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      Familial Non-autoimmune Hyperthyroidism in Family Members Across Four Generations Due To a Novel Disease-causing Variant in The Thyrotropin Receptor Gene

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          Abstract

          Activating disease-causing variants in the thyrotropin-receptor ( TSHR) gene are associated with familial or sporadic congenital non-autoimmune hyperthyroidism. Familial non-autoimmune hyperthyroidism (FNAH) is a rare form of hyperthyroidism with 41 families reported so far in the TSHR gene mutation database. We present clinical and genetic features of 11 patients with FNAH across four generations of a Slovenian family. They all developed clinical features of hyperthyroidism but did not show characteristics of autoimmune hyperthyroidism. Members of the initially diagnosed generation were diagnosed as hyperthyrotic after they developed cardiac complications (rhythm disorders, thromboembolic events, cardiac insufficiency), while patients in the younger generations were diagnosed earlier, and consequently, early cardiovascular complications were less frequent. All patients had a novel heterozygous TSHR variant NP_ 000360.2: p.Met453Val (NM_000369.2: c.1357A>G) predicted to be pathogenic. Therefore, besides expending the mutational spectrum of the activating TSHR variants in FNAH, our experience with this multi-generation family confirms the need for early diagnosis and appropriate treatment of FNAH.

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          Most cited references 24

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          Standards and Guidelines for the Interpretation of Sequence Variants: A Joint Consensus Recommendation of the American College of Medical Genetics and Genomics and the Association for Molecular Pathology

          The American College of Medical Genetics and Genomics (ACMG) previously developed guidance for the interpretation of sequence variants. 1 In the past decade, sequencing technology has evolved rapidly with the advent of high-throughput next generation sequencing. By adopting and leveraging next generation sequencing, clinical laboratories are now performing an ever increasing catalogue of genetic testing spanning genotyping, single genes, gene panels, exomes, genomes, transcriptomes and epigenetic assays for genetic disorders. By virtue of increased complexity, this paradigm shift in genetic testing has been accompanied by new challenges in sequence interpretation. In this context, the ACMG convened a workgroup in 2013 comprised of representatives from the ACMG, the Association for Molecular Pathology (AMP) and the College of American Pathologists (CAP) to revisit and revise the standards and guidelines for the interpretation of sequence variants. The group consisted of clinical laboratory directors and clinicians. This report represents expert opinion of the workgroup with input from ACMG, AMP and CAP stakeholders. These recommendations primarily apply to the breadth of genetic tests used in clinical laboratories including genotyping, single genes, panels, exomes and genomes. This report recommends the use of specific standard terminology: ‘pathogenic’, ‘likely pathogenic’, ‘uncertain significance’, ‘likely benign’, and ‘benign’ to describe variants identified in Mendelian disorders. Moreover, this recommendation describes a process for classification of variants into these five categories based on criteria using typical types of variant evidence (e.g. population data, computational data, functional data, segregation data, etc.). Because of the increased complexity of analysis and interpretation of clinical genetic testing described in this report, the ACMG strongly recommends that clinical molecular genetic testing should be performed in a CLIA-approved laboratory with results interpreted by a board-certified clinical molecular geneticist or molecular genetic pathologist or equivalent.
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            A method and server for predicting damaging missense mutations

