Researchers at the Big Data Institute and colleagues have developed a new method for understanding the relationships between different DNA sequences and where they come from.

This information has widespread applications, from understanding the development of viruses, such as SARS-CoV-2, the strain of coronavirus that causes COVID-19, to precision medicine, an approach to disease treatment and prevention that takes into account individual genetic information. The study is published in GENETICS and is the featured paper in the September 2024 edition.  

Genetics is rapidly becoming part of our everyday lives. Nearly every week sees another newspaper headline about genetics and human ancestry, with huge datasets of DNA sequences routinely generated and used for medical study.

We can make sense of this genomic big data by working out the historical process that created it ‒ in other words, where the DNA sequences came from. If we take a small section of someone’s DNA we know it must have come from one of their two parents in the last generation, and previously from one of their four grandparents in the generation before that, and so on. This means we can represent the history of different sections of DNA by tracing them backwards through time.

If we do this for a large set of DNA sequences from different people, we can build up a set of genetic “family trees,” a genealogy of DNA sequences. This grand network of inheritance is sometimes called an ancestral recombination graph (ARG). Previous work by the same research group has shown that such networks can be used not only to illuminate the history of our genome, but also to compress DNA data and speed up genetic analyses.

Lead author and evolutionary geneticist at the Big Data Institute, Dr Yan Wong said, “There has been surprisingly little consensus on exactly how to represent such an ancestral recombination graph on a computer. In this study, we outline a simple and efficient encoding of genetic genealogies in which each ancestor can be thought of as a fragmentary length of DNA, or ‘ancestral genome’ at some point in the past. The history of today’s genetic sequences is traced back through those ancestral genomes, keeping track of which chunks of DNA were inherited from which ancestors.”

By using this simple scheme, recording genome-to-genome transmission of information, the study shows that the same genetic ancestry can be stored to different degrees of precision. This means relationships between different DNA sequences can be represented without having to know or guess the precise timing of joins and splits that underlie the true history of inheritance. The researchers also show that their description of genetic inheritance is flexible enough to deal with the wide variety of different methods that researchers currently use to reconstruct genetic history.

The approach allows scientists to store and analyze large amounts of genetic data on a standard laptop, and it generalizes to any species of life on earth. For example, it forms the basis of a “unified genealogy” of over 7,000 publicly available whole human genome sequences that the researchers released previously. They are currently creating a genetic genealogy of millions of SARS-CoV-2 genomes, collected over the span of the coronavirus pandemic, which will allow analysis of the recent history of the virus, pinpointing the emergence of novel mixed (or “recombinant”) strains. Dr Wong added, “We hope that this formal standard for how to represent genetic genealogies can help to unify the field of genetic history and make it easier for scientists to analyze, share and compare results. This will be crucial as we move into an era of genomic medicine, where genetic data will be used to diagnose and treat diseases, and where understanding the history of our genomes will be key to understanding our health and ancestry.”

As part of its scope, G3 Genes|Genomes|Genetics is dedicated to reporting new methods and technologies of significant benefit to the genetics community. Here, we highlight a selection of new analysis pipelines and software developments from the August 2024 issue that promise to improve research and practical applications in their respective subfields. These advances include easy and ready-to-use genomics tools that improve data management and analysis and overcome long-time challenges, emphasizing the ongoing progress and innovation happening in genomics.

An easy-to-use phylogenetic analysis pipeline

A new turn-key pipeline called OrthoPhyl has answered the call to improve the phylogenetic analysis of bacterial genomes. Developed by Middlebrook et al., OrthoPhyl can analyze up to 1,200 input genomes and reconstruct high-resolution phylogenetic trees based on whole genome codon alignments from diverse bacterial clades.

The beauty of OrthoPhyl is that it streamlines a usually complex, multi-step process requiring extensive bioinformatics expertise and computing resources into a multi-threaded tool that runs from a single command.

With more than 2 million publicly available bacterial genomes in NCBI’s GenBank database, OrthoPhyl can help research groups in the fields of bacterial phylogenetics and taxonomy take advantage of existing datasets to inform their ongoing analyses amid the ever-expanding sea of bacterial diversity.

