The genetic architecture of pediatric cardiomyopathy.

To understand the genetic contribution to primary pediatric cardiomyopathy, we performed exome sequencing in a large cohort of 528 children with cardiomyopathy. Using clinical interpretation guidelines and targeting genes implicated in cardiomyopathy, we identified a genetic cause in 32% of affected individuals. Cardiomyopathy sub-phenotypes differed by ancestry, age at diagnosis, and family history. Infants < 1 year were less likely to have a molecular diagnosis (p < 0.001). Using a discovery set of 1,703 candidate genes and informatic tools, we identified rare and damaging variants in 56% of affected individuals. We see an excess burden of damaging variants in affected individuals as compared to two independent control sets, 1000 Genomes Project (p < 0.001) and SPARK parental controls (p < 1 × 10-16). Cardiomyopathy variant burden remained enriched when stratified by ancestry, variant type, and sub-phenotype, emphasizing the importance of understanding the contribution of these factors to genetic architecture. Enrichment in this discovery candidate gene set suggests multigenic mechanisms underlie sub-phenotype-specific causes and presentations of cardiomyopathy. These results identify important information about the genetic architecture of pediatric cardiomyopathy and support recommendations for clinical genetic testing in children while illustrating differences in genetic architecture by age, ancestry, and sub-phenotype and providing rationale for larger studies to investigate multigenic contributions.

Splicing predictions, minigene analyses, and ACMG-AMP clinical classification of 42 germline PALB2 splice-site variants.

PALB2 loss-of-function variants confer high risk of developing breast cancer. Here we present a systematic functional analysis of PALB2 splice-site variants detected in approximately 113,000 women in the large-scale sequencing project Breast Cancer After Diagnostic Gene Sequencing (BRIDGES; https://bridges-research.eu/). Eighty-two PALB2 variants at the intron-exon boundaries were analyzed with MaxEntScan. Forty-two variants were selected for the subsequent splicing functional assays. For this purpose, three splicing reporter minigenes comprising exons 1-12 were constructed. The 42 potential spliceogenic variants were introduced into the minigenes by site-directed mutagenesis and assayed in MCF-7/MDA-MB-231 cells. Splicing anomalies were observed in 35 variants, 23 of which showed no traces or minimal amounts of the expected full-length transcripts of each minigene. More than 30 different variant-induced transcripts were characterized, 23 of which were predicted to truncate the PALB2 protein. The pathogenicity of all variants was interpreted according to an in-house adaptation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology (ACMG-AMP) variant classification scheme. Up to 23 variants were classified as pathogenic/likely pathogenic. Remarkably, three ±1,2 variants (c.49-2A>T, c.108+2T>C, and c.211+1G>A) were classified as variants of unknown significance, as they produced significant amounts of either in-frame transcripts of unknown impact on the PALB2 protein function or the minigene full-length transcripts. In conclusion, we have significantly contributed to the ongoing effort of identifying spliceogenic variants in the clinically relevant PALB2 cancer susceptibility gene. Moreover, we suggest some approaches to classify the findings in accordance with the ACMG-AMP rationale. © 2021 The Authors. The Journal of Pathology published by John Wiley & Sons, Ltd on behalf of The Pathological Society of Great Britain and Ireland.

Feasibility and outcome of reproducible clinical interpretation of high-dimensional molecular data: a comparison of two molecular tumor boards.

