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PubMed2025

Quantifying evidence for phenotypic specificity (PP4) for syndromic phenotypes: Large-scale integration of rare germline FH variants from diagnostic laboratory testing for HLRCC and renal cancer.

PURPOSE: Hereditary leiomyomatosis and renal cell cancer (HLRCC) is a rare cancer susceptibility syndrome exclusively attributable to pathogenic variants in FH (HGNC:3700). This article quantitatively weights the phenotypic context (PP4/PS4) of such very rare variants in FH. METHODS: We collated clinical diagnostic testing data on germline FH variants from 387 individuals with HLRCC and 1780 individuals with renal cancer and compared the frequency of "very-rare" variants in each phenotypic cohort with 562,295 population controls. We generated pan-gene very rare variant likelihood ratios (PG-VRV-LRs), domain-specific likelihood ratios for missense variants (DS-VRMV-LR) using spatial clustering analysis, and log2.08 likelihood ratios (LLRs) as applicable within the updated American College of Medical Genetics and Genomics/Association for Molecular Pathology variant classification framework. RESULTS: For HLRCC, the PG-VRV-LR was estimated to be 2669.4 (95% CI 1843.4-3881.2, LLR 10.77) for truncating variants and 214.7 (95% CI 185.0-246.9, LLR 7.33) for missense variants. For renal cancer, the PG-VRV-LR was 95.5 (95% CI 48.9-183.0, LLR 6.23) for truncating variants and 5.8 (95% CI 3.5-9.3, LLR 2.39) for missense variants. Clustering analysis in HLRCC cases revealed 3 "hotspot" regions wherein the DS-VRMV-LR increased to 1226.9. CONCLUSION: These data provide quantitative measures for very rare missense and truncating variants in FH, which reflect the differing phenotypic specificity of HLRCC and renal cancer and may be applicable in clinical variant classification.

Humans↗

PubMed2025

Optimized phenotyping of complex morphological traits: enhancing discovery of common and rare genetic variants.

Genotype-phenotype (G-P) analyses for complex morphological traits typically utilize simple, predetermined anatomical measures or features derived via unsupervised dimension reduction techniques (e.g. principal component analysis (PCA) or eigen-shapes). Despite the popularity of these approaches, they do not necessarily reveal axes of phenotypic variation that are genetically relevant. Therefore, we introduce a framework to optimize phenotyping for G-P analyses, such as genome-wide association studies (GWAS) of common variants or rare variant association studies (RVAS) of rare variants. Our strategy is two-fold: (i) we construct a multidimensional feature space spanning a wide range of phenotypic variation, and (ii) within this feature space, we use an optimization algorithm to search for directions or feature combinations that are genetically enriched. To test our approach, we examine human facial shape in the context of GWAS and RVAS. In GWAS, we optimize for phenotypes exhibiting high heritability, estimated from either family data or genomic relatedness measured in unrelated individuals. In RVAS, we optimize for the skewness of phenotype distributions, aiming to detect commingled distributions that suggest single or few genomic loci with major effects. We compare our approach with eigen-shapes as baseline in GWAS involving 8246 individuals of European ancestry and in gene-based tests of rare variants with a subset of 1906 individuals. After applying linkage disequilibrium score regression to our GWAS results, heritability-enriched phenotypes yielded the highest SNP heritability, followed by eigen-shapes, while commingling-based traits displayed the lowest SNP heritability. Heritability-enriched phenotypes also exhibited higher discovery rates, identifying the same number of independent genomic loci as eigen-shapes with a smaller effective number of traits. For RVAS, commingling-based traits resulted in more genes passing the exome-wide significance threshold than eigen-shapes, while heritability-enriched phenotypes lead to only a few associations. Overall, our results demonstrate that optimized phenotyping allows for the extraction of genetically relevant traits that can specifically enhance discovery efforts of common and rare variants, as evidenced by their increased power in facial GWAS and RVAS.

Humans↗

PubMed2026

Genetic Correlation Between Brain Imaging Phenotypes and Externalizing Behavior: A Large-Scale LDSC Analysis of UK Biobank IDPs.

