Andrew Kern
Senior Editor

Andrew Kern is an Evergreen Professor in the Department of Biology and the Institute for Ecology and Evolution at the University of Oregon. His research combines modern machine learning methods with classical probabilistic approaches and large-scale simulation to gain insight into population genetic and evolutionary biological questions. His lab focuses on methods development, creating new tools that empower the field to gain insights that weren’t attainable previously. One fundamental thread that has run through his entire research career is understanding the impact of natural selection on genetic variation in natural populations including models such as humans, mosquitoes, and fruit flies as well as non-model systems such as barnacles and octopuses.  He completed his ScB in Biology at Brown University and his PhD in Population Genetics at the University of California, Davis. Kern was an NIH Ruth Kirschstein National Research Service Award postdoctoral fellow at the University of California, Santa Cruz where he studied Computational Biology under the mentorship of David Haussler. Before arriving at the University of Oregon, Kern served as an Assistant Professor of Biology at Dartmouth College, and both an Assistant and Associate Professor of Genetics at Rutgers University.

Alexander Edward Lipka
Senior Editor

Alexander Edward Lipka leads a research team at the University of Illinois that applies cutting-edge statistical approaches to quantitative genetics analyses, resulting in more accurate quantification of genomic signals underlying phenotypic variation and prediction of breeding values of agronomically important traits. His lab also develops freely available software that enables the broader research community to apply these approaches to their own work. Here are some examples of publications from his lab:

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Antonis Rokas
Senior Editor

Antonis Rokas holds the Cornelius Vanderbilt Chair in Biological Sciences and is a Professor in the Departments of Biological Sciences and Biomedical Informatics at Vanderbilt University. He also serves as the Founding Director of the Vanderbilt Evolutionary Studies Initiative, an interdisciplinary center that unites scholars from diverse disciplines with broad interests and expertise in evolution-related fields. Research in the Rokas lab focuses on the study of the DNA record to gain insight into the patterns and processes of evolution. Using computational and experimental approaches, their current studies aim to understand the molecular foundations of the fungal lifestyle, the reconstruction of the tree of life, and the evolution of human pregnancy. Rokas is a Guggenheim Fellow (2018), a Fellow of the American Academy of Microbiology (2019), and an American Association for the Advancement of Science Fellow (2020).

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There is no simple way to make a brain, even in a creature as small as a fruit fly. As an embryonic fly develops into adulthood, its central nervous system (CNS) expands almost 100-fold in mass. Neuronal, glial, immune, and vascular cells—in both the CNS and the peripheral nervous system (PNS)—must work in harmony to build the structures responsible for controlling movement and behavior. Since structure dictates function, the size and shape of the CNS must be tightly regulated, but the genes and pathways involved in the process have yet to be fully described.

In a recent study published in the September issue of G3: Genes|Genomes|Genetics, Lacin et al. use the power of forward genetics in Drosophila larvae to identify genes controlling nervous system shape. Using the robust genetic manipulation toolkit available in Drosophila, they further identify a glial subtype-specific molecular profile that functionally subdivides glia along the peripheral-central axis.

Their screen used the classic mutagenesis agent ethyl methanesulfonate (EMS) to randomly introduce mutations, generating more than 12,000 mutant lines that carried mutations specifically on the second chromosome. The authors screened for larval mutants with dramatically altered CNS shapes, sorting them into three categories: widened, elongated, or misshapen. Through a combination of genetic mapping, complementation analysis, and whole genome sequencing, they identified 50 mutant alleles across 17 genes that encode transcription factors, enzymes, signaling receptors, tumor suppressors, and basement membrane proteins.

Four of the mutant alleles were found in the senseless-2 (sens-2) gene, which encodes a zinc-finger domain transcription factor; these alleles caused massive elongation of the ventral nerve cord (the Drosophila equivalent to the spinal cord) that manifested very early in the first-instar larvae (see Figure 1). To understand the cellular basis for the mutant sens-2 CNS elongation phenotype, the authors generated an antibody against the Sens-2 protein and found it localized to most glia on peripheral nerves—but not in any CNS glial cells.

