Alina Elahie

A population-scale map of structural variants in Qatar reveals medically actionable genetic effects that standard approaches miss, highlighting the growing importance of inclusive genomics for precision medicine.

Precision medicine promises to tailor prevention and treatment strategies to individual genetic profiles, yet much of this vision relies on data from populations of European ancestry and on genetic variants that are relatively easy to detect¹. A study by Aliyev et al. provides a comprehensive analysis of structural variants (SVs) in over 6,000 individuals from the Qatari population, leveraging Qatar’s advanced genomics infrastructure and high consanguinity rates to reveal genetic diversity with direct implications for disease, diagnostics, and therapy¹. The authors link SVs to medically relevant traits such as kidney function, body composition, and extreme obesity, highlighting the clinical importance of investigating underrepresented populations¹. Using whole-genome sequencing (WGS) and detailed phenotyping, they show that SVs contribute substantially to these traits, often independently of single-nucleotide variants (SNVs)¹.

By organizing large DNA rearrangements across thousands of genomes, the study maps a rich landscape of deletions, duplications, and other SVs¹. Many of these variants are underrepresented in global reference datasets yet common in Qatar, frequently affecting genes implicated in Mendelian disease and complex traits¹.This work elevates SVs from an afterthought to a central player in precision medicine for Arab and neighbouring populationsand fills a geographic and demographic gap in human genomics research^1,2.

The Qatari cohort brings this biological potential of SVs into focus. Unlike SNVs, many SVs involve the loss or gain of larger segments of DNA, leading to substantial changes in the number of functional copies of a gene, known as gene dosage (Fig. 1)³. Deletions spanning coding regions can eliminate protein production, duplications can increase gene dosage, and more complex rearrangements can disrupt regulatory elements^3,6,8. Large studies of genetically diverse populations suggest that each person carries a small number of rare SVs that alter the dosage or structure of several genes^3,4.  In populations with high consanguinity, long stretches of DNA inherited from a shared ancestor, known as autozygosity, increase the likelihood that rare variants occur in homozygous form, occasionally causing a gene to become completely inactivated in otherwise healthy individuals^3,4,5,8.

To appreciate the magnitude of this hidden variation, it is useful to consider the broader spectrum of human genetic diversity (Fig. 1)³. While precision medicine has largely focused on SNVs (<50 bp), SVs (≥50 bp) encompass both balanced variants, such as inversions, and unbalanced variants, including deletions and duplications³. Although numerically less frequent than SNVs, the larger size of SVs often results in greater functional impact, from eliminating protein production to altering gene regulation^1,3.

Figure 1: The spectrum of human genetic variation. Genomic diversity spans from single-nucleotide variants (SNVs) to large-scale chromosomal rearrangements³. While precision medicine has historically focused on SNVs (<50 bp), Aliyev et al. highlight the clinical importance of SV’s (≥50 bp)³. These include balanced variants (no net DNA change, such as inversions) and unbalanced variants (dosage-altering deletions and duplications)³. Although SVs are numerically less frequent than SNVs, their size often leads to greater functional impact, including gene knockouts and regulatory disruptions observed in the Qatari population^1,3. Advances in WGS enable detection of complex SV classes that are often missed by microarrays or exome sequencing^1,3,6. Figure adapted from Collins, R. L. & Talkowski, M. E.³ and created in BioRender.

A key strength of this study is its population context. High rates of consanguinity in Qatar increase the likelihood that rare variants, including SVs, appear in homozygous form^3,8. The authors identify over 180 genes disrupted by homozygous SVs and demonstrate that these disruptions have measurable biological consequences¹. By measuring protein levels, they confirm that gene knockouts reduce or eliminate protein production, linking genotype to molecular phenotype¹. This connection moves beyond cataloguing genetic variation and towards understanding its functional impact.

Importantly, the study also highlights the importance of SVs outside of traditional protein-coding regions, which are often overlooked in clinical genetics¹. By examining individuals with extreme trait values, the authors identified homozygous deletions with large effects on kidney function, body composition, and obesity¹. For example, a deletion in a regulatory region on chromosome 19 is linked to obesity, showing how changes in non-coding DNA can influence key biological processes. Focusing only on protein-coding genes risks missing important disease-causing variants¹.

From a clinical perspective, 3.2% of individuals carry variants in medically actionable genes, and nearly one-third of these would have been missed if only SNVs were considered¹. These findings underscore the need to incorporate SVs into clinical genomics pipelines. For instance, by updating screening arrays or developing sequencing approaches that capture larger DNA rearrangements, enabling more comprehensive detection of disease-relevant variants. Currently, Qatar uses a population-specific screening array (QChip1) that primarily detects small variants and misses many structural alterations⁷. The SV catalogue generated in this study helps identify which missing variants are most relevant locally, informing the design of next-generation screening tools^1,7.

Technology limitations also remain important. Short-read sequencing, even at high coverage, misses many complex SVs and performs poorly in repetitive regions⁹. In contrast, long-read assemblies and regional pangenomes are already revealing tens of megabases of sequence absent from standard references, along with additional SVs of clinical relevance^6,8,9. Looking ahead, long-read sequencing promises improved SV detection, refined breakpoints, and discovery of complex rearrangements⁶.

By examining DNA changes alongside gene activity and cellular behavior, the study provides a clearer picture of how SVs influence human health and biology. This study illuminates a previously hidden layer of human genetic diversity, demonstrating that SVs, particularly in non-coding regions can exert profound effects on health and disease¹. By focusing on a population historically underrepresented in genomics, this work advances precision medicine for the Arabian Peninsula while providing insights relevant to global health².

References

1. Aliyev, E. et al. The biomedical landscape of genomic structural variation in the Qatari population. Nat. Commun. 17, 1–15 (2026).

2. Cole, A. J. et al. The landscape of genomic structural variation in Indigenous Australians. Nature 624, 610–617 (2023).

3. Collins, R. L. & Talkowski, M. E. Diversity and consequences of structural variation in the human genome. Nat. Rev. Genet. 26, 443–462 (2025).

4. Daw Elbait, G. et al. A population-specific major allele reference genome from the United Arab Emirates population. Front. Genet. 12, 660428 (2021)

5. Mezzavilla, M. et al. Ancestry-related distribution of runs of homozygosity and functional variants in Qatari population. BMC Genom. Data 23, 73 (2022).

6. Ramaswamy, S. et al. Middle Eastern genetic variation improves clinical annotation of the human genome. J. Pers. Med. 12, 423 (2022).

7. Rodriguez-Flores, J. L. et al. The QChip1 knowledgebase and microarray for precision medicine in Qatar. npj Genom. Med. 7, 3 (2022).

8. Saleheen, D. et al. Human knockouts and phenotypic analysis in a cohort with a high rate of consanguinity. Nature 544, 331–336 (2017).

9. Scott, A. J., Chiang, C. & Hall, I. M. Structural variants are a major source of gene expression differences in humans and often affect multiple nearby genes. Genome Res. 31, 2249–2257 (2021).

Melissa Elgie

Cutting-edge analysis of high-grade gliomas (HGG) classifies these aggressive brain or central nervous system cancers into three groups based on their molecular and clinical characteristics, providing insight on effective treatment regimens for patients¹.

Imagine being tasked with organizing a library of books into groups based on their common genres, target audiences, and number of pages. This task would leave many people wondering where to begin. Such a task is low stakes if it’s just about being orderly, but if it could change the course of someone’s life, things get a lot more serious. Add into the mix that the books being organized are extremely heterogenous, and you get the dilemma facing clinicians in the treatment of high-grade gliomas (HGG), a motley group of cancers that are among the most dangerous to human health². Fernandez et al. tackle this challenge in their recent study in which they categorized HGG into three subgroups¹. Their characterization of this complex type of cancer may have direct implications for how such tumours are treated in the future, streamlining clinical interventions and improving the patient experience¹.

Fernandez et al. sought to determine whether the molecular and clinical differences in HGG could be explained by genetic mutations in the body’s DNA repair and copying systems, which are responsible for cutting out mismatched DNA sequences and replacing them with the correct DNA and replicating DNA, respectively¹. To that end, they studied 162 HGG samples collected from 152 patients from the International Replication Repair Deficiency Consortium database, which they analyzed in terms of their genetic characteristics and clinical presentation¹. The researchers hypothesized that in general, primary mismatch-repair-deficient HGG (priMMRD-HGG, a specific type of glioma characterized by a particular genetic profile) could be stratified into one of three groups based on the mutations they harbour: priMMRD-1 (deficiencies in DNA repair and in the enzyme responsible for copying DNA), priMMRD-2 (just DNA repair deficient), or priMMRD-3 (DNA repair deficiency and mutation in a metabolic enzyme) (Figure 1)^1,3.

Figure 1: Summary of characteristics of glioma subgroups as defined by Fernandez et al.¹. The characteristics of each glioma subgroup (priMMRD-1, priMMRD-2, and priMMRD-3) are presented, including their molecular and genetic profiles, typical age of onset, and response to immunotherapy¹. Figure created in BioRender.com.

Critically, the group found that priMMRD-1 HGG samples were characterized by inactivation of DNA repair genes due to the presence of two mutated copies of DNA repair genes compared to priMMRD-2 and priMMRD-3, which harboured only one mutated copy of DNA repair genes¹. Given that priMMRD-1 tumours were seen to arise at a significantly younger age compared to the other two categories of HGG, the researchers proposed that this could be a determining factor in age of onset¹. A next course of action could be to determine external factors beyond the number functional genes present that could impact age of onset, such as potential immune system deficiencies¹.

