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IntroductionThere has been best online viagra considerable interest in elucidating the contribution of genetic factors to the development of common diseases and using this information for better prediction of disease risk. The common disease common variant hypothesis predicts that variants that are common in the population play a role in disease susceptibility.1 Genome-wide association studies (GWAS) using single nucleotide polymorphism (SNP) arrays were developed as a mechanism by which to investigate these genetic factors and it was hoped this best online viagra would lead to identification of variants associated with disease risk and subsequent development of predictive tests. Variants identified as associated with particular traits by these studies, for the large part, are SNPs that individually have a minor effect on disease risk and hence, by themselves, cannot be reliably used in disease prediction. Looking at the aggregate impact of these SNPs in the form of a polygenic score (PGS) appeared to be one possible means of using this information to predict disease.2 It is thought this best online viagra will be of benefit as our genetic make-up is largely stable from birth and dictates a ‘baseline risk’ on which external influences act and modulate.

Therefore, PGS are a potential mechanism to act as a risk predictor by capturing information on this genetic liability.The use of PGS as a predictive biomarker is being explored in a number of different disease areas, including cancer,3 4 psychiatric disorders,5–7 metabolic disorders (diabetes,8 obesity9) and coronary artery disease (CAD).10 The proposed applications range from aiding disease diagnosis, informing selection of therapeutic interventions, improvement of risk prediction, informing disease screening and, on a personal level, informing life planning. Therefore, genetic risk information in the form of a PGS is considered to have potential in informing both best online viagra clinical and individual-level decision-making.Recent advances in statistical techniques, improved computational power and the availability of large data sets have led to rapid developments in this area over the past few years. This has resulted in a variety of approaches to construction of models for score calculation and the investigation of these scores for prediction of common diseases.11 Several review articles aimed at researchers with a working knowledge of this field have been produced.6 11–17 In this article, we provide an overview of the key aspects of PGS construction to assist clinicians and researchers in other areas of academia to gain an understanding of the processes involved in score construction. We also consider the implications of evolving methodologies for the development of applications of PGS in healthcare.Evolution in polygenic model construction methodologiesTerminology with respect to PGS has best online viagra evolved over time, reflecting evolving approaches and methodology.

Other terms include PGS, polygenic risk score, polygenic load, genotype score, genetic burden, polygenic hazard score, genetic risk score (GRS), metaGRS and allelic risk score. Throughout this article we use the terms polygenic models to refer to the method used to calculate an output in best online viagra the form of a PGS. Different polygenic models can be used to calculate a PGS and analysis of these scores can be used to examine associations with particular markers or to predict an individuals risk of diseases.12Usual practice in calculating PGS is as a weighted sum of a number of risk alleles carried by an individual, where the risk alleles and their weights are defined by SNPs and their measured effects (figure 1).11 Polygenic models have been constructed using a few, hundreds or thousands of SNPs, and more recently SNPs across the whole genome. Consequently, determining which SNPs to include and the disease-associated best online viagra weighting to assign to SNPs are important aspects of model construction (figure 2).18 These aspects are influenced by available genotype data and effect size estimates as well as the methodology employed in turning this information into model parameters (ie, weighted SNPs).Polygenic score calculation.

This calculation aggregates the SNPs and their weights selected for a polygenic score. Common diseases are thought to be influenced by many genetic variants with small individual effect sizes, such that meaningful risk prediction necessitates examining the aggregated impact of these multiple variants including best online viagra their weightings. PGS, polygenic score." data-icon-position data-hide-link-title="0">Figure 1 Polygenic score calculation. This calculation aggregates the SNPs and their weights selected for best online viagra a polygenic score.

Common diseases are thought to be influenced by many genetic variants with small individual effect sizes, such that meaningful risk prediction necessitates examining the aggregated impact best online viagra of these multiple variants including their weightings. PGS, polygenic score.Construction of a polygenic score. In the process of developing a polygenic best online viagra score, numerous models are tested and then compared. The model that performs best (as determined by one or more measures) is then selected for validation in the external data set.

GWAS, genome-wide association studies." data-icon-position data-hide-link-title="0">Figure 2 Construction of best online viagra a polygenic score. In the process of developing a polygenic score, numerous models are tested and then compared. The model that performs best best online viagra (as determined by one or more measures) is then selected for validation in the external data set. GWAS, genome-wide association studies.Changes in data availability over time have had an impact on the approach taken in SNP selection and weighting.

Early studies to identify variants associated with common diseases took the form best online viagra of candidate gene studies. The small size of candidate gene studies, the limitation of technologies available for genotyping and stringent significance thresholds meant that these studies investigated fewer variants and those that were identified with disease associations had relatively large effect sizes.19 Taken together, this meant that a relatively small number of variants were available for consideration for inclusion in a polygenic model.20 21 Furthermore, weighting parameters for these few variants were often simplistic, such as counts of the number of risk alleles carried, ignoring their individual effect sizes.16The advent of GWAS enabled assessment of SNPs across the genome, leading to the identification of a larger number of disease-associated variants and therefore more variants suitable for inclusion in a polygenic model. In addition, the increasing number of individuals in the association studies meant that the power of these studies increased, allowing for more precise estimates best online viagra of effect sizes.19 Furthermore, some theorised that lowering stringent significance thresholds set for SNP–trait associations could also identify SNPs that might play a part in disease risk.11 16 This resulted in more options with respect to polygenic model parameters of SNPs to include and weights to assign to them. However, the inclusion of more SNPs and direct application of GWAS effect sizes as a weighting parameter does not always equate to better predictive performance.4 16 This is because GWAS do not provide perfect information with respect to the causal SNP, the effect sizes or the number of SNPs that contribute to the trait.

Therefore, different methods have best online viagra been developed to address these issues and optimise predictive performance of the score. Current common practice is to construct models with different iterations of SNPs and weighting, with assessment of the performance of each to identify the optimum configuration of SNPs and their weights (figure 2).Methods used in SNP selection and weighting assignmentSome methods of model development will initially involve selection of SNPs followed by optimisation of weighting, whereas others may involve optimisation of weightings for all SNPs that have been genotyped using their overall GWAS effect sizes, the linkage disequilibrium (LD) and an estimate of the proportion of SNPs that are expected to contribute to the risk.22LD is the phenomenon where some SNPs are coinherited more frequently with other SNPs due to their close proximity on the genome. Segments with strong LD between SNPs are referred to as haplotype best online viagra blocks. This phenomenon means that GWAS often identify multiple SNPs in the same haplotype block associated with disease and the true causal SNP is not known.

As models have started to assess more SNPs, careful consideration is required to take into account possible correlation between SNPs as a result of this best online viagra phenomenon. Correlation between SNPs can lead to double counting of best online viagra SNPs and association redundancy, where multiple SNPs in a region of LD are identified as being associated with the outcome. This can lead to reduction in the predictive performance of the model. Therefore, processes for filtering best online viagra SNPs and using one SNP (tag SNP) to act as a marker in an area of high LD, through LD thinning, were developed.

Through these processes SNPs correlated with other SNPs in a block are removed, by either pruning or clumping. Pruning ignores p value thresholds and ‘eliminates’ SNPs by a process of iterative comparison between a pair of SNPs to assess if they are correlated, and subsequently could remove SNPs that are deemed to have best online viagra evidence of association. Clumping (also known as informed pruning) is guided by GWAS p values and chooses the most significant SNP, therefore keeping the most significant SNP within a block.23 This is all done with the aim of pinpointing relatively small areas of the genome that contribute to risk of the trait. Different significance thresholds may be used to select SNPs from this subgroup for inclusion in models.Poor performance of a model can result from imperfect tagging with the underlying causal SNP.16 This is because the causal SNP that is associated with best online viagra disease might not be in LD with the tag SNP that is in the model but is in LD with another SNP which is not in the model.

This particularly occurs where the LD and variant frequency differs between population groups.24 An alternate approach to filter SNPs is stepwise regression where SNPs are selected based on how much the SNPs improve the model’s performance. This is a statistical approach and does not consider the impact of LD or effect size.As described above, best online viagra early studies used simple weighting approaches or directly applied effect sizes from GWAS as weighting parameters for SNPs. However, application of effect sizes as a weighting parameter directly from a GWAS may not be optimal, due to differences in the population that the GWAS was conducted in and the target population. Also as described above, LD and best online viagra the fact that not all SNPs may contribute to the trait mean that these effect sizes from GWAS are imperfect estimates.

Therefore, methods have been developed that adjust effect size estimates from GWAS using statistical techniques which make assumptions about factors such as the number of causal SNPs, level of LD between SNPs or knowledge of their potential function to better reflect their impact on a trait. Numerous statistical methodologies have been developed to improve weighting with a view to enhancing the discriminative power of a PGS.25 26 Examples of some methodological approaches are LDpred,22 winner’s curse correction,23 empirical Bayes estimation,27 shrinkage regression (Lasso),28 linear best online viagra mixed models,29 with more being developed or tested. An additional improvement on the methods is to embed non-genetic information (eg, age-specific ORs).6 Determination of which methodology or hybrid of methodologies is most appropriate for various settings and conditions is continuously being explored and is evolving with new statistical approaches developing at a rapid pace.In summary, model development has evolved in an attempt to gain the most from available GWAS data and address some of the issues that arise due to working with data sets which cannot be directly translated into parameters for prediction models. The different approaches taken in SNP selection best online viagra and weighting, and the impact on the predictive performance of a model are important to consider when assessing different models.

This is because different approaches to PGS modelling can achieve the same or a similar level of prediction. From a health system implementation perspective, particular approaches may be preferred following practical considerations and trade-offs between obtaining genotype data, processes for score construction and model performance best online viagra. In addition, the degree to which these parameters need to be optimised will also be best online viagra impacted by the input data and validation data set, and the quality control procedures that need to be applied to these data sets.12Sources of input data for score constructionKey to the development of a polygenic model is the availability of data sets that can provide input parameters for model construction. Genotype data used in model construction can either be available as raw GWAS data or provided as GWAS summary statistics.

Data in the raw format are individual-level data from a SNP array and may not have undergone basic quality control such as assessment of missingness, sex discrepancy checks, deviation from Hardy-Weinberg equilibrium, heterozygosity rate, relatedness or assessment for outliers.30 31 Availability of raw GWAS data allows for different polygenic models to be developed because of the richness of the data, however computational best online viagra issues arise because of the size of the data sets. Data based on genome sequencing, as opposed to SNP arrays, could also be used in model construction. There have been limited studies of PGS developed from this form of data due to limitations in data availability, which is mainly due to cost restraints.15 32 Individual-level genomic data are also often not available to researchers due to privacy concerns.Due to these issues, the focus of polygenic model development has therefore been on using well-powered GWAS summary statistics.33 These are available from open access repositories and best online viagra contain summary information such as the allele positions, ORs, CIs and allele frequency, without containing confidential information on individuals. These data sets have usually been through the basic quality control measures mentioned above.

There are, however, no standards for publicly available files, meaning some further processing steps may be required, in particular when various best online viagra data sets are combined for a meta-analysis. Quality control on summary statistics is only possible if information such as missing genotype rate, minor allele frequency, Hardy-Weinberg equilibrium failures and non-Mendelian transmission rates is provided.12Processing of GWAS data may include additional quality control steps, imputation and filtering of the SNP information, which can be done at the level of genotype or summary statistics data. SNP arrays used in GWAS only have common SNPs represented on them as they rely on LD between best online viagra SNPs to cover the entire genome. As described above, one tag SNP on the array can represent many other SNPs.