BACKGROUND: Structured and harmonized implementation of molecular tumor boards (MTB) for the clinical interpretation of molecular data presents a current challenge for precision oncology. Heterogeneity in the interpretation of molecular data was shown for patients even with a limited number of molecular alterations. Integration of high-dimensional molecular data, including RNA- (RNA-Seq) and whole-exome sequencing (WES), is expected to further complicate clinical application. To analyze challenges for MTB harmonization based on complex molecular datasets, we retrospectively compared clinical interpretation of WES and RNA-Seq data by two independent molecular tumor boards. METHODS: High-dimensional molecular cancer profiling including WES and RNA-Seq was performed for patients with advanced solid tumors, no available standard therapy, ECOG performance status of 0-1, and available fresh-frozen tissue within the DKTK-MASTER Program from 2016 to 2018. Identical molecular profiling data of 40 patients were independently discussed by two molecular tumor boards (MTB) after prior annotation by specialized physicians, following independent, but similar workflows. Identified biomarkers and resulting treatment options were compared between the MTBs and patients were followed up clinically. RESULTS: A median of 309 molecular aberrations from WES and RNA-Seq (n = 38) and 82 molecular aberrations from WES only (n = 3) were considered for clinical interpretation for 40 patients (one patient sequenced twice). A median of 3 and 2 targeted treatment options were identified per patient, respectively. Most treatment options were identified for receptor tyrosine kinase, PARP, and mTOR inhibitors, as well as immunotherapy. The mean overlap coefficient between both MTB was 66%. Highest agreement rates were observed with the interpretation of single nucleotide variants, clinical evidence levels 1 and 2, and monotherapy whereas the interpretation of gene expression changes, preclinical evidence levels 3 and 4, and combination therapy yielded lower agreement rates. Patients receiving treatment following concordant MTB recommendations had significantly longer overall survival than patients receiving treatment following discrepant recommendations or physician's choice. CONCLUSIONS: Reproducible clinical interpretation of high-dimensional molecular data is feasible and agreement rates are encouraging, when compared to previous reports. The interpretation of molecular aberrations beyond single nucleotide variants and preclinically validated biomarkers as well as combination therapies were identified as additional difficulties for ongoing harmonization efforts.

Consideration of the Cycle Threshold Values from Real-Time RT-PCR SARS-CoV-2 Interpretation for the Clinicians: Analysis of 339 Positive Cases from a Referral Laboratory in Jakarta, Indonesia.

BACKGROUND: real-time RT-PCR was recommended by WHO for COVID-19 diagnosis. The cycle threshold (Ct) values were expected to have an association with clinical manifestation. However, the diagnostic modalities such as quantitative molecular detection and virus isolation were not yet available for the routine test. This study has been conducted to analyze the relationship between the Ct values of qualitative rRT-PCR and the clinical manifestation and to describe the factors determining the result. METHODS: from March to April 2020, specimens were sent to our laboratory from different healthcare centers in Jakarta. The patient's characteristic and clinical manifestation were extracted from the specimen's epidemiology forms. The specimens extracted and tested using rRT-PCR, and the Ct value were collected. The data were analyzed using the appropriate statistic test. RESULTS: from 339 positive results, the mild to moderate case was 176 (52%) and the severe cases was 163 (48%). Female was dominant in the mild to moderate cases (58%), while the male was prevalent in the severe cases (60%). The median age for mild to moderate case was 35 years old and severe cases was 49 years old. Statistical analysis found relationship between both group with gender (p = 0.001) and age (p < 0.001), but not with the Ct value. CONCLUSION: many variables in specimen sampling and processing could affect the Ct value result. In addition, the disease's severity was depended with the host immune response, regardless the number of virus. There was suggested no significant difference between the Ct values of mild-moderate and severe COVID-19, and thus should not be loosely interpreted.

The Clinical Genome and Ancestry Report: An interactive web application for prioritizing clinically implicated variants from genome sequencing data with ancestry composition.

Genome sequencing is positioned as a routine clinical work-up for diverse clinical conditions. A commonly used approach to highlight candidate variants with potential clinical implication is to search over locus- and gene-centric knowledge databases. Most web-based applications allow a federated query across diverse databases for a single variant; however, sifting through a large number of genomic variants with combination of filtering criteria is a substantial challenge. Here we describe the Clinical Genome and Ancestry Report (CGAR), an interactive web application developed to follow clinical interpretation workflows by organizing variants into seven categories: (1) reported disease-associated variants, (2) rare- and high-impact variants in putative disease-associated genes, (3) secondary findings that the American College of Medical Genetics and Genomics recommends reporting back to patients, (4) actionable pharmacogenomic variants, (5) focused reports for candidate genes, (6) de novo variant candidates for trio analysis, and (7) germline and somatic variants implicated in cancer risk, diagnosis, treatment and prognosis. For each variant, a comprehensive list of external links to variant-centric and phenotype databases are provided. Furthermore, genotype-derived ancestral composition is used to highlight allele frequencies from a matched population since some disease-associated variants show a wide variation between populations. CGAR is an open-source software and is available at https://tom.tch.harvard.edu/apps/cgar/.

