Will Blockchain Technology Usher in a Healthcare Data Revolution?
The hacker collective known as the Dark Overlord first surfaced in June 2016, when it advertised more than 600,000 patient files from three U.S. healthcare organizations for sale on the dark web. The group, which also attempted to extort ransom from its victims, soon offered another 9 million records pilfered from health insurance companies and provider networks across the country.
Since 2009, federal regulators have counted nearly 5,000 major data breaches in the United States alone, affecting some 260 million individuals.
Last October, apparently seeking publicity as well as cash, the hackers stole a trove of potentially scandalous data from a celebrity plastic surgery clinic in London—including photos of in-progress genitalia- and breast-enhancement surgeries. "We have TBs [terabytes] of this shit. Databases, names, everything," a gang representative told a reporter. "There are some royal families in here."
Bandits like these are prowling healthcare's digital highways in growing numbers. Since 2009, federal regulators have counted nearly 5,000 major data breaches in the United States alone, affecting some 260 million individuals. Although hacker incidents represent less than 20 percent of the total breaches, they account for almost 80 percent of the affected patients. Such attacks expose patients to potential blackmail or identity theft, enable criminals to commit medical fraud or file false tax returns, and may even allow hostile state actors to sabotage electric grids or other infrastructure by e-mailing employees malware disguised as medical notices. According to the consulting agency Accenture, data theft will cost the healthcare industry $305 billion between 2015 and 2019, with annual totals doubling from $40 billion to $80 billion.
Blockchain could put patients in control of their own data, empowering them to access, share, and even sell their medical information as they see fit.
One possible solution to this crisis involves radically retooling the way healthcare data is stored and shared—by using blockchain, the still-emerging information technology that underlies cryptocurrencies such as Bitcoin. And blockchain-enabled IT systems, boosters say, could do much more than prevent the theft of medical data. Such networks could revolutionize healthcare delivery on many levels, creating efficiencies that would reduce medical errors, improve coordination between providers, drive down costs, and give researchers unprecedented insights into patterns of disease. Perhaps most transformative, blockchain could put patients in control of their own data, empowering them to access, share, and even sell their medical information as they see fit. Widespread adoption could result in "a new kind of healthcare economy, in which data and services are quantifiable and exchangeable, with strong guarantees around both the security and privacy of sensitive information," wrote W. Brian Smith, chief scientist of healthcare-blockchain startup PokitDok, in a recent white paper.
Around the world, entrepreneurs, corporations, and government agencies are hopping aboard the blockchain train. A survey by the IBM Institute for Business Value, released in late 2016, found that 16 percent of healthcare executives in 16 countries planned to begin implementing some form of the technology in the coming year; 90 percent planned to launch a pilot program in the next two years. In 2017, Estonia became the first country to switch its medical-records system to a blockchain-based framework. Great Britain and Dubai are exploring a similar move. Yet in countries with more fragmented health systems, most notably the U.S., the challenges remain formidable. Some of the most advanced healthcare applications envisioned for blockchain, moreover, raise technological and ethical questions whose answers may not arrive anytime soon.
By creating a detailed, comprehensive, and immutable timeline of medical transactions, blockchain-based recordkeeping could help providers gauge a patient's long-term health patterns in a way that's never before been possible.
What Exactly Is Blockchain, Anyway?
To understand the buzz around blockchain, it's necessary to grasp (at least loosely) how the technology works. Ordinary digital recordkeeping systems rely on a central administrator that acts as gatekeeper to a treasury of data; if you can sneak past the guard, you can often gain access to the entire hoard, and your intrusion may go undetected indefinitely. Blockchain, by contrast, employs a network of synchronized, replicated databases. Information is scattered among these nodes, rather than on a single server, and is exchanged through encrypted, peer-to-peer pathways. Each transaction is visible to every computer on the network, and must be approved by a majority in order to be successfully completed. Each batch of transactions, or "block," is date- and time-stamped, marked with the user's identity, and given a cryptographic code, which is posted to every node. These blocks form a "chain," preserved in an electronic ledger, that can be read by all users but can't be edited. Any unauthorized access, or attempt at tampering, can be quickly neutralized by these overlapping safeguards. Even if a hacker managed to break into the system, penetrating deeply would be extraordinarily difficult.