Externalizing has been associated with differences in brain structure and function; however, it remains unclear whether these associations reflect shared common-variant genetic influences. Cross-trait linkage disequilibrium score regression was used to estimate genome-wide genetic correlations between externalizing genome-wide association study (GWAS) results and 3,935 brain imaging-derived phenotypes from the UK Biobank BIG40 resource. The imaging phenotypes covered structural magnetic resonance imaging (MRI), diffusion MRI, susceptibility-weighted imaging, resting-state functional MRI, and task-based functional MRI. Results were included in the primary analysis when the imaging phenotype had positive single-nucleotide polymorphism (SNP) heritability, a heritability Z statistic of at least 1.96, a mean GWAS chi-square statistic of at least 1.02, at least 200,000 regression SNPs, and a complete LDSC result without a fatal error. Technical imaging quality-control phenotypes were excluded from biological inference. Individual results were corrected using the Benjamini-Hochberg false discovery rate procedure. Aggregated Cauchy association tests (ACATs) were used to evaluate evidence across all imaging phenotypes and within predefined imaging categories. Statistical power, simultaneous confidence bounds, and alternative quality-control definitions were examined in sensitivity analyses. Of the 3,935 imaging phenotypes, 3,716 produced estimable genetic correlations, 2,980 met the primary LDSC quality-control criteria, and 2,967 were classified as biological imaging phenotypes. No individual phenotype survived false discovery rate correction. The smallest unadjusted P value was 0.0005, and the minimum adjusted q value was 0.486. The distribution of genetic correlations was centered near zero, with a median genetic correlation of 0.0014 and a median absolute genetic correlation of 0.0338. ACAT provided no evidence of an aggregate association across all biological imaging phenotypes (P = 0.302), and no predefined imaging category survived multiple-testing correction. The median minimum detectable genetic correlation at 80% power was 0.216. Bonferroni-adjusted simultaneous confidence intervals were fully contained within the interval [-0.30, 0.30] for 80.0% of phenotypes in the primary analysis and 88.0% under the stringent heritability quality-control definition. Broad and stringent sensitivity analyses produced the same overall conclusions. In this study, no statistically robust evidence of genome-wide genetic correlations between externalizing and individual UK Biobank brain imaging phenotypes was found. Nevertheless, small, localized, mixed-direction, or developmentally specific genetic effects remain possible.

Journal Article↗

PubMed2025

Charting the phenotypic landscape of mitochondrial diseases through a systematic evaluation of pathogenic mitochondrial DNA and nuclear gene variants.

PURPOSE: Primary mitochondrial diseases (PMD) arise from variants in the mitochondrial or nuclear genomes. Phenotype-based recognition of specific PMD genotypes remains difficult, prolonging the diagnostic odyssey. We expanded the MitoPhen database to characterize phenotypic variation across PMD more systematically. METHODS: Individual-level data on mitochondrial DNA disorders, nuclear-encoded mitochondrial diseases, and single large-scale mitochondrial DNA deletions were manually curated with Human Phenotype Ontology (HPO) terms to produce MitoPhen v2. Principal-component analysis summarized system-level abnormalities; HPO-level enrichment and mean phenotype-similarity scores were then used to distinguish common PMD genotypes. RESULTS: MitoPhen v2 adds 3940 individuals to the original release, now encompassing 1597 publications, 10,626 individuals, and 117 genotypes. Among 7586 affected cases, 72,861 HPO terms were recorded. Principal-component analysis revealed 6 phenotype dimensions capturing most system-level variance. At the HPO level, we observed genotype-specific enrichments and identified 111 gene-phenotype links absent from the current HPO database. Using MT-TL1, single large-scale mitochondrial DNA deletions, and POLG as exemplars, phenotype-similarity scores reliably separated individuals with these genotypes from those without. CONCLUSION: MitoPhen v2 enabled systematic, genotype-aware analysis of heterogeneous PMD phenotypes and highlighted the diagnostic value of structured, individual-level data. Phenotype-similarity metrics from such data sets can refine variant interpretation in large rare-disease cohorts and provide a transferable framework for other phenotypically complex genetic disorders.

Humans↗

PubMed2025

Divergent and stabilizing selection shape the phenotypic space of Arabidopsis thaliana.

Why do we observe some plant phenotypes but not others? The multivariate phenotypic space occupied by individuals or species often reveals both limits and phenotypes strikingly deviating from main syndromes. These observations are usually thought to indicate, respectively, inviable trait combinations and unique phenotypes adapted to specific environments. However, the evolutionary drivers underlying trait covariations often remain unclear. Here, we characterized the phenotypic space of Arabidopsis thaliana by comparing 713 wild accessions collected across the globe with 2,544 artificially-created recombinant individuals. This, combined with the detection of adaptive processes operating within species, allowed us to elucidate the roles of natural selection as a driver of phenotypic (co)variations within A. thaliana. We found that the phenotypic space of this species is constrained and driven by varying levels of divergent and stabilizing selection across different traits. Moreover, at the margins of the European geographic range, strong directional selection favored outlier phenotypes characterized by very late flowering and variation in a WRKY transcription factor gene. Genome analyses revealed that these extreme phenotypes may be explained by hybridization between ancestral and modern lineages of A. thaliana. Our findings demonstrate how interplays between population history and natural selection shape phenotypic diversity in a plant species.

