Archaeologists have long known how to extract millennia-old stories from a single tooth buried in an ancient ruin—and now geneticists have the tools to join them. Advances made in the last several years have enabled researchers to sequence tiny amounts of DNA preserved in very old specimens, such as the material inside a tooth from the Stone Age. But this ancient DNA (aDNA) is often severely degraded, limiting its use. In GENETICS, Joshua Schraiber describes a new statistical approach to getting the most from these old samples and reports how he used the method to uncover secrets about the relationships between ancient humans and modern ones.

A major obstacle to understanding humans’ recent evolutionary history has been the inability to infer much about it using genetic data from people living today. If restricted to data from modern people, we would be locked out of information of great scientific and cultural relevance. The genetic relationships between ancient and modern populations can provide clues about migrations that occurred thousands or tens of thousands of years ago and help us better understand our histories. For example, researchers recently found that many people living in South America today are in part descended from an ancient North American group called the Clovis people.

Solving these kinds of puzzles is where aDNA shines—if you know how to use it. Schraiber found that the best way to determine genetic relationships among modern and ancient populations using degraded samples of aDNA is to sequence multiple ancient samples at low coverage rather than fewer samples at high coverage. After applying his new method to existing genetic data from 230 West Eurasian people who lived 8500–2300 years ago, Schraiber discovered that none of them came from populations that are direct ancestors of any modern European populations tested.

Schraiber’s analysis also suggests many ancient European people separated into small populations with little gene flow among them, and that most of these local groups died out, leaving a limited genetic legacy in modern European people. His results further imply that the oldest populations were the smallest, although this must be tested further because of the complicated nature of drift time, one of the parameters used in the analysis. Since drift time also complicates other types of analysis, Schraiber anticipates that methods to fully account for the troublesome variable will allow development of a fuller picture of these results.

If validated, the increase in effective population size over time predicted by Schraiber’s method would be interesting to compare to archaeological information about ancient humans, especially because many current ideas hinge on a link between agriculture and the rise of larger, more interconnected societies. Perhaps new scientific evidence would put to rest some debates about ancient humans’ lives—but it would surely spawn even more questions, too.

CITATION:

Joshua G. Schraiber. Assessing the Relationship of Ancient and Modern Populations.
GENETICS, 208(1), 383-398.
DOI: 10.1534/genetics.117.300448
http://www.genetics.org/content/208/1/383

Palm fronds crunch under a researcher’s foot as she hikes through a rainforest in Madagascar looking for a spot to release a tiny, omnivorous ball of fur with bulging eyes—a mouse lemur. This creature, the smallest type of primate, is an important research subject: it has just yielded a blood sample, skin cells, and an abundance of physical and behavioral data. The researcher and her team have big plans for the little lemur—they hope it will soon become a genetic model organism that will help us better understand many aspects of primate biology, behavior, and health, including lemur and human diseases.

In the June issue of GENETICS, Ezran et al. explain their decision to pursue genetic research on these diminutive primates. The idea began as a project for three high school laboratory interns to find an appropriate model organism for primate genetics—no small feat, given that there are over 500 known primate species.

The genetic models we currently depend on, such as mice, can’t recapitulate all of primate biology. Genetic research on mice has led to countless important discoveries, but their physiology and behavior differ in many ways from that of humans and other primates. For example, in humans, the fatal lung disease cystic fibrosis is caused by dysfunction in a single gene, but mice with the same defective gene do not show symptoms of the disease. Human-like behavior, such as the use of tools and sophisticated vocal communication, is also impossible to study in the existing genetic model organisms, so it’s critical to find genetic models closer to humans on the evolutionary tree.

The researchers considered many factors. The candidate would need short reproduction times and relatively large numbers of offspring to allow many genetic crosses on a practical timescale, but these traits are found in few primates. The ideal model would also be small, inexpensive to maintain, and easy to work with—it’s no use trying to do genetics with large or dangerous animals. They also took conservation status into account; if a species might be threatened by using it as a model, it was ruled out.

