shinyseg: a web application for flexible cosegregation and sensitivity analysis.

MOTIVATION: Cosegregation analysis is a powerful tool for identifying pathogenic genetic variants, but its implementation remains challenging. Existing software is either limited in scope or too demanding for many end users. Moreover, current solutions lack methods for assessing the robustness of cosegregation evidence, which is important due to its reliance on uncertain estimates. RESULTS: We present shinyseg, a comprehensive web application for clinical cosegregation analysis. Our app streamlines penetrance specification based on either liability classes or epidemiological data such as risks, hazard ratios, and age of onset distribution. In addition, it incorporates sensitivity analyses to assess the robustness of cosegregation evidence, and offers support in clinical interpretation. AVAILABILITY AND IMPLEMENTATION: The shinyseg app is freely available at https://chrcarrizosa.shinyapps.io/shinyseg, with documentation and complete R source code on https://chrcarrizosa.github.io/shinyseg and https://github.com/chrcarrizosa/shinyseg.

Multiscale spatial mapping of cell populations across anatomical sites in healthy human skin and basal cell carcinoma.

Our understanding of how human skin cells differ according to anatomical site and tumour formation is limited. To address this, we have created a multiscale spatial atlas of healthy skin and basal cell carcinoma (BCC), incorporating in vivo optical coherence tomography, single-cell RNA sequencing, spatial global transcriptional profiling, and in situ sequencing. Computational spatial deconvolution and projection revealed the localisation of distinct cell populations to specific tissue contexts. Although cell populations were conserved between healthy anatomical sites and in BCC, mesenchymal cell populations including fibroblasts and pericytes retained signatures of developmental origin. Spatial profiling and in silico lineage tracing support a hair follicle origin for BCC and demonstrate that cancer-associated fibroblasts are an expansion of a POSTN+ subpopulation associated with hair follicles in healthy skin. RGS5+ pericytes are also expanded in BCC suggesting a role in vascular remodelling. We propose that the identity of mesenchymal cell populations is regulated by signals emanating from adjacent structures and that these signals are repurposed to promote the expansion of skin cancer stroma. The resource we have created is publicly available in an interactive format for the research community.

A 39 kb structural variant causing Lynch Syndrome detected by optical genome mapping and nanopore sequencing.

Lynch Syndrome (LS) is a hereditary cancer syndrome caused by pathogenic germline variants in one of the four mismatch repair (MMR) genes MLH1, MSH2, MSH6 and PMS2. It is characterized by a significantly increased risk of multiple cancer types, particularly colorectal and endometrial cancer, with autosomal dominant inheritance. Access to precise and sensitive methods for genetic testing is important, as early detection and prevention of cancer is possible when the variant is known. We present here two unrelated Norwegian families with family histories strongly suggestive of LS, where immunohistochemical and microsatellite instability analyses indicated presence of a pathogenic variant in MSH2, but targeted exon sequencing and multiplex ligation-dependent probe amplification (MLPA) were negative. Using Bionano optical genome mapping, we detected a 39 kb insertion in the MSH2 gene. Precise mapping of the insertion breakpoints and inserted sequence was performed by low-coverage whole-genome sequencing with an Oxford Nanopore MinION. The same variant was present in both families, and later found in other families from the same region of Norway, indicative of a founder event. To our knowledge, this is the first diagnosis of LS caused by a structural variant using these technologies. We suggest that structural variant detection be performed when LS is suspected but not confirmed with first-tier standard genetic testing.

Systematic review of deep learning image analyses for the diagnosis and monitoring of skin disease.

Skin diseases affect one-third of the global population, posing a major healthcare burden. Deep learning may optimise healthcare workflows through processing skin images via neural networks to make predictions. A focus of deep learning research is skin lesion triage to detect cancer, but this may not translate to the wider scope of >2000 other skin diseases. We searched for studies applying deep learning to skin images, excluding benign/malignant lesions (1/1/2000-23/6/2022, PROSPERO CRD42022309935). The primary outcome was accuracy of deep learning algorithms in disease diagnosis or severity assessment. We modified QUADAS-2 for quality assessment. Of 13,857 references identified, 64 were included. The most studied diseases were acne, psoriasis, eczema, rosacea, vitiligo, urticaria. Deep learning algorithms had high specificity and variable sensitivity in diagnosing these conditions. Accuracy of algorithms in diagnosing acne (median 94%, IQR 86-98; n = 11), rosacea (94%, 90-97; n = 4), eczema (93%, 90-99; n = 9) and psoriasis (89%, 78-92; n = 8) was high. Accuracy for grading severity was highest for psoriasis (range 93-100%, n = 2), eczema (88%, n = 1), and acne (67-86%, n = 4). However, 59 (92%) studies had high risk-of-bias judgements and 62 (97%) had high-level applicability concerns. Only 12 (19%) reported participant ethnicity/skin type. Twenty-four (37.5%) evaluated the algorithm in an independent dataset, clinical setting or prospectively. These data indicate potential of deep learning image analysis in diagnosing and monitoring common skin diseases. Current research has important methodological/reporting limitations. Real-world, prospectively-acquired image datasets with external validation/testing will advance deep learning beyond the current experimental phase towards clinically-useful tools to mitigate rising health and cost impacts of skin disease.

