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Over the past two decades, genetic testing has undergone major shifts in its accessibility and in its nature. Historically, it primarily involved analysis of single genes selected on the basis of symptoms or family history, and was available only to a few. Now, options range from diagnostic clinical genomic tests, to broader screens offered to 'healthy' populations, to direct-to-consumer tests offering to explore ancestry. As genetic testing becomes an increasingly 'everyday' encounter, we sought to explore how the topics of genetics (and genomics) were considered in the Mass Observation Project, an archive of writing by 'ordinary' people about everyday life in Britain. 55% of the 147 respondents had personal experience of genetic testing or knew someone who had, typically to explore ancestry. Responses often gave the sense of genetic testing as a powerful tool in healthcare with results that were fairly definitive. Genomic testing was typically written about as an amplification of genetic testing, generating information of similar solidity. Writers threaded together personal experiences with insights drawn from a wide variety of media, often quite old, in outlining their ideas. While many positioned genetics as outside their remit, respondents engaged in depth with the opportunities and challenges raised, advocating for ethical/societal considerations to form a key part of decision-making regarding genetics. Our analysis shows that people without prior experience of clinical genetic testing may yet have a wealth of experiences and exposures sculpting their expectations as to what testing stands to bring. Consent conversations may benefit from exploring these.

Karyotype analysis is a core component of medical genetics education, yet many students find it challenging, particularly in large classes where opportunities for individualized feedback are limited. Digital tools may help address these constraints, but evidence on their use across different class sizes remains limited. This study examined the use of an interactive digital tool to support learning in karyotype analysis within undergraduate medical education. The study involved 252 first-year medical students who used an interactive digital tool integrated into the medical genetics curriculum and delivered asynchronously through the university's virtual learning platform. Students completed structured online activities requiring the construction and interpretation of karyograms from real metaphase images. Data were collected using pre- and post-activity surveys assessing self-reported knowledge, diagnostic confidence, and perceptions of learning. Descriptive statistics and comparative analyses were used to examine changes over time and to explore differences between small and large teaching cohorts. Following the intervention, students reported improved clarity of key genetic concepts and increased confidence in identifying chromosomal abnormalities compared with pre-activity measures. Most participants also perceived the tool as useful for supporting their learning and reported high levels of engagement with the activity. Similar patterns were observed in both small and large cohorts, suggesting that the digital tool functioned comparably across different class sizes. The findings indicate that interactive digital tools can successfully support the acquisition of complex scientific domains, particularly in contexts characterized by large enrollments and limited access to laboratory-based instruction. Although conducted within medical genetics education, the study has broader relevance for higher education settings seeking scalable approaches to teaching conceptually demanding content. The results may inform educational policy and curriculum decisions related to the integration of digital resources in health professions education. Not applicable.

Matt is an unusually talented scholar, with seminal contributions in many areas (cognitive ability, environmental influences, substance use, etc.). One key theme that undergirds and unites his work is his development of novel research designs and experiments of nature capable of estimating causal inferences with unusual precision. In this contribution to his Festshrift, I illuminate key models and experiments developed by Matt and their contributions to the field. In lieu of new empirical data, I also present a new idiographic behavioral genetic research design - to be called 'narrative non-shared environmental identification' - in which we leverage idiographic, person-specific measurement tools to meaningfully study the effective non-shared environment for the first time.

