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Thousands of genomes reveal the wild wolf genes in most dogs’ DNA

wild wolf genes in most dogs’ DNA: New research analyzing 2,693 dog genomes reveals most modern dogs carry wolf DNA from ancient hybridization. Discover how wolf genes helped dogs survive and what this means for breeds from Chihuahuas to sled dogs.

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wild wolf genes in most dogs’ DNA
Modern wolves and dogs both descend from an ancient wolf population that lived alongside woolly mammoths and cave bears.
Iza Lyson/500px Prime via Getty Images

Thousands of genomes reveal the wild wolf genes in most dogs’ DNA

Audrey T. Lin, Smithsonian Institution and Logan Kistler, Smithsonian Institution

Dogs were the first of any species that people domesticated, and they have been a constant part of human life for millennia. Domesticated species are the plants and animals that have evolved to live alongside humans, providing nearly all of our food and numerous other benefits. Dogs provide protection, hunting assistance, companionship, transportation and even wool for weaving blankets.

Dogs evolved from gray wolves, but scientists debate exactly where, when and how many times dogs were domesticated. Ancient DNA evidence suggests that domestication happened twice, in eastern and western Eurasia, before the groups eventually mixed. That blended population was the ancestor of all dogs living today.

Molecular clock analysis of the DNA from hundreds of modern and ancient dogs suggests they were domesticated between around 20,000 and 22,000 years ago, when large ice sheets covered much of Eurasia and North America. The first dog identified in the archaeological record is a 14,000-year-old pup found in Bonn-Oberkassel, Germany, but it can be difficult to tell based on bones whether an animal was an early domestic dog or a wild wolf.

Despite the shared history of dogs and wolves, scientists have long thought these two species rarely mated and gave birth to hybrid offspring. As an evolutionary biologist and a molecular anthropologist who study domestic plants and animals, we wanted to take a new look at whether dog-wolf hybridization has really been all that uncommon.

Little interbreeding in the wild

Dogs are not exactly descended from modern wolves. Rather, dogs and wolves living today both derive from a shared ancient wolf population that lived alongside woolly mammoths and cave bears.

In most domesticated species, there are often clear, documented patterns of gene flow between the animals that live alongside humans and their wild counterparts. Where wild and domesticated animals’ habitats overlap, they can breed with each other to produce hybrid offspring. In these cases, the genes from wild animals are folded into the genetic variation of the domesticated population.

For example, pigs were domesticated in the Near East over 10,000 years ago. But when early farmers brought them to Europe, they hybridized so frequently with local wild boar that almost all of their Near Eastern DNA was replaced. Similar patterns can be seen in the endangered wild Anatolian and Cypriot mouflon that researchers have found to have high proportions of domestic sheep DNA in their genomes. It’s more common than not to find evidence of wild and domesticated animals interbreeding through time and sharing genetic material.

That wolves and dogs wouldn’t show that typical pattern is surprising, since they live in overlapping ranges and can freely interbreed.

Dog and wolf behavior are completely different, though, with wolves generally organized around a family pack structure and dogs reliant on humans. When hybridization does occur, it tends to be when human activities – such as habitat encroachment and hunting – disrupt pack dynamics, leading female wolves to strike out on their own and breed with male dogs. People intentionally bred a few “wolf dog” hybrid types in the 20th century, but these are considered the exception.

a wolfish looking dog lies on the ground behind a metal fence
Luna Belle, a resident of the Wolf Sanctuary of Pennsylvania, which is home to both wolves and wolf dogs.
Audrey Lin

Tiny but detectable wolf ancestry

To investigate how much gene flow there really has been between dogs and wolves after domestication, we analyzed 2,693 previously published genomes, making use of massive publicly available datasets.

These included 146 ancient dogs and wolves covering about 100,000 years. We also looked at 1,872 modern dogs, including golden retrievers, Chihuahuas, malamutes, basenjis and other well-known breeds, plus more unusual breeds from around the world such as the Caucasian ovcharka and Swedish vallhund.