Whole-exome sequencing in moyamoya patients of Northern-European origin identifies gene variants involved in Nitric Oxide metabolism: A pilot study.

Introduction: Moyamoya disease (MMD) is a chronic cerebrovascular steno-occlusive disease of largely unknown etiology. Variants in the RNF213 gene are strongly associated with MMD in East-Asia. In MMD patients of Northern-European origin, no predominant susceptibility variants have been identified so far. Research question: Are there specific candidate genes associated with MMD of Northern-European origin, including the known RNF213 gene? Can we establish a hypothesis for MMD phenotype and associated genetic variants identified for further research? Material and methods: Adult patients of Northern-European origin, treated surgically for MMD at Oslo University Hospital between October 2018 to January 2019 were asked to participate. WES was performed, with subsequent bioinformatic analysis and variant filtering. The selected candidate genes were either previously reported in MMD or known to be involved in angiogenesis. The variant filtering was based on variant type, location, population frequency, and predicted impact on protein function. Results: Analysis of WES data revealed nine variants of interest in eight genes. Five of those encode proteins involved in nitric oxide (NO) metabolism: NOS3, NR4A3, ITGAV, GRB7 and AGXT2. In the AGXT2 gene, a de novo variant was detected, not previously described in MMD. None harboured the p.R4810K missense variant in the RNF213 gene known to be associated with MMD in East-Asian patients. Discussion and conclusion: Our findings suggest a role for NO regulation pathways in Northern-European MMD and introduce AGXT2 as a new susceptibility gene. This pilot study warrants replication in larger patient cohorts and further functional investigations.

The use of segregation analysis in interpretation of sequence variants in SMAD3: A case report.

BACKGROUND: While representing a significant improvement, the introduction of next-generation sequencing in genetic diagnosis also prompted new challenges. Despite widely recognized consensus guidelines for the interpretation of sequence variants, many variants remain unclassified or are discordantly interpreted. In heritable thoracic aortic aneurysms with dissection (HTAAD), most cases are caused by a heterozygous, private missense mutation, possibly contributing to the relatively common reports of variants with uncertain significance in this group. Segregation analysis necessitates advanced likelihood-based methods typically inaccessible to non-experts and is hampered by reduced penetrance, possible phenocopies, and non-availability of DNA from deceased relatives. METHODS: In this report, challenges in variant interpretation and the use of segregation analyses were illustrated in two families with a suspected HTAAD disorder. The R package segregatr, a novel implementation of full-likelihood Bayes factor (FLB), was performed to explore the cosegregation of the variants in these families. CONCLUSION: Using the R package segregatr, cosegregation in the reported families concluded with strong and supporting evidence for pathogenicity. Surveillance of families in a multidisciplinary team enabling systematic phenotype description for standardized segregation analysis with a robust calculation method may be imperative for reliable variant interpretation in HTAAD.

Levels of nitramines and nitrosamines in lake drinking water close to a CO2 capture plant: A modelling perspective.

While CO2 capture is considered a key climate change mitigation option, we must ensure that global implementation occurs without causing harm to the local environment and the human health. The most mature option for capture is using amines, which however, is associated with a risk of contaminating nearby drinking water sources with carcinogenic nitramines (NAs) and nitrosamines (NSAs). Here we present the first process-based simulation of NAs and NSAs in a catchment-lake system with the input of previously modelled atmospheric deposition rates. Considering full-scale CO2 capture at the Oslo waste incineration plant in Norway, future (∼10 y) levels in a nearby lake approach the national drinking water limit. We further quantified the effect of hydrological and biogeochemical processes and identified those with the highest sensitivity (NA biodegradation). The uncertainty of the results is presented by a probabilistic distribution (Monte Carlo analysis), incorporating variability in catchment, lake, and literature NA and NSA parameter values. This modelling tool allows for the site-specific assessment of the abovementioned risks related to amine-based CO2 capture and aspires to contribute to the sound evaluation of costly amine emission reduction measures.