Ageing-related diseases (ARDs) display diverse phenotypes yet share an age-dependent rise in incidence, suggesting mechanistic links with ageing processes. We examined whether ageing-related genes differ systematically from genes associated with multiple ARD clusters. Across 57 ARDs from UK Biobank, network analyses showed that ageing-related genes, although rarely ARD-associated, lie significantly closer to many ARDs through greater-than-chance proximity in protein-protein interaction (PPI) and KEGG networks. Consistent with this network overlap, ageing-related genes tend to occupy intermediate tiers in KEGG signalling cascades and exhibit strong coexpression with disease-associated genes as well as low tissue specificity, supporting that they play regulatory roles across multiple tissues. In contrast, genes associated with multiple ARDs are enriched for immune disorders and tend to have fewer ARD-associated neighbors in PPI networks. Accordingly, genes associated with multiple ARDs also tend to be located in terminal branches of KEGG pathways and show high tissue specificity coupled with weak coexpression with other ARD genes. Lastly, machine learning integration based on gene-disease network topology identified candidate ageing-related genes enriched for intracellular signal transduction and programmed cell death. Altogether, this work reveals two genetic architectures of multi-ARD influence: a cross-tissue regulatory mechanism enriched in ageing-related genes, and a tissue-specific, immune-driven mechanism among pleiotropic disease-related genes.

As larger genomic data sets become available for wild study populations, the need for flexible and efficient methods to estimate and predict quantitative genetic parameters, such as the adaptive potential and measures for genetic change, increases. Even though animal and plant breeders, as well as the field of human genomics, have produced a wealth of methods, wild study systems often face challenges due to larger effective population sizes, environmental heterogeneity and higher spatio-temporal variation. Existing approaches either rely on two-step procedures, where residuals from a pre-fitted model are used as the response in a second analysis, or can become computationally inefficient as model complexity and data size increase. We therefore adapt methods from animal breeding to account for the complexity typically present in wild animal populations. The core idea is to approximate breeding values as a linear combination of principal components (PCs), where the PC effects are shrunk with Bayesian ridge regression. The result is a computationally efficient and scalable approach, denoted Bayesian principal component ridge regression (BPCRR). A case-study for a Norwegian house sparrow meta-population, as well as simulations, illustrate that the method efficiently estimates the additive genetic variance and accurately predicts breeding values. In order to assess whether BPCRR predicts informative breeding values, we also apply BPCRR to track micro-evolutionary change across time and space in the house sparrow system. To make the method accessible, we provide coded examples and data.

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Understanding how landscape features influence gene flow in natural populations is a central goal of landscape genetics. In this study, we evaluated the status of multiple populations of Camellia chekiangoleosa-a provincially protected plant in Jiangxi Province with significant ecological and economic value-in the Laohunao Nature Reserve. We then performed an analysis of their genetic diversity and spatial genetic structure to inform the development of science-based conservation strategies. The findings indicated that the genetic diversity of C. chekiangoleosa was on par with that of the majority of other species within the genus. Sub-populations exhibit a certain degree of genetic differentiation (F ST=0.065). The application of STRUCTURE and principal component analysis (PCA) unveiled a distinct pattern of geographical clustering across the 9 sub-populations. Multiple matrix regression with randomization (MMRR) revealed that both environmental isolation and geographical isolation have wielded a pronounced impact on genetic distance. The MEMGENE analysis further discerned spatial genetic structure, which was influenced by the ridges and valleys that demarcate Mount Ruozhushan (RZS), Mount Zhushan (ZS), and Mount Shitoushan (STS). Mountain ridges exerted a more pronounced influence on genetic differentiation compared to valleys. In summary, this study underscores that topographic features play a pivotal role in shaping the spatial genetic structure of C. chekiangoleosa.