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Finally, we included genomes from about 300 “village dogs.” These are not pets but are free-living animals that are dependent on their close association with human environments.

We traced the evolutionary histories of all of these canids by looking at maternal lineages via their mitochondrial genomes and paternal lineages via their Y chromosomes. We used highly sensitive computational methods to dive into the dogs’ and wolves’ nuclear genomes – that is, the genetic material contained in their cells’ nuclei.

We found the presence of wild wolf genes in most dog genomes and the presence of dog genes in about half of wild wolf genomes. The sign of the wolf was small but it was there, in the form of tiny, almost imperceptible chunks of continuous wolf DNA in dogs’ chromosomes. About two-thirds of breed dogs in our sample had wolf genes from crossbreeding that took place roughly 800 generations ago, on average.

While our results showed that larger, working dogs – such as sled dogs and large guardian dogs that protect livestock – generally have more wolf ancestry, the patterns aren’t universal. Some massive breeds such as the St. Bernard completely lack wolf DNA, but the tiny Chihuahua retains detectable wolf ancestry at 0.2% of its genome. Terriers and scent hounds typically fall at the low end of the spectrum for wolf genes.

a dog curled up on the sidewalk in a town
A street – or free-ranging – dog in Tbilisi, Georgia.
Alexkom000/Wikimedia Commons, CC BY

We were surprised that every single village dog we tested had pieces of wolf DNA in their genomes. Why would this be the case? Village dogs are free-living animals that make up about half the world’s dogs. Their lives can be tough, with short life expectancy and high infant mortality. Village dogs are also associated with pathogenic diseases, including rabies and canine distemper, making them a public health concern.

More often than predicted by chance, the stretches of wolf DNA we found in village dog genomes contained genes related to olfactory receptors. We imagine that olfactory abilities influenced by wolf genes may have helped these free-living dogs survive in harsh, volatile environments.

The intertwining of dogs and wolves

Because dogs evolved from wolves, all of dogs’ DNA is originally wolf DNA. So when we’re talking about the small pieces of wolf DNA in dog genomes, we’re not referring to that original wolf gene pool that’s been kicking around over the past 20,000 years, but rather evidence for dogs and wolves continuing to interbreed much later in time.

A wolf-dog hybrid with one of each kind of parent would carry 50% dog and 50% wolf DNA. If that hybrid then lived and mated with dogs, its offspring would be 25% wolf, and so on, until we see only small snippets of wolf DNA present.

The situation is similar to one in human genomes: Neanderthals and humans share a common ancestor around half a million years ago. However, Neanderthals and our species, Homo sapiens, also overlapped and interbred in Eurasia as recently as a few thousand generations ago, shortly before Neanderthals disappeared. Scientists can spot the small pieces of Neanderthal DNA in most living humans in the same way we can see wolf genes within most dogs.

two small tan dogs walking on pavement on a double lead leash
Even tiny Chihuahuas contain a little wolf within their doggy DNA.
Westend61 via Getty Images

Our study updates the previously held belief that hybridization between dogs and wolves is rare; interactions between these two species do have visible genetic traces. Hybridization with free-roaming dogs is considered a threat to conservation efforts of endangered wolves, including Iberian, Italian and Himalayan wolves. However, there also is evidence that dog-wolf mixing might confer genetic advantages to wolves as they adapt to environments that are increasingly shaped by humans.

Though dogs evolved as human companions, wolves have served as their genetic lifeline. When dogs encountered evolutionary challenges such as how to survive harsh climates, scavenge for food in the streets or guard livestock, it appears they’ve been able to tap into wolf ancestry as part of their evolutionary survival kit.The Conversation

Audrey T. Lin, Research Associate in Anthropology, Smithsonian Institution and Logan Kistler, Curator of Archaeobotany and Archaeogenomics, National Museum of Natural History, Smithsonian Institution

This article is republished from The Conversation under a Creative Commons license. Read the original article.