Imputation of SNPs is common in GWAS and best online viagra describes the process of predicting genotypes that have not been directly genotyped but are statistically inferred (imputed) based on haplotype blocks from a reference sequence.33–35 Often association tests between the imputed SNPs and trait are repeated. As genotype imputation requires individual-level data, researchers have proposed summary statistics imputation as a mechanism to infer the association between untyped SNPs and a trait. The performance of imputation has been best online viagra evaluated and shown that, with certain limitations, summary statistics imputation is an efficient and cost-effective methodology to identify loci associated with traits when compared with imputation done on genotypes.36An alternative source of input data for the selection of SNPs and their weightings is through literature or in existing databases, where already known trait-associated SNPs and their effect sizes are used as the input parameters in model development. A number of studies have taken this approach37 38 and it is possible to use multiple sources when developing various polygenic models and establishing the preferred parameters to use.Currently, there does not appear to be one methodology that works across all contexts and traits, each trait will need to be assessed to determine which method is the most suitable for the trait being evaluated.

For example, four different polygenic model construction strategies were explored for three skin cancer subtypes4 by using data on SNPs and their effect sizes from different sources, such as the best online viagra latest GWAS meta-analysis results, the National Human Genome Research Institute (NHGRI) EBI GWAS catalogue, UK Biobank GWAS summary statistics with different thresholds and GWAS summary statistics with LDpred. In this setting for basal cell carcinoma and melanoma, the meta-analysis and catalogue-derived models were found to perform similarly but that the latter was ultimately used as it included more SNPs. For squamous cell carcinoma the meta-analysis-derived model best online viagra performed better than the catalogue-derived model. This demonstrates how each disease subtype, model construction best online viagra strategy and data set can have their own limitations and advantages.Knowledge of the sources of input data and its subsequent use in model development is important in understanding the limitations of available models.

Models that are developed using data sets that reflect the population in which prediction is to be carried out will perform better. For example, data collected from a symptomatic or high-risk population may not be suitable as an input data set for the development of a polygenic model that will be used for disease prediction in best online viagra the general population. Large GWAS studies were previously focused on high-risk individuals, such as patients with breast cancer with a strong family history or known pathogenic variants in BRCA1 or BRCA2. These studies would not be suitable for the development best online viagra of PGS for use in the general population but can inform risk assessment in high-risk individuals.

The source of the data for SNP selection and weighting also has implications for downstream uses and validation. For example, variant frequency and LD patterns best online viagra can vary between populations and this can translate to poor performance of the polygenic model if the external validation population is different from that of the input data set.39–41 Furthermore, the power and validity of polygenic analyses are influenced by the input data sources.12 42From a model to a scorePGS can be calculated using one of the methodologies discussed above. The resulting PGS units of measurement depend on which measurement is used for the weighting.12 For example, the weightings may have been calculated based on logOR for discrete traits or linear regression coefficient (β/beta) in continuous traits from univariate regression tests carried out in the GWAS. The resulting scores are then usually transformed to a standard normal distribution to give best online viagra scores ranging from −1 to 1, or 0 to 100 for ease of interpretation.

This enables further examination of the association between the score and a trait and the predictive ability of different scores generated by different models. Similar to best online viagra other biomarker analyses, this involves using the PGS as a predictor of a trait with other covariates (eg, age, smoking, and so on) added, if appropriate, in a target sample. Examination of differences in the distribution of scores in cases and controls, or by examining differences in traits between different strata of PGS can enable assessment of predictive ability (figure 3). Common practice is for individual-level PGS best online viagra values to be used to stratify populations into distinct groups of risk based on percentile cut-off or threshold values (eg, the top 1%).Example distribution of polygenic scores across a population.

Thresholds can be set to stratify risk as low (some), average (most) and high (some)." data-icon-position data-hide-link-title="0">Figure 3 Example distribution of polygenic scores across a population. Thresholds can be set to stratify risk as low (some), average (most) and high (some).Model validationPolygenic model development is reliant on further data sets for model testing and validation and the composition of these data sets is important in ensuring that the models are appropriate for a particular best online viagra purpose. The development best online viagra of a model to calculate a PGS involves refinement of the previously discussed input parameters, and selection of the ‘best’ of several models based on performance (figure 2). Therefore, a testing/training data set is often required to assess the model’s ability to accurately predict the trait of interest.

This is often a data set that is independent of the base/input/discovery data set best online viagra. It may comprise a subset of the discovery data set that is only used for testing and was not included in the initial development of the model but should ideally be a separate independent data set.Genotype and phenotype data are needed in these data sets. Polygenic models are used to best online viagra calculate PGS for individuals in the training data set and regression analysis is performed with the PGS as a predictor of a trait. Other covariates may also be included, if appropriate.

This testing phase can be considered a process for identifying models with better overall performance and/or informing best online viagra refinements needed. Hence, this phase often involves comparison of different models that are developed using the same input data set to identify those models that have optimal performance.The primary purpose is to determine which model best discriminates between cases and controls. The area under the best online viagra curve (AUC) or the C-statistic is the most commonly used measure in assessing discriminative ability. It has been criticised as being an insensitive measure that is not able to fully capture all aspects of predictive ability.

For instance, in some instances, AUC can remain unchanged between models but the individuals within are categorised into a different risk group.43 Alternative metrics that have been used to evaluate model performance include increase in risk difference, integrated discrimination improvement, R2 (estimate of variance explained by the PGS after best online viagra covariate adjustment), net classification index and the relative risk (highest percentile vs lowest percentile). A clear understanding on how to interpret the performance within various settings is important in determining which model is most suitable.44As per normal practice when developing any prediction model, polygenic models with the optimal performance in a testing/training data set should be further validated in external data sets. External data sets are best online viagra critical in validation of models and assessment of generalisability, hence must also conform to the desired situations in which a model is to be used. The goal is to find a model with suitable parameters of predictive performance in data sets outside of those in which it was developed.

Ideally, external best online viagra validation requires replication in independent data sets. Few existing polygenic models have been validated to this extent, the focus being rather on the development of new models rather than evaluation of existing ones. One example where replication has been carried out is in the field of CAD, where the GPSCAD45 and metaGRSCAD10 polygenic models (both developed best online viagra using UK Biobank data) were evaluated in a Finnish population cohort.46 Predictive ability was found to be lower in the Finnish population. This is likely to be due to the differences in genetic structure of this population and the population best online viagra of the data set used for polygenic model development.

Research is ongoing to evaluate polygenic models in other populations and strategies are being developed to ensure the same performance when used more widely, possibly through reweighting and adjustment of the scores.47Moving towards clinical applicationsPGS are thought to be useful information that could improve risk estimation and provide an avenue for disease prevention and deciding treatment strategies. There are indications from a number of fields that genetic information in the form of PGS can act as independent biomarkers best online viagra and aid stratification.11 16 48 However, the clinical benefits of stratification using a PGS and the implications for clinical practice are only just beginning to be examined. The use of PGS as part of existing risk prediction tools or as a stand-alone predictor has been suggested. This latter option may be true for diseases where knowledge or predictive ability with other risk factors is limited, such as in prostate cancer.49 In either case, polygenic models need to be individually examined to determine suitability and applicability for the specific clinical question.50 Despite some commercial companies developing PGS,51 52 currently PGS are not an established part of clinical practice.Integration into clinical practice requires evaluation of best online viagra a PGS-based test.

An important concept to consider in this regard is the distinction between an assay and a test. This has been previously discussed with respect to genetic test evaluation.53 best online viagra 54 It is worth examining this concept as applied to PGS, as their evaluation is reliant on a clear understanding of the test to be offered. As outlined by Zimmern and Kroese,54 the method used to analyse a substance in a sample is considered the assay, whereas a test is the use of an assay within a specific context. With respect to PGS, the process of developing a model to best online viagra derive a score can be considered the assay, while the use of this model for a particular disease, population and purpose can be considered the test.

This distinction is important when assessing if studies are reporting on assay performance as opposed to test performance. It is our view that, with respect to polygenic models, progress has been made with respect to assay development, but PGS-based tests best online viagra are yet to be developed and evaluated. This can enable a clearer understanding of their potential clinical utility and issues that may arise for clinical implementation.11 18 55 It is clear that this is still an evolving field, and going forward different models may be required for different traits due to their underlying genetic architecture,26 different clinical contexts and needs.Clinical contexts where risk stratification is already established practice are most likely where implementation of PGS will occur first. Risk prediction models based on non-genetic factors have been developed for many conditions and are used in best online viagra clinical care, for example, in cardiovascular disease over 100 such models exist.56 In such contexts, how a PGS and its ability to predict risk compared with, or improves on, these existing models is being investigated.3 44 57–61 The extent to which PGS improves prediction, as well as the cost implications of including this, is likely to impact on implementation.Integration of PGS into clinical practice, for any application, requires robust and validated mechanisms to generate these scores.

Therefore, given the numerous models available, an assessment of their suitability as part of a test is required. Parameters or guidelines with respect to aspects of model performance and metrics that could assist in selecting best online viagra the model to take forward as a PGS-based test are limited and need to be addressed. Currently, there are different mechanisms to generate PGS and have arisen in response to the challenges in aggregating large-scale genomic data for prediction. For example, a review reported 29 PGS models for breast cancer from 22 publications.62 Due to there being a number best online viagra of different methodologies to generate a score, numerous models may exist for the same condition and each of the resulting models could perform differently.

Models may perform differently because the population, measured outcome or context of the development data sets used to generate the models is diverse, for example, a score for risk of breast cancer versus a breast cancer subtype.44 63 This diversity, alongside the lack of established best practice and standardised reporting in publications, makes comparison and evaluation of polygenic models for best online viagra use in clinical settings challenging. It is clear that moving the field forward is reliant on transparent reporting and evaluation. Recommendations for best practices on the reporting of polygenic models in literature have been proposed14 64 as well as a best online viagra database,65 66 which could allow for such comparisons. Statements and guidelines for risk prediction model development, such as the Genetic Risk Prediction Studies and Transparent Reporting of a Multivariable Prediction Model for Individual Prognosis or Diagnosis (TRIPOD), already exist, but are not consistently used.

TRIPOD explicitly covers the development and validation of prediction models for both diagnosis and prognosis, for all medical domains.One clear issue is generalisability and drop in performance of polygenic models once they are applied in a population group different from the one in which they were developed.22 46 67–70 This is an ongoing challenge in genomics as most GWAS, from which most PGS are being developed, have been conducted in European-Caucasian populations.71 Efforts to improve representation are underway72 and there are attempts to reweight/adjust scores when applied to different population groups which are showing some potential but need further research.47 Others have demonstrated that models developed in more diverse population groups have improved performance when applied to external data sets in different populations.24 73 It is important to consider this issue when moving towards clinical applications as it may pose an best online viagra ethical challenge if the PGS is not generalisable.A greater understanding of different complex traits and the impact of pleiotropy is only beginning to be investigated.74 There is growing appreciation of the role of pleiotropy as multiple variants have been identified to be associated with multiple traits and exert diverse effects, providing insight into overlapping mechanisms.75 76 This, together with the impact of population stratification, genetic relatedness, ascertainment and other sources of heterogeneity leading to spurious signals and reduced power in genetic association studies, all impacting on the predictive ability of PGS in different populations and for different diseases.While many publications report on model development and evaluation, often there is a lack of clarity on intended purpose,50 77 leading to uncertainties as to the clinical pathways in which implementation is envisaged. A clear description of intended use within clinical pathways is a central component in evaluating the use of an application with any form of PGS and in considering practical implications, such as mechanisms of obtaining the score, incorporation into health records, interpretation of scores, relevant cut-offs for intervention initiation, mechanisms for feedback of results and costs, among other issues. These parameters will also be impacted by best online viagra the polygenic model that is taken forward for implementation. Meaning that there are still some important questions that need to be addressed to determine how and where PGS could work within current healthcare systems, particularly at a population level.78It is widely accepted that genotyping using arrays is a lower cost endeavour in comparison to genome sequencing, making the incorporation of PGS into routine healthcare an attractive proposition.