Arabidopsis↗

PubMed2024

Quantitative natural history modeling of HPDL-related disease based on cross-sectional data reveals genotype-phenotype correlations.

PURPOSE: Biallelic HPDL variants have been identified as the cause of a progressive childhood-onset movement disorder, with a broad clinical spectrum from severe neurodevelopmental disorder to juvenile-onset pure hereditary spastic paraplegia type 83. This study aims at delineating the geno- and phenotypic spectra of patients with HPDL-related disease, quantitatively modeling the natural history, and uncovering genotype-phenotype associations. METHODS: A cross-sectional analysis of 90 published and 1 novel case was performed, using a Human-Phenotype-Ontology-based approach. Unsupervised phenotypic clustering was used alongside in silico analyses to identify distinct patient subgroups. RESULTS: The study models the natural history of the HPDL-related disease in a global cohort, clarifying the molecular and phenotypic spectrum and identifying 3 distinct subgroups characterized by differences in onset, clinical trajectories, and survival. It establishes genotype-phenotype associations, showing that the presence of moderately pathogenic missense variants in 1 allele leads to a milder, spastic paraplegic phenotype with later disease onset, whereas biallelic, highly pathogenic missense or truncating variants are associated with a more severe phenotype and reduced life span. CONCLUSION: Quantitative and unbiased natural history modeling in HPDL-related disease reveals significant genotype-phenotype associations, providing a foundation for variant interpretation, anticipatory guidance, and choice of outcome measures in future prospective and functional studies.

Humans↗

PubMed2024

Longitudinal characterization of impulsivity phenotypes boosts signal for genomic correlates and heritability.

Genomic correlates of impulsivity have been identified in several genome-wide association studies (GWAS) using cross-sectional designs, but no studies have investigated the molecular genetic correlates of impulsivity phenotypes using longitudinally constructed traits. In 3860 unrelated European participants in the Avon Longitudinal Study of Parents and Children (ALSPAC), we constructed longitudinal phenotypes for delay discounting and impulsive personality traits (as measured by the UPPS-P impulsive behavior scales) via assessment at ages 24, 26, and 28. We conducted GWASs of impulsivity using both cross-sectional and longitudinal phenotypes, estimated heritability and their phenotypic and genetic correlations, and evaluated their association with recently-developed polygenic risk scores (PRSs) for the impulsivity indicators themselves and also related psychiatric conditions. Latent growth curve modeling revealed a stable intercept over time for all impulsivity phenotypes. High genetic correlation of cross-sectional measures over time suggested a stable genetic component for delay discounting (rg = 0.53-0.99) and sensation seeking (rg = 0.99). Heritability estimates of the stable longitudinal phenotypes substantively improved as compared to their cross-sectional counterparts, revealing a significant SNP-heritability for delay discounting (0.22; p = 0.03) and sensation seeking (0.35; p = 0.0007). Consistent with previous reports, GWAS and gene-based analyses revealed associations between specific longitudinal impulsivity indicators and CADM2 and NCAM1 genes. The PRSs for the impulsivity indicators and disorders related to self-regulation were also significantly associated with longitudinal impulsivity traits. Finally, we validated the associations between longitudinal impulsivity phenotypes and their PRSs in an independent 13-wave longitudinal study (n = 1019) and the benefit of longitudinal phenotypes in simulation studies. In this first longitudinal genetic study of impulsivity traits, the results revealed stable genomic correlates of delay discounting and sensation seeking over time and further validated the utility of recently-developed PRSs, both in relation to the observed traits and in connecting them to psychiatric disorders. More generally, these findings support using latent intercepts as novel longitudinal phenotypes to boost signal for heritability and genomic correlates of mechanisms contributing to psychiatric disease liability.

Humans↗

PubMed2026

Genomic insights into stroke recovery: cross-phenotype associations.