QuickPed: an online tool for drawing pedigrees and analysing relatedness.

BACKGROUND: The ubiquity of pedigrees in many scientific areas calls for versatile and user-friendly software. Previously published online pedigree tools have limited support for complex pedigrees and do not provide analysis of relatedness between pedigree members. RESULTS: We introduce QuickPed, a web application for interactive pedigree creation and analysis. It supports complex inbreeding and comes with a rich built-in library of common and interesting pedigrees. The program calculates all standard coefficients of relatedness, including inbreeding, kinship and identity coefficients, and offers specialised plots for visualising relatedness. It also implements a novel algorithm for describing pairwise relationships in words. CONCLUSION: QuickPed is a user-friendly pedigree tool aimed at researchers, case workers and teachers. It contains a number of features not found in other similar tools, and represents a significant addition to the body of pedigree software by making advanced relatedness analyses available for non-bioinformaticians.

Angiotensin-Converting Enzyme 2 Expression Is Detectable in Keratinocytes, Cutaneous Appendages, and Blood Vessels by Multiplex RNA In Situ Hybridization.

OBJECTIVE: The angiotensin-converting enzyme 2 (ACE2) receptor mediates uptake of SARS-CoV-2, the virus responsible for COVID-19. Previous work analyzing publicly available bulk RNA-sequencing data sets has shown the expression of ACE2 in human keratinocytes. This finding is potentially relevant for the etiology of COVID-19-associated rashes and might also suggest a possible entry mechanism for the SARS-CoV-2 virus. In this study, the authors examined the spatial localization of ACE2 mRNA in vivo. METHODS AND RESULTS: The authors analyzed several publicly available single-cell RNA-sequencing data sets. They determined spatial localization of ACE2 mRNA using multiplex RNA in situ hybridization in human skin. CONCLUSIONS: Both analyses supported ACE2 expression in keratinocytes and skin vasculature, which could reflect a potential cutaneous entry point for SARS-CoV-2, particularly in damaged or broken skin. Moreover, ACE2 expression in vascular endothelial cells may support direct, virally mediated mechanisms in the etiology of the chilblain-like acral eruption that is seen in patients with COVID-19.

Gradient boosting approaches can outperform logistic regression for risk prediction in cutaneous allergy.

BACKGROUND: Contact allergy is a major clinical and public health challenge. It is important to identify individuals who are at risk and perform patch testing to identify relevant allergens. Predicting clinical risk on the basis of input parameters is common in clinical medicine and traditionally has been achieved with linear models. OBJECTIVES: We hypothesized that the risk of a clinically relevant positive patch test could be predicted according to clinical and demographic parameters. METHODS: We compared the predictive accuracy of logistic regression with more sophisticated machine learning approaches such as gradient boosting, in the prediction of patch testing results. RESULTS: We found that both logistic regression and more sophisticated machine learning approaches were able to predict the risk of positive patch tests. For certain predictions, including the overall risk of a clinically relevant positive patch test, gradient boosting approaches can outperform logistic regression. CONCLUSIONS: These findings suggest that complex nonlinear interactions between input variables are relevant in risk prediction. While a risk prediction model cannot replace the judgment of an experienced clinician, quantifying the risk of a clinically relevant positive patch test result has the potential to assist in decision making and to inform discussions with patients.

Joint DNA-based disaster victim identification.

We address computational and statistical aspects of DNA-based identification of victims in the aftermath of disasters. Current methods and software for such identification typically consider each victim individually, leading to suboptimal power of identification and potential inconsistencies in the statistical summary of the evidence. We resolve these problems by performing joint identification of all victims, using the complete genetic data set. Individual identification probabilities, conditional on all available information, are derived from the joint solution in the form of posterior pairing probabilities. A closed formula is obtained for the a priori number of possible joint solutions to a given DVI problem. This number increases quickly with the number of victims and missing persons, posing computational challenges for brute force approaches. We address this complexity with a preparatory sequential step aiming to reduce the search space. The examples show that realistic cases are handled efficiently. User-friendly implementations of all methods are provided in the R package dvir, freely available on all platforms.

Making decisions in missing person identification cases with low statistical power.