The study also elucidates differences in immune system activation across the three HGG groups, with priMMRD-1 and priMMRD-2 exhibiting increased expression of 108 key immunity genes compared to priMMRD-3¹. Given that gliomas are known to suppress the human immune system, the authors take a logical approach in inquiring about the immune profiles of these cancers to possibly determine if immune system-enhancing interventions could combat this effect^1,4,5. To promote the immune system’s anti-tumour activity, researchers could leverage epigenetic changes to DNA, a process that naturally occurs to chemically modify DNA without changing its sequence⁵. Epigenetics-based therapeutics could be combined with immune checkpoint inhibitors (ICI, an immunotherapy) to oppose the immunosuppressive abilities of gliomas^1,5.

In analyzing treatment responses in each HGG group, Fernandez and colleagues investigated samples derived from patients who had been treated with ICI and found that priMMRD-1 patients exhibited better survival compared to priMMRD-2 and priMMRD-3¹. The authors point out in their paper that priMMRD-1 gliomas respond well to immunotherapy, and this presents a more favourable treatment option for children whom clinicians want to spare from harsh radiation therapy (Figure 1)¹. This contrasts with the priMMRD-2 group, which has a variable response to immunotherapy, to which the authors suggest a combination treatment regimen that could include immunotherapies¹. The final group, priMMRD-3 gliomas, does not respond well to immunotherapies, so the group suggests that targeted inhibitors could be used alongside immunotherapy to increase survival for those diagnosed with this type of glioma¹.

While the authors’ work appears to promote research into immunotherapy advances, there are limitations to implementing these treatments for gliomas, which could explain the less favourable response of priMMRD-2 and priMMRD-3 to immunotherapy^1,6. Such obstacles include difficulties with therapies navigating to the brain and the immunosuppressive tumour environment⁶. Despite these challenges, Fernandez et al. do not count immunotherapy out, and this may be for good reason^1,7. An up-and-coming treatment known as oncolytic virus therapy uses viruses to selectively infect cancer cells⁷. This approach to glioma treatment can stimulate the immune system through the release of cellular material from the tumour after the virus kills the cancer cells⁷. These cellular components, such as proteins, can be picked up by antigen-presenting cells, which signal to immune cells to destroy the cancer⁷. Indeed, options such as oncolytic virus therapy clearly illustrate the potential that lies within the immune system to mount lethal, targeted attacks on glioma cells⁷.

This body of work by Fernandez et al. classified high-grade gliomas into three subgroups based on their molecular and clinical characteristics to provide insight on optimal treatment practices for each group¹. Fernandez et al. propose that priMMRD-HGG should be considered for WHO classifications of central nervous system tumors in the future¹. Based on their work, the authors appear to suggest a goal to work toward: investigating how patients with the three different subgroups of HGG respond to different therapy regimens in clinical trials¹. This could take the form of uncovering novel immunotherapeutic avenues^6,7 . The insights from this study equip researchers with new knowledge on how to leverage glioma’s unique molecular intricacies to tailor medicine to specific patients¹.

References

1.           Fernandez, N. R. et al. Patterns of hypermutation shape tumorigenesis and immunotherapy response in mismatch-repair-deficient glioma. Nature Genetics 2025 58:1 58, 132–142 (2025).

2.           Higginbottom, S. L., Tomaskovic-Crook, E. & Crook, J. M. Considerations for modelling diffuse high-grade gliomas and developing clinically relevant therapies. Cancer Metastasis Rev. 42, 507 (2023).

3.           Pirozzi, C. J. & Yan, H. The implications of IDH mutations for cancer development and therapy. Nature Reviews Clinical Oncology 2021 18:10 18, 645–661 (2021).

4.           Xu, S., Tang, L., Li, X., Fan, F. & Liu, Z. Immunotherapy for glioma: Current management and future application. Cancer Lett. 476, 1–12 (2020).

5.           Riyas Mohamed, F. R. & Yaqinuddin, A. Epigenetic reprogramming and antitumor immune responses in gliomas: a systematic review. Medical Oncology 2025 42:6 42, 213- (2025).

6.           Sadowski, K. et al. Revolutionizing Glioblastoma Treatment: A Comprehensive Overview of Modern Therapeutic Approaches. Int. J. Mol. Sci. 25, 5774 (2024).

7.           Zhang, X. et al. Oncolytic virus therapy for glioma: current clinical trials and overcoming key obstacles. Int. Immunopharmacol. 166, 115547 (2025).

Hannah Fraser

A large-scale genome wide association study (GWAS) connects psychiatric disease risk with drug targets, guiding genomics informed drug development.

For many patients with psychiatric disorders, identifying an effective medication often involves prolonged trial-and-error prescribing¹. Could human genetics help break this cycle? A recent study by Hatoum et al.² suggests this may be possible by revealing overlap between genetic risk loci and the molecular targets of existing psychiatric medications². 

Psychiatric disorders place a major burden on healthcare systems worldwide, yet drug development for these conditions lags behind many other therapeutic areas³. Current treatments for disorders such as schizophrenia and depression primarily target dopamine and serotonin pathways that were identified decades ago. Many widely used psychiatric drugs were not originally designed to treat mental illness¹. For example, early antidepressants were first identified during tuberculosis drug trials and were developed without a clear understanding of the underlying disease biology⁴. As a result, psychiatric treatment remains largely nonspecific, forcing patients to cycle through multiple medications before finding one that is effective. Even then, therapies may provide limited benefit or cause significant side effects¹.

These limitations in current treatments have led researchers to look for new, biologically backed methods of drug development. In recent years, genome-wide association studies (GWAS) have identified hundreds of genetic variants linked to psychiatric disease risk². GWAS survey the genomes of large populations to detect variants that occur more frequently in individuals with a disease than in those without it, providing new insight into the biological processes underlying these conditions⁵. However, translating these statistical associations into therapeutic applications remains challenging, as many GWAS signals do not represent true causal variants, and predictive value may vary across populations⁶. To bridge this gap, researchers must determine whether GWAS risk loci map onto molecular pathways targeted by existing therapies.

Hatoum et al. provide a proof-of-principle that GWAS can be used to evaluate psychiatric drug targets². They directly compared genetic risk signals with known drug-target relationships. The study used a drug-set enrichment strategy, which tests whether medications whose targets overlap GWAS risk loci are overrepresented among approved psychiatric drugs. Unlike the traditionally used post GWAS gene-set enrichment analysis, this approach evaluates overlap at the level of drugs rather than individual genes (Figure 1). A key advantage of drug-set enrichment analysis is that it directly evaluates whether genetically implicated pathways correspond to existing medications, making the results more clinically meaningful.

Figure 1. Workflow of the GWAS-based drug enrichment analysis used by Hatoum et al.² Psychiatric GWAS summary statistics were used to identify significant single nucleotide polymorphisms (SNPs). SNPs were then mapped to nearby genes using proximity-based methods. Genes that were identified were linked to drug targets using the DGIdb and Connectivity Map databases. Drug-set enrichment analysis was then performed to test whether medications targeting GWAS associated genes were overrepresented among psychiatric drugs. Results were outputted as odds ratios (OR), representing the strength of enrichment among psychiatric drug classes.

Using this approach, the authors saw significant enrichment for medications used to treat schizophrenia, bipolar disorder, major depressive disorder, and substance use disorders, but not for ADHD, PTSD, generalized anxiety disorder, or insomnia². This suggests that many drugs already used in psychiatry act on biological pathways implicated by genomic studies. Schizophrenia displayed the strongest enrichment, with an odds ratio (OR) exceeding 27, where an OR greater than 1 indicates enrichment. This means that drugs whose targets overlapped with schizophrenia risk loci were far more common among approved treatments than would be expected by chance².

This enrichment extended beyond dopamine-related targets that historically dominated schizophrenia drug development. Although dopamine-associated genes accounted for a substantial portion of the enrichment, removing these genes did not eliminate the overall signal. This suggests that molecular schizophrenia risk is not driven solely by dopamine signalling, but that additional biological pathways are implicated. This may expand the range of potential therapeutic targets and support the development of treatments that move beyond traditional dopamine-based approaches. Similarly, the study found overlap between GWAS-associated risk genes and drug target pathways, including glutamate signaling in schizophrenia, calcium channel genes in bipolar disorder, and opioid and nicotinic receptor pathways in substance use disorders². Together, these findings suggest that GWAS can support current drug targets while also pointing to new ones.

This work has important implications for translating genetic discoveries into clinical practice. One promising application of GWAS is drug repurposing, in which existing psychiatric medications developed for one condition are identified as potential treatments for another⁷. Because repurposed drugs have already undergone clinical testing, they can reach patients faster and at lower cost⁷. GWAS can be used to identify drugs whose molecular targets overlap genetically supported disease pathways, helping prioritize promising candidates for repurposing. Drug development in psychiatry is expensive and often unsuccessful¹. Incorporating genetic data into the early stages of research and development could help narrow down which targets are worth pursuing and lower the financial barrier of developing new treatments.

Although using GWAS to prioritize and evaluate psychiatric drug targets is promising, several limitations remain. Psychiatric diagnoses often include patients with differing underlying biology, making drug target selection more difficult². Most GWAS datasets also rely heavily on individuals of European ancestry, which limits how broadly these findings can be applied. Sex-specific genetic effects are frequently overlooked because GWAS pools male and female samples, even though sex differences are well documented in psychiatric disease risk². GWAS results alone cannot guide treatment decisions, and experimental validation and clinical trials are required before these findings can be translated into the clinic².