However, we were unable best online viagra to find any studies reporting on the use or associated costs of such technology for population screening. Studies are beginning to examine use case scenarios and model cost-effectiveness, but this has only been in very few, specific investigations.79 80 Costs will also be influenced by the testing technology and by the downstream consequences of testing, which is likely to differ depending on specific applications that are developed and the pathways in which such tests are incorporated. This is particularly the case in screening or primary care settings, where such testing is currently not an established part of care pathways and may require additional resources, not least best online viagra as a result of the volume of testing that could be expected. Moving forward, the clinical role of PGS needs to be developed further, including defining the clinical applications as well as supporting evidence, for example, on the effect of clinical outcomes, the feasibility for use in routine practice and cost-effectiveness.ConclusionThere is a large amount of diversity in the PGS field with respect to model development approaches, and this continues to evolve.

There is rapid progress which is being driven best online viagra by the availability of larger data sets, primarily from GWAS and concomitant developments in statistical methodologies. As understanding and knowledge develops, the usefulness and appropriateness of polygenic models for different diseases and contexts are being explored. Nevertheless, this is still an emerging field, with best online viagra a variable evidence base demonstrating some potential. The validity of PGS needs to be clearly demonstrated, and their applications evaluated prior to clinical implementation..

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Menopause is “the permanent cessation of menstruation women viagra near me that occurs after the loss of ovarian activity” (3). This data brief describes sleep duration and sleep quality among nonpregnant women aged 40–59 by menopausal status. The age range selected for this analysis reflects the focus on midlife sleep health. In this analysis, 74.2% of women are premenopausal, 3.7% are women viagra near me perimenopausal, and 22.1% are postmenopausal.

Keywords. Insufficient sleep, menopause, National Health Interview Survey Perimenopausal women viagra near me women were more likely than premenopausal and postmenopausal women to sleep less than 7 hours, on average, in a 24-hour period.More than one in three nonpregnant women aged 40–59 slept less than 7 hours, on average, in a 24-hour period (35.1%) (Figure 1). Perimenopausal women were most likely to sleep less than 7 hours, on average, in a 24-hour period (56.0%), compared with 32.5% of premenopausal and 40.5% of postmenopausal women. Postmenopausal women were significantly more likely than premenopausal women to sleep less than 7 hours, on average, in a 24-hour period.

Figure 1 women viagra near me. Percentage of nonpregnant women aged 40–59 who slept less than 7 hours, on average, in a 24-hour period, by menopausal status. United States, 2015image icon1Significant women viagra near me quadratic trend by menopausal status (p <. 0.05).NOTES.

Women were postmenopausal if they had gone without a menstrual cycle for more than 1 year or were in surgical menopause after the removal of their ovaries. Women were perimenopausal if they no longer had a menstrual cycle and their last menstrual cycle was 1 year ago women viagra near me or less. Women were premenopausal if they still had a menstrual cycle. Access data table women viagra near me for Figure 1pdf icon.SOURCE.

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Figure 2 women viagra near me. Percentage of nonpregnant women aged 40–59 who had trouble falling asleep four times or more in the past week, by menopausal status. United States, 2015image icon1Significant linear trend by menopausal status (p women viagra near me <. 0.05).NOTES.

Women were postmenopausal if they had gone without a menstrual cycle for more than 1 year or were in surgical menopause after the removal of their ovaries. Women were perimenopausal if they no longer had a menstrual cycle and their last menstrual cycle was 1 year women viagra near me ago or less. Women were premenopausal if they still had a menstrual cycle. Access data table for women viagra near me Figure 2pdf icon.SOURCE.

NCHS, National Health Interview Survey, 2015. The percentage of women aged 40–59 who had trouble staying asleep four women viagra near me times or more in the past week varied by menopausal status.More than one in four nonpregnant women aged 40–59 had trouble staying asleep four times or more in the past week (26.7%) (Figure 3). The percentage of women aged 40–59 who had trouble staying asleep four times or more in the past week increased from 23.7% among premenopausal, to 30.8% among perimenopausal, and to 35.9% among postmenopausal women. Postmenopausal women were significantly more likely than premenopausal women to have trouble staying asleep four times or more in the past week.

Figure 3 women viagra near me. Percentage of nonpregnant women aged 40–59 who had trouble staying asleep four times or more in the past week, by menopausal status. United States, 2015image icon1Significant women viagra near me linear trend by menopausal status (p <. 0.05).NOTES.

Women were postmenopausal if they had gone without a menstrual cycle for more than 1 year or were in surgical menopause after the removal of their ovaries. Women were women viagra near me perimenopausal if they no longer had a menstrual cycle and their last menstrual cycle was 1 year ago or less. Women were premenopausal if they still had a menstrual cycle. Access data women viagra near me table for Figure 3pdf icon.SOURCE.

NCHS, National Health Interview Survey, 2015. The percentage of women aged 40–59 who did not wake up feeling well rested 4 days or more in the past week varied by menopausal status.Nearly one in two nonpregnant women aged 40–59 did not wake up feeling well rested 4 days or more in the past week (48.9%) (Figure 4). The percentage of women in this age group who did not wake up feeling well rested 4 days or more in the past week increased from 47.0% among premenopausal women to 49.9% among perimenopausal and women viagra near me 55.1% among postmenopausal women. Postmenopausal women were significantly more likely than premenopausal women to not wake up feeling well rested 4 days or more in the past week.

Figure 4 women viagra near me. Percentage of nonpregnant women aged 40–59 who did not wake up feeling well rested 4 days or more in the past week, by menopausal status. United States, 2015image icon1Significant linear trend by menopausal status (p <. 0.05).NOTES.

Women were postmenopausal if they had gone without a menstrual cycle for more than 1 year or were in surgical menopause after the removal of their ovaries. Women were perimenopausal if they no longer had a menstrual cycle and their last menstrual cycle was 1 year ago or less. Women were premenopausal if they still had a menstrual cycle. Access data table for Figure 4pdf icon.SOURCE.

NCHS, National Health Interview Survey, 2015. SummaryThis report describes sleep duration and sleep quality among U.S. Nonpregnant women aged 40–59 by menopausal status. Perimenopausal women were most likely to sleep less than 7 hours, on average, in a 24-hour period compared with premenopausal and postmenopausal women.

In contrast, postmenopausal women were most likely to have poor-quality sleep. A greater percentage of postmenopausal women had frequent trouble falling asleep, staying asleep, and not waking well rested compared with premenopausal women. The percentage of perimenopausal women with poor-quality sleep was between the percentages for the other two groups in all three categories. Sleep duration changes with advancing age (4), but sleep duration and quality are also influenced by concurrent changes in women’s reproductive hormone levels (5).

Because sleep is critical for optimal health and well-being (6), the findings in this report highlight areas for further research and targeted health promotion. DefinitionsMenopausal status. A three-level categorical variable was created from a series of questions that asked women. 1) “How old were you when your periods or menstrual cycles started?.

€. 2) “Do you still have periods or menstrual cycles?. €. 3) “When did you have your last period or menstrual cycle?.

€. And 4) “Have you ever had both ovaries removed, either as part of a hysterectomy or as one or more separate surgeries?. € Women were postmenopausal if they a) had gone without a menstrual cycle for more than 1 year or b) were in surgical menopause after the removal of their ovaries. Women were perimenopausal if they a) no longer had a menstrual cycle and b) their last menstrual cycle was 1 year ago or less.

Premenopausal women still had a menstrual cycle.Not waking feeling well rested. Determined by respondents who answered 3 days or less on the questionnaire item asking, “In the past week, on how many days did you wake up feeling well rested?. €Short sleep duration. Determined by respondents who answered 6 hours or less on the questionnaire item asking, “On average, how many hours of sleep do you get in a 24-hour period?.

€Trouble falling asleep. Determined by respondents who answered four times or more on the questionnaire item asking, “In the past week, how many times did you have trouble falling asleep?. €Trouble staying asleep. Determined by respondents who answered four times or more on the questionnaire item asking, “In the past week, how many times did you have trouble staying asleep?.

€ Data source and methodsData from the 2015 National Health Interview Survey (NHIS) were used for this analysis. NHIS is a multipurpose health survey conducted continuously throughout the year by the National Center for Health Statistics. Interviews are conducted in person in respondents’ homes, but follow-ups to complete interviews may be conducted over the telephone. Data for this analysis came from the Sample Adult core and cancer supplement sections of the 2015 NHIS.

For more information about NHIS, including the questionnaire, visit the NHIS website.All analyses used weights to produce national estimates. Estimates on sleep duration and quality in this report are nationally representative of the civilian, noninstitutionalized nonpregnant female population aged 40–59 living in households across the United States. The sample design is described in more detail elsewhere (7). Point estimates and their estimated variances were calculated using SUDAAN software (8) to account for the complex sample design of NHIS.

Linear and quadratic trend tests of the estimated proportions across menopausal status were tested in SUDAAN via PROC DESCRIPT using the POLY option. Differences between percentages were evaluated using two-sided significance tests at the 0.05 level. About the authorAnjel Vahratian is with the National Center for Health Statistics, Division of Health Interview Statistics. The author gratefully acknowledges the assistance of Lindsey Black in the preparation of this report.

ReferencesFord ES. Habitual sleep duration and predicted 10-year cardiovascular risk using the pooled cohort risk equations among US adults. J Am Heart Assoc 3(6):e001454. 2014.Ford ES, Wheaton AG, Chapman DP, Li C, Perry GS, Croft JB.

Associations between self-reported sleep duration and sleeping disorder with concentrations of fasting and 2-h glucose, insulin, and glycosylated hemoglobin among adults without diagnosed diabetes. J Diabetes 6(4):338–50. 2014.American College of Obstetrics and Gynecology. ACOG Practice Bulletin No.

141. Management of menopausal symptoms. Obstet Gynecol 123(1):202–16. 2014.Black LI, Nugent CN, Adams PF.

Tables of adult health behaviors, sleep. National Health Interview Survey, 2011–2014pdf icon. 2016.Santoro N. Perimenopause.

From research to practice. J Women’s Health (Larchmt) 25(4):332–9. 2016.Watson NF, Badr MS, Belenky G, Bliwise DL, Buxton OM, Buysse D, et al. Recommended amount of sleep for a healthy adult.

A joint consensus statement of the American Academy of Sleep Medicine and Sleep Research Society. J Clin Sleep Med 11(6):591–2. 2015.Parsons VL, Moriarity C, Jonas K, et al. Design and estimation for the National Health Interview Survey, 2006–2015.

National Center for Health Statistics. Vital Health Stat 2(165). 2014.RTI International. SUDAAN (Release 11.0.0) [computer software].

2012. Suggested citationVahratian A. Sleep duration and quality among women aged 40–59, by menopausal status. NCHS data brief, no 286.

Hyattsville, MD. National Center for Health Statistics. 2017.Copyright informationAll material appearing in this report is in the public domain and may be reproduced or copied without permission. Citation as to source, however, is appreciated.National Center for Health StatisticsCharles J.

Rothwell, M.S., M.B.A., DirectorJennifer H. Madans, Ph.D., Associate Director for ScienceDivision of Health Interview StatisticsMarcie L. Cynamon, DirectorStephen J. Blumberg, Ph.D., Associate Director for Science.

NCHS Data best online viagra Buy levitra online Brief No. 286, September 2017PDF Versionpdf icon (374 KB)Anjel Vahratian, Ph.D.Key findingsData from the National Health Interview Survey, 2015Among those aged 40–59, perimenopausal women (56.0%) were more likely than postmenopausal (40.5%) and premenopausal (32.5%) women to sleep less than 7 hours, on average, in a 24-hour period.Postmenopausal women aged 40–59 were more likely than premenopausal women aged 40–59 to have trouble falling asleep (27.1% compared with 16.8%, respectively), and staying asleep (35.9% compared with 23.7%), four times or more in the past week.Postmenopausal women aged 40–59 (55.1%) were more likely than premenopausal women aged 40–59 (47.0%) to not wake up feeling well rested 4 days or more in the past week.Sleep duration and quality are important contributors to health and wellness. Insufficient sleep best online viagra is associated with an increased risk for chronic conditions such as cardiovascular disease (1) and diabetes (2). Women may be particularly vulnerable to sleep problems during times of reproductive hormonal change, such as after the menopausal transition. Menopause is “the permanent cessation of menstruation that occurs after the loss of ovarian best online viagra activity” (3).

