as the Pandora's box of potential ethical and legal issues that it raises for future consideration. "I think this story is one along a continuum of stories we've had on the issue of identifiability based on genomic information in the past decade," said Amy McGuire, a biomedical ethics professor at Baylor College of Medicine. "It does raise really interesting and important questions about privacy, and socially, how we respond to these types of scientific advancements. A lot of our focus from a policy and ethics perspective is to protect privacy."
McGuire, who is also the Director of the Center for Medical Ethics and Health Policy at Baylor, added that while protecting privacy is very important, "the bigger issue is how do we understand and use genetic information and avoid harming people." While we've taken "baby steps," she said, towards enacting laws in the U.S. that fight genetic determinism—such as the Genetic Information and Nondiscrimination Act, which prohibits discrimination based on genetic information in health insurance and employment—some areas remain unprotected, such as for life insurance and disability.
J. Craig Venter showing slides from his recent study on facial prediction at the Summit Conference in Los Angeles on Nov. 3, 2017.
(Courtesy of Kira Peikoff)
Physical reconstructions like those in Venter's study could also be inappropriately used by law enforcement, said Leslie Francis, a law and philosophy professor at the University of Utah, who has written about the ethical and legal issues related to sharing genomic data.
"If [Venter's] findings, or findings like them, hold up, the implications would be significant," Francis said. Law enforcement is increasingly using DNA identification from genetic material left at crime scenes to weed out innocent and guilty suspects, she explained. This adds another potentially complicating layer.
"There is a shift here, from using DNA sequencing techniques to match other DNA samples—as when semen obtained from a rape victim is then matched (or not) with a cheek swab from a suspect—to using DNA sequencing results to predict observable characteristics," Francis said. She added that while the former necessitates having an actual DNA sample for a match, the latter can use DNA to pre-emptively (and perhaps inaccurately) narrow down suspects.
"My worry is that if this [the study's methodology] turns out to be sort-of accurate, people will think it is better than what it is," said Francis. "If law enforcement comes to rely on it, there will be a host of false positives and false negatives. And we'll face new questions, [such as] 'Which is worse? Picking an innocent as guilty, or failing to identify someone who is guilty?'"
Risking Privacy Involves a Tradeoff
When people voluntarily risk their own privacy, that involves a tradeoff, McGuire said. A 2014 study that she conducted among people who were very sick, or whose children were very sick, found that more than half were willing to share their health information, despite concerns about privacy, because they saw a big benefit in advancing research on their conditions.
"We've focused a lot of our policy attention on restricting access, but we don't have a system of accountability when there's a breach."
"To make leaps and bounds in medicine and genomics, we need to create a database of millions of people signing on to share their genetic and health information in order to improve research and clinical care," McGuire said. "They are going to risk their privacy, and we have a social obligation to protect them."
That also means "punishing bad actors," she continued. "We've focused a lot of our policy attention on restricting access, but we don't have a system of accountability when there's a breach."
Even though most people using genetic information have good intentions, the consequences if not are troubling. "All you need is one bad actor who decimates the trust in the system, and it has catastrophic consequences," she warned. That hasn't happened on a massive scale yet, and even if it did, some experts argue that obtaining the data is not the real risk; what is more concerning is hacking individuals' genetic information to be used against them, such as to prove someone is unfit for a particular job because of a genetic condition like Alzheimer's, or that a parent is unfit for custody because of a genetic disposition to mental illness.
Venter, in fact, told an audience at the recent Summit conference in Los Angeles that his new study's approach could not only predict someone's physical appearance from their DNA, but also some of their psychological traits, such as the propensity for an addictive personality. In the future, he said, it will be possible to predict even more about mental health from the genome.
What is most at risk on a massive scale, however, is not so much genetic information as demographic identifiers included in medical records, such as birth dates and social security numbers, said Francis, the law and philosophy professor. "The much more interesting and lucrative security breaches typically involve not people interested in genetic information per se, but people interested in the information in health records that you can't change."
Hospitals have been hacked for this kind of information, including an incident at the Veterans Administration in 2006, in which the laptop and external hard drive of an agency employee that contained unencrypted information on 26.5 million patients were stolen from the employee's house.
So, what can people do to protect themselves? "Don't share anything you wouldn't want the world to see," Francis said. "And don't click 'I agree' without actually reading privacy policies or terms and conditions. They may surprise you."
Avalanche rescue dogs train to find and dig out people buried in snow slides
By the time he is three, Lume, like Huckleberry, will be a fully trained avy pup and will join seven other avy dogs on Breckenridge ski patrol team. Some of the team members, both human and canine, are also certified to work with Colorado Rapid Avalanche Deployment, a coordinated response team that works with the Summit County Sheriff’s office for avalanche emergencies outside of the ski slopes’ boundaries.
There have been 19 avalanche deaths in the U.S. this season, according to avalanche.org, which tracks slides; eight in Colorado. During the entirety of last season there were 17. Avalanche season runs from November through June, but avalanches can occur year-round.
High tech and high stakes
Complementing avy dogs’ ability to smell people buried in a slide, avalanche detection, rescue and recovery is becoming increasingly high tech. There are transceivers, signal locators, ground scanners and drones, which are considered “games changers” by many in avalanche rescue and recovery
For a person buried in an avalanche, the chance of survival plummets after 20 minutes, so every moment counts.
A drone can provide thermal imaging of objects caught in a slide; what looks like a rock from far away might be a human with a heat signature. Transceivers, also known as beacons, send a signal from an avalanche victim to a companion. Signal locators, like RECCO reflectors which are often sewn directly into gear, can echo back a radar signal sent by a detector; most ski resorts have RECCO detector units.