Stroke is a major cause of long-term disability with variable recovery. While clinical factors such as initial severity play a role, genetic factors are increasingly recognized as important contributors to stroke recovery. Genotype studies are generally focused on a single post-stroke behavioural domain, but some genes might relate to broad mechanisms of plasticity. This study therefore aimed to identify cross-phenotypic genetic variants associated across two or more stroke recovery domains. DNA from Stroke, Stress, Rehabilitation, and Genetics study participants was genotyped, resulting in 9 814 610 variants. In order to examine cross-phenotypic results, we first conducted genome-wide association studies on the six recovery domains: motor (grip force), cognition (Telephone Montreal Cognitive Assessment), depression (Patient Health Questionnaire-8), stress (Primary Care Post-Traumatic Stress Disorder Screen), functional status (Stroke Impact Scale-Activities of Daily Living), and disability (modified Rankin Scale 0-2 versus 3-6), some of which were tested longitudinally, yielding nine phenotypes. Models were adjusted for age, sex, initial severity (NIH Stroke Scale score), and ancestry. Cross-phenotype associations were identified by evaluating single nucleotide polymorphisms (SNPs) associated (P < 5e-5) with multiple phenotypes. To determine how these genetic variants may relate to biological mechanisms of recovery, we conducted gene enrichment analyses. Participants (n = 565, 59% male) had mild-moderate initial stroke severity (median acute NIH Stroke Scale score = 4). After accounting for the correlation structure among the nine phenotypes, we observed 319 cross-phenotypic SNPs, 3.45 times the expected number. Five of the cross-phenotypic SNPs were linked to genes relevant to neural development, function and plasticity, e.g. ERICH1 (rs11778883-C), FOX3 (rs55726768-G), LIFR-AS1 (rs76401391-T), RPS6KA2 (rs113518460-C) and TUBGCP2 (rs147150392-C), as were enrichments in RAB5-EEA1, CTNNA1-CTNNB1, CIN85-SH3GL2 and ELMO1-DOCK2 complexes. Multiple gene enrichments were found, e.g. Stroke Impact Scale-Activities of Daily Living and Patient Health Questionnaire 8 at 3 months were enriched for CREB phosphorylation, which is important for long-term potentiation. We identified cross-phenotypic SNPs associated with multiple behavioural domains of stroke recovery. Some of these genes encode, or regulate, druggable proteins. These genetic factors are not well captured by clinical or neuroimaging assessments and so provide a unique window into stroke recovery. These findings, if validated, suggest that some genes may be broadly important to stroke recovery.

GWAS↗

PubMed2026

Osteoarthritis phenotypes: advancing precision medicine through clinical, structural, and molecular stratification.

PURPOSE: Osteoarthritis (OA) is now understood as a heterogeneous syndrome driven by diverse biological, biomechanical, metabolic, genetic, and molecular mechanisms. This variability explains differences in disease progression and treatment response, challenging the traditional "one-size-fits-all" approach. This review highlights OA phenotyping as a key step toward precision medicine, focusing on clinical, structural, and molecular classifications that inform individualized care. METHODS: A narrative review was conducted using a non-systematic search of major databases and Osteoarthritis Research Society International sources (2010-2026). Evidence was thematically synthesized across clinical, imaging, and molecular domains to characterize OA phenotypes and their potential relevance to precision medicine. RESULTS: Multiple OA phenotypes were identified: inflammatory, metabolic, biomechanical, cartilage-subchondral, pain-sensitization, and aging/senescence. These exhibit distinct clinical features, risk factors, and therapeutic responses. Imaging-based phenotypes (e.g., inflammatory, meniscus-cartilage, subchondral bone, atrophic, hypertrophic) and molecular endotypes (low turnover, structural damage, systemic inflammation) further refine stratification. Pain-structure discordance is notable in sensitization phenotypes and may predict poorer surgical outcomes. Joint-specific variations and emerging genomic and epigenetic insights underscore disease complexity. Advances in imaging, biomarkers, and machine learning may enable earlier detection and patient clustering, though clinical application remains limited. CONCLUSION: Phenotype- and endotype-based classification represents a critical advancement toward precision OA management. Tailored interventions based on stratification hold promise for improving outcomes; however, clinical translation remains limited by overlapping phenotypes, lack of validated biomarkers, and inconsistent results from phenotype-driven trials. Wider clinical adoption requires standardized definitions, validation across joints, and integration of multimodal diagnostic tools into routine practice.

Humans↗

PubMed2026

The bidirectional genetic causality between immunocyte phenotypes and dilated cardiomyopathy: A bidirectional Mendelian randomization study.

