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      Congenital Hypogonadotropic Hypogonadism with Anosmia and Gorlin Features Caused by a PTCH1 Mutation Reveals a New Candidate Gene for Kallmann Syndrome

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          Background: Two loci (CHD7 and SOX10) underlying Kallmann syndrome (KS) were discovered through clinical and genetic analysis of CHARGE and Waardenburg syndromes, conditions that include congenital anosmia caused by olfactory bulb (CA/OBs) defects and congenital hypogonadotropic hypogonadism (CHH). We hypothesized that other candidate genes for KS could be discovered by analyzing rare syndromes presenting with these signs. Study Design, Size, Duration: We first investigated a family with Gorlin-Goltz syndrome (GGS) in which affected members exhibited clinical signs suggesting KS. Participants/Materials, Methods: Proband and family members underwent detailed clinical assessment. The proband received detailed neuroendocrine evaluation. Genetic analyses included sequencing the PTCH1 gene at diagnosis, followed by exome analyses of causative or candidate KS/CHH genes, in order to exclude contribution to the phenotypes of additional mutations. Exome analyses in additional 124 patients with KS/CHH probands with no additional GGS signs. Results: The proband exhibited CA, absent OBs on magnetic resonance imaging, and had CHH with unilateral cryptorchidism, consistent with KS. Pulsatile Gonadotropin-releasing hormone (GnRH) therapy normalized serum gonadotropins and increased testosterone levels, supporting GnRH deficiency. Genetic studies revealed 3 affected family members harbor a novel mutation of PTCH1 (c.838G> T; p.Glu280*). This unreported nonsense deleterious mutation results in either a putative truncated Ptch1 protein or in an absence of translated Ptch1 protein related to nonsense mediated messenger RNA decay. This heterozygous mutation cosegregates in the pedigree with GGS and CA with OBs aplasia/hypoplasia and with CHH in the proband suggesting a genetic linkage and an autosomal dominant mode of inheritance. No pathogenic rare variants in other KS/CHH genes cosegregated with these phenotypes. In additional 124 KS/CHH patients, 3 additional heterozygous, rare missense variants were found and predicted in silico to be damaging: p.Ser1203Arg, p.Arg1192Ser, and p.Ile108Met. Conclusion: This family suggests that the 2 main signs of KS can be included in GGS associated with PTCH1 mutations. Our data combined with mice models suggest that PTCH1 could be a novel candidate gene for KS/CHH and reinforce the role of the Hedgehog signaling pathway in pathophysiology of KS and GnRH neuron migration.

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          Most cited references 84

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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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              SIFT: Predicting amino acid changes that affect protein function.

               P C Ng (2003)
              Single nucleotide polymorphism (SNP) studies and random mutagenesis projects identify amino acid substitutions in protein-coding regions. Each substitution has the potential to affect protein function. SIFT (Sorting Intolerant From Tolerant) is a program that predicts whether an amino acid substitution affects protein function so that users can prioritize substitutions for further study. We have shown that SIFT can distinguish between functionally neutral and deleterious amino acid changes in mutagenesis studies and on human polymorphisms. SIFT is available at http://blocks.fhcrc.org/sift/SIFT.html.

                Author and article information

                S. Karger AG
                December 2020
                20 February 2020
                : 111
                : 1-2
                : 99-114
                aDepartment of Endocrinology, Reims University Hospital, Reims, France
                bUniversity of Reims Champagne-Ardenne, Reims, France
                cDepartement of Genetic, Reims University Hospital, Reims, France
                dDepartment of Molecular Genetics, Pharmacogenomics, and Hormonology, Assistance Publique-Hôpitaux de Paris, Hôpital Bicêtre, Le Kremlin-Bicêtre, France
                eDepartment of Otolaryngology, Reims University Hospital, Reims, France
                fDepartment of Dermatology, Reims University Hospital, Reims, France
                gDepartment of Neuroradiology, Reims University Hospital, Reims, France
                hBoston College, William F. Connell School of Nursing, Chestnut Hill, Massachusetts, USA
                iService of Endocrinology, Diabetology and Metabolism, Lausanne University Hospital, Lausanne, Switzerland
                jCentre for Neuroendocrinology, Department of Immunology, Faculty of Health Sciences, University of Pretoria, Pretoria, South Africa
                kInstitute for Infectious Diseases and Molecular Medicine, University of Cape Town, Cape Town, South Africa
                lUniversity Paris-Saclay, Le Kremlin-Bicêtre, France
                mDepartment of Reproductive Endocrinology, Assistance Publique-Hôpitaux de Paris, Bicêtre Hospital, Le Kremlin-Bicêtre, France
                nINSERM U1185, Paris Saclay Medical School, Le Kremlin-Bicêtre, France
                Author notes
                *Prof. Jacques Young, Department of Reproductive Endocrinology, Bicêtre Hospital, 78, rue du General Leclerc, FR–94275 Le Kremlin-Bicetre (France), jacques.young@aphp.fr
                506640 Neuroendocrinology 2021;111:99–114
                © 2020 S. Karger AG, Basel

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                Page count
                Figures: 4, Tables: 1, Pages: 16
                Research Article


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