With all these factors in mind, the group settled on the genus Microcebus: the mouse lemurs. They’re about twice as closely related to humans as are rodents, they’re the fastest-developing primates, they have large litter sizes, they are abundant—and working with them in the wild is virtually free. There’s also an existing body of research on natural mouse lemur populations detailing their biology, evolutionary relationships, and the structure of their populations. Studies of these wild lemurs have shown they have good memories and communicate vocally in their social groups, making them excellent models for those aspects of human behavior. And demonstrating their amenability to laboratory studies, several research colonies already exist around the world; one such colony has been studied for over 50 years. A final benefit of using these animals is that there’s a large amount of standing genetic variation in their native populations—so finding interesting mutations can be as simple as sifting through existing variants.

Ramping up research on mouse lemurs could even have benefits for the local Malagasy people. Ezran et al. describe a high school program they have established in Madagascar’s Vatovavy-Fitovinany Region, an area known for its rich rainforests. The program aims to train the students as citizen scientists using the natural laboratory surrounding their school. One day, these students may be the researchers following the lives of these tiny primates and identifying the genes that influence them, putting their skills to use in learning from their bountiful natural environment.

CITATION:

Ezran, C.; Karanewsky, C.; Pendleton, J.; Sholtz, A.; Krasnow, M.; Willick, J.; Razafindrakoto, A.; Zohdy, S.; Albertelli, M.; Krasnow, M. The Mouse Lemur, a Genetic Model Organism for Primate Biology, Behavior, and Health.
GENETICS, 206(2), 651-664.
DOI: 10.1534/genetics.116.199448
http://www.genetics.org/content/206/2/651

When it comes to genotyping technology, poop genetics is stuck in the 1990s. While most geneticists are now awash in genome-scale data from thousands of individuals, those who depend on  fecal and other non-invasively collected samples still rely on old-school, boutique panels of a dozen or so genetic markers.

But feces — along with fur, feathers, and urine — is critically important stuff for understanding the population genetics, ecology, evolution, behavior, and conservation of wild animals. Many are too elusive or endangered to allow collection of blood samples, and even for common species it is a logistical nightmare to immobilize and draw blood from large numbers of animals in the field. In the latest issue of GENETICS, Snyder-Mackler et al. describe tools that promise to advance studies of such samples into the genomic era.

Patrick Chiyo collecting noninvasive samples from elephants in Amboseli National Park. Photo courtesy Jenny Tung.

Noninvasively collected samples have the obvious advantage of easy access. “We have freezers and freezers full of baboon poop,” says study co-leader Jenny Tung (Duke University). Tung’s group works on behavior and  genetics in a wild baboon population in Kenya. But though abundant, poop also presents serious challenges for standard genetic analysis. The DNA present in noninvasive samples is typically a fragmented mixture of host and contaminant sequence. For example, only around 1% of the DNA in a fecal sample comes from the animal that produced the poop. Most of the rest is microbial.

These limitations were first overcome in the 1980s and 1990s, and the ability to analyze DNA from noninvasive samples revolutionized the field. Using such samples not only allowed geneticists to understand the genetic diversity and viability of endangered animals, it allowed them to empirically test important theories about animal behavior and evolution.

“There are many examples. Noninvasive sampling of chimps, baboons, rhesus macaques and other primates revealed that animals really do bias their behavior towards relatives, even paternal relatives that are likely more difficult for an individual to identify as kin,” says Tung. “And in baboons, it also showed that males provide some paternal care to their offspring, which wasn’t expected for a polygamous primate.”

But the genotyping methods used in such studies have changed surprisingly little over the last twenty years. For the most part, researchers still use small groups of carefully validated markers, usually based on stretches of short tandem repeat sequences (microsatellites). This means the field has mostly missed out on the benefits of genomics that have become routine for medical researchers and those who work with laboratory organisms.

“Microsatellite approaches still work. But over the last 5 or 10 years it has become impossible to ignore the way genome-scale datasets allow you to answer entirely different questions,” says Tung.

For example, data on how a genome varies across a population can provide crucial evidence of the evolutionary and demographic forces that have shaped it. Genomic data can also trace in detail the mergers and separations of mixing populations.

Vet, a female yellow baboon, and her children in Amboseli National Park. Photo courtesy Susan Alberts.