Accurate genotype phasing and inference of grandparental haplotypes

To improve the analysis of complex plant genomes, Montero-Tena et al. have developed a new computational pipeline called haploMAGIC, which lets researchers identify locations of recombination known as genome-wide crossovers (COs) in multi-parent populations. haploMAGIC uses single-nucleotide polymorphism (SNP) data and known pedigree information to accurately phase genotypes, i.e., determine which alleles were inherited from each parent, and to reconstruct grandparental haplotypes, i.e., determine which alleles were inherited from each grandparent.

When tested on real-world data, haploMAGIC improved upon existing methods by using different levels of haploblock filtering to prevent false-positive COs—a common limitation—even as rates of genotyping errors increased. haploMAGIC can also distinguish between COs and gene conversions. By learning more about the position and frequency of genetic recombination events in complex plant genomes, breeders can better manage and expand genetic variation in their breeding programs.

A complete HiC/HiFi assembly pipeline

The USDA-ARS AgPest100 Initiative aims to create high-quality genome assemblies of pest insects that threaten agricultural production. However, the high cost and time currently needed to produce and manage these assemblies often hinders progress.

Molik et al. set out to address this challenge by developing a new Hi-C/high-fidelity (HiFi) sequencing genomic assembly pipeline called only the best (otb) using the Nextflow programming language. They then used otb to create a HiC/HiFi genome of the two-lined spittlebug, a significant agricultural pest that is not well understood. Overall, otb was able to streamline the process and reduce manual input and analysis time—including time spent organizing data and installing and calibrating bioinformatic tools.

By saving time, otb can significantly reduce costs for large genomic projects like AgPest100 and pave the way for new discoveries. Indeed, the HiC/HiFi assembly of the spittlebug genome represents a first step toward better understanding this plant-eating pest, which may lead to new, sustainable ways to manage it.

Assigning triploids to their diploid parents

Roche et al. have developed the first publicly-available, ready-to-use software for assigning triploid fish to their diploid parents. Triploidy means that an organism has three sets of chromosomes instead of two, and sterile triploids are commonly used in aquaculture breeding programs for their better yield and growth and to prevent genetic contamination of wild fish populations. The authors improve upon existing frameworks by updating the parentage assignment R package APIS to support triploids with diploid parentage.

When assessed with simulated and real datasets, APIS accurately assigned triploid offspring to their diploid parents using both likelihood and exclusion methods. The new software represents a key tool for establishing pedigrees in fish farming.

We’ve all suffered a cut from a blade, some broken glass, or even a sheet of paper. The smallest of wounds can cause infections and become detrimental if they don’t heal, so luckily for most of us, our immune system steps in to do the job. Just as the immune system kicks off a cascade of events to heal a cut, an individual cell kicks off a cascade of signals to manage disruption to its cell membrane. However, the molecular mechanisms that underlie cellular wound healing are quite complex, and we don’t have a complete picture of the phenomenon. In a recent study published in the August issue of GENETICS, Mitsutoshi Nakamura and Susan M. Parkhurst flesh out additional details of the process.

In eukaryotic cells, a structural protein called actin forms the cytoskeleton that underlies the cell membrane. When the cell cortex (cytoskeleton and membrane) is wounded, vesicles are recruited to temporarily plug the opening, and a ring of actin filaments and myosin fibers assembles around the site to rapidly close the wound. After the wound closes, the patch job is removed, and the cytoskeleton and cell membrane are remodeled to their normal states. Actin remodeling requires the activity of the Rho family of small guanosine triphosphatases (GTPases), including the guanine nucleotide exchange factors RhoGEF2 and RhoGEF3.

One of the earliest events after a cell is wounded is a swift influx of calcium from the extracellular space into the cell. The uniform inflow of calcium across the wound site recruits specific factors to precise locations—but how this occurs is still an open question. We do know, however, that a group of proteins called annexins bind specific phospholipids in a calcium-dependent manner and play a conserved role in wound healing. The authors previously showed that annexin AnxB9 is rapidly recruited to wounds and plays a vital role in actin stabilization in the Drosophila cell wound model by recruiting RhoGEF2 to the site. Interestingly, they found that AnxB9 is not required for RhoGEF3 recruitment.