This data brief describes sleep duration and sleep quality among nonpregnant women aged 40–59 by menopausal status. The age range selected for this analysis reflects the focus on midlife sleep health. In this analysis, 74.2% of women best online viagra are premenopausal, 3.7% are perimenopausal, and 22.1% are postmenopausal. Keywords. Insufficient sleep, menopause, National Health Interview Survey Perimenopausal women were more likely than premenopausal and postmenopausal women to sleep less than best online viagra 7 hours, on average, in a 24-hour period.More than one in three nonpregnant women aged 40–59 slept less than 7 hours, on average, in a 24-hour period (35.1%) (Figure 1).

Perimenopausal women were most likely to sleep less than 7 hours, on average, in a 24-hour period (56.0%), compared with 32.5% of premenopausal and 40.5% of postmenopausal women. Postmenopausal women were significantly more likely than premenopausal women to sleep less than 7 hours, on average, in a 24-hour period. Figure 1 best online viagra. Percentage of nonpregnant women aged 40–59 who slept less than 7 hours, on average, in a 24-hour period, by menopausal status. United States, 2015image icon1Significant quadratic best online viagra trend by menopausal status (p <.

0.05).NOTES. Women were postmenopausal if they had gone without a menstrual cycle for more than 1 year or were in surgical menopause after the removal of their ovaries. Women were perimenopausal if they no longer best online viagra had a menstrual cycle and their last menstrual cycle was 1 year ago or less. Women were premenopausal if they still had a menstrual cycle. Access data table for Figure best online viagra 1pdf icon.SOURCE.

NCHS, National Health Interview Survey, 2015. The percentage of women aged 40–59 who had trouble falling asleep four times or more in the past week varied by menopausal status.Nearly one in five nonpregnant women aged 40–59 had trouble falling asleep four times or more best online viagra in the past week (19.4%) (Figure 2). The percentage of women in this age group who had trouble falling asleep four times or more in the past week increased from 16.8% among premenopausal women to 24.7% among perimenopausal and 27.1% among postmenopausal women. Postmenopausal women were significantly more likely than premenopausal women to have trouble falling asleep four times or more in the past week. Figure 2 best online viagra.

Percentage of nonpregnant women aged 40–59 who had trouble falling asleep four times or more in the past week, by menopausal status. United States, 2015image icon1Significant linear trend best online viagra by menopausal status (p <. 0.05).NOTES. Women were postmenopausal if they had gone without a menstrual cycle for more than 1 year or were in surgical menopause after the removal of their ovaries. Women were best online viagra perimenopausal if they no longer had a menstrual cycle and their last menstrual cycle was 1 year ago or less.

Women were premenopausal if they still had a menstrual cycle. Access data best online viagra table for Figure 2pdf icon.SOURCE. NCHS, National Health Interview Survey, 2015. The percentage of women aged 40–59 who had trouble staying asleep four times or more in the past best online viagra week varied by menopausal status.More than one in four nonpregnant women aged 40–59 had trouble staying asleep four times or more in the past week (26.7%) (Figure 3). The percentage of women aged 40–59 who had trouble staying asleep four times or more in the past week increased from 23.7% among premenopausal, to 30.8% among perimenopausal, and to 35.9% among postmenopausal women.

Postmenopausal women were significantly more likely than premenopausal women to have trouble staying asleep four times or more in the past week. Figure 3 best online viagra. Percentage of nonpregnant women aged 40–59 who had trouble staying asleep four times or more in the past week, by menopausal status. United States, 2015image icon1Significant linear trend best online viagra by menopausal status (p <. 0.05).NOTES.

Women were postmenopausal if they had gone without a menstrual cycle for more than 1 year or were in surgical menopause after the removal of their ovaries. Women were perimenopausal if they no longer had a menstrual cycle and their last menstrual cycle was 1 best online viagra year ago or less. Women were premenopausal if they still had a menstrual cycle. Access data best online viagra table for Figure 3pdf icon.SOURCE. NCHS, National Health Interview Survey, 2015.

The percentage of women aged 40–59 who did not wake up feeling well rested 4 days or more in the past week varied by menopausal status.Nearly one in two nonpregnant women aged 40–59 did not wake up feeling well rested 4 days or more in the past week (48.9%) (Figure 4). The percentage of women best online viagra in this age group who did not wake up feeling well rested 4 days or more in the past week increased from 47.0% among premenopausal women to 49.9% among perimenopausal and 55.1% among postmenopausal women. Postmenopausal women were significantly more likely than premenopausal women to not wake up feeling well rested 4 days or more in the past week. Figure 4 best online viagra. Percentage of nonpregnant women aged 40–59 who did not wake up feeling well rested 4 days or more in the past week, by menopausal status.

United States, 2015image icon1Significant linear trend by menopausal status (p <. 0.05).NOTES. Women were postmenopausal if they had gone without a menstrual cycle for more than 1 year or were in surgical menopause after the removal of their ovaries. Women were perimenopausal if they no longer had a menstrual cycle and their last menstrual cycle was 1 year ago or less. Women were premenopausal if they still had a menstrual cycle.

Access data table for Figure 4pdf icon.SOURCE. NCHS, National Health Interview Survey, 2015. SummaryThis report describes sleep duration and sleep quality among U.S. Nonpregnant women aged 40–59 by menopausal status. Perimenopausal women were most likely to sleep less than 7 hours, on average, in a 24-hour period compared with premenopausal and postmenopausal women.

In contrast, postmenopausal women were most likely to have poor-quality sleep. A greater percentage of postmenopausal women had frequent trouble falling asleep, staying asleep, and not waking well rested compared with premenopausal women. The percentage of perimenopausal women with poor-quality sleep was between the percentages for the other two groups in all three categories. Sleep duration changes with advancing age (4), but sleep duration and quality are also influenced by concurrent changes in women’s reproductive hormone levels (5). Because sleep is critical for optimal health and well-being (6), the findings in this report highlight areas for further research and targeted health promotion.

DefinitionsMenopausal status. A three-level categorical variable was created from a series of questions that asked women. 1) “How old were you when your periods or menstrual cycles started?. €. 2) “Do you still have periods or menstrual cycles?.

€. 3) “When did you have your last period or menstrual cycle?. €. And 4) “Have you ever had both ovaries removed, either as part of a hysterectomy or as one or more separate surgeries?. € Women were postmenopausal if they a) had gone without a menstrual cycle for more than 1 year or b) were in surgical menopause after the removal of their ovaries.

Women were perimenopausal if they a) no longer had a menstrual cycle and b) their last menstrual cycle was 1 year ago or less. Premenopausal women still had a menstrual cycle.Not waking feeling well rested. Determined by respondents who answered 3 days or less on the questionnaire item asking, “In the past week, on how many days did you wake up feeling well rested?. €Short sleep duration. Determined by respondents who answered 6 hours or less on the questionnaire item asking, “On average, how many hours of sleep do you get in a 24-hour period?.

€Trouble falling asleep. Determined by respondents who answered four times or more on the questionnaire item asking, “In the past week, how many times did you have trouble falling asleep?. €Trouble staying asleep. Determined by respondents who answered four times or more on the questionnaire item asking, “In the past week, how many times did you have trouble staying asleep?. € Data source and methodsData from the 2015 National Health Interview Survey (NHIS) were used for this analysis.

NHIS is a multipurpose health survey conducted continuously throughout the year by the National Center for Health Statistics. Interviews are conducted in person in respondents’ homes, but follow-ups to complete interviews may be conducted over the telephone. Data for this analysis came from the Sample Adult core and cancer supplement sections of the 2015 NHIS. For more information about NHIS, including the questionnaire, visit the NHIS website.All analyses used weights to produce national estimates. Estimates on sleep duration and quality in this report are nationally representative of the civilian, noninstitutionalized nonpregnant female population aged 40–59 living in households across the United States.

The sample design is described in more detail elsewhere (7). Point estimates and their estimated variances were calculated using SUDAAN software (8) to account for the complex sample design of NHIS. Linear and quadratic trend tests of the estimated proportions across menopausal status were tested in SUDAAN via PROC DESCRIPT using the POLY option. Differences between percentages were evaluated using two-sided significance tests at the 0.05 level. About the authorAnjel Vahratian is with the National Center for Health Statistics, Division of Health Interview Statistics.

The author gratefully acknowledges the assistance of Lindsey Black in the preparation of this report. ReferencesFord ES. Habitual sleep duration and predicted 10-year cardiovascular risk using the pooled cohort risk equations among US adults. J Am Heart Assoc 3(6):e001454. 2014.Ford ES, Wheaton AG, Chapman DP, Li C, Perry GS, Croft JB.

Associations between self-reported sleep duration and sleeping disorder with concentrations of fasting and 2-h glucose, insulin, and glycosylated hemoglobin among adults without diagnosed diabetes. J Diabetes 6(4):338–50. 2014.American College of Obstetrics and Gynecology. ACOG Practice Bulletin No. 141.

Management of menopausal symptoms. Obstet Gynecol 123(1):202–16. 2014.Black LI, Nugent CN, Adams PF. Tables of adult health behaviors, sleep. National Health Interview Survey, 2011–2014pdf icon.

2016.Santoro N. Perimenopause. From research to practice. J Women’s Health (Larchmt) 25(4):332–9. 2016.Watson NF, Badr MS, Belenky G, Bliwise DL, Buxton OM, Buysse D, et al.

Recommended amount of sleep for a healthy adult. A joint consensus statement of the American Academy of Sleep Medicine and Sleep Research Society. J Clin Sleep Med 11(6):591–2. 2015.Parsons VL, Moriarity C, Jonas K, et al. Design and estimation for the National Health Interview Survey, 2006–2015.

National Center for Health Statistics. Vital Health Stat 2(165). 2014.RTI International. SUDAAN (Release 11.0.0) [computer software]. 2012.

Suggested citationVahratian A. Sleep duration and quality among women aged 40–59, by menopausal status. NCHS data brief, no 286. Hyattsville, MD. National Center for Health Statistics.

2017.Copyright informationAll material appearing in this report is in the public domain and may be reproduced or copied without permission. Citation as to source, however, is appreciated.National Center for Health StatisticsCharles J. Rothwell, M.S., M.B.A., DirectorJennifer H. Madans, Ph.D., Associate Director for ScienceDivision of Health Interview StatisticsMarcie L. Cynamon, DirectorStephen J.

Blumberg, Ph.D., Associate Director for Science.

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AbstractIntroduction informative post does viagra really work. We report a very rare case of familial breast cancer and diffuse gastric cancer, with germline pathogenic variants in both BRCA1 and CDH1 genes. To the best of our knowledge, this is the first report of such an association.Family description does viagra really work.

The proband is a woman diagnosed with breast cancer at the age of 52 years. She requested genetic counselling in 2012, at the age of 91 years, because of a history of breast cancer in her daughter, her sister, her niece and her paternal does viagra really work grandmother and was therefore concerned about her relatives. Her sister and maternal aunt also had gastric cancer.

She was tested for several genes associated with hereditary breast cancer.Results does viagra really work. A large deletion of BRCA1 from exons 1 to 7 and two CDH1 pathogenic cis variants were identified.Conclusion. This complex situation is challenging for genetic counselling and management of at-risk individuals.cancer does viagra really work.