What the lab can and cannot do: clinical interpretation of drug testing results.

Urine drug testing is one of the objective tools available to assess adherence. To monitor adherence, quantitative urinary results can assist in differentiating "new" drug use from "previous" (historical) drug use. "Spikes" in urinary concentration can assist in identifying patterns of drug use. Coupled chromatographic-mass spectrometric methods are capable of identifying very small amounts of analyte and can make clinical interpretation rather challenging, specifically for drugs that have a longer half-life. Polypharmacy is common in treatment and rehabilitation programs because of co-morbidities. Medications prescribed for comorbidities can cause drug-drug interaction and phenoconversion of genotypic extensive metabolizers into phenotypic poor metabolizers of the treatment drug. This can have significant impact on both pharmacokinetic (PK) and pharmacodynamic properties of the treatment drug. Therapeutic drug monitoring (TDM) coupled with PKs can assist in interpreting the effects of phenoconversion. TDM-PKs reflects the cumulative effects of pathophysiological changes in the patient as well as drug-drug interactions and should be considered for treatment medications/drugs used to manage pain and treat substance abuse. Since only a few enzyme immunoassays for TDM are available, this is a unique opportunity for clinical laboratory scientists to develop TDM-PK protocols that can have a significant impact on patient care and personalized medicine. Interpretation of drug screening results should be done with caution while considering pharmacological properties and the presence or absence of the parent drug and its metabolites. The objective of this manuscript is to review and address the variables that influence interpretation of different drugs analyzed from a rehabilitation and treatment programs perspective.

InterVar: Clinical Interpretation of Genetic Variants by the 2015 ACMG-AMP Guidelines.

In 2015, the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published updated standards and guidelines for the clinical interpretation of sequence variants with respect to human diseases on the basis of 28 criteria. However, variability between individual interpreters can be extensive because of reasons such as the different understandings of these guidelines and the lack of standard algorithms for implementing them, yet computational tools for semi-automated variant interpretation are not available. To address these problems, we propose a suite of methods for implementing these criteria and have developed a tool called InterVar to help human reviewers interpret the clinical significance of variants. InterVar can take a pre-annotated or VCF file as input and generate automated interpretation on 18 criteria. Furthermore, we have developed a companion web server, wInterVar, to enable user-friendly variant interpretation with an automated interpretation step and a manual adjustment step. These tools are especially useful for addressing severe congenital or very early-onset developmental disorders with high penetrance. Using results from a few published sequencing studies, we demonstrate the utility of InterVar in significantly reducing the time to interpret the clinical significance of sequence variants.

Reduced penetrance of pathogenic ACMG variants in a deeply phenotyped cohort study and evaluation of ClinVar classification over time.

PURPOSE: We studied the penetrance of pathogenically classified variants in an elderly Dutch population from the Rotterdam Study, for which deep phenotyping is available. We screened the 59 actionable genes for which reporting of known pathogenic variants was recommended by the American College of Medical Genetics and Genomics (ACMG), and demonstrate that determining what constitutes a known pathogenic variant can be quite challenging. METHODS: We defined "known pathogenic" as classified pathogenic by both ClinVar and the Human Gene Mutation Database (HGMD). In 2628 individuals, we performed exome sequencing and identified known pathogenic variants. We investigated the clinical records of carriers and evaluated clinical events during 25 years of follow-up for evidence of variant pathogenicity. RESULTS: Of 3815 variants detected in the 59 ACMG genes, 17 variants were considered known pathogenic. For 14/17 variants the ClinVar classification had changed over time. Of 24 confirmed carriers of these variants, we observed at least one clinical event possibly caused by the variant in only three participants (13%). CONCLUSION: We show that the definition of "known pathogenic" is often unclear and should be approached carefully. Additionally variants marked as known pathogenic do not always have clinical impact on their carriers. Definition and classification of true (individual) expected pathogenic impact should be defined carefully.