In the current study, Nakamura and Parkhurst show that two additional Drosophila annexins, AnxB10 and AnxB11, are also rapidly recruited to distinct sites around the wound within seconds of injury and that they, in turn, recruit RhoGEF2 and RhoGEF3. The three annexins at the center of their work must find their way to specific locations, and they have non-redundant functions in stabilizing the formation of the actomyosin ring around the wound, which sets the stage for RhoGTPase-mediated repair. The authors show that, while the repair process can begin under reduced-calcium conditions, it is inefficient and ultimately unsuccessful.

Calcium signals are widely known as a second messenger and are crucial for many processes. In addition to its impacts on wound healing, an imbalance in calcium homeostasis is found in cancer, muscular dystrophy, and diabetes. Understanding the dynamics of calcium-mediated annexin recruitment may inform the development of therapeutic strategies to enhance cellular repair mechanisms. For instance, targeting annexin functions or modulating calcium signaling pathways could offer new avenues for treating injuries and diseases characterized by impaired wound repair. Continued research in this area promises to unveil further nuances of this vital cellular process—with potential applications in regenerative medicine and beyond.

Epigenetics has the potential to help us understand key differences in how divergent species control gene expression. Recent work published by Brown et al. in GENETICS delves into the epigenetic mechanisms of Pristionchus pacificus, providing significant insights into the evolutionary dynamics of epigenetic regulation.

Many developmental traits are sensitive to environmental factors, and the differences in how close evolutionary relatives respond to their environments can help demystify development. The nematode Pristionchus pacificus has been established as a comparative system to the well-studied Caenorhabditis elegans, but a thorough exploration of the conservation of epigenetic pathways between the two species has not been conducted—until now.

P. pacificus is known for its remarkable morphological plasticity, especially in its feeding structures. It appears to be a perfect model to study the epigenetic regulation of these adaptive changes; however, its relative newness as a model system means its epigenetic “toolkit” isn’t well-defined. To manipulate the proteins and modifications involved in the epigenetics of plasticity, they first must be identified.

To address this gap, Brown et al. began with an in-silico approach to identify potential epigenetic genes, followed by biochemical analysis to identify histone posttranslational modifications. By orthology, they then predicted which proteins might be responsible for adding or removing these marks. Their work provides a comprehensive “epigenetic toolkit” for P. pacificus and reveals significant differences in epigenetic machinery between P. pacificus and C. elegans, highlighting the evolutionary flexibility of epigenetic regulation and underscoring the importance of understanding species-specific epigenetic landscapes.

One of the authors’ most striking findings is that P. pacificus lacks the repressive PRC2 complex, which is usually crucial for histone methylation. Surprisingly, the enzymatic product H3K27me3 is still present, suggesting an unknown methyltransferase is responsible for this modification. The revelation that P. pacificus can maintain a critical histone modification while missing its canonical enzyme opens the door to myriad new paths of investigation.

This work serves as a foundational resource for future studies on developmental plasticity and epigenetic regulation in P. pacificus. It also provides a comparative framework for studying similar mechanisms in other species, offering new avenues for research in evolutionary biology and epigenetics.

Advances in technology have allowed geneticists to sequence a slew of unique animal, plant, and fungi to species over the past thirty years. Public databases currently house tens of thousands of eukaryotic genome assemblies, but a relative few include an estimate of the total genome size for their respective species. Genome size (or C-value) varies widely, even at the species level, largely due to noncoding DNA, which is often dismissed as “junk” DNA. The standard metrics used to characterize assemblies don’t get at size and chromosome number—the fundamental structure of genomes. Without this foundational information, a new study in GENETICS asks: “Are our genome assemblies good enough?”

To determine whether existing assemblies match the estimated genome size for their corresponding species, author Carl Hjelmen designed an R script to pull information from four NCBI databases: Assembly, BioSample, Sequence Read Archive (SRA), and Taxonomy. Starting from the >40,000 available eukaryotic genome assemblies, he analyzed the ~15,000 animal, plant, and fungi genomes that had existing size estimates. He also used karyotype databases to determine the haploid chromosome number for mammals, dipterans, coleopterans, amphibians, polyneopterans.

Taking into account Kingdom, the sequencing platform used, and common assembly statistics, Hjelmen devised a metric called “Proportional difference from genome size” to determine how closely a given assembly length came to matching the estimated genome size. If the assembly was within 10% of the estimate, he considered it “good.” 