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Forgotten Genius Fridays

Valerie Thomas: NASA Engineer, Inventor, and STEM Trailblazer

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Last Updated on February 10, 2026 by Daily News StaffValerie Thomas

Valerie Thomas is a true pioneer in the world of science and technology. A NASA engineer and physicist, she is best known for inventing the illusion transmitter, a groundbreaking device that creates 3D images using concave mirrors. This invention laid the foundation for modern 3D imaging and virtual reality technologies.

Beyond her inventions, Thomas broke barriers as an African American woman in STEM, mentoring countless young scientists and advocating for diversity in science and engineering. Her work at NASA’s Goddard Space Flight Center helped advance satellite technology and data visualization, making her contributions both innovative and enduring.

In our latest short video, we highlight Valerie Thomas’ remarkable journey—from her early passion for science to her groundbreaking work at NASA. Watch and be inspired by a true STEM pioneer whose legacy continues to shape the future of space and technology.

🎥 Watch the video here: https://youtu.be/P5XTgpcAoHw

Dive into “The Knowledge,” where curiosity meets clarity. This playlist, in collaboration with STMDailyNews.com, is designed for viewers who value historical accuracy and insightful learning. Our short videos, ranging from 30 seconds to a minute and a half, make complex subjects easy to grasp in no time. Covering everything from historical events to contemporary processes and entertainment, “The Knowledge” bridges the past with the present. In a world where information is abundant yet often misused, our series aims to guide you through the noise, preserving vital knowledge and truths that shape our lives today. Perfect for curious minds eager to discover the ‘why’ and ‘how’ of everything around us. Subscribe and join in as we explore the facts that matter.  https://stmdailynews.com/the-knowledge/

Forgotten Genius Fridays

https://stmdailynews.com/the-knowledge-2/forgotten-genius-fridays/

🧠 Forgotten Genius Fridays

A Short-Form Series from The Knowledge by STM Daily News

Every Friday, STM Daily News shines a light on brilliant minds history overlooked.

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Forgotten Genius Fridays is a weekly collection of short videos and articles dedicated to inventors, innovators, scientists, and creators whose impact changed the world—but whose names were often left out of the textbooks.

From life-saving inventions and cultural breakthroughs to game-changing ideas buried by bias, our series digs up the truth behind the minds that mattered.

Each episode of The Knowledge runs 30–90 seconds, designed for curious minds on the go—perfect for YouTube Shorts, TikTok, Reels, and quick reads.

Because remembering these stories isn’t just about the past—it’s about restoring credit where it’s long overdue.

 🔔 New episodes every Friday

📺 Watch now at: stmdailynews.com/the-knowledge

 🧠 Now you know.  

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    Rod: A creative force, blending words, images, and flavors. Blogger, writer, filmmaker, and photographer. Cooking enthusiast with a sci-fi vision. Passionate about his upcoming series and dedicated to TNC Network. Partnered with Rebecca Washington for a shared journey of love and art. View all posts


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The Knowledge

Beneath the Waves: The Global Push to Build Undersea Railways

Undersea railways are transforming transportation, turning oceans from barriers into gateways. Proven by tunnels like the Channel and Seikan, these innovations offer cleaner, reliable connections for passengers and freight. Ongoing projects in China and Europe, alongside future proposals, signal a new era of global mobility beneath the waves.

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Train traveling through underwater tunnel
Trains beneath the ocean are no longer science fiction—they’re already in operation.

For most of modern history, oceans have acted as natural barriers—dividing nations, slowing trade, and shaping how cities grow. But beneath the waves, a quiet transportation revolution is underway. Infrastructure once limited by geography is now being reimagined through undersea railways.

Undersea rail tunnels—like the Channel Tunnel and Japan’s Seikan Tunnel—proved decades ago that trains could reliably travel beneath the ocean floor. Today, new projects are expanding that vision even further.