The association between immunocyte phenotypes and dilated cardiomyopathy (DCM) has been explored, however the exact pathogenesis of the relationship between immune cells and DCM is unclear. This bidirectional two-sample Mendelian randomization (MR) research aims to further validate the causal link between 731 immunocyte phenotypes and DCM. Summary statistics from a genome-wide association study data of individuals with European ancestry were utilized, including 1444 DCM cases and 353,937 controls, as well as 3757 European adults for the 731 immunocyte phenotypes. Causal effects were estimated using inverse variance weighted, MR-Egger regression, weight median estimator, weighted mode, and simple mode. Sensitivity analysis was conducted to confirm data robustness and feasibility. Based on the inverse variance weighted findings, 14 immunocyte phenotypes were risk factors for DCM (P < .05, odds ratio [OR] > 1), while 15 immunocyte phenotypes exhibited a protective effect on DCM (P < .05, OR < 1). The results of reverse MR analysis suggested evidence that DCM occurrence might elevate the levels of 17 immunocyte phenotypes (P < .05, OR > 1) and decrease the levels of 9 immunocyte phenotypes (P < .05, OR < 1). Our research indicated that CD28 on secreting regulatory T cell could mitigate the occurrence of DCM, and reciprocally, the progression of DCM could reduce the level of CD28 on secreting regulatory T cell. This study confirmed the bidirectional genetic predictive relationship between immunocyte phenotypes and DCM, underscoring the complex interplay between DCM and the immune system.

Cardiomyopathy, Dilated↗

PubMed2026

Phenotypes of Hereditary Diseases Associated With Rauch-Steindl Syndrome.

PURPOSE: Prenatal phenotypic manifestations of genetic disorders associated with NSD2 variants remain poorly characterized. This study presents our institutional experience with the prenatal diagnosis of NSD2-associated genetic disorders, specifically Rauch-Steindl syndrome (RAUST), aiming to improve understanding of both the molecular and clinical features of RAUST. METHODS: We performed a retrospective analysis of six fetuses and one adult diagnosed with RAUST at our institution and thoroughly reviewed the prenatal ultrasound reports of six fetuses. Prenatal and postnatal phenotypes of RAUST cases were summarized alongside findings from previously published literature. Correlations between NSD2 variant locations, variant types, and phenotypes were analyzed. Additionally, protein modeling was used to visualize structural changes in NSD2 protein before and after C-terminal variants. We integrated single-cell transcriptomic and gene expression data from multiple public databases to investigate spatiotemporal expression patterns of NSD2 during human fetal development. RESULTS: Fetal growth restriction (FGR) was the most prevalent prenatal manifestation in RAUST fetuses, followed by microcephaly. Bilateral renal hypoplasia emerged as a novel prenatal ultrasonographic feature. Postnatally, speech and motor developmental delays were the most commonly reported phenotypes, followed by physical developmental delays and intellectual disability. Genotype-phenotype correlation analysis revealed an association between N-terminal truncating variants in NSD2 and impaired fetal growth parameters. Notably, C-terminal truncating variants-predicted not to directly impact NSD2 functional domains-also exerted disease-causing effects. CONCLUSION: This study provides a comprehensive analysis of prenatal phenotypes in RAUST cases, enriching the prenatal phenotypic spectrum of the disease and facilitating early diagnosis and clinical management of RAUST. Furthermore, our genotype-phenotype correlation findings lay a foundational basis for future research into the complex molecular mechanisms underlying NSD2-associated genetic disorders.

Humans↗

PubMed2026

First-line drug-resistant tuberculosis among children under 15 years in Ethiopia: insights from phenotypic and whole-genome sequencing approaches.

BACKGROUND: Childhood drug-resistant tuberculosis is often underdiagnosed and inadequately characterized due to the paucibacillary nature of the disease. This study aimed to assess resistance to first-line anti-tuberculosis drugs in children using phenotypic drug susceptibility testing and whole-genome sequencing. METHODS: A retrospective-prospective study was conducted on culture-confirmed childhood tuberculosis cases in Ethiopia (2017–2023). Phenotypic drug susceptibility testing was performed on 110 Mycobacterium tuberculosis complex isolates. Whole-genome sequencing was completed for 85 of these isolates, which were analyzed using the TB-Profiler and MTBSeq pipelines. We assessed the sensitivity, specificity, predictive values, and kappa agreement of whole-genome sequencing compared with phenotypic drug susceptibility testing. RESULTS: Phenotypic resistance to at least one first-line anti-TB drug was observed in 26/110 (23.6%) of the examined isolates, with isoniazid resistance being the most frequent, 23/110 (20.9%), followed by rifampicin resistance, 18/110 (16.4%). TB-Profiler showed almost perfect agreement with phenotypic drug susceptibility testing for rifampicin (sensitivity 94.4%, kappa = 0.96) and isoniazid (sensitivity 91.3%, kappa = 0.91), whereas MTBSeq showed slightly lower performance. Both pipelines demonstrated moderate to weak agreement with phenotypic drug susceptibility testing for detecting resistance to ethambutol, pyrazinamide, and streptomycin. The most frequently observed resistance mutations among phenotypically resistant isolates were rpoB (Ser450Leu), katG (S315Thr), embB (Met306Ile), and pncA (C-11 A > G) for rifampicin, isoniazid, ethambutol, and pyrazinamide, respectively. Discrepancies between genotypic and phenotypic drug susceptibility testing were observed across all first-line anti-TB drug-resistant isolates, particularly for ethambutol and pyrazinamide. CONCLUSION: We found a high prevalence of isoniazid resistance, along with rifampicin resistance, underscoring the need for early detection in vulnerable groups. Whole-genome sequencing showed good accuracy for these drugs, with TB-Profiler performing best. CLINICAL TRIAL NUMBER: Not applicable.