He found that almost half of the assemblies analyzed were outside of 10% of the genome size estimate for their species. Most were smaller than the estimates, suggesting that some assemblies are missing information. The larger the genome size, the more dramatic the deviation tended to be—which wasn’t surprising considering that larger eukaryotic genomes often carry more of that so-called “junk” DNA. (Nongenic DNA—a friendlier way to describe the regions of the genome that don’t code for proteins—might turn out to be more informative than its reputation would suggest, points out Hjelmen.)

Hjelmen also discovered a positive relationship between late-replicating heterochromatin and assembly/genome size deviation. When genomes contained more heterochromatin, the assembly was more likely to be missing DNA; he argues that this “lost information” should be highly sought after when studying populations and their health. And though the results were modest, long-read technologies appeared more likely to assemble genomes near that 10% cutoff.

This study points out the limitations of widely used genome metrics like N50 (which narrowly measures contiguity) and BUSCO value (which describes completeness of core sets of genes). To shrink this analytic gap, Hjelmen proposes a new structural unit: “PN50,” or proportional N50 value, which contextualizes N50 values by relating them to estimated genome size and haploid chromosome number. Adding PN50 to the current mix of metrics could increase the rigor of genome research, offering insight into the less-studied structural components of assemblies and supporting universal assembly comparison.

Like a genetic time capsule, mitochondria contain the secrets of their ancient bacterial origins in their own genomes (mtDNA), separate from the nuclear genomes (nucDNA) that tend to be in the spotlight. mtDNA is especially vulnerable to UV damage as the cellular powerhouses are located in the cytoplasm, near the thin cell membrane. While UV damage to nuclear DNA triggers the nucleotide excision repair (NER) pathway to replace the damaged segment, mtDNA has more limited repair mechanisms and may instead rely on degradation to mitigate the damage.

Recent research published in the May issue of GENETICS by Waneka et al. revisits this assumption, reporting evidence that mitochondria may have unique DNA repair pathways to address UV damage in Arabidopsis thaliana and Saccharomyces cerevisiae.

UV damage produces cyclobutane pyrimidine dimers (CPDs), which result in bulky distortions in the DNA structure. In response, NER creates single-stranded incisions upstream and downstream of the dimer to excise the damaged fragment, which is then replaced by a polymerase using the opposite strand as a template.

The excision process results in damage-containing oligonucleotides with distinct size distributions, which is a useful characteristic for investigating damage repair. Bacterial NER produces fragments of ~10 to 13 nt, while eukaryotic nuclear NER produces fragments of ~23 to 30 nt. In an approach called excision repair sequencing (XR-seq), antidamage antibodies recognize and capture the fragments for subsequent sequencing analysis, allowing researchers to map the locations of active DNA repair across the genome.

To explore the effects of UV damage on mitochondrial DNA in plants, yeast, and flies, Waneka et al. analyzed previously published XR-seq datasets from UV-irradiated A. thaliana, S. cerevisiae, and D. melanogaster S2 cells. They found that mtDNA fragments had distinct size distributions compared to nucDNA fragments. In S. cerevisiae, the mtDNA fragments were predominantly 26 nt in size, while in A. thaliana, they were 28 nt. Both species also had additional fragment peaks at regular size intervals—2 nt in S. cerevisiae and 4 nt in A. thaliana. In D. melanogaster, however, mtDNA fragments did not follow the same size patterns.

Among the most frequent fragments, the CPDs were located in common positions relative to the read end. This suggests that UV damage results in mtDNA fragments of specific sizes and positions relative to the damaged location—a known characteristic of NER.

However, Waneka et al. also presented an alternative explanation, hypothesizing that the distinct fragment sizes could also be produced by a yet uncharacterized damage-induced mtDNA degradation pathway. They emphasized that many questions remain as the bacterial origins of mtDNA make it difficult to predict which genes may be involved in the response to UV damage.

What we do know is that the accumulation of mutations in mtDNA can contribute to metabolic and neurodegenerative diseases, and that UV damage to mtDNA is linked to skin aging and melanoma. Thus, further research is needed to understand the yet-unrevealed damage-induced NER and/or degradation mechanisms which may protect mitochondrial DNA. Either of these possibilities point to the exciting prospect of novel maintenance or processing in response to exogenous damage.

