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      Diagnostic yield of a multigene sequencing approach in children classified as idiopathic short stature

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          Abstract

          Objective

          Most children with short stature remain without an etiologic diagnosis after extensive clinical and laboratory evaluation and are classified as idiopathic short stature (ISS). This study aimed to determine the diagnostic yield of a multigene analysis in children classified as ISS.

          Design and methods

          We selected 102 children with ISS and performed the genetic analysis as part of the initial investigation. We developed customized targeted panel sequencing, including all genes already implicated in the isolated short-stature phenotype. Rare and deleterious single nucleotide or copy number variants were assessed by bioinformatic tools.

          Results

          We identified 20 heterozygous pathogenic (P) or likely pathogenic (LP) genetic variants in 17 of 102 patients (diagnostic yield = 16.7%). Three patients had more than one P/LP genetic alteration. Most of the findings were in genes associated with the growth plate differentiation: IHH ( n  = 4), SHOX ( n  = 3), FGFR3 ( n  = 2), NPR2 ( n  = 2), ACAN ( n  = 2), and COL2A1 ( n  = 1) or involved in the RAS/MAPK pathway: NF1 ( n  = 2), PTPN11 ( n  = 1), CBL ( n  = 1), and BRAF ( n  = 1). None of these patients had clinical findings to guide a candidate gene approach. The diagnostic yield was higher among children with severe short stature (35% vs 12.2% for height SDS ≤ or > −3; P = 0.034). The genetic diagnosis had an impact on clinical management for four children.

          Conclusion

          A multigene sequencing approach can determine the genetic etiology of short stature in up to one in six children with ISS, removing the term idiopathic from their clinical classification.

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          Most cited references38

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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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            The mutational constraint spectrum quantified from variation in 141,456 humans

            Genetic variants that inactivate protein-coding genes are a powerful source of information about the phenotypic consequences of gene disruption: genes that are crucial for the function of an organism will be depleted of such variants in natural populations, whereas non-essential genes will tolerate their accumulation. However, predicted loss-of-function variants are enriched for annotation errors, and tend to be found at extremely low frequencies, so their analysis requires careful variant annotation and very large sample sizes 1 . Here we describe the aggregation of 125,748 exomes and 15,708 genomes from human sequencing studies into the Genome Aggregation Database (gnomAD). We identify 443,769 high-confidence predicted loss-of-function variants in this cohort after filtering for artefacts caused by sequencing and annotation errors. Using an improved model of human mutation rates, we classify human protein-coding genes along a spectrum that represents tolerance to inactivation, validate this classification using data from model organisms and engineered human cells, and show that it can be used to improve the power of gene discovery for both common and rare diseases.
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              Meta-analysis of genome-wide association studies for height and body mass index in ∼700000 individuals of European ancestry

              Recent genome-wide association studies (GWAS) of height and body mass index (BMI) in ∼250000 European participants have led to the discovery of ∼700 and ∼100 nearly independent single nucleotide polymorphisms (SNPs) associated with these traits, respectively. Here we combine summary statistics from those two studies with GWAS of height and BMI performed in ∼450000 UK Biobank participants of European ancestry. Overall, our combined GWAS meta-analysis reaches N ∼700000 individuals and substantially increases the number of GWAS signals associated with these traits. We identified 3290 and 941 near-independent SNPs associated with height and BMI, respectively (at a revised genome-wide significance threshold of P < 1 × 10-8), including 1185 height-associated SNPs and 751 BMI-associated SNPs located within loci not previously identified by these two GWAS. The near-independent genome-wide significant SNPs explain ∼24.6% of the variance of height and ∼6.0% of the variance of BMI in an independent sample from the Health and Retirement Study (HRS). Correlations between polygenic scores based upon these SNPs with actual height and BMI in HRS participants were ∼0.44 and ∼0.22, respectively. From analyses of integrating GWAS and expression quantitative trait loci (eQTL) data by summary-data-based Mendelian randomization, we identified an enrichment of eQTLs among lead height and BMI signals, prioritizing 610 and 138 genes, respectively. Our study demonstrates that, as previously predicted, increasing GWAS sample sizes continues to deliver, by the discovery of new loci, increasing prediction accuracy and providing additional data to achieve deeper insight into complex trait biology. All summary statistics are made available for follow-up studies.

                Author and article information

                Journal
                Endocr Connect
                Endocr Connect
                EC
                Endocrine Connections
                Bioscientifica Ltd (Bristol )
                2049-3614
                12 October 2022
                01 December 2022
                : 11
                : 12
                : e220214
                Affiliations
                [1 ]Unidade de Endocrinologia Genetica (LIM 25) , Hospital das Clínicas da Faculdade de Medicina, Universidade de São Paulo (USP), Sao Paulo, Brasil
                [2 ]Unidade de Endocrinologia do Desenvolvimento , Laboratorio de Hormonios e Genetica Molecular (LIM42), Hospital das Clinicas da Faculdade de Medicina, Universidade de Sao Paulo (USP), Sao Paulo, Brasil
                [3 ]Departamento de Pediatria , Faculdade de Ciencias Medicas da Santa Casa de Sao Paulo, Sao Paulo, Brasil
                [4 ]Disciplina de Endocrinologia , Departamento de Medicina Interna, Faculdade de Ciências Medicas, Universidade do Estado do Rio de Janeiro, Rio de Janeiro, Brasil
                [5 ]Serviço de Endocrinologia , Unidade de Crescimento, Santa Casa de Belo Horizonte, Belo Horizonte, Minas Gerais, Brasil
                [6 ]Departamento de Medicina , Faculdade de Ciencias Medicas da Santa Casa de Sao Paulo, Sao Paulo, Brasil
                [7 ]Serviço de Endocrinologia do Instituto de Puericultura e Pediatria Martagao Gesteira/Universidade Federal do Rio de Janeiro , Rio de Janeiro, Brasil
                [8 ]Centro de Pesquisa em Genoma Humano e Células-Tronco , Departamento de Genética e Biologia Evolutiva, Instituto de Biociências, Universidade de Sao Paulo, São Paulo, Brasil
                [9 ]Division of Metabolism , Endocrinology and Diabetes, Department of Internal Medicine, University of Michigan, Ann Arbor, Michigan, USA
                Author notes
                Correspondence should be addressed to A A L Jorge: alexj@ 123456usp.br
                Author information
                http://orcid.org/0000-0002-1628-7881
                http://orcid.org/0000-0002-7207-5576
                http://orcid.org/0000-0003-2567-7360
                Article
                EC-22-0214
                10.1530/EC-22-0214
                9716379
                36373817
                435e0eb4-a132-4cab-82ba-c3b29040b017
                © The authors

                This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.

                History
                : 04 October 2022
                : 12 October 2022
                Categories
                Research

                idiopathic short stature,multigene sequencing analysis,genetic,mutation

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