Research suggests that Ground Penetrating Radar (GPR), an electromagnetic tool used by geophysicists to pull images from inside the ground, could be used to locate an avalanche victim. A new study from the Department of Energy’s Sandia National Laboratories suggests that a computer program developed to pinpoint the source of a chemical or biological terrorist attack could also be used to find someone submerged in an avalanche. The search algorithm allows for small robots (described as cockroach-sized) to “swarm” a search area. Researchers say that this distributed optimization algorithm can help find avalanche victims four times faster than current search mechanisms. For a person buried in an avalanche, the chance of survival plummets after 20 minutes, so every moment counts.
An avy dog in training is picking up scent
Sarah McLear
While rescue gear has been evolving, predicting when a slab will fall remains an emerging science — kind of where weather forecasting science was in the 1980s. Avalanche forecasting still relies on documenting avalanches by going out and looking,” says Ethan Greene, director of the Colorado Avalanche Information Center (CAIC). “So if there's a big snowstorm, and as you might remember, most avalanches happened during snowstorms, we could have 10,000 avalanches that release and we document 50,” says Greene. “Avalanche forecasting is essentially pattern recognition,” he adds--and understanding the layering structure of snow.
However, determining where the hazards lie can be tricky. While a dense layer of snow over a softer, weaker layer may be a recipe for an avalanche, there’s so much variability in snowpack that no one formula can predict the trigger. Further, observing and measuring snow at a single point may not be representative of all nearby slopes. Finally, there’s not enough historical data to help avalanche scientists create better prediction models.
That, however, may be changing.
Last year, an international group of researchers created computer simulations of snow cover using 16 years of meteorological data to forecast avalanche hazards, publishing their research in Cold Regions Science and Technology. They believe their models, which categorize different kinds of avalanches, can support forecasting and determine whether the avalanche is natural (caused by temperature changes, wind, additional snowfall) or artificial (triggered by a human or animal).
With smell receptors ranging from 800 million for an average dog, to 4 billion for scent hounds, canines remain key to finding people caught in slides.
With data from two sites in British Columbia and one in Switzerland, researchers built computer simulations of five different avalanche types. “In terms of real time avalanche forecasting, this has potential to fill in a lot of data gaps, where we don't have field observations of what the snow looks like,” says Simon Horton, a postdoctoral fellow with the Simon Fraser University Centre for Natural Hazards Research and a forecaster with Avalanche Canada, who participated in the study. While complex models that simulate snowpack layers have been around for a few decades, they weren’t easy to apply until recently. “It's been difficult to find out how to apply that to actual decision-making and improving safety,” says Horton. If you can derive avalanche problem types from simulated snowpack properties, he says, you’ll learn “a lot about how you want to manage that risk.”
The five categories include “new snow,” which is unstable and slides down the slope, “wet snow,” when rain or heat makes it liquidly, as well as “wind-drifted snow,” “persistent weak layers” and “old snow.” “That's when there's some type of deeply buried weak layer in the snow that releases without any real change in the weather,” Horton explains. “These ones tend to cause the most accidents.” One step by a person on that structurally weak layer of snow will cause a slide. Horton is hopeful that computer simulations of avalanche types can be used by scientists in different snow climates to help predict hazard levels.
Greene is doubtful. “If you have six slopes that are lined up next to each other, and you're going to try to predict which one avalanches and the exact dimensions and what time, that's going to be really hard to do. And I think it's going to be a long time before we're able to do that,” says Greene.
What both researchers do agree on, though, is that what avalanche prediction really needs is better imagery through satellite detection. “Just being able to count the number of avalanches that are out there will have a huge impact on what we do,” Greene says. “[Satellites] will change what we do, dramatically.” In a 2022 paper, scientists at the University of Aberdeen in England used satellites to study two deadly Himalayan avalanches. The imaging helped them determine that sediment from a 2016 ice avalanche plus subsequent snow avalanches contributed to the 2021 avalanche that caused a flash flood, killing over 200 people. The researchers say that understanding the avalanches characteristics through satellite imagery can inform them how one such event increases the magnitude of another in the same area.
Avy dogs trainers hide in dug-out holes in the snow, teaching the dogs to find buried victims
Sarah McLear
Lifesaving combo: human tech and Mother Nature’s gear
Even as avalanche forecasting evolves, dogs with their built-in rescue mechanisms will remain invaluable. With smell receptors ranging from 800 million for an average dog, to 4 billion for scent hounds, canines remain key to finding people caught in slides. (Humans in comparison, have a meager 12 million.) A new study published in the Journal of Neuroscience revealed that in dogs smell and vision are connected in the brain, which has not been found in other animals. “They can detect the smell of their owner's fingerprints on a glass slide six weeks after they touched it,” says Nicholas Dodman, professor emeritus at Cummings School of Veterinary Medicine at Tufts University. “And they can track from a boat where a box filled with meat was buried in the water, 100 feet below,” says Dodman, who is also co-founder and president of the Center for Canine Behavior Studies.
Another recent study from Queens College in Belfast, United Kingdom, further confirms that dogs can smell when humans are stressed. They can also detect the smell of a person’s breath and the smell of the skin cells of a deceased person.
The emerging avalanche-predicting human-made tech and the incredible nature-made tech of dogs’ olfactory talents is the lifesaving “equipment” that Leffler believes in. Even when human-made technology develops further, it will be most efficient when used together with the millions of dogs’ smell receptors, Leffler believes. “It is a combination of technology and the avalanche dog that will always be effective in finding an avalanche victim.”