Hatoum et al.’s study shows that GWAS results can be used for more than statistical risk prediction. By linking genetic risk signals to existing psychiatric medications, the group shows how human genetics can be used to ask more practical drug development questions². As genetic datasets continue to grow, this type of approach could help guide more targeted research and reduce the reliance on trial-and-error treatment strategies. While further experimental and clinical testing will still be required, this work highlights one way genetics can begin to play a more direct role in shaping future psychiatric therapies.

References

1.         Smoller, J. W. Psychiatric Genetics and the Future of Personalized Treatment. Depress Anxiety 31, 893–898 (2014).

2.         Hatoum, A. S. et al. Psychiatric genome-wide association study enrichment shows promise for future psychopharmaceutical discoveries. Commun Med 5, 176 (2025).

3.         Brewster, P. R., Bari, S. M. I., Walker, G. M. & Werfel, T. A. Current and Future Directions of Drug Delivery for the Treatment of Mental Illnesses. Adv Drug Deliv Rev 197, 114824 (2023).

4.         Hillhouse, T. M. & Porter, J. H. A brief history of the development of antidepressant drugs: From monoamines to glutamate. Exp Clin Psychopharmacol 23, 1–21 (2015).

5.         Uffelmann, E. et al. Genome-wide association studies. Nat Rev Methods Primers 1, 59 (2021).

6.         Scheinfeldt, L. B., Schmidlen, T. J., Gerry, N. P. & Christman, M. F. Challenges in Translating GWAS Results to Clinical Care. Int J Mol Sci 17, 1267 (2016).

7.         Woodward, D. J. et al. Identification of drug repurposing candidates for the treatment of anxiety: a genetic approach. Psychiatry Res 326, 115343 (2023).

Sarah Hammond

A large-scale, multi-ancestry investigation reveals novel loci and biological pathways involved in the pathogenesis of anxiety, highlighting the importance of using diverse genomes in research.

Over 300 million people worldwide suffer from an anxiety disorder, yet the genetic basis of these conditions remains poorly understood¹. Despite the prevalence of anxiety disorders having increased by more than 55% in the last three decades, genomic research in this area remains underrepresented as compared to other psychiatric conditions such as major depressive disorder (MDD) and post-traumatic stress disorder (PTSD)¹. Anxiety disorders affect individuals worldwide, so it is essential that genetic studies capture the genomic variability present across different ancestries. However, most genomic research to date has one striking limitation: studies are largely based on Caucasian European populations. Presently almost 80% of participants in published genome-wide association studies (GWAS) are of European descent². This lack of diversity narrows our view of human genetic variation, limits discovery, and underserves many populations. Beyond ethical and public health considerations, there is strong scientific justification for using multi-ancestry data, including more accurate effect-size estimates and broader generalizability². The study by Friligkou et al. takes a major step toward addressing these gaps and demonstrates the discovery and progress that can be found using multi-ancestry genomic data to study the genetic bases of anxiety disorders³.

The authors analyzed a strikingly large and diverse dataset, drawing on 1,266,780 participants (97,383 anxiety cases) from multiple databases encompassing five ancestries: European, African, admixed American, South Asian, and East Asian. To better understand the pathogenesis of anxiety, they integrated genome-wide, transcriptome-wide, and proteomic-wide association analyses (Figure 1). The goal of these studies was to find an association of either common genetic variants, changes in gene expression, or changes in protein regulation with anxiety.

The authors conducted ancestry-specific and cross-ancestry GWAS, ultimately identifying 41 loci associated with anxiety including 10 novel loci and one specific to individuals of African ancestry (Figure 1C). These discoveries were possible only because the dataset encompassed multiple ancestries. Per the authors, the novel findings of these GWAS quadruple the gene discoveries reported by previous studies, which highlights how the integration of multi-ancestry data from multiple databases can enrich the analysis.

Beyond identifying novel genes, the authors investigated how gene and protein expression levels are altered in anxiety disorders using a transcriptome-wide association study (TWAS) and a proteome-wide association study (PWAS) (Figure 1A-B). This multi-omics approach offers insight into the biological processes underlying anxiety. The tissue-specific and cross-tissue TWAS identified 211 transcriptome-wide associations with the strongest association being to a variation of a DRD2 locus in the cerebellar hemisphere. Several genes – including CTNND1, KHK, and NEK4 – were identified as being associated with anxiety in both the PWAS and TWAS, strengthening the evidence that they play a role in the pathogenesis of anxiety.

Figure 1 | Manhattan plots of PWAS (A), TWAS (B) and GWAS (C) statistics related to anxiety. Friligkou et al show which genes (labeled) have convergent evidence across all analyses. The x axis shows the genomic location of the gene on chromosomes 1-22. The y axis shows −log10(P value) obtained from two-sided statistical tests. A higher point indicates that there is a stronger statistical significance for the association of the gene with anxiety. Dashed lines represent the significance threshold from the Bonferroni multiple testing correction. The TWAS data shown is obtained from the multi-tissue analysis and the GWAS data shown is obtained from the European cohort. Adapted from Figure 3³.

Notably, the DRD2 gene encodes the dopamine D2 receptor, and variation in its expression has been found to be associated with anxiety⁴. DRD2 is already a major therapeutic target for antipsychotics, and it has been proposed as a target for anxiety disorders⁵. From both PWAS and TWAS investigations, they found the strongest evidence for association of the CTNND1 gene with anxiety. This association has been previously described, and this gene has also been associated with depression⁵. CTNND1 encodes a protein which regulates important processes in the central nervous system, and mice without CTNND1 were found to have anxiety-like behaviors⁵. Another gene of interest, KHK, identified across GWAS, TWAS and PWAS, is involved in fructose metabolism, and animal models have previously demonstrated that there may be a role in early-life fructose exposure to depression and anxiety⁶. Lastly, the NEK4 gene which is involved in cell cycle regulation and cell division, has also previously been identified as a drug candidate gene for bipolar disorder and MDD⁷. The involvement of many of these genes in multiple psychiatric conditions highlights the pleiotropic nature of anxiety disorders, prompting the authors to investigate what other conditions may have genetic overlap with anxiety.

The authors demonstrate that anxiety disorders do share much of their genetic basis with other psychiatric and physical conditions. After performing statistical analyses to investigate causation, the authors found that most genetic associations showed shared pleiotropy rather than direct causal influence – only a small subset of traits show some evidence favoring causation. There is a substantial genetic overlap between anxiety and both psychiatric and physical health conditions. They observed a substantial overlap with gastrointestinal and pain-related phenotypes. Interestingly, only about 10% of the influential variants for anxiety pathogenesis did not overlap with those for MDD. This suggests that the genetic basis of anxiety disorders is likely due to disruptions in shared biological pathways rather than a disorder-specific mechanism.

Friligkou et al. provide a clear example of how diverse population datasets can expand the discovery of new genetic associations³. They discovered novel anxiety risk loci and gained insight into biological pathways involved in the pathogenesis of anxiety, surpassing the insights from previous research studies which used only European data. The authors also clearly show how intertwined anxiety disorders are with other psychiatric disorders and even how they may overlap with physical health. Unfortunately, the limited data available for non-European populations reduced the statistical power in these populations and prohibited further ancestry-specific gene discovery. However, with worldwide efforts to expand genomic databases, these large genomic studies will become more robust and able to provide more detailed information on worldwide genomic variation. When genomic research reflects global diversity, we will gain a deeper, more accurate understanding of how biological mechanisms shape anxiety across populations.

References

1.        Javaid, S. F. et al. Epidemiology of anxiety disorders: global burden and sociodemographic associations. Middle East Current Psychiatry 2023 30:1 30, 44- (2023).

2.        Peterson, R. E. et al. Genome-wide Association Studies in Ancestrally Diverse Populations: Opportunities, Methods, Pitfalls, and Recommendations. Cell 179, 589–603 (2019).

3.        Friligkou, E. et al. Gene discovery and biological insights into anxiety disorders from a large-scale multi-ancestry genome-wide association study. Nat. Genet. 56, 2036–2045 (2024).

4.        Ike, K. G. O. et al. The human neuropsychiatric risk gene Drd2 is necessary for social functioning across evolutionary distant species. Mol. Psychiatry 29, 518–528 (2024).

5.        Li, W. et al. Genome-wide meta-analysis, functional genomics and integrative analyses implicate new risk genes and therapeutic targets for anxiety disorders. Nature Human Behaviour 2023 8:2 8, 361–379 (2023).

6.        Hyldgaard Andersen, S., Black, T., Grassi-Oliveira, R. & Wegener, G. Can early-life high fructose exposure induce long-term depression and anxiety-like behaviours? – A preclinical systematic review. Brain Res. 1814, (2023).

7.        Gong, B., Xiao, C., Feng, Y. & Shen, J. NEK4: prediction of available drug targets and common genetic linkages in bipolar disorder and major depressive disorder. Front. Psychiatry 16, 1414015 (2025).

Isabella Harmic

A new triple-marker diagnostic test offers a non-invasive method to screen for gastric cancer and the potential to address the difficulty physicians face to diagnose this disease.