Around the world, engineers and governments are investing in undersea railways—tunnels that allow high-speed trains to travel beneath oceans and seas. Once considered science fiction, these projects are now operational, under construction, or actively being planned.

image 3

Undersea Rail Is Already a Reality

Japan’s Seikan Tunnel and the Channel Tunnel between the United Kingdom and France proved decades ago that undersea railways are not only possible, but reliable. These tunnels carry passengers and freight beneath the sea every day, reshaping regional connectivity.

Undersea railways are cleaner than short-haul flights, more resilient than bridges, and capable of lasting more than a century. As climate pressures and congestion increase, rail beneath the sea is emerging as a practical solution for future mobility.

What’s Being Built Right Now

China is currently constructing the Jintang Undersea Railway Tunnel as part of the Ningbo–Zhoushan high-speed rail line, while Europe’s Fehmarnbelt Fixed Link will soon connect Denmark and Germany beneath the Baltic Sea. These projects highlight how transportation and technology are converging to solve modern mobility challenges.

The Mega-Projects Still on the Drawing Board

Looking ahead, proposals such as the Helsinki–Tallinn Tunnel and the long-studied Strait of Gibraltar rail tunnel could reshape global affairs by linking regions—and even continents—once separated by water.

Why Undersea Rail Matters

The future of transportation may not rise above the ocean—but run quietly beneath it.


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Special Education Is Turning to AI to Fill Staffing Gaps—But Privacy and Bias Risks Remain

With special education staffing shortages worsening, schools are using AI to draft IEPs, support training, and assist assessments. Experts warn the benefits come with major risks—privacy, bias, and trust.

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Seth King, University of Iowa

With special education staffing shortages worsening, schools are using AI to draft IEPs, support training, and assist assessments. Experts warn the benefits come with major risks—privacy, bias, and trust.
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In special education in the U.S., funding is scarce and personnel shortages are pervasive, leaving many school districts struggling to hire qualified and willing practitioners.

Amid these long-standing challenges, there is rising interest in using artificial intelligence tools to help close some of the gaps that districts currently face and lower labor costs.

Over 7 million children receive federally funded entitlements under the Individuals with Disabilities Education Act, which guarantees students access to instruction tailored to their unique physical and psychological needs, as well as legal processes that allow families to negotiate support. Special education involves a range of professionals, including rehabilitation specialists, speech-language pathologists and classroom teaching assistants. But these specialists are in short supply, despite the proven need for their services.

As an associate professor in special education who works with AI, I see its potential and its pitfalls. While AI systems may be able to reduce administrative burdens, deliver expert guidance and help overwhelmed professionals manage their caseloads, they can also present ethical challenges – ranging from machine bias to broader issues of trust in automated systems. They also risk amplifying existing problems with how special ed services are delivered.

Yet some in the field are opting to test out AI tools, rather than waiting for a perfect solution.

A faster IEP, but how individualized?

AI is already shaping special education planning, personnel preparation and assessment.

One example is the individualized education program, or IEP, the primary instrument for guiding which services a child receives. An IEP draws on a range of assessments and other data to describe a child’s strengths, determine their needs and set measurable goals. Every part of this process depends on trained professionals.

But persistent workforce shortages mean districts often struggle to complete assessments, update plans and integrate input from parents. Most districts develop IEPs using software that requires practitioners to choose from a generalized set of rote responses or options, leading to a level of standardization that can fail to meet a child’s true individual needs.

Preliminary research has shown that large language models such as ChatGPT can be adept at generating key special education documents such as IEPs by drawing on multiple data sources, including information from students and families. Chatbots that can quickly craft IEPs could potentially help special education practitioners better meet the needs of individual children and their families. Some professional organizations in special education have even encouraged educators to use AI for documents such as lesson plans.