The present work proposes a general strategy for dealing with missing person identification cases through DNA-database search. Our main example is the identification of abducted children in the last civic-dictatorship of Argentina, known as the "Missing Grandchildren of Argentina". Particularly we focus on those pedigrees where few, or only distant relatives of the missing person are available, resulting in low statistical power. For such complex cases we provide a statistical method for selecting a likelihood ratio (LR) threshold for each pedigree based on error rates. Furthermore, we provide an open-source user friendly software for computing LR thresholds and error rates. The strategy described in the paper could be applied to other large-scale cases of DNA-based identification hampered by low statistical power.

Developmental cell programs are co-opted in inflammatory skin disease.

The skin confers biophysical and immunological protection through a complex cellular network established early in embryonic development. We profiled the transcriptomes of more than 500,000 single cells from developing human fetal skin, healthy adult skin, and adult skin with atopic dermatitis and psoriasis. We leveraged these datasets to compare cell states across development, homeostasis, and disease. Our analysis revealed an enrichment of innate immune cells in skin during the first trimester and clonal expansion of disease-associated lymphocytes in atopic dermatitis and psoriasis. We uncovered and validated in situ a reemergence of prenatal vascular endothelial cell and macrophage cellular programs in atopic dermatitis and psoriasis lesional skin. These data illustrate the dynamism of cutaneous immunity and provide opportunities for targeting pathological developmental programs in inflammatory skin diseases.

Differential Glial Activation in Early Epileptogenesis-Insights From Cell-Specific Analysis of DNA Methylation and Gene Expression in the Contralateral Hippocampus.

Background and Aims: Morphological changes in mesial temporal lobe epilepsy with hippocampal sclerosis (mTLE-HS) are well-characterized. Yet, it remains elusive whether these are a consequence of seizures or originate from a hitherto unknown underlying pathology. We recently published data on changes in gene expression and DNA methylation in the ipsilateral hippocampus (ILH) using the intracortical kainate mouse model of mTLE-HS. In order to explore the effects of epileptic activity alone and also to further disentangle what triggers morphological alterations, we investigated glial and neuronal changes in gene expression and DNA methylation in the contralateral hippocampus (CLH). Methods: The intracortical kainic acid mouse model of mTLE-HS was used to elicit status epilepticus. Hippocampi contralateral to the injection site from eight kainate-injected and eight sham mice were extracted and shock frozen at 24 h post-injection. Glial and neuronal nuclei were sorted by flow cytometry. Alterations in gene expression and DNA methylation were assessed using reduced representation bisulfite sequencing and RNA sequencing. The R package edgeR was used for statistical analysis. Results: The CLH featured substantial, mostly cell-specific changes in both gene expression and DNA methylation in glia and neurons. While changes in gene expression overlapped to a great degree between CLH and ILH, alterations in DNA methylation did not. In the CLH, we found a significantly lower number of glial genes up- and downregulated compared to previous results from the ILH. Furthermore, several genes and pathways potentially involved in anti-epileptogenic effects were upregulated in the CLH. By comparing gene expression data from the CLH to previous results from the ILH (featuring hippocampal sclerosis), we derive potential upstream targets for epileptogenesis, including glial Cox2 and Cxcl10. Conclusion: Despite the absence of morphological changes, the CLH displays substantial changes in gene expression and DNA methylation. We find that gene expression changes related to potential anti-epileptogenic effects seem to dominate compared to the pro-epileptogenic effects in the CLH and speculate whether this imbalance contributes to prevent morphological alterations like neuronal death and reactive gliosis.

The CHAOS-7 geomagnetic field model and observed changes in the South Atlantic Anomaly.

We present the CHAOS-7 model of the time-dependent near-Earth geomagnetic field between 1999 and 2020 based on magnetic field observations collected by the low-Earth orbit satellites Swarm, CryoSat-2, CHAMP, SAC-C and Ørsted, and on annual differences of monthly means of ground observatory measurements. The CHAOS-7 model consists of a time-dependent internal field up to spherical harmonic degree 20, a static internal field which merges to the LCS-1 lithospheric field model above degree 25, a model of the magnetospheric field and its induced counterpart, estimates of Euler angles describing the alignment of satellite vector magnetometers, and magnetometer calibration parameters for CryoSat-2. Only data from dark regions satisfying strict geomagnetic quiet-time criteria (including conditions on IMF B z and B y at all latitudes) were used in the field estimation. Model parameters were estimated using an iteratively reweighted regularized least-squares procedure; regularization of the time-dependent internal field was relaxed at high spherical harmonic degree compared with previous versions of the CHAOS model. We use CHAOS-7 to investigate recent changes in the geomagnetic field, studying the evolution of the South Atlantic weak field anomaly and rapid field changes in the Pacific region since 2014. At Earth's surface a secondary minimum of the South Atlantic Anomaly is now evident to the south west of Africa. Green's functions relating the core-mantle boundary radial field to the surface intensity show this feature is connected with the movement and evolution of a reversed flux feature under South Africa. The continuing growth in size and weakening of the main anomaly is linked to the westward motion and gathering of reversed flux under South America. In the Pacific region at Earth's surface between 2015 and 2018 a sign change has occurred in the second time derivative (acceleration) of the radial component of the field. This acceleration change took the form of a localized, east-west oriented, dipole. It was clearly recorded on ground, for example at the magnetic observatory at Honolulu, and was seen in Swarm observations over an extended region in the central and western Pacific. Downward continuing to the core-mantle boundary, we find this event originated in field acceleration changes at low latitudes beneath the central and western Pacific in 2017.