University of Minnesota researchers have successfully mapped the complete genome of the endangered Przewalski’s horse. Once extinct in the wild, the species now has a population of around 2,000 animals thanks to conservation efforts.

The study, published in the journal G3, was led by Nicole Flack and Lauren Hughes, researchers at the College of Veterinary Medicine, along with Christopher Faulk, a professor in the College of Food, Agricultural and Natural Resource Sciences. University of Minnesota students contributed to the genome sequencing through Faulk’s animal science course. 

“The genome is the basic blueprint for an animal and tells us what makes a species unique and also tells us about the health of a population,” said Faulk. “My students worked together to produce the highest quality Przewalski’s horse genome in the world.”

Researchers can now use this as a tool to make accurate predictions about what gene mutations mean for Przewalski’s horse health and conservation.  

“Studying genes without a good reference is like doing a 3 billion-piece puzzle without the picture on the box,” said Flack. “Przewalski’s horse researchers studying mutations in an important gene need a good reference picture to compare their puzzle with.” 

Researchers used a blood sample from Varuschka, a 10-year-old Przewalski’s mare at the Minnesota Zoo, to construct a representative map of genes for the species. The zoo has long been active in Przewalski’s horse breeding and management, with over 50 foals born since the 1970s. 

“We were excited to partner with the University of Minnesota to preserve the genetic health of the species as their populations continue to recover, both in zoos and in the wild,” said Anne Rivas, doctor of veterinary medicine at the Minnesota Zoo. “We are thrilled to offer our community the opportunity to see the horse as the results of our conservation efforts.” 

The cutting-edge technology sequencing used to construct the genome uses a small machine about the size of a soda can. Its portability means this method could be adapted for further study of wild Przewalski’s horses in remote locations.

Future applications of the reference genome may include studying genes that help the horse adapt to environmental changes, identifying mutations associated with specific traits or diseases, and informing future breeding decisions to help improve upon genetic diversity. Given the extreme population bottleneck that occurred during the near-extinction of Przewalski’s horse, such understanding is crucial for continued breeding efforts.

Aquaculture experts at the Roslin Institute have collaborated with industry partner Atlantic Aqua Farms to map the complete set of chromosomes for the blue mussel, an important commercial species in Europe and North America.

Researchers aim to support mussel farming and improve disease resistance using advanced gene sequencing technologies.

The high-quality genome map identifies over 65,000 genes, providing a comprehensive blueprint of the mussel’s genetic makeup.

This development is particularly important for the aquaculture industry, which relies on efficient and sustainable breeding practices to meet the growing demand for mussels.

Aquaculture Breeding

In Prince Edward Island, Canada, where the world’s only commercial mussel hatchery exists, this information will allow farmers to select mussels with desirable traits.

For instance, the new data will enable farmers to breed mussels with stronger byssus threads, which are crucial for the mussels to attach securely to ropes, ensuring a more stable yield.

Additionally, the genomic insights will help in selecting mussels that grow faster and produce more meat, enhancing overall productivity for mussel farms.

Disease Resistance

The mapped genome allows scientists to study the immune responses of different mussel populations, enabling researchers to identify how certain populations are better able to withstand threats posed by climate change and emerging diseases. This can lead to targeted breeding programmes that enhance disease resistance.

This will help reduce losses due to illness and improve the health and sustainability of mussel populations, the research team says.

Ecosystem conservation

This research not only benefits commercial aquaculture, but also contributes to the conservation of wild mussel populations by ensuring their health and genetic diversity, researchers explain.

Blue mussels can spread and establish themselves in non-native regions, affecting local ecosystems. Accessing detailed genomic data will enable scientists to track and mitigate the impact of these invasive populations, preserving the balance of marine environments.

In the coming months, the research team plans to explore the genetic diversity of blue mussels in Scotland, leveraging the complete genome map for more detailed analyses.

This research was published in the G3 Genes, Genomes, Genetics journal. The project was funded by Genome Canada and carried out in close collaboration with Atlantic Aqua Farms.