The good news for poop genomics is that short-read next-generation sequencing methods are well suited to the fragmented DNA found in noninvasive samples. These methods have been famously adapted for analyzing a sample type that also suffers from vanishingly small amounts of target sequence: ancient DNA. The bad news is that the expensive, intensive approaches that work well for a precious sample of Neanderthal bone are not practical for a geneticist facing a freezer full of poop.

About six years ago, Tung’s friend and colleague George (PJ) Perry published a major advance that allowed large-scale resequencing from noninvasive samples. It was based on a method known as sequence capture, which enriches for host sequence using synthetic RNA “baits” to capture the target DNA. Tung was excited by the possibilities of the methods, but realized it was still too expensive for most applications. This was partly because the baits had to be custom-designed and synthesized for the species of interest. The method also had the drawback of only capturing a tiny fraction of the genome, while consuming large amounts of sample.

“Even fecal samples are exhaustible,” says Tung. “We have a lot of irreplaceable samples from dead animals, for instance. If we’re going to use them up, we want to cover all our bases and gather data on a truly genome-wide scale.”

So Tung’s group and their collaborators worked to modify and scale up Perry’s protocol. They also constructed the baits in a considerably cheaper way, using in vitro transcription of RNA from baboon DNA templates, sidestepping the need for custom synthesis. The new protocol had more modest input DNA requirements and could enrich the target DNA by 40-fold.

But getting enough sequence per sample was just the beginning. Xiang Zhou (University of Michigan) led the group’s efforts to develop tools to analyze data from the new method. Zhou says one of the reasons microsatellites became so popular was the availability of standard and easy-to-use software for assigning paternity from the data. “If people are going to transition to a new method, we thought it would be incredibly important that we package our models into software that will make it as easy as possible,” says Zhou.

But to develop something comparable for low-coverage sequence, the team faced two major challenges: the data is simultaneously much richer (more sequence) and much lower quality (more uncertainty). To deal with the large quantity of data they needed much more computationally efficient algorithms. They also had to factor in the lower data quality, which makes it  impossible to use the simpler approaches that work when the genotype at each site is known with certainty. Instead, they incorporated the error rate across all the sites in the genome, generating a sophisticated statistical model.

One of (several) freezers in the Tung lab containing boxes of fecal samples. Photo courtesy Jenny Tung.

Using the new capture method and the paternity assignment software (called WHODAD), the team were able to construct pedigrees from baboon fecal samples that almost perfectly  matched those created using traditional analysis of high-quality DNA from blood. In short, despite the low coverage of the genome (typically less than 1x), and the resulting very high uncertainty of the genotype at any one site, the trends in the data were more than enough to reconstruct family relationships.

But what about cost? Lead author Noah Snyder-Mackler gave the project the pet name “fecal alchemy” because it aims to transform poop into a data goldmine. But not every researcher can afford gold — most labs must use the cheapest tool that will get the job done. Tung says they included a cost analysis in the paper because they are regularly asked about the price of making the switch.

“Right now it costs about twice as much to produce 1x coverage of the entire baboon genome as it does to type 14 microsatellites. But the amount of information you get is much greater! So if you’re thinking in terms of cost per genotype, our method is way more cost effective. But in terms of absolute amounts it’s more expensive. In the end the cost-benefit decision depends on what questions you’re trying to answer,” says Tung. “Of course we’d like to get it even cheaper and more efficient and more robust. We’re working on it!”

FUNDING

This work was partly funded by the National Science Foundation DEB through an EAGER grant, with co-funding from NSF Biological Anthropology.

CITATION

Noah Snyder-Mackler, William H. Majoros, Michael L. Yuan, Amanda O. Shaver, Jacob B. Gordon, Gisela H. Kopp, Stephen A. Schlebusch, Jeffrey D. Wall,Susan C. Alberts, Sayan Mukherjee, Xiang Zhou, Jenny Tung (2016). Efficient Genome-Wide Sequencing and Low-Coverage Pedigree Analysis from Noninvasively Collected Samples. Genetics, 203(2), 699-714.

http://www.genetics.org/content/203/2/699

DOI: 10.1534/genetics.116.187492