COV-OBS.x2: 180 years of geomagnetic field evolution from ground-based and satellite observations.

We present the geomagnetic field model COV-OBS.x2 that covers the period 1840-2020. It is primarily constrained by observatory series, satellite data, plus older surveys. Over the past two decades, we consider annual differences of 4-monthly means at ground-based stations (since 1996), and virtual observatory series derived from magnetic data of the satellite missions CHAMP (over 2001-2010) and Swarm (since 2013). A priori information is needed to complement the constraints carried by geomagnetic records and solve the ill-posed geomagnetic inverse problem. We use for this purpose temporal cross-covariances associated with auto-regressive stochastic processes of order 2, whose parameters are chosen so as to mimic the temporal power spectral density observed in paleomagnetic and observatory series. We aim this way to obtain as far as possible realistic posterior model uncertainties. These can be used to infer for instance the core dynamics through data assimilation algorithms, or an envelope for short-term magnetic field forecasts. We show that because of the projection onto splines, one needs to inflate the formal model error variances at the most recent epochs, in order to account for unmodeled high frequency core field changes. As a by-product of the core field model, we co-estimate the external magnetospheric dipole evolution on periods longer than 2 years. It is efficiently summarized as the sum of a damped oscillator (of period 10.5 years and decay rate 55 years), plus a short-memory (6 years) damped random walk.

Prioritising family members for genotyping in missing person cases: A general approach combining the statistical power of exclusion and inclusion.

Missing person identification typically involves genetic matching of a person of interest against relatives of the missing person. In cases with few available relatives, exhumations or other substantial efforts may be necessary in order to secure adequate statistical power. We propose a simulation approach for solving prioritisation problems arising in such cases. Conditioning on the already typed individuals we estimate the power of each alternative, both to detect the true person, and to exclude false candidates. Graphical summaries of the simulations are given in complementary power plots, facilitating interpretation and decision making. Through a series of examples originating from the well-known Missing grandchildren of Argentina we demonstrate that our method may untangle complex prioritisation problems and other power-related questions. In particular we offer novel insights in recent cases where only children of the potential match are available for testing. We also show that X-chromosomal markers may give high statistical power in missing person identification, but that this requires careful selection of relatives for genotyping. All simulations, power calculations and plots are done with the R package forrel.

Phenotypic spectrum and transcriptomic profile associated with germline variants in TRAF7.

PURPOSE: Somatic variants in tumor necrosis factor receptor-associated factor 7 (TRAF7) cause meningioma, while germline variants have recently been identified in seven patients with developmental delay and cardiac, facial, and digital anomalies. We aimed to define the clinical and mutational spectrum associated with TRAF7 germline variants in a large series of patients, and to determine the molecular effects of the variants through transcriptomic analysis of patient fibroblasts. METHODS: We performed exome, targeted capture, and Sanger sequencing of patients with undiagnosed developmental disorders, in multiple independent diagnostic or research centers. Phenotypic and mutational comparisons were facilitated through data exchange platforms. Whole-transcriptome sequencing was performed on RNA from patient- and control-derived fibroblasts. RESULTS: We identified heterozygous missense variants in TRAF7 as the cause of a developmental delay-malformation syndrome in 45 patients. Major features include a recognizable facial gestalt (characterized in particular by blepharophimosis), short neck, pectus carinatum, digital deviations, and patent ductus arteriosus. Almost all variants occur in the WD40 repeats and most are recurrent. Several differentially expressed genes were identified in patient fibroblasts. CONCLUSION: We provide the first large-scale analysis of the clinical and mutational spectrum associated with the TRAF7 developmental syndrome, and we shed light on its molecular etiology through transcriptome studies.