Breastcancer. Gastricclinical geneticsgenetic screening/counsellingmolecular geneticsIntroductionGLI-Kruppel family member 3 (GLI3) encodes for a zinc finger transcription factor which does viagra really work plays a key role in the sonic hedgehog (SHH) signalling pathway essential in both limb and craniofacial development.1 2 In hand development, SHH is expressed in the zone of polarising activity (ZPA) on the posterior side of the handplate. The ZPA expresses SHH, creating a gradient of SHH from the posterior to the anterior side of the handplate.

In the presence of SHH, full length GLI3-protein is produced (GLI3A), whereas absence of SHH causes cleavage of GLI3 into its repressor form (GLI3R).3 4 Abnormal expression of this SHH/GLI3R gradient can cause both preaxial and does viagra really work postaxial polydactyly.2Concordantly, pathogenic DNA variants in the GLI3 gene are known to cause multiple syndromes with craniofacial and limb involvement, such as. Acrocallosal syndrome5 (OMIM. 200990), Greig cephalopolysyndactyly does viagra really work syndrome6 (OMIM.

175700) and Pallister-Hall syndrome7 (OMIM. 146510). Also, in non-syndromic polydactyly, such as preaxial polydactyly-type 4 (PPD4, OMIM.

174700),8 pathogenic variants in GLI3 have been described. Out of these diseases, Pallister-Hall syndrome is the most distinct entity, defined by the presence of central polydactyly and hypothalamic hamartoma.9 The other GLI3 syndromes are defined by the presence of preaxial and/or postaxial polydactyly of the hand and feet with or without syndactyly (Greig syndrome, PPD4). Also, various mild craniofacial features such as hypertelorism and macrocephaly can occur.

Pallister-Hall syndrome is caused by truncating variants in the middle third of the GLI3 gene.10–12 The truncation of GLI3 causes an overexpression of GLI3R, which is believed to be the key difference between Pallister-Hall and the GLI3-mediated polydactyly syndromes.9 11 Although multiple attempts have been made, the clinical and genetic distinction between the GLI3-mediated polydactyly syndromes is less evident. This has for example led to the introduction of subGreig and the formulation of an Oro-facial-digital overlap syndrome.10 Other authors, suggested that we should not regard these diseases as separate entities, but as a spectrum of GLI3-mediated polydactyly syndromes.13Although phenotype/genotype correlation of the different syndromes has been cumbersome, clinical and animal studies do provide evidence that distinct regions within the gene, could be related to the individual anomalies contributing to these syndromes. First, case studies show isolated preaxial polydactyly is caused by both truncating and non-truncating variants throughout the GLI3 gene, whereas in isolated postaxial polydactyly cases truncating variants at the C-terminal side of the gene are observed.12 14 These results suggest two different groups of variants for preaxial and postaxial polydactyly.

Second, recent animal studies suggest that posterior malformations in GLI3-mediated polydactyly syndromes are likely related to a dosage effect of GLI3R rather than due to the influence of an altered GLI3A expression.15Past attempts for phenotype/genotype correlation in GLI3-mediated polydactyly syndromes have directly related the diagnosed syndrome to the observed genotype.10–12 16 Focusing on individual hand phenotypes, such as preaxial and postaxial polydactyly and syndactyly might be more reliable because it prevents misclassification due to inconsistent use of syndrome definition. Subsequently, latent class analysis (LCA) provides the possibility to relate a group of observed variables to a set of latent, or unmeasured, parameters and thereby identifying different subgroups in the obtained dataset.17 As a result, LCA allows us to group different phenotypes within the GLI3-mediated polydactyly syndromes and relate the most important predictors of the grouped phenotypes to the observed GLI3 variants.The aim of our study was to further investigate the correlation of the individual phenotypes to the genotypes observed in GLI3-mediated polydactyly syndromes, using LCA. Cases were obtained by both literature review and the inclusion of local clinical cases.

Subsequently, we identified two subclasses of limb anomalies that relate to the underlying GLI3 variant. We provide evidence for two different phenotypic and genotypic groups with predominantly preaxial and postaxial hand and feet anomalies, and we specify those cases with a higher risk for corpus callosum anomalies.MethodsLiterature reviewThe Human Gene Mutation Database (HGMD Professional 2019) was reviewed to identify known pathogenic variants in GLI3 and corresponding phenotypes.18 All references were obtained and cases were included when they were diagnosed with either Greig or subGreig syndrome or PPD4.10–12 Pallister-Hall syndrome and acrocallosal syndrome were excluded because both are regarded distinct syndromes and rather defined by the presence of the non-hand anomalies, than the presence of preaxial or postaxial polydactyly.13 19 Isolated preaxial or postaxial polydactyly were excluded for two reasons. The phenotype/genotype correlations are better understood and both anomalies can occur sporadically which could introduce falsely assumed pathogenic GLI3 variants in the analysis.

Additionally, cases were excluded when case-specific phenotypic or genotypic information was not reported or if these two could not be related to each other. Families with a combined phenotypic description, not reducible to individual family members, were included as one case in the analysis.Clinical casesThe Sophia Children’s Hospital Database was reviewed for cases with a GLI3 variant. Within this population, the same inclusion criteria for the phenotype were valid.

Relatives of the index patients were also contacted for participation in this study, when they showed comparable hand, foot, or craniofacial malformations or when a GLI3 variant was identified. Phenotypes of the hand, foot and craniofacial anomalies of the patients treated in the Sophia Children's Hospital were collected using patient documentation. Family members were identified and if possible, clinically verified.

Alternatively, family members were contacted to verify their phenotypes. If no verification was possible, cases were excluded.PhenotypesThe phenotypes of both literature cases and local cases were extracted in a similar fashion. The most frequently reported limb and craniofacial phenotypes were dichotomised.

The dichotomised hand and foot phenotypes were preaxial polydactyly, postaxial polydactyly and syndactyly. Broad halluces or thumbs were commonly reported by authors and were dichotomised as a presentation of preaxial polydactyly. The extracted dichotomised craniofacial phenotypes were hypertelorism, macrocephaly and corpus callosum agenesis.

All other phenotypes were registered, but not dichotomised.Pathogenic GLI3 variantsAll GLI3 variants were extracted and checked using Alamut Visual V.2.14. If indicated, variants were renamed according to standard Human Genome Variation Society nomenclature.20 Variants were grouped in either missense, frameshift, nonsense or splice site variants. In the group of frameshift variants, a subgroup with possible splice site effect were identified for subgroup analysis when indicated.

Similarly, nonsense variants prone for nonsense mediated decay (NMD) and nonsense variants with experimentally confirmed NMD were identified.21 Deletions of multiple exons, CNVs and translocations were excluded for analysis. A full list of included mutations is available in the online supplementary materials.Supplemental materialThe location of the variant was compared with five known structural domains of the GLI3 gene. (1) repressor domain, (2) zinc finger domain, (3) cleavage site, (4) activator domain, which we defined as a concatenation of the separately identified transactivation zones, the CBP binding domain and the mediator binding domain (MBD) and (5) the MID1 interaction region domain.1 6 22–24 The boundaries of each of the domains were based on available literature (figure 1, exact locations available in the online supplementary materials).

The boundaries used by different authors did vary, therefore a consensus was made.In this figure the posterior probability of an anterior phenotype is plotted against the location of the variant, stratified for the type of mutation that was observed. For better overview, only variants with a location effect were displayed. The full figure, including all variant types, can be found in the online supplementary figure 1.

Each mutation is depicted as a dot, the size of the dot represents the number of observations for that variant. If multiple observations were made, the mean posterior odds and IQR are plotted. For the nonsense variants, variants that were predicted to produce nonsense mediated decay, are depicted using a triangle.

Again, the size indicates the number of observations." data-icon-position data-hide-link-title="0">Figure 1 In this figure the posterior probability of an anterior phenotype is plotted against the location of the variant, stratified for the type of mutation that was observed. For better overview, only variants with a location effect were displayed. The full figure, including all variant types, can be found in the online supplementary figure 1.

Each mutation is depicted as a dot, the size of the dot represents the number of observations for that variant. If multiple observations were made, the mean posterior odds and IQR are plotted. For the nonsense variants, variants that were predicted to produce nonsense mediated decay, are depicted using a triangle.

Again, the size indicates the number of observations.Supplemental materialLatent class analysisTo cluster phenotypes and relate those to the genotypes of the patients, an explorative analysis was done using LCA in R (R V.3.6.1 for Mac. Polytomous variable LCA, poLCA V.1.4.1.). We used our LCA to detect the number of phenotypic subgroups in the dataset and subsequently predict a class membership for each case in the dataset based on the posterior probabilities.In order to make a reliable prediction, only phenotypes that were sufficiently reported and/or ruled out were feasible for LCA, limiting the analysis to preaxial polydactyly, postaxial polydactyly and syndactyly of the hands and feet.

Only full cases were included. To determine the optimal number of classes, we fitted a series of models ranging from a one-class to a six-class model. The optimal number of classes was based on the conditional Akaike information criterion (cAIC), the non adjusted and the sample-size adjusted Bayesian information criterion (BIC and aBIC) and the obtained entropy.25 The explorative LCA produces both posterior probabilities per case for both classes and predicted class membership.

Using the predicted class membership, the phenotypic features per class were determined in a univariate analysis (χ2, SPSS V.25). Using the posterior probabilities on latent class (LC) membership, a scatter plot was created using the location of the variant on the x-axis and the probability of class membership on the y-axis for each of the types of variants (Tibco Spotfire V.7.14). Using these scatter plots, variants that give similar phenotypes were clustered.Genotype/phenotype correlationBecause an LC has no clinical value, the correlation between genotypes and phenotypes was investigated using the predictor phenotypes and the clustered phenotypes.

First, those phenotypes that contribute most to LC membership were identified. Second those phenotypes were directly related to the different types of variants (missense, nonsense, frameshift, splice site) and their clustered locations. Quantification of the relation was performed using a univariate analysis using a χ2 test.

Because of our selection criteria, meaning patients at least have two phenotypes, a multivariate using a logistic regression analysis was used to detect the most significant predictors in the overall phenotype (SPSS V.25). Finally, we explored the relation of the clustered genotypes to the presence of corpus callosum agenesis, a rare malformation in GLI3-mediated polydactyly syndromes which cannot be readily diagnosed without additional imaging.ResultsWe included 251 patients from the literature and 46 local patients,10–12 16 21 26–43 in total 297 patients from 155 different families with 127 different GLI3 variants, 32 of which were large deletions, CNVs or translocations. In six local cases, the exact variant could not be retrieved by status research.The distribution of the most frequently observed phenotypes and variants are presented in table 1.

Other recurring phenotypes included developmental delay (n=22), broad nasal root (n=23), frontal bossing or prominent forehead (n=16) and craniosynostosis (n=13), camptodactyly (n=8) and a broad first interdigital webspace of the foot (n=6).View this table:Table 1 Baseline phenotypes and genotypes of selected populationThe LCA model was fitted using the six defined hand/foot phenotypes. Model fit indices for the LCA are displayed in table 2. Based on the BIC, a two-class model has the best fit for our data.

The four-class model does show a gain in entropy, however with a higher BIC and loss of df. Therefore, based on the majority of performance statistics and the interpretability of the model, a two-class model was chosen. Table 3 displays the distribution of phenotypes and genotypes over the two classes.View this table:Table 2 Model fit indices for the one-class through six-class model evaluated in our LCAView this table:Table 3 Distribution of phenotypes and genotypes in the two latent classes (LC)Table 1 depicts the baseline phenotypes and genotypes in the obtained population.

Note incomplete data especially in the cranium phenotypes. In total 259 valid genotypes were present. In total, 289 cases had complete data for all hand and foot phenotypes (preaxial polydactyly, postaxial polydactyly and syndactyly) and thus were available for LCA.