We discuss here physics and evolution of globular proteins with a focus on the basic units of their structure and function, closed loops and elementary functional loops. A starting point of the journey here is a prebiotic evolution, in which short linear peptides and corresponding RNA duplexes with traits determined by demands to survive and to move on in the harshness of the Origin of Life emerged first. Next, we follow the fate of ring-like peptides, which apparently were the "Dayhoff fragments" that passed through abiogenic transition, formed first functional domains followed by their inclusion in multidomain structures, formation of complexes, assemblies, and molecular machines in later stages. I argue that physics, specifically polymer nature of protein chains, not only served as a determinant of the basic units of proteins-closed loops but also could play a decisive role in determining the size of another important structural unit-domain-based on the polymer nature of nucleic acids that governed the optimal ring closure size of DNA molecules. The protein closed loops served as scaffold for carrying elementary functions-descendants of ancient ring-like peptides, combinations of which provided the modern protein function universe. Another alliance between physics and evolution discussed here is the allosteric regulation of protein function, which is based on the structural dynamics underlying the allosteric signal transduction and its regulatory role. While the physics drive the structure-based conserved patterns of allosteric signalling determined by the folds, the evolution brings in a sequence diversity, allowing to alternate the allosteric communication and to make it archetypal for distinct functional (super)families using the same fold as the structural platform. Considering only few above aspects, I show that important lessons from the hand-in-hand walk of physics and evolution already helped to achieve the current state-of-the-art in understanding of protein structure and function. I conclude, however, that there is still even more to learn from Nature about long and remaining mainly mysterious 3.5 billion years endeavor.

Biological manipulation via physical stimuli such as light and magnetism has become a central goal in modern biotechnology. Among these modalities, magnetic fields offer unique advantages, including deep tissue penetration and untethered interventions in living systems. An ideal platform for such a magnetogenetic toolkit would be a genetically encodable protein with tunable magnetic features under physiological conditions. However, the development of such tools has been hindered by the lack of robust and stable protein scaffolds with strong intrinsic magnetic properties. Inspired by animal magnetoreception in nature, here, we rationally designed and systematically screened single-chain variants of the magnetoreceptor MagR. Through nine iterative rounds of design and experimental validation, we generated 25 constructs and ultimately identified a stable single-chain-dimer-based-tetramer, SDT-MagR, as the optimal magnetic molecular platform. This engineered protein exhibits exceptional structural stability and state-dependent magnetic behavior, showing ferrimagnetic-like characteristics in the solid state and paramagnetic behavior in solution. With enhanced magnetic susceptibility, purified SDT-MagR can be directly attracted by a magnet in vitro, establishing it as a promising new platform for future biomagnetic manipulation and magnetogenetics applications.

The protease TMPRSS2 facilitates coronavirus infections, yet its mechanism of viral glycoprotein recognition remains unclear. Here we show that, following ACE2 engagement of the SARS-CoV-2 spike (S) inducing the early fusion intermediate conformation (E-FIC), TMPRSS2 cleaves the R815 S2' site and promotes fusogenic conformational changes leading to viral entry. We unveil TMPRSS2 recognition of S2', identify key residues modulating binding specificity and demonstrate that S2' site-directed broadly neutralizing antibodies target E-FIC and inhibit viral entry by blocking TMPRSS2 access. We computationally designed stabilized E-FIC as a vaccine candidate, overcoming the transient nature of this state. We describe a TMPRSS2-directed monoclonal antibody inhibiting several coronaviruses, including SARS-CoV-2 variants and protecting mice against SARS-CoV-2 challenge. These results outline the mechanistic role of TMPRSS2 and S2' site-directed antibodies in coronavirus entry.

A distinct form of chronic kidney disease of unknown etiology (CKDu) has emerged in tropical regions of Sri Lanka, predominantly affecting individuals aged 30-60 years in the North Central Province. Unlike conventional chronic kidney disease (CKD), CKDu occurs independently of diabetes or hypertension and is characterized by tubulointerstitial damage, including tubular atrophy, interstitial inflammation, and fibrosis. Epidemiological studies showed familial clustering, suggesting an underlying genetic predisposition. This study aimed to identify genetic variants associated with CKDu in Sri Lankan populations using whole-exome sequencing (WES). Eighty-six individuals (47 CKDu patients and 39 controls) were recruited from endemic and non-endemic regions. Physiological, biochemical, and geographic parameters were recorded. DNA extracted from blood was subjected to WES to identify variants associated with CKDu. Results: A total of 171 unique variants across 121 genes were identified. Among the most prevalent genes were ATXN3, LFNG, PNLDC1, LINC02456, and HLA-DRB1. In the case-control comparison, only LFNG showed statistically significant enrichment in affected individuals, whereas signals in ATXN3, PNLDC1, and LINC02456 were not statistically significant, but have an association with renal dysfunction, and thus are included as hypothesis-generating variant observations. HLA-DRB1 variants showed trends toward a protective haplotype. LFNG showed the greatest prevalence in affected individuals (71.7%), followed by PNLDC1 (63%), ATXN3 (56%), FIP1L1 (41%), and HLA-DRB1 (32%). Conclusion: Findings suggest genetic variants in combination with environmental factors may contribute to CKDu susceptibility in the Sri Lankan population. We underscore the multi-factorial nature of CKDu and highlight the need for integrative genomic and environmental research to elucidate disease mechanisms and inform targeted prevention strategies.