Measurement uncertainty of ß-lactam antibiotics results: estimation and clinical impact on therapeutic drug monitoring.

Background Despite that measurement uncertainty data should facilitate an appropriate interpretation of measured values, there are actually few reported by clinical laboratories. We aimed to estimate the measurement uncertainty of some ß-lactam antibiotics (ß-LA), and to evaluate the impact of reporting the measurement uncertainty on clinicians' decisions while guiding antibiotic therapy. Methods Measurement uncertainty of ß-LA (aztreonam [ATM], cefepime [FEP], ceftazidime [CAZ], and piperacillin [PIP]) values, obtained by an UHPLC-MS/MS based-method, was estimated using the top-down approach called the single laboratory validation approach (EUROLAB guidelines). Main uncertainty sources considered were related to calibrators' assigned values, the intermediate precision, and the bias. As part of an institutional program, patients with osteoarticular infections are treated with ß-LA in continuous infusion and monitored to assure values at least 4 times over the minimal inhibitory concentration (4×MIC). We retrospectively evaluated the impact of two scenarios of laboratory reports on clinicians' expected decisions while monitoring the treatment: reports containing only the ß-LA values, or including the ß-LA coverage intervals (ß-LA values and their expanded measurement uncertainties). Results The relative expanded uncertainties for ATM, FEP, CAZ and PIP were lower than 26.7%, 26.4%, 28.8%, and 25.5%, respectively. Reporting the measurement uncertainty, we identified that clinicians may modify their decision especially in cases where 4×MIC values were within the ß-LA coverage intervals. Conclusions This study provides a simple method to estimate the measurement uncertainty of ß-LA values that can be easily applied in clinical laboratories. Further studies should confirm the potential impact of reporting measurement uncertainty on clinicians' decision-making while guiding antibiotic therapy.

Clinical relevance of HEp-2 indirect immunofluorescent patterns: the International Consensus on ANA patterns (ICAP) perspective.

The indirect immunofluorescence assay (IIFA) on HEp-2 cells is widely used for detection of antinuclear antibodies (ANA). The dichotomous outcome, negative or positive, is integrated in diagnostic and classification criteria for several systemic autoimmune diseases. However, the HEp-2 IIFA test has much more to offer: besides the titre or fluorescence intensity, it also provides fluorescence pattern(s). The latter include the nucleus and the cytoplasm of interphase cells as well as patterns associated with mitotic cells. The International Consensus on ANA Patterns (ICAP) initiative has previously reached consensus on the nomenclature and definitions of HEp-2 IIFA patterns. In the current paper, the ICAP consensus is presented on the clinical relevance of the 29 distinct HEp-2 IIFA patterns. This clinical relevance is primarily defined within the context of the suspected disease and includes recommendations for follow-up testing. The discussion includes how this information may benefit the clinicians in daily practice and how the knowledge can be used to further improve diagnostic and classification criteria.

Navigating the nuances of clinical sequence variant interpretation in Mendelian disease.

Understanding clinical genetic test results in the era of next-generation sequencing has become increasingly complex, necessitating clear and thorough guidelines for sequence variant interpretation. To meet this need the American College of Medical Genetics and Genomics (ACMG) and the Association for Molecular Pathology (AMP) published guidelines for a systematic approach for sequence variant interpretation in 2015. This framework is intended to be adaptable to any Mendelian condition, promoting transparency and consistency in variant interpretation, yet its comprehensive nature yields important challenges and caveats that end users must understand. In this review, we address some of these nuances and discuss the evolving efforts to refine and adapt this framework. We also consider the added complexity of distinguishing between variant-level interpretations and case-level conclusions, particularly in the context of the large gene panel approach to clinical diagnostics.