Combined, for phenotype/genotype correlation 258 cases were available with complete genotypes and complete hand and foot phenotypes.Table 2 depicts the model fit indices for all models that have been fitted to our data.Table 3 depicts the distribution of phenotypes and genotypes over the two assigned LCs. Hand and foot phenotypes were used as input for the LCA, thus are all complete cases. Malformation of the cranium and genotypes do have missing cases.

Note that for the LCA, full case description was required, resulting in eight cases due to incomplete phenotypes. Out of these eight, one also had a genotype that thus needed to be excluded. Missingness of genotypic data was higher in LC2, mostly due to CNVs (table 1).In 54/60 cases, a missense variant produced a posterior phenotype.

Likewise, splice site variants show the same phenotype in 23/24 cases (table 3). For both frameshift and nonsense variants, this relation is not significant (52 anterior vs 54 posterior and 26 anterior vs 42 posterior, respectively). Therefore, only for nonsense and frameshift variants the location of the variant was plotted against the probability for LC2 membership in figure 1.

A full scatterplot of all variants is available in online supplementary figure 1.Figure 1 reveals a pattern for these nonsense and frameshift variants that reveals that variants at the C-terminal of the gene predict anterior phenotypes. When relating the domains of the GLI3 protein to the observed phenotype, we observe that the majority of patients with a nonsense or frameshift variant in the repressor domain, the zinc finger domain or the cleavage site had a high probability of an LC2/anterior phenotype. This group contains all variants that are either experimentally determined to be subject to NMD (triangle marker in figure 1) or predicted to be subject to NMD (diamond marker in figure 1).

Frameshift and nonsense variants in the activator domain result in high probability for an LC1/posterior phenotype. These variants will be further referred to as truncating variants in the activator domain.The univariate relation of the individual phenotypes to these two groups of variants are estimated and presented in table 4. In our multivariate analysis, postaxial polydactyly of the foot and hand are the strongest predictors (Beta.

2.548, p<0001 and Beta. 1.47, p=0.013, respectively) for patients to have a truncating variant in the activator domain. Moreover, the effect sizes of preaxial polydactyly of the hand and feet (Beta.

ˆ’0.797, p=0123 and −1.772, p=0.001) reveals that especially postaxial polydactyly of the foot is the dominant predictor for the genetic substrate of the observed anomalies.View this table:Table 4 Univariate and multivariate analysis of the phenotype/genotype correlationTable 4 shows exploration of the individual phenotypes on the genotype, both univariate and multivariate. The multivariate analysis corrects for the presence of multiple phenotypes in the underlying population.Although the craniofacial anomalies could not be included in the LCA, the relation between the observed anomalies and the identified genetic substrates can be studied. The prevalence of hypertelorism was equally distributed over the two groups of variants (47/135 vs 21/47 respectively, p<0.229).

However for corpus callosum agenesis and macrocephaly, there was a higher prevalence in patients with a truncating variant in the activator domain (3/75 vs 11/41, p<0.001. OR. 8.8, p<0.001) and 42/123 vs 24/48, p<0.05).

Noteworthy is the fact that 11/14 cases with corpus callosum agenesis in the dataset had a truncating variant in the activator domain.DiscussionIn this report, we present new insights into the correlation between the phenotype and the genotype in patients with GLI3-mediated polydactyly syndromes. We illustrate that there are two LCs of patients, best predicted by postaxial polydactyly of the hand and foot for LC1, and the preaxial polydactyly of the hand and foot and syndactyly of the foot for LC2. Patients with postaxial phenotypes have a higher risk of having a truncating variant in the activator domain of the GLI3 gene which is also related to a higher risk of corpus callosum agenesis.

These results suggest a functional difference between truncating variants on the N-terminal and the C-terminal side of the GLI3 cleavage site.Previous attempts of phenotype to genotype correlation have not yet provided the clinical confirmation of these assumed mechanisms in the pathophysiology of GLI3-mediated polydactyly syndromes. Johnston et al have successfully determined the Pallister-Hall region in which truncating variants produce a Pallister-Hall phenotype rather than Greig syndrome.11 However, in their latest population study, subtypes of both syndromes were included to explain the full spectrum of observed malformations. In 2015, Demurger et al reported the higher incidence of corpus callosum agenesis in the Greig syndrome population with truncating mutations in the activator domain.12 Al-Qattan in his review summarises the concept of a spectrum of anomalies dependent on haplo-insufficiency (through different mechanisms) and repressor overexpression.13 However, he bases this theory mainly on reviewed experimental data.

Our report is the first to provide an extensive clinical review of cases that substantiate the phenotypic difference between the two groups that could fit the suggested mechanisms. We agree with Al-Qattan et al that a variation of anomalies can be observed given any pathogenic variant in the GLI3 gene, but overall two dominant phenotypes are present. A population with predominantly preaxial anomalies and one with postaxial anomalies.

The presence of preaxial or postaxial polydactyly and syndactyly is not mutually exclusive for one of these two subclasses. Meaning that preaxial polydactyly can co-occur with postaxial polydactyly. However, truncating mutations in the activator domain produce a postaxial phenotype, as can be derived from the risk in table 4.

The higher risk of corpus callosum agenesis in this population shows that differentiating between a preaxial phenotype and a postaxial phenotype, instead of between the different GLI3-mediated polydactyly syndromes, might be more relevant regarding diagnostics for corpus callosum agenesis.We chose to use LCA as an exploratory tool only in our population for two reasons. First of all, LCA can be useful to identify subgroups, but there is no ‘true’ model or number of subgroups you can detect. The best fitting model can only be estimated based on the available measures and approximates the true subgroups that might be present.

Second, LC membership assignment is a statistical procedure based on the posterior probability, with concordant errors of the estimation, rather than a clinical value that can be measured or evaluated. Therefore, we decided to use our LCA only in an exploratory tool, and perform our statistics using the actual phenotypes that predict LC membership and the associated genotypes. Overall, this method worked well to differentiate the two subgroups present in our dataset.

However, outliers were observed. A qualitative analysis of these outliers is available in the online supplementary data.The genetic substrate for the two phenotypic clusters can be discussed based on multiple experiments. Overall, we hypothesise two genetic clusters.

One that is due to haploinsufficiency and one that is due to abnormal truncation of the activator. The hypothesised cluster of variants that produce haploinsufficiency is mainly based on the experimental data that confirms NMD in two variants and the NMD prediction of other nonsense variants in Alamut. For the frameshift variants, it is also likely that the cleavage of the zinc finger domain results in functional haploinsufficiency either because of a lack of signalling domains or similarly due to NMD.

Missense variants could cause haploinsufficiency through the suggested mechanism by Krauss et al who have illustrated that missense variants in the MID1 domain hamper the functional interaction with the MID1-α4-PP2A complex, leading to a subcellular location of GLI3.24 The observed missense variants in our study exceed the region to which Krauss et al have limited the MID-1 interaction domain. An alternative theory is suggested by Zhou et al who have shown that missense variants in the MBD can cause deficiency in the signalling of GLI3A, functionally implicating a relative overexpression of GLI3R.22 However, GLI3R overexpression would likely produce a posterior phenotype, as determined by Hill et al in their fixed homo and hemizygous GLI3R models.15 Therefore, our hypothesis is that all included missense variants have a similar pathogenesis which is more likely in concordance with the mechanism introduced by Krauss et al. To our knowledge, no splice site variants have been functionally described in literature.

However, it is noted that the 15 and last exon encompasses the entire activator domain, thus any splice site mutation is by definition located on the 5′ side of the activator. Based on the phenotype, we would suggest that these variants fail to produce a functional protein. We hypothesise that the truncating variants of the activator domain lead to overexpression of GLI3R in SHH rich areas.

In normal development, the presence of SHH prevents the processing of full length GLI34 into GLI3R, thus producing the full length activator. In patients with a truncating variant of the activator domain of GLI3, thus these variants likely have the largest effect in SHH rich areas, such as the ZPA located at the posterior side of the hand/footplate. Moreover, the lack of posterior anomalies in the GLI3∆699/- mouse model (hemizygous fixed repressor model) compared with the GLI3∆699/∆699 mouse model (homozygous fixed repressor model), suggesting a dosage effect of GLI3R to be responsible for posterior hand anomalies.15 These findings are supported by Lewandowski et al, who show that the majority of the target genes in GLI signalling are regulated by GLI3R rather than GLI3A.44 Together, these findings suggest a role for the location and type of variant in GLI3-mediated syndromes.Interestingly, the difference between Pallister-Hall syndrome and GLI3-mediated polydactyly syndromes has also been attributed to the GLI3R overexpression.

However, the difference in phenotype observed in the cases with a truncating variant in the activator domain and Pallister-Hall syndrome suggest different functional consequences. When studying figure 1, it is noted that the included truncating variants on the 3′ side of the cleavage site seldomly affect the CBP binding region, which could provide an explanation for the observed differences. This binding region is included in the Pallister-Hall region as defined by Johnston et al and is necessary for the downstream signalling with GLI1.10 11 23 45 Interestingly, recent reports show that pathogenic variants in GLI1 can produce phenotypes concordant with Ellis von Krefeld syndrome, which includes overlapping features with Pallister-Hall syndrome.46 The four truncating variants observed in this study that do affect the CBP but did not result in a Pallister-Hall phenotype are conflicting with this theory.

Krauss et al postulate an alternative hypothesis, they state that the MID1-α4-PP2A complex, which is essential for GLI3A signalling, could also be the reason for overlapping features of Opitz syndrome, caused by variants in MID1, and Pallister-Hall syndrome. Further analysis is required to fully appreciate the functional differences between truncating mutations that cause Pallister-Hall syndrome and those that result in GLI3-mediated polydactyly syndromes.For the clinical evaluation of patients with GLI3-mediated polydactyly syndromes, intracranial anomalies are likely the most important to predict based on the variant. Unfortunately, the presence of corpus callosum agenesis was not routinely investigated or reported thus this feature could not be used as an indicator phenotype for LC membership.

Interestingly when using only hand and foot phenotypes, we did notice a higher prevalence of corpus callosum agenesis in patients with posterior phenotypes. The suggested relation between truncating mutations in the activator domain causing these posterior phenotypes and corpus callosum agenesis was statistically confirmed (OR. 8.8, p<0.001).

Functionally this relation could be caused by the GLI3-MED12 interaction at the MBD. Pathogenic DNA variants in MED12 can cause Opitz-Kaveggia syndrome, a syndrome in which presentation includes corpus callosum agenesis, broad halluces and thumbs.47In conclusion, there are two distinct phenotypes within the GLI3-mediated polydactyly population. Patients with more posteriorly and more anteriorly oriented hand anomalies.

Furthermore, this difference is related to the observed variant in GLI3. We hypothesise that variants that cause haploinsufficiency produce anterior anomalies of the hand, whereas variants with abnormal truncation of the activator domain have more posterior anomalies. Furthermore, patients that have a variant that produces abnormal truncation of the activator domain, have a greater risk for corpus callosum agenesis.

Thus, we advocate to differentiate preaxial or postaxial oriented GLI3 phenotypes to explain the pathophysiology as well as to get a risk assessment for corpus callosum agenesis.Data availability statementData are available upon reasonable request.Ethics statementsPatient consent for publicationNot required.Ethics approvalThe research protocol was approved by the local ethics board of the Erasmus MC University Medical Center (MEC 2015-679)..

AbstractIntroduction Buy propecia from canada best online viagra. We report a very rare case of familial breast cancer and diffuse gastric cancer, with germline pathogenic variants in both BRCA1 and CDH1 genes. To the best of our knowledge, this is the first report of best online viagra such an association.Family description.

The proband is a woman diagnosed with breast cancer at the age of 52 years. She requested genetic counselling in 2012, at the age of 91 years, because of a history best online viagra of breast cancer in her daughter, her sister, her niece and her paternal grandmother and was therefore concerned about her relatives. Her sister and maternal aunt also had gastric cancer.