The correlation between type 2 diabetes mellitus (T2DM) and heart failure has been extensively studied; however, the causal nature of this relationship remains a topic of debate. Our study employs a two-sample Mendelian randomization (MR) approach to rigorously examine the causal relationship between T2DM and heart failure. By utilizing summary statistics from genome-wide association studies (GWAS) involving 655,666 individuals for T2DM (including 61,714 cases and 1178 controls) and 977,323 individuals for heart failure (including 47,309 cases and 930,014 controls), we aimed to provide strong evidence for a causal association. The analysis revealed a significant causal relationship, with individuals with T2DM exhibiting a 7% increased odds of heart failure (inverse variance weighting [IVW]: OR = 1.07, 95% CI = 1.04-1.10, P = 1.07 × 10-6). This finding highlights the significance of T2DM as an independent risk factor for heart failure and suggests that MR analysis could be a valuable tool for the early identification of heart failure risk in diabetic populations. Our results emphasize the need for enhanced screening and preventive measures for heart failure in patients with T2DM, potentially leading to improved patient outcomes through early diagnosis and intervention.

Alphaviruses are mosquito-borne viruses that can infect the central nervous system (CNS) and cause encephalomyelitis, which is a rare but dangerous complication from infection. In mice, this can be studied in a model of infection with Sindbis virus (SINV), which infects neurons and causes neurological disease. Due to the non-renewable nature of neurons, the immune response in the CNS is specialized to prevent neuronal damage or death, even if they are infected. Therefore, insights into the nuances of antiviral immunity in the CNS provide a better understanding of disease pathogenesis and mechanisms of recovery. Type I interferons (IFNs) are critically important for survival; they are an innate antiviral defense mechanism that consists mainly of IFNα and IFNβ. Although both use the same receptor, type-specific differences between IFNα and IFNβ have been described in other contexts. To this end, Ifnb-/- mice were used to elucidate the role of IFNβ in recovery from alphavirus encephalomyelitis. IFNβ-deficient mice have intact IFNα expression and downstream signaling, but symptomatic disease occurs earlier and is more severe. This is accompanied by increased virus replication in the early stages of infection. Microgliosis is reduced in Ifnb-/- mice compared to wildtype, but inflammatory cytokine/chemokine levels are higher and associated with alterations in monocyte and NK cell recruitment into the CNS. Ifnb-/- mice have no deficiencies in the expression of factors known to be required for viral clearance. Therefore, IFNβ modulates the early stages of the immune response and facilitates restriction of virus replication, contributing to delayed disease onset.