“This research project marks a significant advancement in aquaculture. It showcases how genomic research can provide practical solutions for commercial aquaculture and environmental conservation,” says Dr. Tim Regan, Career Track Fellow, Roslin Institute.

GSA’s Starter Culture Microgrant Program provides up to $2,000 in funding to GSA members to support  new, impactful initiatives that will benefit the genetics community. This volunteer-driven program includes a quarterly proposal review process by the Microgrant Review Committee. 

What is the committee looking for? 

We fund small projects that can make a big difference to the genetics community. We get particularly excited by applications that come up with new ideas for community outreach. 

A variety of projects can be funded, including summer camps, virtual presentations, and scientific workshops. However, a key aspect to consider is their impact: We look for evidence that the initiative will be transformative at some level, be it by engaging with broad and diverse audiences or underrepresented groups in science, or by fulfilling an unmet need in the community. 

We also like to ensure that, regardless of the applicant’s career stage, there is a solid connection between their background and the initiative they propose, such as a bioinformatics professor proposing a bioinformatics bootcamp. It is also important that we are convinced that the microgrant budget is both adequate and needed to deliver the initiative.

What type of projects are we looking for?

We do not want to limit your creativity, so we encourage you to apply with any amazing ideas you have! Just remember: Your project should clearly respond to a community need, have a broad and far-reaching impact, and benefit as many scientists and communities as possible. 

One of the main criteria we evaluate is the impact your initiative will have on your community and assurance the audience covers a breadth of research areas, geographic regions, institution types, and other elements that foster a wide range of knowledge and expertise.  . We want to see projects that fulfill a need in your community or benefit the community at large, such as genetics-focused STEM fairs or similar events that help expand access to genetics knowledge, or seminar series open to all early career scientists at your institution and across several institutions. 

We are also eager to fund projects in areas with limited access to science resources and communication. Seminar series, lecture workshops, hands-on training workshops in developing fields such as bioinformatics, and science communication initiatives are great ideas for your project, especially if these activities would not occur without  funding from this program.

What details are needed?

When preparing your Starter Culture Microgrant proposal it is essential that you include detailed, precise, and relevant information to effectively communicate the merits and feasibility of your project. Here’s a breakdown of what to include to make your proposal stand out:

1. Clear and specific objectives:

Begin with a well-defined statement of your project’s goals. Clarify what the project aims to achieve, the expected impact, and ensure these objectives are measurable, achievable, and relevant – this helps the committee understand your vision and the structured planning behind it.

2. Detailed project plan:

Provide an in-depth description of the activities and methodologies your project will employ and include a timeline with key milestones and phases. For example, if you’re proposing a symposium, list the topics to be covered, types of sessions (e.g., workshops, keynote speeches), and the format of each session. Describe the selection criteria for speakers and how the event will offer novel insights compared to existing symposia. For another example, if your project is about providing education or training to your community, explain the curriculum or content and teaching methods to be used.

3. Rationale and need:

Explain the significance of your project by highlighting the specific issues or gaps your project addresses and why it is timely. Your rationale should connect with the broader goals of the Starter Culture Microgrant Program, demonstrating alignment with program objectives.

4. Target audience and beneficiaries: 

Identify the direct beneficiaries of your project, provide detailed demographic information, and describe how the project meets their needs. Discuss the expected changes or benefits for this group, emphasizing the direct impact of your initiative.

5. Outcomes, impact measurement, and evaluation plan:

Detail expected outcomes and how you will measure the project’s impact. Include specific metrics or indicators, such as participant feedback, post-event surveys, or measurable changes in participant knowledge. This section demonstrates your commitment and the anticipated impact of your project.

6. Support and collaboration:

Mention any additional support, such as co-sponsorships, partnerships, or endorsements from relevant organizations. If you have received or are seeking other grants, specify how these support your current proposal and demonstrate broader validation of your project, and why this particular grant is also needed for your initiative.

7. Detailed budget justification:

Provide a line-item budget where every expense is justified in relation to project activities. If you’re requesting funds for materials, specify quantities, costs per unit, and total cost. For example, if updating educational materials, provide a breakdown of costs involved in updating each set of slides or resources. If the budget includes stipends or honoraria, explain the rationale behind the amount and the responsibilities covered by these payments. This transparency enhances your proposal’s credibility.