As of 2022, Gastric cancer (GC) is the fifth most diagnosed cancer type worldwide, and responsible for 6.8% of cancer-related deaths¹. It remains relatively common even in first world countries where the eradication of Helicobacter pylori, a known risk factor, has decreased its incidence^1,2. To combat the outstanding burden of GC, Lee et al. have developed a new diagnostic test using circulating tumor DNA (ctDNA) that has the capability to accurately and non-invasively detect early stages of the cancer².

Using ctDNA to find genetic markers of disease is a rising trend in the field of cancer diagnostics. ctDNA is classified as a form of cell-free DNA (cfDNA), or DNA circulating in the bloodstream because of natural cellular processes, but originating from cancer cells (Figure 1)³. As such, this DNA can be easily accessed by a non-invasive blood sample for a series of analysis, one component of which focusing on methylation patterns. Methylation is a covalent marker that is not incorporated into the DNA sequence, rather it functions like an on/off switch, indicating which genes are actively expressed. One of the main advantages of ctDNA analysis is that it can detect cancers without any physical manifestation of symptoms – which is often the case with GC.

Figure 1. Analytic tests performed on cfDNA and its clinical applications. Tumors, by their nature, increase vascularization (blood flow) in the surrounding tissue to allow for greater access to essentials like oxygen and nutrients⁴. As such, it is common that DNA released by cancer cells from processes such as cell death, secretion, etc. makes its way into the bloodstream (left side of the figure; Liquid Biopsy); as diagramed by Dao et al. This circulating tumor DNA (ctDNA) can be accessed from plasma, which is extracted from a blood sample. Several properties of ctDNA can be of interest (middle of the figure; Sample Analysis), including the amount present in the blood which can be related to the size/burden of the tumor, the mutations in the DNA which can provide information on the tumor’s biology, and the methylation patterns present in the ctDNA. All of this analysis provides relevant information that can advise clinical progression, however, this paper focuses on the impacts of methylation, which informs physicians about gene expression levels (right side of the figure; Clinical Application)³. Figure taken from ³.

Often, early stages of GC present asymptomatically until it becomes extremely advanced, whereby the five-year survival rate drops to approximately 30%^2,5,6. Although an early diagnosis increases survival, many physicians hesitate to recommend screening for or even diagnose symptomatic GC because its symptoms replicate many other diseases⁶. Additionally, screening is typically only offered to those of high-risk status, or individuals who were exposed to multiple risk factors for the disease (e.g. H. pylori infection, increased age, etc.)⁵. The current gold standard to screen and diagnose GC is endoscopy, however, this technique possesses its own limitations. Specifically, endoscopy is highly invasive, reducing patient participation, and exhibits reduced accuracy for detecting early GC. In fact, an analysis of endoscopy effectiveness by Pimenta-Melo et al. found that the technique misses 1 in 10 GC cases, the majority of which were in the early stages of development⁷.

Unfortunately, a frequently encountered issue in the use of ctDNA-based diagnostic approaches is decreased sensitivity in early stage cancers with lower tumor cell counts, which produce less ctDNA in the bloodstream³. Despite this difficulty, Lee et al. rose to the challenge of designing an effective GC ctDNA test. They began by selecting two candidate genes from a database of over 1000 genome-wide methylation samples assembled by The Cancer Genome Atlas that exhibited elevated methylation profiles unique to GC samples, but not the other 32 cancer types documented. Their first candidate, GHR, produced the GC-specific methylation pattern they expected. As did their second candidate GLRB, although it also exhibited elevated methylation in colorectal cancer (CRC) samples. The authors validated their findings by measuring the methylation of these same genes in cancer and non-cancerous cell lines. Finally, they tested their genetic markers on a retrospective case-control cohort of 60 GC patient cases and 40 non-affected controls, of which 73% of the cases had stage 1 cancer. They found the methylation status of their chosen genes remained highly predictive, although as a final controlling measure they implemented a third gene with CRC-specific methylation, GATM, as a reference to prevent mistakes in conflating GC with CRC².

Lee et al.’s final triple-marker test, measuring the methylation of GHR, GLRB, and GATM, had a sensitivity of 82% and specificity of 90% in stage 1 GC patients. Moreover, their trend in sensitivity suggested increased accuracy in tandem with later stages of the cancer. Although Lee et al. were not the group to develop the most accurate, non-invasive test for GC, their test does have the highest sensitivity at detecting stage 1 GC, indicating its promise as a tool to screen for early disease. Three other notable high accuracy diagnostic tests have been developed using ctDNA methylation profiles, however, they all suffered decreases in sensitivity with stage 1 GC². The most accurate of the three, a triple-marker test developed by Anderson et al., had an overall sensitivity of 85% and specificity of 86%, but the sensitivity dropped to 50% for stage 1 GC⁸.

However, it would also be untrue to describe Lee et al.’s work as perfect. There are some limitations to the distributions of participants in the case-control study, such as a lack of controlling for H. pylori infections amongst cases, to see if this characteristic methylation profile is present in everyone exposed to the pathogen or only those who develop cancer. Additionally, they recruited a low number of participants from stages 2-4 of GC. In the future, both of these limitations could be addressed by applying their triple-marker diagnostic test to a larger, more generalized cohort of patients to ensure it remains accurate in a wider implementation².

A new technique is needed to bridge the gap between diagnosis and early stage GC.  Particularly one that avoids patient discomfort to allow for generalized screening of the population, not just those of high-risk status. Lee et al.’s triple marker test provides a promising solution. Their test is straightforward, analysing the methylation profiles of only three genes, and non-invasive, requiring just a regular blood sample to conduct. As such, it would be simpler to implement clinically and reach a much greater number of patients. With accessible screening, this test has great potential to increase early disease detection and save countless lives.

References

1.         Bray, F. et al. Global cancer statistics 2022: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA. Cancer J. Clin. 74, 229–263 (2024).

2.         Lee, Y. Y., An, J., Han, J., Moon, Y. & Lee, S.-I. Plasma-based digital PCR assay for early detection of gastric cancer using multiple methylation biomarkers. Sci. Rep. 16, 1727 (2025).

3.         Dao, J. et al. Using cfDNA and ctDNA as Oncologic Markers: A Path to Clinical Validation. Int. J. Mol. Sci. 24, 13219 (2023).

4.         De Palma, M. & Hanahan, D. Milestones in tumor vascularization and its therapeutic targeting. Nat. Cancer 5, 827–843 (2024).

5.         Karimi, P., Islami, F., Anandasabapathy, S., Freedman, N. D. & Kamangar, F. Gastric Cancer: Descriptive Epidemiology, Risk Factors, Screening, and Prevention. Cancer Epidemiol. Biomarkers Prev. 23, 700–713 (2014).

6.         Xia, J. Y. & Aadam, A. A. Advances in screening and detection of gastric cancer. J. Surg. Oncol. 125, 1104–1109 (2022).

7.         Pimenta-Melo, A. R., Monteiro-Soares, M., Libânio, D. & Dinis-Ribeiro, M. Missing rate for gastric cancer during upper gastrointestinal endoscopy: a systematic review and meta-analysis. Eur. J. Gastroenterol. Hepatol. 28, 1041 (2016).

8.         Anderson, B. W. et al. Detection of Gastric Cancer with Novel Methylated DNA Markers: Discovery, Tissue Validation, and Pilot Testing in Plasma. Clin. Cancer Res. Off. J. Am. Assoc. Cancer Res. 24, 5724–5734 (2018).

Yixin He

The study establishes mosaic loss of the Y chromosome (mLOY) as an important, independent risk factor of cardiovascular death in men, suggesting a new perspective of precision medicine.

Cardiovascular disease (CVD) is a leading threat to male health¹, yet a fundamental scientific question remains unsolved: approximately half of all CVD cases in men cannot be explained by traditional risk factors like blood pressure and cholesterol². A recent study published in the European Heart Journal provides an understanding of why these sex-related disparities in prevalence and outcome may occur³. The authors identify mosaic loss of the Y chromosome (mLOY), a common age-related mutation in men’s blood cells, as a strong and independent risk factor of mortality in patients with CVD. More importantly, the study reveals that this risk is highly dependent on genetic predisposition to interstitial myocardial fibrosis, which is a profibrotic protein signature (figure 1). This work provides a new foundation for identifying high-risk men and developing sex-specific intervention strategies.

Figure 1 | The authors propose that LOY in leukocytes is associated with myocardial fibrosis and contributes to increased cardiovascular disease mortality. The figure is taken from³.

The researchers began by analyzing data from the well-established Ludwigshafen Risk and Cardiovascular Health (LURIC) study⁴. They selected 1,698 male participants who had undergone coronary angiography and stratified them into two groups based on the proportion of Y chromosome loss in their white blood cells, using a clinically defined threshold of >17%. Initial analysis revealed that individuals with low LOY had significantly lower mortality during a follow-up of approximate 10 years compared to those with high LOY. Their mortality rate was comparable to that of women in the same cohort, who typically have lower mortality⁵, suggesting a link between LOY and poor prognosis.

However, this association could be confounded by other factors. For instance, LOY is itself a common marker of aging, and age is the strongest risk factor of CVD. So, is the effect of LOY independent, or does it merely reflect the influence of age and other variables?

To answer this, the team employed a Cox proportional hazards model, a statistical tool that quantifies the independent effect of a specific factor on event risk after controlling for others⁶. They included LOY status in the model while adjusting for over ten traditional risk factors, including age, smoking status, body mass index, and etc. The results demonstrated that high LOY is an independent risk marker, separate from all these conventional factors. It was associated with a 41% higher risk of all-cause mortality (hazard ratio 1.41). Using a more refined model that accounts for competing risks, high LOY was linked to a 49% increased risk of cardiovascular death (hazard ratio1.49) and a striking 165% increased risk of fatal myocardial infarction (hazard ratio 2.65).