Early-stage diagnosis of paroxysmal atrial fibrillation (PAF) is challenging owing to its asymptomatic nature. However, the genetic factors underlying PAF and predictive utility of polygenic risk scores (PRSs) for PAF in Asian populations remain elusive. We aimed to explore the PAF-associated genetic variants in a Japanese cohort and evaluate the predictive performance of PAF-specific PRSs. This study included 2,604 participants. Following exclusion, quality control, and genotype imputation, a genome-wide association study (GWAS) was conducted. The predictive performance of 30 sets of PRS models constructed across various thresholds was evaluated using three machine learning methods. Model performance was assessed using area under the curve (AUC) and SHapley Additive exPlanations (SHAP). The GWAS using 1,038 PAF cases and 744 controls identified 82 genome-wide significant variants (P&#x2009;<&#x2009;5&#x2009;&#xd7;&#x2009;10-8), all on chromosome 4q25. Of these, 80 variants clustered upstream of PITX2, and two were located in LINC01438. Fine mapping identified two independent intergenic signals, with rs2200732 as the lead single-nucleotide polymorphism. The best PRS-only model achieved an AUC of >0.70, which was improved up to 0.737 in additive models incorporating both PRS and clinical variables. SHAP analysis consistently ranked PRS as the most influential predictor among the clinical variables included in this study. These results suggest that genetic risk, particularly at the established 4q25/PITX2 locus, contributes substantially to PAF susceptibility in this Japanese cohort and that PRS may improve early risk stratification when integrated with clinical risk factors.

Visual camouflage evolution in animals depends on both light's interaction with its surroundings and the limits of what the natural observers of the camouflage can see. Changes in lighting - such as those caused by shifting weather - can quickly alter how animals and their surroundings appear due to the generation of shadows both cast onto objects and from self-shading. The extent and nature of these changes will depend on the three-dimensional (3D) structure of the environment. Despite the apparent effects of lighting on object and background appearance, the enormity of interactions and the diversity of animal camouflage methods and appearances pose challenges to investigating the combined effects of lighting and habitat structure on camouflage. Genetic algorithms and mathematical methods for generating animal patterns provide a potential solution for investigating camouflage's broad feature space by evolving artificial prey under different lighting conditions within different habitats. Here, an online artificial evolution experiment was used to examine the effect of lighting, both direct and diffuse, and habitat geometry on camouflage. Lighting and geometry changed the appearance of the evolved prey, and the predictive power of common measures of camouflage. Crucially, lighting condition systematically altered how contrasting the prey-targets' internal patterning was and interacted with habitat geometry to affect the evolved pattern shapes, colours, and countershading. Our work demonstrates the importance of considering the relative geometry and lighting of an environment when determining the function of animal colouration and the adaptive value of camouflage.

Over the last two decades, we have routinely published phylogenies describing the evolution of organisms and viruses using protein structure information. The structure-based trees offer several advantages over traditional approaches, including improved resolution of basal branches of the trees, a more realistic representation of evolutionary events such as gene duplication, loss, gain, and horizontal gene transfer, better handling of fast-evolving (micro)-organisms and organisms with parasitic tendencies (viruses), and reduced susceptibility to artifacts arising from sequence alignment and reconstruction in complex genomic datasets. Here, we present a generic protocol for phylogenomic analysis of molecular structure, which is timely considering the recent revolution in AI-driven models of protein structure prediction. While the protocol is illustrated with viral protein structures, the procedure is generic in nature and can be easily adapted for other structures (e.g., RNA) and molecular characteristics (e.g., molecular functions, pathways), thereby enriching the phylogenetic toolkit available to molecular biologists.