Sample budget:

Let’s say your project is a hybrid genome engineering symposia. Your budget should include a breakdown of supplies needed, speaker travel reimbursements, printed marketing materials, and other costs you might incur: 

• $500 – Snacks and refreshments for 100 in-person participants 
• $280 – Invited speaker’s travel reimbursement  
• $200 – Print cost of posters, banners, and fliers  
• $980 – Total requested funding

8. Sustainability or future directions:

If your project is part of a larger initiative, describe how it will have an impact beyond the funding period. Discuss plans for scaling, potential future funding, or integration into broader practices, showing the long-term vision beyond the grant period.

Once you’re ready, use the Starter Culture Microgrant Application checklist to develop your application for submission. We’re excited to see the innovative ideas you’ll bring to the genetics community. Submit your proposal today! 

Humans have an incredible ability to recognize friends and family members from far away. This cognitive function helps us find the one familiar face hidden among hundreds of unfamiliar ones; it also informs us about their attention or emotional state of mind. Those with face blindness, however, must rely on alternative cues to recognize familiar individuals. In a new study, published in the June issue of GENETICS, Sun et al. apply classical genetic mapping strategies to identify the first gene involved in recognizing faces.

Face recognition relies on intricate neuronal processes that first identify individual elements of a face—blue eyes, heavy eyebrows, or small ears—before detecting the spatial relationship among these elements. Researchers have studied the neuronal circuits underlying facial recognition since the 1970s, and they have identified several brain regions that contain clusters of face-selective neurons. In contrast, our current understanding of the molecular players responsible for this complex cognitive behavior is non-existent.

Congenital prosopagnosia (CP) is a hereditary condition that causes face blindness. When neurobiologist Yi Rao, senior author on the study, learned of CP, he realized that identifying its genetic cause could help us begin to define the molecules that make up the facial recognition machinery.

Rao’s group started searching for individuals who struggle with identifying faces around 2007 but faced several hurdles along the way, the first of which was reliably identifying patients. “We thought it would be straightforward to check the phenotype versus the genotype. But it turns out [that the field] was using different questionnaires and behavioral assays. So, we had to come up with a standard ourselves,” Rao said. Along the way, his team learned that questionnaires were more reliable than most face recognition behavioral assays. “People have adopted different compensation strategies: they recognize people by their hair, by their voice, or their gestures. So, some of them can pass a face recognition assay. But if you really ask them, they will admit that they actually have problems recognizing faces in daily life,” Rao explains.

Once the researchers had developed a reliable patient phenotyping pipeline, they started screening individuals and were surprised to identify a large family with prosopagnosia among their own staff members on campus. They next performed linkage analysis, analyzing single nucleotide polymorphisms that segregate with CP to identify genomic regions responsible for the face blind phenotype. Combining the results of their linkage analysis with whole genome sequencing, Rao’s team identified a potential disease-causing mutation in MCTP2, a calcium-binding protein. They then screened an additional 3,000 people for face blindness, finding MCTP2 mutations in several other affected families.

Not all prosopagnosia families carry MCTP2 mutations, which means there are more genes involved in face recognition for scientists to discover. However, this study has now identified the first piece of the molecular machinery responsible for our ability to recognize familiar faces.

Some of the identified pathogenic mutations reside in a primate-specific isoform, causing Rao to suspect that this isoform acquired a novel function that unlocked face recognition. Identifying what precisely that function is will not be trivial. “The problem is that the mutation is not in the calcium-binding domain, so we don’t have a cellular or molecular assay to study [MCTP2] function,” says Rao. “We would have to rely on making knock-in mutations in monkeys [to study their consequence on gene function],” he adds.

Despite these difficulties, Rao is committed to continue this line of research, hoping to identify the precise nature and function of the molecular machinery that mediates this higher cognitive behavior. He plans on following the approach researchers took to understand the molecular machinery underlying circadian rhythm by piecing together the pathway one element at a time through in-depth genetic and molecular studies.

“We will have to keep working on it, and it is hard because you have to use other species (atypical animal models, like non-human primates), but I believe this is the first time we’ve been able to study [the genetics of] a higher cognitive function,” Rao concludes.