After establishing LOY as an independent predictor of CVD, the researchers sought to investigate its underlying mechanisms. Previous research has shown that LOY in hematopoietic cells promotes cardiac fibrosis in mouse models⁷, a condition that increases myocardial stiffness and can lead to arrhythmias⁸. To investigate the interaction between LOY and cardiac fibrosis, the researchers first applied a quantitative metric, the weighted genetic risk score(wGRS) for myocardial fibrosis, measuring each patient’s genetic susceptibility for cardiac fibrosis. They found that the harm from LOY exhibits synergy with this genetic background. In men predisposed to fibrosis (wGRS > 0), LOY increased the risk of cardiovascular death by 114% (hazard ratio 2.14). Conversely, in men not predisposed to fibrosis (wGRS ≤ 0), the excess risk from LOY was completely absent (hazard ratio 1.02). This strongly suggests a functional biological interaction between LOY and fibrotic pathways, indicating that LOY likely exerts its deleterious effect by promoting fibrosis.

Building on these findings, the researchers next investigated the relationship between LOY and myocardial fibrosis at the cellular level. They performed genome-wide methylation analysis within the LURIC cohort, comparing blood samples from men with high and low LOY. This revealed 298 differentially methylated genes, suggesting that LOY may reprogram cell function epigenetically. To verify whether these epigenetic changes alter actual gene expression, the team turned to a previous independent single-cell RNA sequencing dataset from seven male patients with severe degenerative aortic stenosis³. Comparing the transcriptomes of LOY cells and normal cells within this dataset, they found that 37 of the 298 genes showed significant expression differences, including several, like ARAP1, BST1, and RPS5, known to be involved in fibrosis regulation^9-11.

Current technology does not allow for the knockout of an entire Y chromosome in vitro. Therefore, the researchers knocked down three candidate genes in macrophages in vitro to mimic the state of LOY cells. The results showed that knocking down RPS5 was sufficient to drive cardiac fibroblasts to produce excess collagen. The study thus provides a compelling, but indirect, evidence chain linking LOY to the promotion of cardiac fibrosis. However, this experimental design still has certain limitations. Whether simply knocking down these three genes respectively in vitro is sufficient to faithfully mimic the cellular state of LOY remains an open question.

In summary, this research establishes an age-related somatic mutation (LOY) as an independent risk factor of CVD in men and elucidates its likely mechanism of action through interaction with cardiac fibrosis pathways. It paves the way for a deeper understanding of why men are disproportionately affected by CVD and for crafting therapeutic strategies tailored specifically to them. Moving forward, developing blood-based LOY assays for risk stratification and designing drug interventions targeting its downstream pathways (e.g., RPS5) hold promise as novel precision medicine strategies.

References

1          Global burden of 288 causes of death and life expectancy decomposition in 204 countries and territories and 811 subnational locations, 1990-2021: a systematic analysis for the Global Burden of Disease Study 2021. Lancet 403, 2100-2132 (2024). https://doi.org:10.1016/s0140-6736(24)00367-2

2          Magnussen, C. et al. Global Effect of Modifiable Risk Factors on Cardiovascular Disease and Mortality. N Engl J Med 389, 1273-1285 (2023). https://doi.org:10.1056/NEJMoa2206916

3          Mas-Peiro, S. et al. Mosaic loss of Y chromosome in monocytes is associated with lower survival after transcatheter aortic valve replacement. Eur Heart J 44, 1943-1952 (2023). https://doi.org:10.1093/eurheartj/ehad093

4          Winkelmann, B. R. et al. Rationale and design of the LURIC study–a resource for functional genomics, pharmacogenomics and long-term prognosis of cardiovascular disease. Pharmacogenomics 2, S1-73 (2001). https://doi.org:10.1517/14622416.2.1.S1

5          Fan, J. et al. The global burden and attributable risk factor analysis of cardiovascular disease between females and males, 1990-2021: findings from the 2021 global burden of disease study. Int J Surg 112, 239-249 (2026). https://doi.org:10.1097/js9.0000000000003397

6          Cox, D. R. Regression Models and Life-Tables. Journal of the Royal Statistical Society. Series B (Methodological) 34, 187-220 (1972).

7          Sano, S. et al. Hematopoietic loss of Y chromosome leads to cardiac fibrosis and heart failure mortality. Science 377, 292-297 (2022). https://doi.org:10.1126/science.abn3100

8          de Jong, S., van Veen, T. A., van Rijen, H. V. & de Bakker, J. M. Fibrosis and cardiac arrhythmias. J Cardiovasc Pharmacol 57, 630-638 (2011). https://doi.org:10.1097/FJC.0b013e318207a35f

9          Zhang, X. et al. Matrine attenuates pathological cardiac fibrosis via RPS5/p38 in mice. Acta Pharmacol Sin 42, 573-584 (2021). https://doi.org:10.1038/s41401-020-0473-8

10        Yuan, Y. et al. BST-1 aggravates aldosterone-induced cardiac hypertrophy via the Ca2+ /CaN/NFATc3 pathway. Gen Physiol Biophys 42, 349-360 (2023). https://doi.org:10.4149/gpb_2022063

11        Jiang, L. et al. Downregulation of the CD151 protects the cardiac function by the crosstalk between the endothelial cells and cardiomyocytes via exosomes. PLoS One 19, e0297121 (2024). https://doi.org:10.1371/journal.pone.0297121

Matt Hudson

A large-scale genomic analysis of women with polycystic ovary syndrome highlights ancestry-specific differences that might be targetable by using drugs approved for other indications.

Polycystic ovary syndrome (PCOS) is a complex condition that affects 11–13% of women worldwide¹. PCOS is diagnosed according to the presence of one of three criteria: excess androgens, irregular or absent menstruation, and/or polycystic ovaries¹. Understandably, this results in extensive heterogeneity in clinical presentation. While previous genome-wide association studies (GWASs) identified population-specific and common susceptibility loci for PCOS across ethnicities, these studies were relatively unpowered and cannot explain most of the heritability of PCOS.

To address the missing genetic components of PCOS heterogeneity, Zhao and colleagues conducted a large-scale GWAS of Chinese women with a combined meta-analysis using European datasets². Zhao et al.’s GWAS stands out thanks to their analyses of shared and ancestry-specific variants, relevant cell types and tissues that may be affected by their significant PCOS variants, bidirectional associations between PCOS and certain relevant traits, pharmacogenomics, and an ancestry-aware polygenic risk score (PRS).

Zhao et al.² significantly increased their sample size for the Chinese PCOS population compared to previous efforts and increased their statistical power by combining their results with four other European datasets^3–5. This increased the number of known PCOS-associated variants by nearly fivefold, spanning variants shared across Chinese and European populations as well as those unique to each. The genes associated with these variants were most strongly expressed in cumulus cells and granulosa cells in the ovaries. Cumulus cells are essentially “nursery” cells responsible for protecting and nourishing developing oocytes⁶, while granulosa cells produce estrogen and help regulate the menstrual cycle⁷. Of note, the authors’ lead variant of interest lies in the gene AMH (anti-Müllerian hormone), which regulates folliculogenesis and is normally tightly temporally regulated in granulosa cells in conjunction with ovulation⁸.

The use of such large and ethnicity-specific populations allowed for a more in-depth analysis of genetic similarities and differences in Chinese and European women with PCOS, distinguishing their study from a “run-of-the-mill” GWAS. These two populations have distinct differences in PCOS presentation (i.e., Asian women more often present with ovarian cysts while European women often present with more obvious forms of hyperandrogenism, like excess body hair)⁹, suggesting a role of natural selection for different traits. This was also supported by population-level genomic analyses of allele fixation and the influence of genetic drift. Significant Chinese-specific variants were more strongly associated with metabolic processes, whereas European-specific variants were more strongly associated with female gonad development and reproductive function. Interestingly, Zhao et al. rationalized this from an anthropological perspective, noting that the skew toward variants in metabolic pathway genes in East Asian populations may reflect a need in deep evolutionary time for these populations to have enhanced fat storage, energy management, and tissue repair in response to environmental and dietary changes². Reflecting this, the Chinese cohort showed an increased association between low-density lipoprotein cholesterol (linked to cardiovascular disease¹⁰) and PCOS, while the European cohort did not. However, both Chinese and European cohorts showed significant associations between higher body mass index, type 2 diabetes, and older age at menopause with an increased risk of PCOS, serving as a reminder that there are important pan-population risk factors (Figure 1).

Figure 1. Similarities and differences in polycystic ovary syndrome (PCOS) presentation and genetics between East Asian and European populations. Chinese women with PCOS more often had genetic variants affecting metabolic processes, whereas European more often had variants affecting gonad development and reproductive function. High low-density lipoprotein (LDL) cholesterol was a risk factor for PCOS in Chinese women, whereas later age at menarche was a risk factor in European women. Important risk factors in both populations were variants in AMH, high body mass index (BMI), type 2 diabetes, and older age at menopause. Created in BioRender.com

As the path from novel in vitro drug testing to clinical trials is time-consuming, expensive, and can often lead to negative results, it is becoming more cost-effective and time-efficient to screen approved drugs based on their targets to match a potential disease treatment. Using a pharmacogenomic approach¹¹, Zhao and colleagues identified several possible candidate drugs that could be reoriented for PCOS treatment using their list of PCOS-associated genes², opening future avenues for research in PCOS pharmacotherapy. While they confirmed that sex hormone modulators like oral contraceptives and fertility drugs (which are already prescribed for PCOS) are likely effective, insulin sensitizers like pioglitazone and metformin likely have potential as treatments in patients with PCOS associated with PPARG variants.