Because blockchain technology shares transaction records throughout a network, it could eliminate communication bottlenecks between different components of the healthcare system (primary care physicians, specialists, nurses, and so on). And because blockchain-based systems are designed to incorporate programs known as "smart contracts," which automate functions previously requiring human intervention, they could reduce dangerous slipups as well as tedious and costly paperwork. For example, when a patient gets a checkup, sees a specialist, and fills a prescription, all these actions could be automatically recorded on his or her electronic health record (EHR), checked for errors, submitted for billing, and entered on insurance claims—which could be adjudicated and reimbursed automatically as well. "Blockchain has the potential to remove a lot of intermediaries from existing workflows, whether digital or nondigital," says Kamaljit Behera, an industry analyst for the consulting firm Frost & Sullivan.
The possible upsides don't end there. By creating a detailed, comprehensive, and immutable timeline of medical transactions, blockchain-based recordkeeping could help providers gauge a patient's long-term health patterns in a way that's never before been possible. In addition to data entered by their caregivers, individuals could use app-based technologies or wearables to transmit other information to their records, such as diet, exercise, and sleep patterns, adding new depth to their medical portraits.
Many experts expect healthcare blockchain to take root more slowly in the U.S. than in nations with government-run national health services.
Smart contracts could also allow patients to specify who has access to their data. "If you get an MRI and want your orthopedist to see it, you can add him to your network instead of carrying a CD into his office," explains Andrew Lippman, associate director of the MIT Media Lab, who helped create a prototype healthcare blockchain system called MedRec that's currently being tested at Beth Israel Deaconess Hospital in Boston. "Or you might make a smart contract to allow your son or daughter to access your healthcare records if something happens to you." Another option: permitting researchers to analyze your data for scientific purposes, whether anonymously or with your name attached.
The Recent History, and Looking Ahead
Over the past two years, a crowd of startups has begun vying for a piece of the emerging healthcare blockchain market. Some, like PokitDok and Atlanta-based Patientory, plan to mint proprietary cryptocurrencies, which investors can buy in lieu of stock, medical providers may earn as a reward for achieving better outcomes, and patients might score for meeting wellness goals or participating in clinical trials. (Patientory's initial coin offering, or ICO, raised more than $7 million in three days.) Several fledgling healthcare-blockchain companies have found powerful corporate partners: Intel for Silicon Valley's PokitDok, Kaiser Permanente for Patientory, Philips for Los Angeles-based Gem Health. At least one established provider network, Change Healthcare, is developing blockchain-based systems of its own. Two months ago, Change launched what it calls the first "enterprise-scale" blockchain network in U.S. healthcare—a system to track insurance claim submissions and remittances.
No one, however, has set a roll-out date for a full-blown, blockchain-based EHR system in this country. "We have yet to see anything move from the pilot phase to some kind of production status," says Debbie Bucci, an IT architect in the federal government's Office of the National Coordinator for Health Information Technology. Indeed, many experts expect healthcare blockchain to take root more slowly here than in nations with government-run national health services. In America, a typical patient may have dealings with a family doctor who keeps everything on paper, an assortment of hospitals that use different EHR systems, and an insurer whose system for processing claims is separate from that of the healthcare providers. To help bridge these gaps, a consortium called the Hyperledger Healthcare Working Group (which includes many of the leading players in the field) is developing standard protocols for blockchain interoperability and other functions. Adding to the complexity is the federal Health Insurance and Portability Act (HIPAA), which governs who can access patient data and under what circumstances. "Healthcare blockchain is in a very nascent stage," says Behera. "Coming up with regulations and other guidelines, and achieving large-scale implementation, will take some time."