Humans↗

PubMed2026

Phenotypic and transcriptomic characterization of biallelic RNU2-2 developmental and epileptic encephalopathy.

OBJECTIVE: A significant proportion of individuals with suspected genetic developmental and epileptic encephalopathies (DEEs) remain unsolved following whole genome sequencing (WGS). Here we describe biallelic RNU2-2 variants causing a recently reported, severe, recessive DEE. METHODS: We screened individuals who have received WGS analyses at the Genomic Medicine Centre Karolinska for Rare Diseases for biallelic RNU2-2 variants. Deep phenotyping was performed through reviewing entire medical histories and phenotypic traits were transcribed to their corresponding Human Phenotype Ontology (HPO) term. HPO terms were used to generate pairwise phenotypic similarity scores and assess for significantly shared phenotype enrichment in the RNU2-2 sub-cohort. RNA sequencing analyses were performed in fibroblast and blood tissues to compare splicing events between RNU2-2 individuals and two independent control groups. RESULTS: We identified 14 individuals from nine families with 12 ultra-rare biallelic RNU2-2 variants clustering in the conserved 5' domains. Genotype data from 13 of 14 individuals has been reported previously as part of a larger cohort. All individuals presented with a highly concordant, severe DEE, characterized by severe to profound intellectual disability, inability to walk or communicate, hyperkinesia, and refractory seizures. Infantile spasms and tonic seizures were the predominant seizure types and a Lennox-Gastaut syndrome-like phenotype was common. These individuals had a significantly similar phenotypic signature when compared with 703 individuals with complex pediatric epilepsies (two-sided Monte Carlo permutation test, p = .005). RNA sequencing analyses showed aberrant splicing, with the most pronounced effects in fibroblast tissues in mutually exclusive exon and alternate 3' splice-site events, which were not detectable in blood. SIGNIFICANCE: We present deep phenotyping data and transcriptomic analyses that provide support for rare, 5' clustering biallelic RNU2-2 variants causing this novel, severe DEE. We propose an RNA sequencing methodology on fibroblast tissue for future validation of RNU2-2 variants.

autosomal recessive disease↗

PubMed2026

Evaluation of Oxford nanopore sequencing for antimicrobial resistance surveillance in Salmonella: comparison with phenotypic antimicrobial susceptibility in a large-scale study.

UNLABELLED: Salmonella is a major zoonotic foodborne pathogen, and antimicrobial resistance (AMR) in Salmonella presents a significant public health challenge. Compared with conventional antimicrobial susceptibility testing (AST), whole-genome sequencing (WGS) provides a more rapid and comprehensive approach to AMR characterization, thereby informing antimicrobial selection and supporting public health surveillance. In this study, Oxford Nanopore Technology (ONT)-based WGS was performed on 1,490 Salmonella isolates collected through nationwide surveillance in Taiwan in 2025. Genotypic resistance inferred from WGS data was compared with phenotypic AST results to assess the performance of ONT-WGS. Overall, WGS-inferred resistance showed high concordance with phenotypic resistance for most antimicrobials. However, major genotype-phenotype discordance was observed, attributed to four categories: (i) breakpoint-dependent classification, (ii) reduced or absent phenotypic expression of resistance genes, (iii) minimum inhibitory concentration (MIC) modulation by ramAp, and (iv) absence of known AMR determinants. Notable discrepancies included tigecycline resistance without known genetic determinants, nalidixic acid resistance linked to ramAp-mediated MIC elevation, and a high prevalence of colistin resistance (35.7%) in S. Enteritidis, with most resistant isolates lacking identifiable AMR determinants. Additionally, a significant proportion of ESBL- and AmpC-producing isolates were classified as susceptible or intermediate to cefotaxime and ceftazidime under CLSI criteria, highlighting the potential for misclassification and treatment failure. These findings demonstrate that ONT-WGS enables accurate and comprehensive AMR characterization by directly identifying resistance determinants and avoiding potential misclassification associated with breakpoint-based AST interpretations. When interpreted appropriately, WGS can support better antimicrobial selection and serve as a valuable alternative to conventional susceptibility testing. IMPORTANCE: Accurate prediction of antimicrobial resistance is essential for appropriate therapy and effective surveillance of Salmonella. However, discordance between genotype-based predictions and phenotypic antimicrobial susceptibility testing (AST) can complicate clinical interpretation. In this nationwide study of 1,490 Salmonella isolates, we show that Oxford Nanopore Technology-based whole-genome sequencing (ONT-WGS) provides rapid and comprehensive detection of antimicrobial resistance determinants with high concordance to phenotypic AST. We further identify four major mechanisms underlying genotype-phenotype discordance, including breakpoint-dependent classification, reduced or absent phenotypic expression of resistance genes, minimum inhibitory concentration (MIC) modulation by ramAp, and the absence of known AMR determinants. These findings demonstrate how WGS can complement conventional AST, improve interpretation of challenging susceptibility results, and strengthen genomic surveillance of emerging antimicrobial-resistant Salmonella.