Zhao et al. also attempted to apply a PRS for PCOS using their GWAS variants by sequentially excluding cohorts to make test populations, using the Chinese cohort as the target population². Perhaps unsurprisingly, the PRS was ineffective in the European cohort alone, while it showed a steady upward trend when applied only to the Chinese cohort and the combined pan-ancestry cohort. Unfortunately, the method to create the PRS likely introduced some bias toward the Chinese cohort due to “batch removal” of cohorts in training – a more unbiased approach using k-fold cross-validation or sample bootstrapping may address this in the future.

While Zhao et al. confirmed the significance of previous PCOS-relevant variants and identified new ones applicable across populations, their findings of more ancestry-specific PCOS-relevant variants suggest the utility of personalized medicine for people with PCOS – or as they put it, “ancestry-specific calibration” of treatment². However, as Zhao et al. only examined Chinese and mixed European datasets, other large-scale GWASs are needed to determine if other populations may benefit from this ancestry-specific analysis. Finally, as this was an association study, functional analyses of these new variants are needed to confirm any role in PCOS in either population. Ultimately, Zhao and colleagues’ ancestry-aware GWAS and meta-analysis of Chinese and European women with PCOS reveals a deeper understanding of the evolution of this condition in humans. Their findings underscore the importance of considering natural genetic diversity in complex conditions and show that there is a role of precision medicine in PCOS treatment.

References

1.           Teede, H. J. et al. Recommendations from the 2023 International Evidence-based Guideline for the Assessment and Management of Polycystic Ovary Syndrome. Fertil. Steril. 120, 767–793 (2023).

2.           Zhao, H. et al. Multi-ancestry genome-wide association analyses of polycystic ovary syndrome. Nat. Genet. 57, 2669–2681 (2025).

3.           Tyrmi, J. S. et al. Leveraging Northern European population history: novel low-frequency variants for polycystic ovary syndrome. Human Reprod. 37, 352–365 (2022).

4.           Kurki, M. I. et al. FinnGen provides genetic insights from a well-phenotyped isolated population. Nature 613, 508–518 (2023).

5.           Day, F. et al. Large-scale genome-wide meta-analysis of polycystic ovary syndrome suggests shared genetic architecture for different diagnosis criteria. PLoS Genet. 14, e1007813 (2018).

6.           Turathum, B., Gao, E. M. & Chian, R. C. The function of cumulus cells in oocyte growth and maturation and in subsequent ovulation and fertilization. Cells 10, 2292 (2021).

7.           Schütz, L. F. & Batalha, I. M. Granulosa cells: central regulators of female fertility. Endocrines 5, 547–565 (2024).

8.           Sova, H. et al. Hormone profiling, including anti-Müllerian hormone (AMH), for the diagnosis of polycystic ovary syndrome (PCOS) and characterization of PCOS phenotypes. Gynecol. Endocrinol. 35, 595–600 (2019).

9.           VanHise, K. et al. Racial and ethnic disparities in polycystic ovary syndrome. Fertil. Steril. 119, 348–354 (2023).

10.        Stanciulescu, L. A., Scafa-Udriste, A. & Dorobantu, M. Exploring the association between low-density lipoprotein subfractions and major adverse cardiovascular outcomes—a comprehensive review. Int. J. Mol. Sci. 24, 6669 (2023).

11.        Sakaue, S. & Okada, Y. GREP: genome for REPositioning drugs. Bioinformatics 35, 3821–3823 (2019).

Alisha Imtiaz

Incorporating genome-wide association study (GWAS) summary statistics with 3D chromatin interactome maps links variants to distal genes and disease-specific pathways possibly implicated in neurodegenerative diseases.

Neurodegenerative conditions like Alzheimer’s disease (AD), schizophrenia, Parkinson’s disease (PD), amyotrophic lateral sclerosis (ALS) and multiple sclerosis (MS) affect more than 57 million people worldwide¹. Despite this, these conditions remain without effective cures. Genome-wide association studies (GWAS) have identified many associated risk variants for neurodegenerative conditions. However, 90% of these are located within non-coding regions², far from known genes, which has made the identification of specific targets in the brain difficult³. Genetic regulation tends to involve chromosomal interactions over long-distances hence, it can be important to consider the 3D organization when studying risk variants. Motivated by this, Askarova et. al identified disease-specific risk genes for 5 neurodegenerative diseases by analyzing 3D interactome maps to link GWAS reported genetic variants with possible distal target genes³. 

Neurodegenerative conditions tend to be genetically heterogenous involving both rare and common genetic variants with varying effect sizes. Due to this complexity, sporadic forms of the disease tend to be very common, making up more than 90% of cases⁴. This highlights the importance of conducting GWAS studies for the identification of risk genes.

Enhancers are DNA elements that can interact over long distances with gene promoters, regions located upstream of genes that initiate transcription, to control gene expression³. Promoters and enhancers have characteristic epigenetic modifications and the chromosomal loops that often allow for the enhancer-promoter interactions are found to be cell-type specific³. Proximity-based sequencing methods have been used to analyze these histone modifications, seen as specific “marks” on the DNA, to identify cell-type specific chromosomal interactions in the brain. Askarova et. al were interested in taking this one step further by utilising these enhancer-to-promoter interactome maps to link GWAS reported variants to cell-type specific distal genes. Chromosomal interactions were mapped using data generated from human cortical neurons, microglia and oligodendrocytes (Figure 1).

Figure 1: Representation of microglia, oligodendrocytes and neurons inside brain cells. The venn diagram shows the microglial disease-risk genes which overlap between Amyotrophic Lateral Sclerosis (ALS), Schizophrenia (SCZ) and Parkinson’s Disease (PD). ALS= Amyotrophic Lateral Sclerosis, PD= Parkinson’s Disease, SCZ= Schizophrenia.  Created in  https://BioRender.com

GWAS summary statistics from previously done studies were incorporated for AD, PD, MS, ALS and schizophrenia. Chromatin-interacting regions in microglia showed enrichment for AD, PD and MS variants whereas neurons and oligodendrocytes showed enrichment for schizophrenia³. These results are consistent with associations of these cell types found in previous studies as well^5,6.

To identify disease risk genes, the researchers utilized a technique known as Hi-C coupled multimarker analysis of genomic annotation (H-MAGMA) (Figure 2)³. MAGMA is an analytical tool that links noncoding variants to the nearest genes, limited by distance⁷. When combined with Hi-C chromatin data, H-MAGMA is able to make connections even between distal genes and variants⁷. Using this method, they were able to find cell-type specific disease risk genes. All conditions, except schizophrenia, had the highest number of risk genes found in microglia. Neurons had the highest risk genes for schizophrenia³. This is biologically consistent because schizophrenia is, at its core, a neural dysconnectivity disorder⁶.

Figure 2: Workflow for the Hi-C coupled multimarker analysis of genomic annotation (H-MAGMA) technique. Step 1: Single nucleotide polymorphisms (SNPs) are annotated within their respective genes and chromatin region using the chromatin interaction data developed. Step 2: Genome-wide association study (GWAS) summary statistics and MAGMA software identifies disease associated risk genes. Image obtained from³.

Interestingly, although several conditions had the highest number of risk genes found within microglia, they were all found to be associated with disease specific pathways with minimal overlap (Table 1). This is consistent with a previous study which also found low pairwise genome-wide genetic correlations (r_g) between neurodegenerative GWAS datasets⁸. The major histocompatibility complex (MHC), associated with immunity, was implicated in both AD and MS but different genes. This suggests that different pathways may be disturbed in each condition and exemplifies the complexity of these diseases. The AD risk genes identified by H-MAGMA include APOE and BIN1 which have previously been implicated in AD as well, further supporting these links^5,9. Distal genes from GWAS variants which have not previously been linked were also identified. These include endosome/endocytosis-associated genes like USP6NL and CNN2, membrane associated genes, gene regulation associated EED and protein homeostasis associated ATXN3.

Similarly, H-MAGMA was also able to identify distal genes implicated in PD including P2RY12 and P2RY13. This further supports a role for P2RY12 in PD pathogenesis which has been shown to be associated with the activation of microglia¹⁰. P2RY12 is also a target for antithrombotic compound clopidogrel¹⁰ and hence, offers potential for repurposing of drugs. Further research studying the role of this gene in models of PD is required.

Although these are some promising results, this H-MAGMA based approach also has some important limitations that should be addressed. The results are directly dependent on the amount and quality of GWAS summary statistics available. If there is a population bias or confounding effects in the GWAS study conducted, this bias will carry over to any genetic inferences made down the line. For example, schizophrenia was found to have the highest number of risk genes identified but this is because schizophrenia had the highest number of variants reported in the GWAS datasets analyzed in this study.

Future directions for this work involve functional validation and study of the disease risk genes and pathways identified. Repeating this analysis for more cell types beyond neurons, microglia and oligodendrocytes and more populations could also offer further insights. The strong enrichment of disease risk genes in microglia further implicates its role in neurodegenerative diseases and should be studied further.