The ethical implications of buying and selling personal genomic data in an electronic marketplace are doubtless open to debate.
How long? Behera, like other analysts, estimates that relatively simple applications, such as revenue-cycle management systems, could become commonplace in the next five years. More ambitious efforts might reach fruition in a decade or so. But once the infrastructure for healthcare blockchain is fully established, its uses could go far beyond keeping better EHRs.
A handful of scientists and entrepreneurs are already working to develop one visionary application: managing genomic data. Last month, Harvard University geneticist George Church—one of the most influential figures in his discipline—launched a business called Nebula Genomics. It aims to set up an exchange in which individuals can use "Neptune tokens" to purchase DNA sequencing, which will be stored in the company's blockchain-based system; research groups will be able to pay clients for their data using the same cryptocurrency. Luna DNA, founded by a team of biotech veterans in San Diego, plans a similar service, as does a Moscow-based startup called the Zenome Project.
Hossein Rahnama, CEO of the mobile-tech company Flybits and director of research at the Ryerson Centre for Cloud and Context-Aware Computing in Toronto, envisions a more personalized way of sharing genomic data via blockchain. His firm is working with a U.S. insurance company to develop a service that would allow clients in their 20s and 30s to connect with people in their 70s or 80s with similar genomes. The young clients would learn how the elders' lifestyle choices had influenced their health, so that they could modify their own habits accordingly. "It's intergenerational wisdom-sharing," explains Rahnama, who is 38. "I would actually pay to be a part of that network."
The ethical implications of buying and selling personal genomic data in an electronic marketplace are doubtless open to debate. Such commerce could greatly expand the pool of subjects for research in many areas of medicine, enabling the kinds of breakthroughs that only Big Data can provide. Yet it could also lead millions to surrender the most private information of all—the secrets of their cells—to buyers with less benign intentions. The Dark Overlord, one might argue, could not hope for a more satisfying victory.
These scenarios, however, are pure conjecture. After the first web page was posted, in 1991, Lippman observes, "a whole universe developed that you couldn't have imagined on Day 1." The same, he adds, is likely true for healthcare blockchain. "Our vision is to make medical records useful for you and for society, and to give you more control over your own identity. Time will tell."
Massive benefits of AI come with environmental and human costs. Can AI itself be part of the solution?
The recent explosion of generative artificial intelligence tools like ChatGPT and Dall-E enabled anyone with internet access to harness AI’s power for enhanced productivity, creativity, and problem-solving. With their ever-improving capabilities and expanding user base, these tools proved useful across disciplines, from the creative to the scientific.
But beneath the technological wonders of human-like conversation and creative expression lies a dirty secret—an alarming environmental and human cost. AI has an immense carbon footprint. Systems like ChatGPT take months to train in high-powered data centers, which demand huge amounts of electricity, much of which is still generated with fossil fuels, as well as water for cooling. “One of the reasons why Open AI needs investments [to the tune of] $10 billion from Microsoft is because they need to pay for all of that computation,” says Kentaro Toyama, a computer scientist at the University of Michigan. There’s also an ecological toll from mining rare minerals required for hardware and infrastructure. This environmental exploitation pollutes land, triggers natural disasters and causes large-scale human displacement. Finally, for data labeling needed to train and correct AI algorithms, the Big Data industry employs cheap and exploitative labor, often from the Global South.
Generative AI tools are based on large language models (LLMs), with most well-known being various versions of GPT. LLMs can perform natural language processing, including translating, summarizing and answering questions. They use artificial neural networks, called deep learning or machine learning. Inspired by the human brain, neural networks are made of millions of artificial neurons. “The basic principles of neural networks were known even in the 1950s and 1960s,” Toyama says, “but it’s only now, with the tremendous amount of compute power that we have, as well as huge amounts of data, that it’s become possible to train generative AI models.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries.