She was best online viagra tested for several genes associated with hereditary breast cancer.Results. A large deletion of BRCA1 from exons 1 to 7 and two CDH1 pathogenic cis variants were identified.Conclusion. This complex situation is challenging for genetic best online viagra counselling and management of at-risk individuals.cancer.

Breastcancer. Gastricclinical geneticsgenetic screening/counsellingmolecular geneticsIntroductionGLI-Kruppel best online viagra family member 3 (GLI3) encodes for a zinc finger transcription factor which plays a key role in the sonic hedgehog (SHH) signalling pathway essential in both limb and craniofacial development.1 2 In hand development, SHH is expressed in the zone of polarising activity (ZPA) on the posterior side of the handplate. The ZPA expresses SHH, creating a gradient of SHH from the posterior to the anterior side of the handplate.

In the presence of SHH, full length GLI3-protein is produced (GLI3A), whereas best online viagra absence of SHH causes cleavage of GLI3 into its repressor form (GLI3R).3 4 Abnormal expression of this SHH/GLI3R gradient can cause both preaxial and postaxial polydactyly.2Concordantly, pathogenic DNA variants in the GLI3 gene are known to cause multiple syndromes with craniofacial and limb involvement, such as. Acrocallosal syndrome5 (OMIM. 200990), Greig best online viagra cephalopolysyndactyly syndrome6 (OMIM.

175700) and Pallister-Hall syndrome7 (OMIM. 146510). Also, in non-syndromic polydactyly, such as preaxial polydactyly-type 4 (PPD4, OMIM.

174700),8 pathogenic variants in GLI3 have been described. Out of these diseases, Pallister-Hall syndrome is the most distinct entity, defined by the presence of central polydactyly and hypothalamic hamartoma.9 The other GLI3 syndromes are defined by the presence of preaxial and/or postaxial polydactyly of the hand and feet with or without syndactyly (Greig syndrome, PPD4). Also, various mild craniofacial features such as hypertelorism and macrocephaly can occur.

Pallister-Hall syndrome is caused by truncating variants in the middle third of the GLI3 gene.10–12 The truncation of GLI3 causes an overexpression of GLI3R, which is believed to be the key difference between Pallister-Hall and the GLI3-mediated polydactyly syndromes.9 11 Although multiple attempts have been made, the clinical and genetic distinction between the GLI3-mediated polydactyly syndromes is less evident. This has for example led to the introduction of subGreig and the formulation of an Oro-facial-digital overlap syndrome.10 Other authors, suggested that we should not regard these diseases as separate entities, but as a spectrum of GLI3-mediated polydactyly syndromes.13Although phenotype/genotype correlation of the different syndromes has been cumbersome, clinical and animal studies do provide evidence that distinct regions within the gene, could be related to the individual anomalies contributing to these syndromes. First, case studies show isolated preaxial polydactyly is caused by both truncating and non-truncating variants throughout the GLI3 gene, whereas in isolated postaxial polydactyly cases truncating variants at the C-terminal side of the gene are observed.12 14 These results suggest two different groups of variants for preaxial and postaxial polydactyly.

Second, recent animal studies suggest that posterior malformations in GLI3-mediated polydactyly syndromes are likely related to a dosage effect of GLI3R rather than due to the influence of an altered GLI3A expression.15Past attempts for phenotype/genotype correlation in GLI3-mediated polydactyly syndromes have directly related the diagnosed syndrome to the observed genotype.10–12 16 Focusing on individual hand phenotypes, such as preaxial and postaxial polydactyly and syndactyly might be more reliable because it prevents misclassification due to inconsistent use of syndrome definition. Subsequently, latent class analysis (LCA) provides the possibility to relate a group of observed variables to a set of latent, or unmeasured, parameters and thereby identifying different subgroups in the obtained dataset.17 As a result, LCA allows us to group different phenotypes within the GLI3-mediated polydactyly syndromes and relate the most important predictors of the grouped phenotypes to the observed GLI3 variants.The aim of our study was to further investigate the correlation of the individual phenotypes to the genotypes observed in GLI3-mediated polydactyly syndromes, using LCA. Cases were obtained by both literature review and the inclusion of local clinical cases.

Subsequently, we identified two subclasses of limb anomalies that relate to the underlying GLI3 variant. We provide evidence for two different phenotypic and genotypic groups with predominantly preaxial and postaxial hand and feet anomalies, and we specify those cases with a higher risk for corpus callosum anomalies.MethodsLiterature reviewThe Human Gene Mutation Database (HGMD Professional 2019) was reviewed to identify known pathogenic variants in GLI3 and corresponding phenotypes.18 All references were obtained and cases were included when they were diagnosed with either Greig or subGreig syndrome or PPD4.10–12 Pallister-Hall syndrome and acrocallosal syndrome were excluded because both are regarded distinct syndromes and rather defined by the presence of the non-hand anomalies, than the presence of preaxial or postaxial polydactyly.13 19 Isolated preaxial or postaxial polydactyly were excluded for two reasons. The phenotype/genotype correlations are better understood and both anomalies can occur sporadically which could introduce falsely assumed pathogenic GLI3 variants in the analysis.

Additionally, cases were excluded when case-specific phenotypic or genotypic information was not reported or if these two could not be related to each other. Families with a combined phenotypic description, not reducible to individual family members, were included as one case in the analysis.Clinical casesThe Sophia Children’s Hospital Database was reviewed for cases with a GLI3 variant. Within this population, the same inclusion criteria for the phenotype were valid.

Relatives of the index patients were also contacted for participation in this study, when they showed comparable hand, foot, or craniofacial malformations or when a GLI3 variant was identified. Phenotypes of the hand, foot and craniofacial anomalies of the patients treated in the Sophia Children's Hospital were collected using patient documentation. Family members were identified and if possible, clinically verified.

Alternatively, family members were contacted to verify their phenotypes. If no verification was possible, cases were excluded.PhenotypesThe phenotypes of both literature cases and local cases were extracted in a similar fashion. The most frequently reported limb and craniofacial phenotypes were dichotomised.

The dichotomised hand and foot phenotypes were preaxial polydactyly, postaxial polydactyly and syndactyly. Broad halluces or thumbs were commonly reported by authors and were dichotomised as a presentation of preaxial polydactyly. The extracted dichotomised craniofacial phenotypes were hypertelorism, macrocephaly and corpus callosum agenesis.

All other phenotypes were registered, but not dichotomised.Pathogenic GLI3 variantsAll GLI3 variants were extracted and checked using Alamut Visual V.2.14. If indicated, variants were renamed according to standard Human Genome Variation Society nomenclature.20 Variants were grouped in either missense, frameshift, nonsense or splice site variants. In the group of frameshift variants, a subgroup with possible splice site effect were identified for subgroup analysis when indicated.

Similarly, nonsense variants prone for nonsense mediated decay (NMD) and nonsense variants with experimentally confirmed NMD were identified.21 Deletions of multiple exons, CNVs and translocations were excluded for analysis. A full list of included mutations is available in the online supplementary materials.Supplemental materialThe location of the variant was compared with five known structural domains of the GLI3 gene. (1) repressor domain, (2) zinc finger domain, (3) cleavage site, (4) activator domain, which we defined as a concatenation of the separately identified transactivation zones, the CBP binding domain and the mediator binding domain (MBD) and (5) the MID1 interaction region domain.1 6 22–24 The boundaries of each of the domains were based on available literature (figure 1, exact locations available in the online supplementary materials).

The boundaries used by different authors did vary, therefore a consensus was made.In this figure the posterior probability of an anterior phenotype is plotted against the location of the variant, stratified for the type of mutation that was observed. For better overview, only variants with a location effect were displayed. The full figure, including all variant types, can be found in the online supplementary figure 1.

Each mutation is depicted as a dot, the size of the dot represents the number of observations for that variant. If multiple observations were made, the mean posterior odds and IQR are plotted. For the nonsense variants, variants that were predicted to produce nonsense mediated decay, are depicted using a triangle.

Again, the size indicates the number of observations." data-icon-position data-hide-link-title="0">Figure 1 In this figure the posterior probability of an anterior phenotype is plotted against the location of the variant, stratified for the type of mutation that was observed. For better overview, only variants with a location effect were displayed. The full figure, including all variant types, can be found in the online supplementary figure 1.

Each mutation is depicted as a dot, the size of the dot represents the number of observations for that variant. If multiple observations were made, the mean posterior odds and IQR are plotted. For the nonsense variants, variants that were predicted to produce nonsense mediated decay, are depicted using a triangle.

Again, the size indicates the number of observations.Supplemental materialLatent class analysisTo cluster phenotypes and relate those to the genotypes of the patients, an explorative analysis was done using LCA in R (R V.3.6.1 for Mac. Polytomous variable LCA, poLCA V.1.4.1.). We used our LCA to detect the number of phenotypic subgroups in the dataset and subsequently predict a class membership for each case in the dataset based on the posterior probabilities.In order to make a reliable prediction, only phenotypes that were sufficiently reported and/or ruled out were feasible for LCA, limiting the analysis to preaxial polydactyly, postaxial polydactyly and syndactyly of the hands and feet.

Only full cases were included. To determine the optimal number of classes, we fitted a series of models ranging from a one-class to a six-class model. The optimal number of classes was based on the conditional Akaike information criterion (cAIC), the non adjusted and the sample-size adjusted Bayesian information criterion (BIC and aBIC) and the obtained entropy.25 The explorative LCA produces both posterior probabilities per case for both classes and predicted class membership.

Using the predicted class membership, the phenotypic features per class were determined in a univariate analysis (χ2, SPSS V.25). Using the posterior probabilities on latent class (LC) membership, a scatter plot was created using the location of the variant on the x-axis and the probability of class membership on the y-axis for each of the types of variants (Tibco Spotfire V.7.14). Using these scatter plots, variants that give similar phenotypes were clustered.Genotype/phenotype correlationBecause an LC has no clinical value, the correlation between genotypes and phenotypes was investigated using the predictor phenotypes and the clustered phenotypes.

First, those phenotypes that contribute most to LC membership were identified. Second those phenotypes were directly related to the different types of variants (missense, nonsense, frameshift, splice site) and their clustered locations. Quantification of the relation was performed using a univariate analysis using a χ2 test.

Because of our selection criteria, meaning patients at least have two phenotypes, a multivariate using a logistic regression analysis was used to detect the most significant predictors in the overall phenotype (SPSS V.25). Finally, we explored the relation of the clustered genotypes to the presence of corpus callosum agenesis, a rare malformation in GLI3-mediated polydactyly syndromes which cannot be readily diagnosed without additional imaging.ResultsWe included 251 patients from the literature and 46 local patients,10–12 16 21 26–43 in total 297 patients from 155 different families with 127 different GLI3 variants, 32 of which were large deletions, CNVs or translocations. In six local cases, the exact variant could not be retrieved by status research.The distribution of the most frequently observed phenotypes and variants are presented in table 1.

Other recurring phenotypes included developmental delay (n=22), broad nasal root (n=23), frontal bossing or prominent forehead (n=16) and craniosynostosis (n=13), camptodactyly (n=8) and a broad first interdigital webspace of the foot (n=6).View this table:Table 1 Baseline phenotypes and genotypes of selected populationThe LCA model was fitted using the six defined hand/foot phenotypes. Model fit indices for the LCA are displayed in table 2. Based on the BIC, a two-class model has the best fit for our data.

The four-class model does show a gain in entropy, however with a higher BIC and loss of df. Therefore, based on the majority of performance statistics and the interpretability of the model, a two-class model was chosen. Table 3 displays the distribution of phenotypes and genotypes over the two classes.View this table:Table 2 Model fit indices for the one-class through six-class model evaluated in our LCAView this table:Table 3 Distribution of phenotypes and genotypes in the two latent classes (LC)Table 1 depicts the baseline phenotypes and genotypes in the obtained population.