Overall, actionable targets and pathways noted in this study offer potential for therapeutic interventions and increased understanding of underlying disease pathology. Furthermore, H-MAGMA highlights a unique approach for analyzing large-scale GWAS datasets that are already available and this study validated this technique. In recent times, the role of regulatory regions in disease progression has been recognized more; hence, methods which analyze genetic interactions within the context of 3D chromosomal organization may just be the missing pieces.

Table 1: The disease-risk genes identified by H-MAGMA tool and their corresponding genetic pathways for the diseases which had highest enrichment in microglial disease risk genes.

Disease	Genes Implicated	Genetic Pathways
Alzheimer’s Disease	APOE, ABCA7, PICALM, BIN1, SORL1, ADAM10, APH1B, INPP5D, ADAMTS4, CASS4, USP6NL, CNN2, RIN3, RAB8B, SPPL2A, STX4, TRIP11, EED, ATXN3	Lipoproteins, amyloid processing, endocytosis and MHC class II
Parkinson’s Disease	LRRK2, RAB29, PLEKHM1STX4, TMEM175, VPS37A, SNCA, ARHGAP27,  LRRK2, SNCA, TMEM175, NDUFAF2, BCKDK, SUCNR1, PINK1, PARK7, SETD1A, FAM47E, KAT8, KANSL1, P2RY12, P2RY13, UBE2Q1, FGF20	Endo-lysosomal pathways, membrane fusion gene, mitochondrial dysfunction, dopamine secretion
Multiple Sclerosis	TAP1, TAP2, TAPBPL, MICB, CIITA, HSPA1B, HSPA1A and HSPA1L, MARCHF1, Tumor Necrosis Factor (TNF), TNFRSF1, CD27, SOCS1, VSIR, IL12RB1, AIF1, BCL10, PTPRC, CORO1A, KAT5	T cell signaling and antigen presentation and processing
Amyotrophic Lateral Sclerosis	ATXN3, CLCN3, SLC12A4, TMEM175, TPP1, KICS2, NEU1, TM6SF1, C9orf72, IDUA, FNBP1, ATP6V1G2, TBK1	Vacuole-associated channels and transporters, autophagy and the lysosome

References

1. Imam, F. et al. The Global Neurodegeneration Proteomics Consortium: biomarker and drug target discovery for common neurodegenerative diseases and aging. Nat Med 31, 2556–2566 (2025).

2. Rogers, B. B. & Cochran, J. N. Non-coding variation in dementias: mechanisms, insights, and challenges. npj Dement. 1, 9 (2025).

3. Askarova, A., Yaa, R. M., Marzi, S. J. & Nott, A. Genetic risk for neurodegenerative conditions is linked to disease-specific microglial pathways. PLoS Genet 21, e1011407 (2025).

4. Firdaus, Z. & Li, X. Unraveling the Genetic Landscape of Neurological Disorders: Insights into Pathogenesis, Techniques for Variant Identification, and Therapeutic Approaches. IJMS 25, 2320 (2024).

5. Efthymiou, A. G. & Goate, A. M. Late onset Alzheimer’s disease genetics implicates microglial pathways in disease risk. Mol Neurodegeneration 12, 43 (2017).

6. Falkai, P. et al. Disturbed Oligodendroglial Maturation Causes Cognitive Dysfunction in Schizophrenia: A New Hypothesis. Schizophrenia Bulletin 49, 1614–1624 (2023).

7.  Sey, N. Y. A., Pratt, B. M. & Won, H. Annotating genetic variants to target genes using H-MAGMA. Nat Protoc 18, 22–35 (2023).

8. Lona-Durazo, F., Reynolds, R. H., Scholz, S. W., Ryten, M. & Gagliano Taliun, S. A. Regional genetic correlations highlight relationships between neurodegenerative disease loci and the immune system. Commun Biol 6, 729 (2023).

9. Nott, A. et al. Brain cell type–specific enhancer–promoter interactome maps and disease – risk association. Science 366, 1134–1139 (2019).

10. Andersen, M. S. et al. Heritability Enrichment Implicates Microglia in Parkinson’s Disease Pathogenesis. Annals of Neurology 89, 942–951 (2021).

Parneet Kaur

By combining ultrasound guidance with AAV-based gene therapy, a fetal pig model highlights a minimally invasive, safety-focused approach to prenatal gene therapy.

Gene therapies are reshaping outcomes for several genetic disorders¹. Yet, for many conditions, such as spinal muscular atrophy (SMA), beta thalassemia and certain lysosomal storage disorders, the pathological process begins during fetal development, meaning that postnatal gene therapies may arrive too late to prevent irreversible damage^1–3. The mismatch between disease onset and treatment timing has intensified interest in prenatal gene therapy, where early intervention could avert disease progression before birth¹. Although the idea of treating disease in the womb has been explored for decades, translation has progressed slowly because delivering therapy safely during pregnancy remains challenging^3,4. A recent study addresses this barrier by using ultrasound-guided intravascular access to deliver an adeno-associated virus serotype 9 (AAV9) vector encoding green fluorescent protein (GFP) to fetal pigs⁴. By prioritizing feasibility and procedural safety over the disease model, the work shifts prenatal gene therapy from proof-of-principle toward clinical integration⁴.

The study uses a clinically inspired, minimally invasive, ultrasound-guided approach to deliver AAV9 vectors to 7 fetal pigs, mirroring procedures used in human prenatal care⁴. Ultrasound imaging visualized fetal position and vascular structures, enabling precise placement of a thin transabdominal needle to inject the vector into the umbilical vein and the left ventricle (Fig.1). The vector expressed GFP as a reporter to track gene expression rather than to provide therapeutic benefit. The entire procedure was completed in under thirty minutes and was well tolerated by both the sow and the fetuses. Aside from one preterm delivery, pregnancies progressed normally, and no maternal complications were observed. This level of safety profile establishes a procedural foundation compatible with clinical translation⁴.

Figure 1: Schematic overview of the production of the AAV9-GFP vector and its ultrasound-guided intravascular delivery in fetal pigs. A) The GFP transgene was packaged into AAV9 particles by co-transfecting packaging cells with plasmids encoding AAV Rep/Cap proteins and adenoviral helper functions. Viral particles were harvested and purified to generate high-titer stocks (approximately 10¹²–10¹³ vector genomes per millilitre). B) The purified AAV9-GFP vector was then delivered in utero using transabdominal ultrasound-guided injection to two areas, the left ventricle and the umbilical vein, mirroring the routine procedure used in human prenatal care. Postnatal evaluations were conducted to assess systemic gene expression patterns, biodistribution, and safety outcomes. Figure adapted from Donfrancesco et al., 2025⁴. Created with BioRender.com and FigureLabs^5,6.

The strategy used by Donfrancesco et al. is best understood in light of the limitations of earlier large-animal studies⁴. Sheep and non-human primate models have demonstrated that viral vectors can reach fetal tissues and drive gene expression, but these experiments relied on open fetal surgery and intracerebroventricular injections^3,4,7. These are substantially invasive, technically demanding approaches and carry significant risks for both the mother and the fetus, making them incompatible with human prenatal care. Large animal models are also challenging to genetically manipulate due to long gestational periods and small litter sizes, limiting their utility for modelling human diseases⁸. The pig model narrows this translation divide as fetal pigs resemble human fetuses in size, anatomy and immune maturation⁹. This compatibility with standard obstetric tools enabled the present study to evaluate minimally invasive fetal procedures under clinically realistic conditions.

With feasibility and safety established, the study investigated whether this gene-delivery approach could achieve systemic distribution of the vector across fetal tissue⁴. The results demonstrated widespread biodistribution, with the highest transgene expression in the liver and heart, and moderate levels in the lungs, spleen, kidneys, and skeletal muscle. Expression was also detected in the brain despite the developing blood-brain barrier, indicating prenatal delivery can reach tissues that are less accessible after birth. High expression in the liver and heart suggests potential applications in cardiomyopathic and early-onset metabolic diseases, such as Pompe disease and urea cycle defects, while brain expression offers an opportunity to target neurodevelopmental conditions, such as SMA². Overall, these findings highlight that the prenatal delivery strategy evaluated in this study could support multi-organ therapeutic applications⁴.

Although broad biodistribution was achieved, the persistence of vector genomes over time remains an important consideration for prenatal gene therapy^4,10,11. In this study, detectable vector genome levels declined by one month of age, likely reflecting dilution as fetal tissues undergo rapid growth and cell division^4,11. This highlights the central challenge: durability. For disorders requiring lifelong gene expression, such as hemophilia or Duchenne muscular dystrophy, additional strategies like integrating a vector or a postnatal booster may be necessary^2,11. In contrast, for disorders such as SMA in which early molecular correction is sufficient to prevent irreversible pathology, even transient gene expression during fetal development could provide lasting therapeutic benefit^2,10,11. This distinction emphasizes the need to tailor prenatal therapy to the disease and its therapeutic window.

Beyond vector persistence, the immune response poses another major challenge for AAV9-based gene therapies^4,10. In this study, neither sows nor piglets developed significant levels of anti-AAV9 antibodies, suggesting that the fetal immune system may confer tolerance to viral capsids. A mild increase in inflammatory cytokines was observed; however, it was not associated with tissue damage, supporting the overall safety of this approach⁴. These observations suggest that prenatal exposure to AAV9 vectors may mitigate immune barriers that often limit the efficacy of postnatal AAV-based therapies¹¹.