Disagreement in normative IGF-I levels may lead to different clinical interpretations and GH dose adjustments in GH deficiency.

INTRODUCTION AND BACKGROUND: Normative data for the iSYS IGF-I assay have been published both in the VARIETE cohort and by Bidlingmaier et al. OBJECTIVE: To investigate whether normative data of the VARIETE cohort lead to differences in Z-scores for total IGF-I and clinical interpretation compared to normative data of Bidlingmaier et al. DESIGN: We used total IGF-I values previously measured by the IDS-iSYS assay in 102 GH-deficient subjects before starting GH treatment and after 12 months of GH treatment. Z-scores were calculated for all samples by using the normative data of the VARIETE cohort and by the normative data reported by Bidlingmaier et al. RESULT: Before GH treatment, Z-scores calculated by using the normative data of the VARIETE cohort were significantly lower than those calculated by the normative data of Bidlingmaier et al: -2.40 (-4.52 to +1.31) (mean [range]) vs. -1.41 (-3.14 to +1.76); P < .001). After 12 months of GH treatment, again the Z-scores based on the normative data of the VARIETE cohort were significantly lower than those based on the normative data of Bidlingmaier et al: -0.65 (-4.32 to +2.79) vs 0.21 (-3.00 to +3.28); P < .001). CONCLUSION: IGF-I Z-scores in 102 GH-deficient subjects differed significantly when normative data from two different sources were used. In daily clinical practice, this would most likely have led to different clinical interpretations and GH dose adjustments.

Differences in levels of albumin, ALT, AST, γ-GT and creatinine in frail, moderately healthy and healthy elderly individuals.

BACKGROUND: Reference intervals are widely used as decision tools, providing the physician with information about whether the analyte values indicate ongoing disease process. Reference intervals are generally based on individuals without diagnosed diseases or use of medication, which often excludes elderly. The aim of the study was to assess levels of albumin, alanine aminotransferase (ALT), aspartate aminotransferase (AST), creatinine and γ-glutamyl transferase (γ-GT) in frail, moderately healthy and healthy elderly indivuduals. METHODS: Blood samples were collected from individuals >80 years old, nursing home residents, in the Elderly in Linköping Screening Assessment and Nordic Reference Interval Project, a total of 569 individuals. They were divided into three cohorts: frail, moderately healthy and healthy, depending on cognitive and physical function. Albumin, ALT, AST, creatinine and γ-GT were analyzed using routine methods. RESULTS: Linear regression predicted factors for 34% of the variance in albumin were activities of daily living (ADL), gender, stroke and cancer. ADLs, gender and weight explained 15% of changes in ALT. For AST levels, ADLs, cancer and analgesics explained 5% of changes. Kidney disease, gender, Mini Mental State Examination (MMSE) and chronic obstructive pulmonary disease explained 25% of the variation in creatinine levels and MMSE explained three per cent of γ-GT variation. CONCLUSIONS: Because a group of people are at the same age, they should not be assessed the same way. To interpret results of laboratory tests in elderly is a complex task, where reference intervals are one part, but far from the only one, to take into consideration.

Annotation of functional impact of voltage-gated sodium channel mutations.

Voltage-gated sodium channels are pore-forming transmembrane proteins that selectively allow sodium ions to flow across the plasma membrane according to the electro-chemical gradient thus mediating the rising phase of action potentials in excitable cells and playing key roles in physiological processes such as neurotransmission, skeletal muscle contraction, heart rhythm, and pain sensation. Genetic variations in the nine human genes encoding these channels are known to cause a large range of diseases affecting the nervous and cardiac systems. Understanding the molecular effect of genetic variations is critical for elucidating the pathologic mechanisms of known variations and in predicting the effect of newly discovered ones. To this end, we have created a Web-based tool, the Ion Channels Variants Portal, which compiles all variants characterized functionally in the human sodium channel genes. This portal describes 672 variants each associated with at least one molecular or clinical phenotypic impact, for a total of 4,658 observations extracted from 264 different research articles. These data were captured as structured annotations using standardized vocabularies and ontologies, such as the Gene Ontology and the Ion Channel ElectroPhysiology Ontology. All these data are available to the scientific community via neXtProt at https://www.nextprot.org/portals/navmut.