In recent months, much attention has gone to the transformative benefits of these technologies. But it’s important to consider that these remarkable advances may come at a price.
AI’s carbon footprint
In their latest annual report, 2023 Landscape: Confronting Tech Power, the AI Now Institute, an independent policy research entity focusing on the concentration of power in the tech industry, says: “The constant push for scale in artificial intelligence has led Big Tech firms to develop hugely energy-intensive computational models that optimize for ‘accuracy’—through increasingly large datasets and computationally intensive model training—over more efficient and sustainable alternatives.”
Though there aren’t any official figures about the power consumption or emissions from data centers, experts estimate that they use one percent of global electricity—more than entire countries. In 2019, Emma Strubell, then a graduate researcher at the University of Massachusetts Amherst, estimated that training a single LLM resulted in over 280,000 kg in CO2 emissions—an equivalent of driving almost 1.2 million km in a gas-powered car. A couple of years later, David Patterson, a computer scientist from the University of California Berkeley, and colleagues, estimated GPT-3’s carbon footprint at over 550,000 kg of CO2 In 2022, the tech company Hugging Face, estimated the carbon footprint of its own language model, BLOOM, as 25,000 kg in CO2 emissions. (BLOOM’s footprint is lower because Hugging Face uses renewable energy, but it doubled when other life-cycle processes like hardware manufacturing and use were added.)
Luckily, despite the growing size and numbers of data centers, their increasing energy demands and emissions have not kept pace proportionately—thanks to renewable energy sources and energy-efficient hardware.
But emissions don’t tell the full story.
AI’s hidden human cost
“If historical colonialism annexed territories, their resources, and the bodies that worked on them, data colonialism’s power grab is both simpler and deeper: the capture and control of human life itself through appropriating the data that can be extracted from it for profit.” So write Nick Couldry and Ulises Mejias, authors of the book The Costs of Connection.
The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
Technologies we use daily inexorably gather our data. “Human experience, potentially every layer and aspect of it, is becoming the target of profitable extraction,” Couldry and Meijas say. This feeds data capitalism, the economic model built on the extraction and commodification of data. While we are being dispossessed of our data, Big Tech commodifies it for their own benefit. This results in consolidation of power structures that reinforce existing race, gender, class and other inequalities.
“The political economy around tech and tech companies, and the development in advances in AI contribute to massive displacement and pollution, and significantly changes the built environment,” says technologist and activist Yeshi Milner, who founded Data For Black Lives (D4BL) to create measurable change in Black people’s lives using data. The energy requirements, hardware manufacture and the cheap human labor behind AI systems disproportionately affect marginalized communities.
AI’s recent explosive growth spiked the demand for manual, behind-the-scenes tasks, creating an industry described by Mary Gray and Siddharth Suri as “ghost work” in their book. This invisible human workforce that lies behind the “magic” of AI, is overworked and underpaid, and very often based in the Global South. For example, workers in Kenya who made less than $2 an hour, were the behind the mechanism that trained ChatGPT to properly talk about violence, hate speech and sexual abuse. And, according to an article in Analytics India Magazine, in some cases these workers may not have been paid at all, a case for wage theft. An exposé by the Washington Post describes “digital sweatshops” in the Philippines, where thousands of workers experience low wages, delays in payment, and wage theft by Remotasks, a platform owned by Scale AI, a $7 billion dollar American startup. Rights groups and labor researchers have flagged Scale AI as one company that flouts basic labor standards for workers abroad.
It is possible to draw a parallel with chattel slavery—the most significant economic event that continues to shape the modern world—to see the business structures that allow for the massive exploitation of people, Milner says. Back then, people got chocolate, sugar, cotton; today, they get generative AI tools. “What’s invisible through distance—because [tech companies] also control what we see—is the massive exploitation,” Milner says.