This study analyses relationships between milk yield, milk composition, and fertility in Murciano-Granadina dairy goats to test whether these associations reflect biological pathways underlying a production-fertility trade-off. Linear and non-linear patterns were examined using canonical discriminant analysis (CDA), regularized canonical correlation analysis (rCCA), and CHAID decision trees. The dataset included 32,693 artificial inseminations from 21,757 does and 29,390 milk records from 21,541 does; fertility was assessed using three indicators accounting for insemination timing and semen type. Mean fertility differed by semen preservation method, with fresh/chilled semen showing 18-20 percentage points higher fertility than frozen/thawed semen. Milk yield showed wide variability (8.7-6704.6&#x2009;kg; mean 686.4&#x2009;kg), whereas milk composition was relatively stable. After multicollinearity control (VIF&#x2009;>&#x2009;178 to <5), CDA and rCCA revealed a low-dimensional structure dominated by a first canonical function, explaining 80.8-88.7% of discriminant variance and 97.3% of shared covariance. This dominant axis reflected an energy balance and metabolic partitioning pathway, with higher milk yield and solids concentration associated with reduced fertility; very high production (>2021&#x2009;kg) consistently coincided with lower fertility, supporting a production-reproduction trade-off. A secondary axis represented an endocrine and lactation-timing pathway linking fertility to temporal variation in milk composition, while semen type constituted an autonomous semen-related pathway with the strongest standardized canonical coefficients. CHAID analysis identified non-linear relationships and biologically meaningful thresholds in milk traits, achieving ~44% classification accuracy and up to 87% reliability for high fertility. Optimal fertility was associated with intermediate ranges of milk yield and composition (protein 3.64-6.98%, fat 6.0-7.9%, dry matter 14-25%, lactose 5.6-6.5%, SCC&#x2009;<&#x2009;5200&#x2009;&#xd7;&#x2009;10&#xb3; cells/mL). Overall, milk yield and composition showed statistically robust but moderate associations with fertility, consistent with reproductive biology multifactorial nature of and gaining relevance when interpreted jointly with metabolic, endocrine, and semen-related factors.

The classification of congenital malformations has been transformed over recent decades by advances in genetic analysis, so that the natural history of many disorders during childhood is well described. However, implications for adult prognosis and survival are often poorly documented. In Apert syndrome, caused by heterozygous germline mutations in the fibroblast growth factor receptor type 2 gene (FGFR2), the question of prognosis is particularly pertinent because FGFR2 is a known cancer driver gene (oncogene) and the identical mutations, when arising somatically, are enriched in specific tumours, notably endometrial carcinoma. We exploited a unique resource provided by a series of 24 UK patients described by Dr Eric Blank in 1960, and used tracing of cancer events and deaths through the National Health Service Central Register to determine the long-term outcome of these individuals until 2013, a period spanning 53 years. Twelve individuals (50%) were still alive and without any cancer registration, at the end of the study; of the remainder, two could not be traced and ten were known to have died, with four deaths related to malignancies. We conclude that Apert syndrome is not, in many affected individuals, associated either with substantial shortening of lifespan, or with a high risk of developing particular types of cancer. Explanation of the lack of strong cancer predisposition, despite the oncogenic nature of the FGFR2 mutations, may lie in the different signalling relationship that a mutant cell has with its neighbours when the mutation is present constitutionally, compared to occurrence as a somatic change.

Several MLLT10-associated single-nucleotide polymorphisms (SNPs) have been identified by genome-wide association studies (GWASs) as germline risk variants for meningioma in predominantly European cohorts, but their relevance in Koreans remains uncertain. We investigated these MLLT10 risk SNPs in Korean meningiomas, assessing differences across two time cohorts and comparing allele frequencies with those observed in other populations. Three MLLT10 SNPs (rs12770228, rs11012732, and rs1243180) were examined in 143 meningiomas from patients aged &#x2264;50 years, comprising 62 fresh-frozen tissues collected during 1999-2003 (Period 1) and 81 formalin-fixed paraffin-embedded tissues from 2006-2023 (Period 2). Three SNPs were detected in 9 of 143 meningiomas (6.3%). While the differences did not reach statistical significance (p > 0.05), minor allele frequencies of all three SNPs were reduced two- to four-fold in Period 2 compared with Period 1. The observed frequencies were similar to those reported in Japanese cohorts but substantially lower than the &#x2265;30% reported in European populations. Despite the limitation of using tumour-derived DNA to assess germline variants, our findings consistently showed that MLLT10 risk SNPs occur at very low frequencies in Koreans, similar to Japanese data and in contrast to Europeans. These results highlight the population-specific nature of MLLT10 variants and underscore the need for large-scale Asian studies for risk SNP analysis in meningiomas.