            To the Editor: Applications of rapidly advancing sequencing technologies exacerbate the need to interpret individual sequence variants. Sequencing of phenotyped clinical subjects will soon become a method of choice in studies of the genetic causes of Mendelian and complex diseases. New exon capture techniques will direct sequencing efforts towards the most informative and easily interpretable protein-coding fraction of the genome. Thus, the demand for computational predictions of the impact of protein sequence variants will continue to grow. Here we present a new method and the corresponding software tool, PolyPhen-2 (http://genetics.bwh.harvard.edu/pph2/), which is different from the early tool PolyPhen1 in the set of predictive features, alignment pipeline, and the method of classification (Fig. 1a). PolyPhen-2 uses eight sequence-based and three structure-based predictive features (Supplementary Table 1) which were selected automatically by an iterative greedy algorithm (Supplementary Methods). Majority of these features involve comparison of a property of the wild-type (ancestral, normal) allele and the corresponding property of the mutant (derived, disease-causing) allele, which together define an amino acid replacement. Most informative features characterize how well the two human alleles fit into the pattern of amino acid replacements within the multiple sequence alignment of homologous proteins, how distant the protein harboring the first deviation from the human wild-type allele is from the human protein, and whether the mutant allele originated at a hypermutable site2. The alignment pipeline selects the set of homologous sequences for the analysis using a clustering algorithm and then constructs and refines their multiple alignment (Supplementary Fig. 1). The functional significance of an allele replacement is predicted from its individual features (Supplementary Figs. 2–4) by Naïve Bayes classifier (Supplementary Methods). We used two pairs of datasets to train and test PolyPhen-2. We compiled the first pair, HumDiv, from all 3,155 damaging alleles with known effects on the molecular function causing human Mendelian diseases, present in the UniProt database, together with 6,321 differences between human proteins and their closely related mammalian homologs, assumed to be non-damaging (Supplementary Methods). The second pair, HumVar3, consists of all the 13,032 human disease-causing mutations from UniProt, together with 8,946 human nsSNPs without annotated involvement in disease, which were treated as non-damaging. We found that PolyPhen-2 performance, as presented by its receiver operating characteristic curves, was consistently superior compared to PolyPhen (Fig. 1b) and it also compared favorably with the three other popular prediction tools4–6 (Fig. 1c). For a false positive rate of 20%, PolyPhen-2 achieves the rate of true positive predictions of 92% and 73% on HumDiv and HumVar, respectively (Supplementary Table 2). One reason for a lower accuracy of predictions on HumVar is that nsSNPs assumed to be non-damaging in HumVar contain a sizable fraction of mildly deleterious alleles. In contrast, most of amino acid replacements assumed non-damaging in HumDiv must be close to selective neutrality. Because alleles that are even mildly but unconditionally deleterious cannot be fixed in the evolving lineage, no method based on comparative sequence analysis is ideal for discriminating between drastically and mildly deleterious mutations, which are assigned to the opposite categories in HumVar. Another reason is that HumDiv uses an extra criterion to avoid possible erroneous annotations of damaging mutations. For a mutation, PolyPhen-2 calculates Naïve Bayes posterior probability that this mutation is damaging and reports estimates of false positive (the chance that the mutation is classified as damaging when it is in fact non-damaging) and true positive (the chance that the mutation is classified as damaging when it is indeed damaging) rates. A mutation is also appraised qualitatively, as benign, possibly damaging, or probably damaging (Supplementary Methods). The user can choose between HumDiv- and HumVar-trained PolyPhen-2. Diagnostics of Mendelian diseases requires distinguishing mutations with drastic effects from all the remaining human variation, including abundant mildly deleterious alleles. Thus, HumVar-trained PolyPhen-2 should be used for this task. In contrast, HumDiv-trained PolyPhen-2 should be used for evaluating rare alleles at loci potentially involved in complex phenotypes, dense mapping of regions identified by genome-wide association studies, and analysis of natural selection from sequence data, where even mildly deleterious alleles must be treated as damaging. Supplementary Material 1
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              CADD: predicting the deleteriousness of variants throughout the human genome

              Abstract Combined Annotation-Dependent Depletion (CADD) is a widely used measure of variant deleteriousness that can effectively prioritize causal variants in genetic analyses, particularly highly penetrant contributors to severe Mendelian disorders. CADD is an integrative annotation built from more than 60 genomic features, and can score human single nucleotide variants and short insertion and deletions anywhere in the reference assembly. CADD uses a machine learning model trained on a binary distinction between simulated de novo variants and variants that have arisen and become fixed in human populations since the split between humans and chimpanzees; the former are free of selective pressure and may thus include both neutral and deleterious alleles, while the latter are overwhelmingly neutral (or, at most, weakly deleterious) by virtue of having survived millions of years of purifying selection. Here we review the latest updates to CADD, including the most recent version, 1.4, which supports the human genome build GRCh38. We also present updates to our website that include simplified variant lookup, extended documentation, an Application Program Interface and improved mechanisms for integrating CADD scores into other tools or applications. CADD scores, software and documentation are available at https://cadd.gs.washington.edu.
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                Author and article information

                Journal
                Balkan J Med Genet
                bjmg
                bjmg
                Balkan Journal of Medical Genetics : BJMG
                Sciendo
                1311-0160
                2199-5761
                November 2020
                23 March 2021
                : 23
                : 2
                : 87-92
                Affiliations
                [1 ]Division of Internal Medicine, General Hospital Izola , Izola, Slovenia
                [2 ]Deparment of Pediatric Endocrinology, Diabetes and Metabolism, University Medical Centre Ljubljana, University Children´s Hospital , Ljubljana, Slovenia
                [3 ]Institute of Biochemistry and Molecular Genetics, University of Ljubljana, Faculty of Medicine , Ljubljana, Slovenia
                Author notes
                [* ] Associate Professor Katarina Trebušak Podkrajšek, Ph.D., Institute of Biochemistry and Molecular Genetics, Faculty of Medicine, University of Ljubljana, Slovenia. Tel: +386-1-543-7669. Fax: +386-1-543-7641. katarina.trebusakpodkrajsek@ 123456mf.uni-lj.si
                Article
                bjmg-2020-0022
                10.2478/bjmg-2020-0022
                8009563
                © 2020 Malej A, Avbelj Stefanija M, Bratanič N, Trebušak Podkrajšek K, published by Sciendo

                This work is licensed under the Creative Commons Attribution-NonCommercial-NoDerivatives 3.0 License.

                Page count
                Pages: 5
                Funding
                This study was supported by funding from the Slovenian Research Agency [research core funding P3-0343 and P1-0170].
                Categories
                Case Report

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