“At Data for Black Lives, we are less concerned with whether AI will become human…[W]e’re more concerned with the growing power of AI to decide who’s human and who’s not,” Milner says. As a decision-making force, AI becomes a “justifying factor for policies, practices, rules that not just reinforce, but are currently turning the clock back generations years on people’s civil and human rights.”
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement.
Nuria Oliver, a computer scientist, and co-founder and vice-president of the European Laboratory of Learning and Intelligent Systems (ELLIS), says that instead of focusing on the hypothetical existential risks of today’s AI, we should talk about its real, tangible risks.
“Because AI is a transverse discipline that you can apply to any field [from education, journalism, medicine, to transportation and energy], it has a transformative power…and an exponential impact,” she says.
AI's accountability
“At the core of what we were arguing about data capitalism [is] a call to action to abolish Big Data,” says Milner. “Not to abolish data itself, but the power structures that concentrate [its] power in the hands of very few actors.”
A comprehensive AI Act currently negotiated in the European Parliament aims to rein Big Tech in. It plans to introduce a rating of AI tools based on the harms caused to humans, while being as technology-neutral as possible. That sets standards for safe, transparent, traceable, non-discriminatory, and environmentally friendly AI systems, overseen by people, not automation. The regulations also ask for transparency in the content used to train generative AIs, particularly with copyrighted data, and also disclosing that the content is AI-generated. “This European regulation is setting the example for other regions and countries in the world,” Oliver says. But, she adds, such transparencies are hard to achieve.
Google, for example, recently updated its privacy policy to say that anything on the public internet will be used as training data. “Obviously, technology companies have to respond to their economic interests, so their decisions are not necessarily going to be the best for society and for the environment,” Oliver says. “And that’s why we need strong research institutions and civil society institutions to push for actions.” ELLIS also advocates for data centers to be built in locations where the energy can be produced sustainably.
Ironically, AI plays an important role in mitigating its own harms—by plowing through mountains of data about weather changes, extreme weather events and human displacement. “The only way to make sense of this data is using machine learning methods,” Oliver says.
Milner believes that the best way to expose AI-caused systemic inequalities is through people's stories. “In these last five years, so much of our work [at D4BL] has been creating new datasets, new data tools, bringing the data to life. To show the harms but also to continue to reclaim it as a tool for social change and for political change.” This change, she adds, will depend on whose hands it is in.
DNA gathered from animal poop helps protect wildlife
On the savannah near the Botswana-Zimbabwe border, elephants grazed contentedly. Nearby, postdoctoral researcher Alida de Flamingh watched and waited. As the herd moved away, she went into action, collecting samples of elephant dung that she and other wildlife conservationists would study in the months to come. She pulled on gloves, took a swab, and ran it all over the still-warm, round blob of elephant poop.
Sequencing DNA from fecal matter is a safe, non-invasive way to track and ultimately help protect over 42,000 species currently threatened by extinction. Scientists are using this DNA to gain insights into wildlife health, genetic diversity and even the broader environment. Applied to elephants, chimpanzees, toucans and other species, it helps scientists determine the genetic diversity of groups and linkages with other groups. Such analysis can show changes in rates of inbreeding. Populations with greater genetic diversity adapt better to changes and environmental stressors than those with less diversity, thus reducing their risks of extinction, explains de Flamingh, a postdoctoral researcher at the University of Illinois Urbana-Champaign.
Analyzing fecal DNA also reveals information about an animal’s diet and health, and even nearby flora that is eaten. That information gives scientists broader insights into the ecosystem, and the findings are informing conservation initiatives. Examples include restoring or maintaining genetic connections among groups, ensuring access to certain foraging areas or increasing diversity in captive breeding programs.
Approximately 27 percent of mammals and 28 percent of all assessed species are close to dying out. The IUCN Red List of threatened species, simply called the Red List, is the world’s most comprehensive record of animals’ risk of extinction status. The more information scientists gather, the better their chances of reducing those risks. In Africa, populations of vertebrates declined 69 percent between 1970 and 2022, according to the World Wildlife Fund (WWF).