How concerns and experiences with medical malpractice affect dermatopathologists' perceptions of their diagnostic practices when interpreting cutaneous melanocytic lesions.

OBJECTIVE: We sought to identify characteristics associated with past malpractice lawsuits and how malpractice concerns may affect interpretive practices. METHODS: We surveyed 207 of 301 (68.8%) eligible dermatopathologists who interpret melanocytic skin lesions in 10 states. The survey assessed dermatopathologists' demographic and clinical practice characteristics, perceptions of how medical malpractice concerns could influence their interpretive practices, and past malpractice lawsuits. RESULTS: Of dermatopathologists, 33% reported past malpractice experiences. Factors associated with being sued included older age (57 vs 48 years, P < .001), lack of board certification or fellowship training in dermatopathology (76.5% vs 53.2%, P = .001), and greater number of years interpreting melanocytic lesions (>20 years: 52.9% vs 20.1%, P < .001). Of participants, 64% reported being moderately or extremely confident in their melanocytic interpretations. Although most dermatopathologists believed that malpractice concerns increased their likelihood of ordering specialized pathology tests, obtaining recuts, and seeking a second opinion, none of these practices were associated with past malpractice. Most dermatopathologists reported concerns about potential harms to patients that may result from their assessments of melanocytic lesions. LIMITATIONS: Limitations of this study include lack of validation of and details about the malpractice suits experienced by participating dermatopathologists. In addition, the study assessed perceptions of practice rather than actual practices that might be associated with malpractice incidents. CONCLUSIONS: Most dermatopathologists reported apprehension about how malpractice affects their clinical practice and are concerned about patient safety irrespective of whether they had actually experienced a medical malpractice suit.

Variability in meta-analysis estimates of continuous outcomes using different standardization and scale-specific re-expression methods.

OBJECTIVES: To explore the impact of using different data standardization and scale-specific re-expression methods (i.e., processes to convert standardized data into scale-specific units) in meta-analyses using standardized mean differences (SMDs). STUDY DESIGN AND SETTING: We used data assessed by the Short Physical Performance Battery and the Barthel Index from a meta-analysis of randomized controlled trials which synthesized evidence of physical activity effectiveness on the functional capacity of hospitalized older adults. We standardized the data using study-specific pooled standard deviations (SDs), an internal, and an external SD references. Bayesian meta-analyses were performed for each method to compare the posterior distributions of the meta-analysis parameters. Posterior estimates were re-expressed into scale-specific units applying different methods established in the Cochrane guidelines. RESULTS: Meta-analysis estimates depend on the used standardization method. Analyses including data standardized using the largest SD reference presented lower estimates with less uncertainty in both scales. The method applied for re-expressing SMDs into scale-specific units impacted in their posterior clinical interpretation. The most similar results across models were obtained when using the same SD reference to standardize and re-express data. CONCLUSION: Different data standardization methods yielded different meta-analysis estimates on the SMD scale. To avoid the introduction of bias, the use of a single scale-specific SD reference to standardize data is recommended and instead of study-specific pooled sample SDs. Meta-analysis software packages may therefore change their default methods to allow this method by a single scale-specific SD. To re-express the SMDs into scale-specific units, we suggest the application of the same SD reference that was used for data standardization.

Genetic constraint at single amino acid resolution in protein domains improves missense variant prioritisation and gene discovery.