Training and diagnosing disabilities

There is also potential for AI systems to help support professional training and development. My own work on personnel development combines several AI applications with virtual reality to enable practitioners to rehearse instructional routines before working directly with children. Here, AI can function as a practical extension of existing training models, offering repeated practice and structured support in ways that are difficult to sustain with limited personnel.

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Some districts have begun using AI for assessments, which can involve a range of academic, cognitive and medical evaluations. AI applications that pair automatic speech recognition and language processing are now being employed in computer-mediated oral reading assessments to score tests of student reading ability.

Practitioners often struggle to make sense of the volume of data that schools collect. AI-driven machine learning tools also can help here, by identifying patterns that may not be immediately visible to educators for evaluation or instructional decision-making. Such support may be especially useful in diagnosing disabilities such as autism or learning disabilities, where masking, variable presentation and incomplete histories can make interpretation difficult. My ongoing research shows that current AI can make predictions based on data likely to be available in some districts.

Privacy and trust concerns

There are serious ethical – and practical – questions about these AI-supported interventions, ranging from risks to students’ privacy to machine bias and deeper issues tied to family trust. Some hinge on the question of whether or not AI systems can deliver services that truly comply with existing law.

The Individuals with Disabilities Education Act requires nondiscriminatory methods of evaluating disabilities to avoid inappropriately identifying students for services or neglecting to serve those who qualify. And the Family Educational Rights and Privacy Act explicitly protects students’ data privacy and the rights of parents to access and hold their children’s data.

What happens if an AI system uses biased data or methods to generate a recommendation for a child? What if a child’s data is misused or leaked by an AI system? Using AI systems to perform some of the functions described above puts families in a position where they are expected to put their faith not only in their school district and its special education personnel, but also in commercial AI systems, the inner workings of which are largely inscrutable.

These ethical qualms are hardly unique to special ed; many have been raised in other fields and addressed by early-adopters. For example, while automatic speech recognition, or ASR, systems have struggled to accurately assess accented English, many vendors now train their systems to accommodate specific ethnic and regional accents.

But ongoing research work suggests that some ASR systems are limited in their capacity to accommodate speech differences associated with disabilities, account for classroom noise, and distinguish between different voices. While these issues may be addressed through technical improvement in the future, they are consequential at present.

Embedded bias

At first glance, machine learning models might appear to improve on traditional clinical decision-making. Yet AI models must be trained on existing data, meaning their decisions may continue to reflect long-standing biases in how disabilities have been identified.

Indeed, research has shown that AI systems are routinely hobbled by biases within both training data and system design. AI models can also introduce new biases, either by missing subtle information revealed during in-person evaluations or by overrepresenting characteristics of groups included in the training data.

Such concerns, defenders might argue, are addressed by safeguards already embedded in federal law. Families have considerable latitude in what they agree to, and can opt for alternatives, provided they are aware they can direct the IEP process.

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By a similar token, using AI tools to build IEPs or lessons may seem like an obvious improvement over underdeveloped or perfunctory plans. Yet true individualization would require feeding protected data into large language models, which could violate privacy regulations. And while AI applications can readily produce better-looking IEPs and other paperwork, this does not necessarily result in improved services.

Filling the gap

Indeed, it is not yet clear whether AI provides a standard of care equivalent to the high-quality, conventional treatment to which children with disabilities are entitled under federal law.

The Supreme Court in 2017 rejected the notion that the Individuals with Disabilities Education Act merely entitles students to trivial, “de minimis” progress, which weakens one of the primary rationales for pursuing AI – that it can meet a minimum standard of care and practice. And since AI really has not been empirically evaluated at scale, it has not been proved that it adequately meets the low bar of simply improving beyond the flawed status quo.

But this does not change the reality of limited resources. For better or worse, AI is already being used to fill the gap between what the law requires and what the system actually provides.

Seth King, Associate Profess of Special Education, University of Iowa

This article is republished from The Conversation under a Creative Commons license. Read the original article.


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