“We put on sterile gloves and use a sterile swab to collect wet mucus and materials from the outside of the dung ball,” says Alida de Flamingh, a postdoctoral researcher at the University of Illinois Urbana-Champaign.
“When people talk about species, they often talk about ecosystems, but they often overlook genetic diversity,” says Christina Hvilsom, senior geneticist at the Copenhagen Zoo. “It’s easy to count (individuals) to assess whether the population size is increasing or decreasing, but diversity isn’t something we can see with our bare eyes. Yet, it’s actually the foundation for the species and populations.” DNA analysis can provide this critical information.
Assessing elephants’ health
“Africa’s elephant populations are facing unprecedented threats,” says de Flamingh, the postdoc, who has studied them since 2009. Challenges include ivory poaching, habitat destruction and smaller, more fragmented habitats that result in smaller mating pools with less genetic diversity. Additionally, de Flamingh studies the microbial communities living on and in elephants – their microbiomes – looking for parasites or dangerous microbes.
Approximately 415,000 elephants inhabit Africa today, but de Flamingh says the number would be four times higher without these challenges. The IUCN Red List reports African savannah elephants are endangered and African forest elephants are critically endangered. Elephants support ecosystem biodiversity by clearing paths that help other species travel. Their very footprints create small puddles that can host smaller organisms such as tadpoles. Elephants are often described as ecosystems’ engineers, so if they disappear, the rest of the ecosystem will suffer too.
There’s a process to collecting elephant feces. “We put on sterile gloves (which we change for each sample) and use a sterile swab to collect wet mucus and materials from the outside of the dung ball,” says de Flamingh. They rub a sample about the size of a U.S. quarter onto a paper card embedded with DNA preservation technology. Each card is air dried and stored in a packet of desiccant to prevent mold growth. This way, samples can be stored at room temperature indefinitely without the DNA degrading.
Earlier methods required collecting dung in bags, which needed either refrigeration or the addition of preservatives, or the riskier alternative of tranquilizing the animals before approaching them to draw blood samples. The ability to collect and sequence the DNA made things much easier and safer.
“Our research provides a way to assess elephant health without having to physically interact with elephants,” de Flamingh emphasizes. “We also keep track of the GPS coordinates of each sample so that we can create a map of the sampling locations,” she adds. That helps researchers correlate elephants’ health with geographic areas and their conditions.
Although de Flamingh works with elephants in the wild, the contributions of zoos in the United States and collaborations in South Africa (notably the late Professor Rudi van Aarde and the Conservation Ecology Research Unit at the University of Pretoria) were key in studying this method to ensure it worked, she points out.
Protecting chimpanzees
Genetic work with chimpanzees began about a decade ago. Hvilsom and her group at the Copenhagen Zoo analyzed DNA from nearly 1,000 fecal samples collected between 2003 and 2018 by a team of international researchers. The goal was to assess the status of the West African subspecies, which is critically endangered after rapid population declines. Of the four subspecies of chimpanzees, the West African subspecies is considered the most at-risk.
In total, the WWF estimates the numbers of chimpanzees inhabiting Africa’s forests and savannah woodlands at between 173,000 and 300,000. Poaching, disease and human-caused changes to their lands are their major risks.
By analyzing genetics obtained from fecal samples, Hvilsom estimated the chimpanzees’ population, ascertained their family relationships and mapped their migration routes.
“One of the threats is mining near the Nimba Mountains in Guinea,” a stronghold for the West African subspecies, Hvilsom says. The Nimba Mountains are a UNESCO World Heritage Site, but they are rich in iron ore, which is used to make the steel that is vital to the Asian construction boom. As she and colleagues wrote in a recent paper, “Many extractive industries are currently developing projects in chimpanzee habitat.”