BACKGROUND: One of the major hurdles in clinical genetics is interpreting the clinical consequences associated with germline missense variants in humans. Recent significant advances have leveraged natural variation observed in large-scale human populations to uncover genes or genomic regions that show a depletion of natural variation, indicative of selection pressure. We refer to this as "genetic constraint". Although existing genetic constraint metrics have been demonstrated to be successful in prioritising genes or genomic regions associated with diseases, their spatial resolution is limited in distinguishing pathogenic variants from benign variants within genes. METHODS: We aim to identify missense variants that are significantly depleted in the general human population. Given the size of currently available human populations with exome or genome sequencing data, it is not possible to directly detect depletion of individual missense variants, since the average expected number of observations of a variant at most positions is less than one. We instead focus on protein domains, grouping homologous variants with similar functional impacts to examine the depletion of natural variations within these comparable sets. To accomplish this, we develop the Homologous Missense Constraint (HMC) score. We utilise the Genome Aggregation Database (gnomAD) 125 K exome sequencing data and evaluate genetic constraint at quasi amino-acid resolution by combining signals across protein homologues. RESULTS: We identify one million possible missense variants under strong negative selection within protein domains. Though our approach annotates only protein domains, it nonetheless allows us to assess 22% of the exome confidently. It precisely distinguishes pathogenic variants from benign variants for both early-onset and adult-onset disorders. It outperforms existing constraint metrics and pathogenicity meta-predictors in prioritising de novo mutations from probands with developmental disorders (DD). It is also methodologically independent of these, adding power to predict variant pathogenicity when used in combination. We demonstrate utility for gene discovery by identifying seven genes newly significantly associated with DD that could act through an altered-function mechanism. CONCLUSIONS: Grouping variants of comparable functional impacts is effective in evaluating their genetic constraint. HMC is a novel and accurate predictor of missense consequence for improved variant interpretation.

The European biological variation study (EuBIVAS): Biological variation data for testosterone, follicle stimulating hormone, prolactin, luteinizing hormone and dehydroepiandrosterone sulfate in men.

BACKGROUND: Knowledge of biological variation (BV) of hormones is essential for interpretation of laboratory tests and for diagnostics of endocrinological and reproductive diseases. There is a lack of robust BV data for many hormones in men. METHODS: We used serum samples collected weekly over 10 weeks from the European Biological Variation Study (EuBIVAS) to determine BV of testosterone, follicle-stimulating hormone (FSH), prolactin, luteinizing hormone (LH) and dehydroepiandrosterone sulfate (DHEA-S) in 38 men. We derived within-subject (CVI) and between-subject (CVG) BV estimates by CV-ANOVA after trend, outlier, and homogeneity analysis and calculated reference change values, index of individuality (II), and analytical performance specifications. RESULTS: The CVI estimates were 10 % for testosterone, 8 % for FSH, 13 % for prolactin, 22 % for LH, and 9 % for DHEA-S, respectively. The IIs ranged between 0.14 for FSH to 0.66 for LH, indicating high individuality. CONCLUSIONS: In this study, we have used samples from the highly powered EuBIVAS study to derive BV estimates for testosterone, FSH, prolactin, LH and DHEA-S in men. Our data confirm previously published BV estimates of testosterone, FSH and LH. For prolactin and DHEA-S BV data for men are reported for the first time.

Structural and functional prediction, evaluation, and validation in the post-sequencing era.

The surge of genome sequencing data has underlined substantial genetic variants of uncertain significance (VUS). The decryption of VUS discovered by sequencing poses a major challenge in the post-sequencing era. Although experimental assays have progressed in classifying VUS, only a tiny fraction of the human genes have been explored experimentally. Thus, it is urgently needed to generate state-of-the-art functional predictors of VUS in silico. Artificial intelligence (AI) is an invaluable tool to assist in the identification of VUS with high efficiency and accuracy. An increasing number of studies indicate that AI has brought an exciting acceleration in the interpretation of VUS, and our group has already used AI to develop protein structure-based prediction models. In this review, we provide an overview of the previous research on AI-based prediction of missense variants, and elucidate the challenges and opportunities for protein structure-based variant prediction in the post-sequencing era.