To determine whether sens-2’s role in determining ventral nerve cord length was specific to its presence in peripheral glia, the authors selectively knocked down its expression in those cells using the Gal4-UAS system. They found that sens-2 expression in peripheral glia is necessary to control CNS structure, and loss in those cells accounted for the observed elongation phenotype. Restoration of sens-2 expression rescued the elongation phenotype.

Lacin et al. were able to establish sens-2 as a marker distinguishing specific glial subtypes along the CNS-PNS axis with a profound impact on gross nervous system structure. In the future, the authors aim to investigate transcriptional targets of sens-2, which could help illuminate mechanisms governing glial development and differentiation in the PNS.

In recent years, the use of expensive -omics technologies to discover cellular heterogeneity at scale has become quite popular in neuroscience research, and the genes identified in these studies need validation and characterization. Here, Lacin et al. present a powerful demonstration that classical genetic studies in invertebrate model systems are still effective at powering neurogenetics and cellular heterogeneity research—at a fraction of the cost.

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.

Eugenics is a stain on the founding of the field of genetics, one that modern geneticists must still reckon with. The Allied Genetics Conference 2024 featured a thought-provoking panel discussion on this subject, moderated by past GSA Presidents Denise Montell and Tracy Johnson. Panelists Katrina Claw, Nathaniel Comfort, Steven Farber, Daniel HoSang, and Jazlyn Mooney shared their expertise on the history of eugenics and the ways its ideas persist in both science and society even today. Their keen insights shed light on the interdisciplinary nature of science, highlighting that anthropology, philosophy, and the humanities are all key in the study of science. 

This event marks the start of important conversations for GSA and the larger genetics and genomics community about the field’s history. GSA understands that scientific research takes place in the context of society, and we strive to build an environment in which all researchers from all backgrounds feel welcome and can thrive. For that, we must look critically at the space in which we conduct research, including reflecting on and learning from the past. Even today, bad actors twist the findings of genetics research to support racist ideology, giving this conversation and reflection notable immediacy. 

While acknowledging that this topic can be uncomfortable, GSA will not oversimplify these discussions; our goal is to face this history directly, even as it may require re-contextualizing luminaries in the field. Only through honest reckoning can we move forward toward an anti-racist scientific enterprise.

GSA fully rejects eugenics ideology, and we commit to understanding how it has lived within and outside our institutions and society at large so we can do better as a field and a scientific community. We will continue to address this topic at GSA, creating space for our community to share their thoughts and experiences and to learn together.

Watch the recording of the panel on our YouTube channel.

Yaniv Brandvain
Associate Editor, Empirical Population Genetics section

Yaniv Brandvain is an Associate Professor of Plant and Microbial Biology at the University of Minnesota working in theoretical and empirical population genomics. He received a BA in Human Ecology from the College of the Atlantic and a PhD in Biology from Indiana University, working on the robe of conflict, cooperation, and co-adaptation in plant evolution and speciation. During his postdoc at the University of California, Davis, he developed evolutionary theory concerning meiotic drive, and he developed population genomic approaches to study the evolutionary origins of self-fertilizing plant species. He is interested in understanding how new plant species arise with a particular interest in how mating systems and genomic conflicts shape plant diversity. His lab combines empirical and theoretical population genomic analyses with collaborative work in empirical systems to study the evolutionary forces shaping flowering plant diversity. He was also named McKnight Land-Grant Professor from the University of Minnesota (2017-2019) for his research efforts and received the Stanley Dagley-Samuel Kirkwood Undergraduate Education Award for his efforts in undergraduate instruction in biostatistics. 

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.

Christos Palaiokostas
Associate Editor, Fish and Complex Traits section

Christos Palaiokostas is an Associate Professor in the Department of Animal Biosciences at the Swedish University of Agricultural Sciences. He is working in the field of aquaculture genetics and breeding. He received his PhD from the Institute of Aquaculture at Stirling University in Scotland, while studying the sex determining system of fish with sexual dimorphism. During his postdoc at the Roslin Institute of Edinburgh University he worked on improving disease resistance in farmed fish using genomics. His research is focused in the application of high-throughput sequencing and genotyping technologies for studying complex traits in aquatic organisms.

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