Even with these advances, several important questions remain unanswered. The study focuses on the safety and efficient delivery of a reporter gene rather than assessing its therapeutic benefit in a disease model⁴. As a result, it remains unclear whether ultrasound-guided delivery of AAV9 vectors can produce sustained functional improvement in genetic conditions that require long-term gene expression. Future research must determine how to extend gene expression without compromising safety. This study moves prenatal gene therapy toward clinical reality by demonstrating that a safe and practical delivery method is achievable in a large-animal model. This fetal pig platform provides a foundation for testing durability, disease-specific models, refining injection techniques, assessing immune tolerance, and optimizing intervention timing⁴.

References

1.         Seth, S., Sen, M., Banerjee, S., Sau, R. & Ray, S. In utero gene therapy: Pioneering diagnostic and therapeutic technologies. Hum. Gene 47, 201527 (2026).

2.         Peddi, N. C., Marasandra Ramesh, H., Gude, S. S., Gude, S. S. & Vuppalapati, S. Intrauterine Fetal Gene Therapy: Is That the Future and Is That Future Now? Cureus https://doi.org/10.7759/cureus.22521 (2022) doi:10.7759/cureus.22521.

3.         Jeanne, M. & Chung, W. K. Opportunities and Challenges of Fetal Gene Therapy. Prenat. Diagn. 45, 764–771 (2025).

4.         Di Donfrancesco, A. et al. Transabdominal ultrasound guided AAV9-GFP delivery in fetal pigs: a translational and minimally invasive model for in utero fetal gene therapy. Gene Ther. 32, 487–496 (2025).

5. BioRender. BioRender (BioRender, https://biorender.com/, accessed 25 Jan 2026).

6. FigureLabs. FigureLabs (FigureLabs, https://www.figurelabs.ai/, accessed 25 Jan 2026

7.Palanki, R., Peranteau, W. H. & Mitchell, M. J. Delivery technologies for in utero gene therapy. Adv. Drug Deliv. Rev. 169, 51–62 (2021).

8.         Mattar, C. N. Z., Chew, W. L. & Lai, P. S. Embryo and fetal gene editing: Technical challenges and progress toward clinical applications. Mol. Ther. – Methods Clin. Dev. 32, 101229 (2024).

9.         Lunney, J. K. et al. Importance of the pig as a human biomedical model. Sci. Transl. Med. 13, eabd5758 (2021).

10. Ronzitti, G., Gross, D.-A. & Mingozzi, F. Human Immune Responses to Adeno-Associated Virus (AAV) Vectors. Front. Immunol. 11, 670 (2020).

11. MacKenzie, T. C. Future AAVenues for In Utero Gene Therapy. Cell Stem Cell 23, 320–321 (2018).

Omer Kleiner

Pronuclear transfer successfully prevents transmission of lethal mtDNA disorders in landmark clinical trial.

Mitochondrial diseases affect an estimated 1 in 5,000 births and remain among the most devastating inherited disorders, with no curative treatments available^1,2. These conditions arise from pathogenic variants in mitochondrial DNA (mtDNA), which is exclusively maternally inherited, often leading to severe multi-organ dysfunction and early mortality in many affected children¹. A groundbreaking study published in the New England Journal of Medicine reports the successful birth of eight healthy babies following mitochondrial donation through pronuclear transfer (PNT). This novel protocol offers families a viable path to having genetically related, healthy children, even if the mother carries homoplasmic or high-heteroplasmic pathogenic mtDNA variants​³.

Newcastle University is a pioneer in mitochondrial donation research, producing extensive preclinical work demonstrating that PNT could reduce mtDNA variant transmission in human embryos⁴. The United Kingdom became the first country to legalize mitochondrial donation in 2015, with the Human Fertilisation and Embryology Authority (HFEA) granting Newcastle’s centre its first treatment license in 2017³. Newcastle University has cultivated an integrated program offering PNT and preimplantation genetic testing (PGT), a complementary screening approach used to identify embryos that already have sufficiently low heteroplasmy levels for safe transfer​³.

Pronuclear Transfer involves microsurgical transplantation of the nuclear genome from the patients’ zygote into a donor zygote that has been enucleated but retains healthy mitochondria (Figure 1)⁵. The procedure is performed 8-13 hours after intracytoplasmic sperm injection, when maternal and paternal haploid genomes are contained in distinct pronuclei. This technique allows children to inherit their parents’ nuclear genes while receiving functional mitochondria from a donor, effectively preventing disease transmission³.

Among 25 patients who underwent oocyte retrieval for PNT, 22 had egg batches warmed and 19 ultimately underwent the procedure. Of 160 attempted PNT procedures between patient and donor zygotes, 127 were successful (79.4%), with 96.1% of resulting embryos remaining intact the following day. Clinical pregnancies were confirmed in 8 of 22 patients (36%) who underwent intracytoplasmic sperm injection for PNT, resulting in eight live births³. These findings demonstrate for the first time in a clinical setting, that PNT is compatible with human embryo viability and can result in healthy live births for a patient population that previously had limited reproductive options.

The outcomes are particularly encouraging when examining mtDNA carryover to newborns. Among the eight newborns, heteroplasmy levels ranged from undetectable to 16%, with five showing undetectable levels, one at 5%, one at 12%, and one at 16%. Levels of maternal pathogenic mtDNA variants were reduced by 95-100% in six newborns and by 77-88% in two others compared to the corresponding enucleated zygotes³. These levels are substantially below the approximately 80% threshold typically required for clinical manifestation of mitochondrial disease⁶. Critically, next-generation sequencing detected only 0.06-0.17% heteroplasmy in three infants whose blood spot tests showed undetectable levels, confirming the remarkable efficacy of the technique. All eight babies were healthy at birth, meeting developmental milestones, with no congenital abnormalities reported at delivery^3,7.

For patients with homoplasmic variants or very high heteroplasmy, PGT is unlikely to identify suitable embryos, since the genetic bottleneck effect that can produce low-heteroplasmy zygotes in moderately affected women does not apply to them. To confirm this, the researchers measured heteroplasmy in the enucleated cytoplasts (the zygote material discarded during PNT) as a proxy for what zygotes would have looked like had these patients attempted PGT instead. In 70% of patients, no zygotes fell below the 30% heteroplasmy transfer threshold, and only 4.5% of all zygotes met this minimum. This validates the patient selection strategy and confirms that PNT provides a crucial alternative for families with homoplasmic or high-heteroplasmy variants who cannot benefit from embryo selection alone³.

These results represent a major breakthrough, yet several important questions remain. There is a phenomenon known as reversion, where small amounts of carried-over maternal mtDNA can amplify over time. This event has been documented in embryonic stem cell lines and warrants lifelong monitoring^4,8. While the two cases showing 12% and 16% neonatal heteroplasmy remain below disease thresholds, understanding why these levels are higher than others is essential. Analysis of cytoplasm carryover scores during PNT showed no marked differences, suggesting that the replicative advantage of patient mtDNA or unequal distribution of heteroplasmy during embryonic development may contribute to the elevated heteroplasmy levels observed in these two cases. Whether these heteroplasmy levels will remain stable across tissues and developmental stages remains unknown, and children born after PNT may therefore require longer-term monitoring protocols than those born after PGT^3,8.   

Alternative approaches to mitochondrial disease prevention continue to emerge, including maternal spindle transfer (MST), a related mitochondrial donation technique performed before fertilization during metaphase II arrest, and improvements to PGT through trophectoderm biopsy at the blastocyst stage^3,5. The relative merits of these different strategies will become clearer as more clinical data accumulate across diverse mtDNA variant types.​

Until more is known about the long-term stability of heteroplasmy levels across tissues and developmental stages, mitochondrial donation should be regarded as a risk-reduction strategy requiring continued clinical follow-up^3,7. This monitoring is especially critical given the risk of reversion, should carried-over maternal mtDNA amplify over time, children currently below disease thresholds could face rising heteroplasmy levels later in life^3,8.

The birth of these eight healthy children marks the beginning of a new era in family planning for those living with mitochondrial disease worldwide.

Figure 1. Pronuclear-Transfer Procedure. a) Illustration demonstrating the pronuclear-transfer procedure. B) The enucleation of patient karyoplasts and the fusion of patient karyoplasts with the donor zygote cytoplast (scale bar, 20 μm). Arrows in the first image indicate the pronuclei, and arrows in the fourth image indicate the karyoplasts.

References

1. Taylor, R. W. & Turnbull, D. M. Mitochondrial DNA mutations in human disease. Nat. Rev. Genet. 6, 389-402 (2005).
2. Gorman, G. S. et al. Mitochondrial diseases. Nat. Rev. Dis. Primers 2, 16080 (2016).
3. Hyslop, L. A. et al. Mitochondrial Donation and Preimplantation Genetic Testing for mtDNA Disease. N. Engl. J. Med. 393, 438-449 (2025).
4. Hyslop, L. A. et al. Towards clinical application of pronuclear transfer to prevent mitochondrial DNA disease. Nature 534, 383-386 (2016).
5. Tachibana, M. et al. Mitochondrial gene replacement in primate offspring and embryonic stem cells. Nature 461, 367-372 (2009).
6. White, S. L. et al. Genetic counseling and prenatal diagnosis for the mitochondrial DNA mutations at nucleotide 8993. Am. J. Hum. Genet. 65, 474-482 (1999).
7. McFarland, R. et al. Mitochondrial Donation within a Reproductive Care Framework. N. Engl. J. Med. 393, 450-461 (2025).
8. Hudson, G., Takeda, Y. & Herbert, M. Reversion after replacement of mitochondrial DNA. Nature 574, E8-E11 (2019).