Analyzing DNA allows researchers to identify individual chimpanzees more accurately than simply observing them, she says. Normally, field researchers would install cameras and manually inspect each picture to determine how many chimpanzees were in an area. But, Hvilsom says, “That’s very tricky. Chimpanzees move a lot and are fast, so it’s difficult to get clear pictures. Often, they find and destroy the cameras. Also, they live in large areas, so you need a lot of cameras.”
By analyzing genetics obtained from fecal samples, Hvilsom estimated the chimpanzees’ population, ascertained their family relationships and mapped their migration routes based upon DNA comparisons with other chimpanzee groups. The mining companies and builders are using this information to locate future roads where they won’t disrupt migration – a more effective solution than trying to build artificial corridors for wildlife.
“The current route cuts off communities of chimpanzees,” Hvilsom elaborates. That effectively prevents young adult chimps from joining other groups when the time comes, eventually reducing the currently-high levels of genetic diversity.
“The mining company helped pay for the genetics work,” Hvilsom says, “as part of its obligation to assess and monitor biodiversity and the effect of the mining in the area.”
Of 50 toucan subspecies, 11 are threatened or near-threatened with extinction because of deforestation and poaching.
Identifying toucan families
Feces aren't the only substance researchers draw DNA samples from. Jeffrey Coleman, a Ph.D. candidate at the University of Texas at Austin relies on blood tests for studying the genetic diversity of toucans---birds species native to Central America and nearby regions. They live in the jungles, where they hop among branches, snip fruit from trees, toss it in the air and catch it with their large beaks. “Toucans are beautiful, charismatic birds that are really important to the ecosystem,” says Coleman.
Of their 50 subspecies, 11 are threatened or near-threatened with extinction because of deforestation and poaching. “When people see these aesthetically pleasing birds, they’re motivated to care about conservation practices,” he points out.
Coleman works with the Dallas World Aquarium and its partner zoos to analyze DNA from blood draws, using it to identify which toucans are related and how closely. His goal is to use science to improve the genetic diversity among toucan offspring.
Specifically, he’s looking at sections of the genome of captive birds in which the nucleotides repeat multiple times, such as AGATAGATAGAT. Called microsatellites, these consecutively-repeating sections can be passed from parents to children, helping scientists identify parent-child and sibling-sibling relationships. “That allows you to make strategic decisions about how to pair (captive) individuals for mating...to avoid inbreeding,” Coleman says.
Jeffrey Coleman is studying the microsatellites inside the toucan genomes.
Courtesy Jeffrey Coleman
The alternative is to use a type of analysis that looks for a single DNA building block – a nucleotide – that differs in a given sequence. Called single nucleotide polymorphisms (SNPs, pronounced “snips”), they are very common and very accurate. Coleman says they are better than microsatellites for some uses. But scientists have already developed a large body of microsatellite data from multiple species, so microsatellites can shed more insights on relations.
Regardless of whether conservation programs use SNPs or microsatellites to guide captive breeding efforts, the goal is to help them build genetically diverse populations that eventually may supplement endangered populations in the wild. “The hope is that the ecosystem will be stable enough and that the populations (once reintroduced into the wild) will be able to survive and thrive,” says Coleman. History knows some good examples of captive breeding success.
The California condor, which had a total population of 27 in 1987, when the last wild birds were captured, is one of them. A captive breeding program boosted their numbers to 561 by the end of 2022. Of those, 347 of those are in the wild, according to the National Park Service.
Conservationists hope that their work on animals’ genetic diversity will help preserve and restore endangered species in captivity and the wild. DNA analysis is crucial to both types of efforts. The ability to apply genome sequencing to wildlife conservation brings a new level of accuracy that helps protect species and gives fresh insights that observation alone can’t provide.
“A lot of species are threatened,” Coleman says. “I hope this research will be a resource people can use to get more information on longer-term genealogies and different populations.”