Note incomplete data especially in the cranium phenotypes. In total 259 valid genotypes were present. In total, 289 cases had complete data for all hand and foot phenotypes (preaxial polydactyly, postaxial polydactyly and syndactyly) and thus were available for LCA.

Combined, for phenotype/genotype correlation 258 cases were available with complete genotypes and complete hand and foot phenotypes.Table 2 depicts the model fit indices for all models that have been fitted to our data.Table 3 depicts the distribution of phenotypes and genotypes over the two assigned LCs. Hand and foot phenotypes were used as input for the LCA, thus are all complete cases. Malformation of the cranium and genotypes do have missing cases.

Note that for the LCA, full case description was required, resulting in eight cases due to incomplete phenotypes. Out of these eight, one also had a genotype that thus needed to be excluded. Missingness of genotypic data was higher in LC2, mostly due to CNVs (table 1).In 54/60 cases, a missense variant produced a posterior phenotype.

Likewise, splice site variants show the same phenotype in 23/24 cases (table 3). For both frameshift and nonsense variants, this relation is not significant (52 anterior vs 54 posterior and 26 anterior vs 42 posterior, respectively). Therefore, only for nonsense and frameshift variants the location of the variant was plotted against the probability for LC2 membership in figure 1.

A full scatterplot of all variants is available in online supplementary figure 1.Figure 1 reveals a pattern for these nonsense and frameshift variants that reveals that variants at the C-terminal of the gene predict anterior phenotypes. When relating the domains of the GLI3 protein to the observed phenotype, we observe that the majority of patients with a nonsense or frameshift variant in the repressor domain, the zinc finger domain or the cleavage site had a high probability of an LC2/anterior phenotype. This group contains all variants that are either experimentally determined to be subject to NMD (triangle marker in figure 1) or predicted to be subject to NMD (diamond marker in figure 1).

Frameshift and nonsense variants in the activator domain result in high probability for an LC1/posterior phenotype. These variants will be further referred to as truncating variants in the activator domain.The univariate relation of the individual phenotypes to these two groups of variants are estimated and presented in table 4. In our multivariate analysis, postaxial polydactyly of the foot and hand are the strongest predictors (Beta.

2.548, p<0001 and Beta. 1.47, p=0.013, respectively) for patients to have a truncating variant in the activator domain. Moreover, the effect sizes of preaxial polydactyly of the hand and feet (Beta.

ˆ’0.797, p=0123 and −1.772, p=0.001) reveals that especially postaxial polydactyly of the foot is the dominant predictor for the genetic substrate of the observed anomalies.View this table:Table 4 Univariate and multivariate analysis of the phenotype/genotype correlationTable 4 shows exploration of the individual phenotypes on the genotype, both univariate and multivariate. The multivariate analysis corrects for the presence of multiple phenotypes in the underlying population.Although the craniofacial anomalies could not be included in the LCA, the relation between the observed anomalies and the identified genetic substrates can be studied. The prevalence of hypertelorism was equally distributed over the two groups of variants (47/135 vs 21/47 respectively, p<0.229).

However for corpus callosum agenesis and macrocephaly, there was a higher prevalence in patients with a truncating variant in the activator domain (3/75 vs 11/41, p<0.001. OR. 8.8, p<0.001) and 42/123 vs 24/48, p<0.05).

Noteworthy is the fact that 11/14 cases with corpus callosum agenesis in the dataset had a truncating variant in the activator domain.DiscussionIn this report, we present new insights into the correlation between the phenotype and the genotype in patients with GLI3-mediated polydactyly syndromes. We illustrate that there are two LCs of patients, best predicted by postaxial polydactyly of the hand and foot for LC1, and the preaxial polydactyly of the hand and foot and syndactyly of the foot for LC2. Patients with postaxial phenotypes have a higher risk of having a truncating variant in the activator domain of the GLI3 gene which is also related to a higher risk of corpus callosum agenesis.

These results suggest a functional difference between truncating variants on the N-terminal and the C-terminal side of the GLI3 cleavage site.Previous attempts of phenotype to genotype correlation have not yet provided the clinical confirmation of these assumed mechanisms in the pathophysiology of GLI3-mediated polydactyly syndromes. Johnston et al have successfully determined the Pallister-Hall region in which truncating variants produce a Pallister-Hall phenotype rather than Greig syndrome.11 However, in their latest population study, subtypes of both syndromes were included to explain the full spectrum of observed malformations. In 2015, Demurger et al reported the higher incidence of corpus callosum agenesis in the Greig syndrome population with truncating mutations in the activator domain.12 Al-Qattan in his review summarises the concept of a spectrum of anomalies dependent on haplo-insufficiency (through different mechanisms) and repressor overexpression.13 However, he bases this theory mainly on reviewed experimental data.

Our report is the first to provide an extensive clinical review of cases that substantiate the phenotypic difference between the two groups that could fit the suggested mechanisms. We agree with Al-Qattan et al that a variation of anomalies can be observed given any pathogenic variant in the GLI3 gene, but overall two dominant phenotypes are present. A population with predominantly preaxial anomalies and one with postaxial anomalies.

The presence of preaxial or postaxial polydactyly and syndactyly is not mutually exclusive for one of these two subclasses. Meaning that preaxial polydactyly can co-occur with postaxial polydactyly. However, truncating mutations in the activator domain produce a postaxial phenotype, as can be derived from the risk in table 4.

The higher risk of corpus callosum agenesis in this population shows that differentiating between a preaxial phenotype and a postaxial phenotype, instead of between the different GLI3-mediated polydactyly syndromes, might be more relevant regarding diagnostics for corpus callosum agenesis.We chose to use LCA as an exploratory tool only in our population for two reasons. First of all, LCA can be useful to identify subgroups, but there is no ‘true’ model or number of subgroups you can detect. The best fitting model can only be estimated based on the available measures and approximates the true subgroups that might be present.

Second, LC membership assignment is a statistical procedure based on the posterior probability, with concordant errors of the estimation, rather than a clinical value that can be measured or evaluated. Therefore, we decided to use our LCA only in an exploratory tool, and perform our statistics using the actual phenotypes that predict LC membership and the associated genotypes. Overall, this method worked well to differentiate the two subgroups present in our dataset.

However, outliers were observed. A qualitative analysis of these outliers is available in the online supplementary data.The genetic substrate for the two phenotypic clusters can be discussed based on multiple experiments. Overall, we hypothesise two genetic clusters.

One that is due to haploinsufficiency and one that is due to abnormal truncation of the activator. The hypothesised cluster of variants that produce haploinsufficiency is mainly based on the experimental data that confirms NMD in two variants and the NMD prediction of other nonsense variants in Alamut. For the frameshift variants, it is also likely that the cleavage of the zinc finger domain results in functional haploinsufficiency either because of a lack of signalling domains or similarly due to NMD.

Missense variants could cause haploinsufficiency through the suggested mechanism by Krauss et al who have illustrated that missense variants in the MID1 domain hamper the functional interaction with the MID1-α4-PP2A complex, leading to a subcellular location of GLI3.24 The observed missense variants in our study exceed the region to which Krauss et al have limited the MID-1 interaction domain. An alternative theory is suggested by Zhou et al who have shown that missense variants in the MBD can cause deficiency in the signalling of GLI3A, functionally implicating a relative overexpression of GLI3R.22 However, GLI3R overexpression would likely produce a posterior phenotype, as determined by Hill et al in their fixed homo and hemizygous GLI3R models.15 Therefore, our hypothesis is that all included missense variants have a similar pathogenesis which is more likely in concordance with the mechanism introduced by Krauss et al. To our knowledge, no splice site variants have been functionally described in literature.

However, it is noted that the 15 and last exon encompasses the entire activator domain, thus any splice site mutation is by definition located on the 5′ side of the activator. Based on the phenotype, we would suggest that these variants fail to produce a functional protein. We hypothesise that the truncating variants of the activator domain lead to overexpression of GLI3R in SHH rich areas.

In normal development, the presence of SHH prevents the processing of full length GLI34 into GLI3R, thus producing the full length activator. In patients with a truncating variant of the activator domain of GLI3, thus these variants likely have the largest effect in SHH rich areas, such as the ZPA located at the posterior side of the hand/footplate. Moreover, the lack of posterior anomalies in the GLI3∆699/- mouse model (hemizygous fixed repressor model) compared with the GLI3∆699/∆699 mouse model (homozygous fixed repressor model), suggesting a dosage effect of GLI3R to be responsible for posterior hand anomalies.15 These findings are supported by Lewandowski et al, who show that the majority of the target genes in GLI signalling are regulated by GLI3R rather than GLI3A.44 Together, these findings suggest a role for the location and type of variant in GLI3-mediated syndromes.Interestingly, the difference between Pallister-Hall syndrome and GLI3-mediated polydactyly syndromes has also been attributed to the GLI3R overexpression.

However, the difference in phenotype observed in the cases with a truncating variant in the activator domain and Pallister-Hall syndrome suggest different functional consequences. When studying figure 1, it is noted that the included truncating variants on the 3′ side of the cleavage site seldomly affect the CBP binding region, which could provide an explanation for the observed differences. This binding region is included in the Pallister-Hall region as defined by Johnston et al and is necessary for the downstream signalling with GLI1.10 11 23 45 Interestingly, recent reports show that pathogenic variants in GLI1 can produce phenotypes concordant with Ellis von Krefeld syndrome, which includes overlapping features with Pallister-Hall syndrome.46 The four truncating variants observed in this study that do affect the CBP but did not result in a Pallister-Hall phenotype are conflicting with this theory.

Krauss et al postulate an alternative hypothesis, they state that the MID1-α4-PP2A complex, which is essential for GLI3A signalling, could also be the reason for overlapping features of Opitz syndrome, caused by variants in MID1, and Pallister-Hall syndrome. Further analysis is required to fully appreciate the functional differences between truncating mutations that cause Pallister-Hall syndrome and those that result in GLI3-mediated polydactyly syndromes.For the clinical evaluation of patients with GLI3-mediated polydactyly syndromes, intracranial anomalies are likely the most important to predict based on the variant. Unfortunately, the presence of corpus callosum agenesis was not routinely investigated or reported thus this feature could not be used as an indicator phenotype for LC membership.

Interestingly when using only hand and foot phenotypes, we did notice a higher prevalence of corpus callosum agenesis in patients with posterior phenotypes. The suggested relation between truncating mutations in the activator domain causing these posterior phenotypes and corpus callosum agenesis was statistically confirmed (OR. 8.8, p<0.001).

Functionally this relation could be caused by the GLI3-MED12 interaction at the MBD. Pathogenic DNA variants in MED12 can cause Opitz-Kaveggia syndrome, a syndrome in which presentation includes corpus callosum agenesis, broad halluces and thumbs.47In conclusion, there are two distinct phenotypes within the GLI3-mediated polydactyly population. Patients with more posteriorly and more anteriorly oriented hand anomalies.

Furthermore, this difference is related to the observed variant in GLI3. We hypothesise that variants that cause haploinsufficiency produce anterior anomalies of the hand, whereas variants with abnormal truncation of the activator domain have more posterior anomalies. Furthermore, patients that have a variant that produces abnormal truncation of the activator domain, have a greater risk for corpus callosum agenesis.

Thus, we advocate to differentiate preaxial or postaxial oriented GLI3 phenotypes to explain the pathophysiology as well as to get a risk assessment for corpus callosum agenesis.Data availability statementData are available upon reasonable request.Ethics statementsPatient consent for publicationNot required.Ethics approvalThe research protocol was approved by the local ethics board of the Erasmus MC University Medical Center (MEC 2015-679)..

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