Microbial Sensitivity Tests↗

PubMed2026

Hybrid genome assembly and phenotypic assays reveal carbohydrate metabolism diversity in Lacticaseibacillus strains.

Investigation of carbohydrate metabolism in lactic acid bacteria is essential for the rational selection of strains for fermentation processes, particularly in emerging applications involving non-conventional substrates or building of synthetic microbial consortia. However, establishing robust genotype-phenotype relationships remains challenging, as gene presence alone often fails to explain observed metabolic traits without considering the genomic context and regulatory architecture. In the present study, we combined hybrid genome assembly (Illumina and Oxford Nanopore) with high-throughput phenotype profiling (Biolog GENIII and PM2A) to investigate carbohydrate utilization in five Lacticaseibacillus strains. Phenotypic assays revealed clear intra- and inter-specific variability in substrate utilization. We therefore investigated whether such differences could be attributed to the organization and regulatory context of carbohydrate-associated loci, rather than to gene presence alone. Functional annotation based on COG and CAZyme databases revealed candidate genomic regions potentially involved in carbohydrate metabolism. Comparative analysis between predicted and experimentally observed substrate usage highlighted specific loci associated with carbohydrate utilization profile. The trehalose (tre) operon was conserved across all strains, while at least two distinct cellobiose-associated loci were detected in each genome. Despite the presence of these loci, L. paracasei strains were unable to metabolize cellobiose, a phenotype likely linked to the presence of a downstream TetR-type transcriptional repressor within the cellobiose (cel) operon. Additionally, a genomic region uniquely found in L. rhamnosus strains was associated with gentiobiose utilization, consistent with phenotypic observations. Overall, these findings highlight the importance of integrating phenotypic validation with complete genome context to support the identification of candidate structural and regulatory determinants of carbohydrate utilization in lactic acid bacteria. KEY POINTS: • Phenotype microarrays reveal metabolic traits of interest in isolated strains. • Regulatory context is key to understanding carbohydrate metabolism differences. • Basis of subspecies-dependent cellobiose metabolism in L. paracasei is provided.

Carbohydrate Metabolism↗

PubMed2025

Predicting natural variation in the yeast phenotypic landscape with machine learning.

Most organismal traits result from the complex interplay of many genetic and environmental factors, making their prediction difficult. Here, we used machine learning (ML) models to explore phenotype predictions for 223 traits measured across 1011 genome-sequenced Saccharomyces cerevisiae strains isolated worldwide. We benchmarked a ML pipeline with multiple linear and non-linear models to predict phenotypes from genotypes and gene expression, and determined gradient boosting machines as the best-performing model. Gene function disruption scores and gene presence/absence emerged as best predictors, suggesting a considerable contribution of the accessory genome in controlling phenotypes. The prediction accuracy broadly varied among phenotypes, with stress resistance being easier to predict compared to growth across nutrients. ML identified relevant genomic features linked to phenotypes, including high-impact variants with established relationships to phenotypes, despite these being rare in the population. Near-perfect accuracies were achieved when other phenomics data mostly in similar conditions were used, suggesting that useful information can be conveyed across phenotypes. Overall, our study underscores the power of ML to interpret the functional outcome of genetic variants.

Genetic Variation↗

PubMed2025

Genotype-phenotype correlations with autism spectrum disorder-related traits in noonan syndrome and noonan syndrome with multiple lentigines: a cross-sectional study.

BACKGROUND: Noonan syndrome (NS) and Noonan syndrome with multiple lentigines (NSML) are neurodevelopmental conditions caused by genetic variants leading to upregulated signaling in the RAS-MAPK pathway. While previous research has focused on genetic variability in cognitive and cardiac phenotypes, behavioral phenotypes, and their correlations across genetic variants and within the PTPN11 gene remain poorly characterized. METHODS: This study included 121 individuals with NS (PTPN11: 88, SOS1: 18, RAF1: 6, KRAS: 2, RIT1: 3, NRAS: 2, LZTR1: 2, SOS2: 1) and seven individuals with NSML (PTPN11), compared to age- and sex-matched typically developing (TD) (N = 71). Behavioral questionnaires assessed social responsiveness and ASD-related traits (using SRS-2), and emotional problems (using CBCL) to identify genetic variant-specific behavioral profiles. Biochemical profiling of SHP2 activity in PTPN11-associated NS variants examined genotype-phenotype relationships. RESULTS: Compared to TD individuals, those with PTPN11-associated NS, NSML, and SOS1-associated NS exhibited clinically elevated scores, indicating increased ASD-related behaviors, poorer social functioning, and heightened emotional problems. Genetic variant comparisons revealed that individuals with PTPN11-associated NS and NSML exhibited greater ASD-related challenges than those with RAF1. Individuals with NSML exhibit elevated attention problems compared to all other genetic groups. Logistic regression results suggested each one-unit increase in SHP2 fold activation for PTPN11-associated NS corresponded to a 64% higher likelihood of markedly elevated restricted and repetitive behaviors, suggesting genotype-phenotype links. LIMITATIONS: Small sample sizes for rarer variants, leading to unequal group sizes across subgroups, with PTPN11 variants comprising most of the NS group. Future research should address these sampling constraints and conduct functional studies to clarify variant impacts. Longitudinal assessments could elucidate behavioral phenotype trajectories. CONCLUSIONS: This study underscores the importance of genetic variant-specific research to understand unique behavioral phenotypes in NS and NSML. Our findings indicate a higher risk for ASD-related symptoms in PTPN11-associated NS and NSML compared to other variants. Additionally, individuals with PTPN11-associated NS and higher SHP2 fold activation exhibited greater impairments in restricted and repetitive behaviors, suggesting SHP2 activation variations may contribute to phenotypic variability. By linking ASD-related symptoms to biochemical predictors in PTPN11-associated NS, this study may inform future targeted treatment approaches.

Humans↗

PubMed2026

Comparative genomics reveals genotype-phenotype concordance and cryptic resistomes in clinical Pseudomonas aeruginosa.

BACKGROUND: Pseudomonas aeruginosa (P. aeruginosa) is a major pathogen because of its adaptability. It shows rapid evolution of multidrug resistance (MDR). Phenotype-based diagnostics often fail to detect silent resistance determinants and early adaptive changes. This study integrates phenotypic profiling with whole-genome sequencing (WGS) to examine resistance architecture in clinical isolates from eastern India. METHODS: From 1295 culture-positive P. aeruginosa specimens collected at a tertiary care hospital in eastern India. Using predefined criteria, representative MDR and non-MDR isolates were selected, including distinct resistance phenotypes, specimen-source diversity, and hospital and community-acquired settings; multivariate analysis of resistance profiles illustrated phenotypic diversity. Antimicrobial susceptibility assessed using VITEK-2 and Kirby-Bauer disk diffusion, species identity confirmed by 16 S rRNA sequencing, and genomic analysis processed through a reference-guided workflow. Antimicrobial Resistance (AMR) determinants were identified through CARD, and phylogenetic tree constructed from 454 publicly available P. aeruginosa genomes. RESULTS: MDR exhibited greater sequence divergence relative to PA14 (~ 69,000 variants) than the non-MDR isolate (~ 58,700 variants), with > 92% coverage at ≥ 30X depth. Strong genotype-phenotype concordance observed in MDR isolates across five antibiotic classes, associated with β-lactamase variants (PDC-67, OXA-396) and regulatory adaptations (ArmR, cprS). The non-MDR isolate harboured gyrA (T83I) resistance-associated mutations, PDC-1, and OXA-847 without phenotypic expression, indicating silent resistome. Phylogenetically, MDR isolates clustered tightly within the phylogeny, while the non-MDR isolate formed a distinct lineage. CONCLUSION: Observed genomic differences align with adaptation under antimicrobial selection, though confirmation requires larger collections. The non-MDR isolate retained a silent resistome. Findings highlight limitations of phenotype-only diagnostics, support genomic data integration, and emphasize transcriptomics for hidden resistance expression and regulatory dynamics.

Pseudomonas aeruginosa↗