1
|
Mew M, Caldwell KA, Caldwell GA. From bugs to bedside: functional annotation of human genetic variation for neurological disorders using invertebrate models. Hum Mol Genet 2022; 31:R37-R46. [PMID: 35994032 PMCID: PMC9585664 DOI: 10.1093/hmg/ddac203] [Citation(s) in RCA: 6] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2022] [Revised: 08/11/2022] [Accepted: 08/17/2022] [Indexed: 02/02/2023] Open
Abstract
The exponential accumulation of DNA sequencing data has opened new avenues for discovering the causative roles of single-nucleotide polymorphisms (SNPs) in neurological diseases. The opportunities emerging from this are staggering, yet only as good as our abilities to glean insights from this surplus of information. Whereas computational biology continues to improve with respect to predictions and molecular modeling, the differences between in silico and in vivo analysis remain substantial. Invertebrate in vivo model systems represent technically advanced, experimentally mature, high-throughput, efficient and cost-effective resources for investigating a disease. With a decades-long track record of enabling investigators to discern function from DNA, fly (Drosophila) and worm (Caenorhabditis elegans) models have never been better poised to serve as living engines of discovery. Both of these animals have already proven useful in the classification of genetic variants as either pathogenic or benign across a range of neurodevelopmental and neurodegenerative disorders-including autism spectrum disorders, ciliopathies, amyotrophic lateral sclerosis, Alzheimer's and Parkinson's disease. Pathogenic SNPs typically display distinctive phenotypes in functional assays when compared with null alleles and frequently lead to protein products with gain-of-function or partial loss-of-function properties that contribute to neurological disease pathogenesis. The utility of invertebrates is logically limited by overt differences in anatomical and physiological characteristics, and also the evolutionary distance in genome structure. Nevertheless, functional annotation of disease-SNPs using invertebrate models can expedite the process of assigning cellular and organismal consequences to mutations, ascertain insights into mechanisms of action, and accelerate therapeutic target discovery and drug development for neurological conditions.
Collapse
Affiliation(s)
- Melanie Mew
- Department of Biological Sciences, The University of Alabama, Tuscaloosa, AL 35487, USA
| | - Kim A Caldwell
- Department of Biological Sciences, The University of Alabama, Tuscaloosa, AL 35487, USA
- Alabama Research Institute on Aging, The University of Alabama, Tuscaloosa, AL 35487, USA
- Center for Convergent Bioscience and Medicine, The University of Alabama, Tuscaloosa, AL 35487, USA
- Departments of Neurobiology and Neurology, Center for Neurodegeneration and Experimental Therapeutics, Nathan Shock Center of Excellence for Research in the Basic Biology of Aging, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Guy A Caldwell
- Department of Biological Sciences, The University of Alabama, Tuscaloosa, AL 35487, USA
- Center for Convergent Bioscience and Medicine, The University of Alabama, Tuscaloosa, AL 35487, USA
- Departments of Neurobiology and Neurology, Center for Neurodegeneration and Experimental Therapeutics, Nathan Shock Center of Excellence for Research in the Basic Biology of Aging, Heersink School of Medicine, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| |
Collapse
|
2
|
Mintoff D, Pace NP, Borg I. Interpreting the spectrum of gamma-secretase complex missense variation in the context of hidradenitis suppurativa—An in-silico study. Front Genet 2022; 13:962449. [PMID: 36118898 PMCID: PMC9478468 DOI: 10.3389/fgene.2022.962449] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2022] [Accepted: 08/08/2022] [Indexed: 11/23/2022] Open
Abstract
Hidradenitis suppurativa (HS) is a disease of the pilosebaceous unit characterized by recurrent nodules, abscesses and draining tunnels with a predilection to intertriginous skin. The pathophysiology of HS is complex. However, it is known that inflammation and hyperkeratinization at the hair follicle play crucial roles in disease manifestation. Genetic and environmental factors are considered the main drivers of these two pathophysiological processes. Despite a considerable proportion of patients having a positive family history of disease, only a minority of patients suffering from HS have been found to harbor monogenic variants which segregate to affected kindreds. Most of these variants are in the ɣ secretase complex (GSC) protein-coding genes. In this manuscript, we set out to characterize the burden of missense pathogenic variants in healthy reference population using large scale genomic dataset thereby providing a standard for comparing genomic variation in GSC protein-coding genes in the HS patient cohort.
Collapse
Affiliation(s)
- Dillon Mintoff
- Department of Pathology, Faculty of Medicine and Surgery, University of Malta, Msida, Malta
- Centre for Molecular Medicine and Biobanking, University of Malta, Msida, Malta
| | - Nikolai P. Pace
- Centre for Molecular Biology and Biobanking, University of Malta, Msida, Malta
- Department of Anatomy, Faculty of Medicine and Surgery, University of Malta, Msida, Malta
- *Correspondence: Nikolai P. Pace,
| | - Isabella Borg
- Department of Pathology, Faculty of Medicine and Surgery, University of Malta, Msida, Malta
- Centre for Molecular Biology and Biobanking, University of Malta, Msida, Malta
- Department of Pathology, Mater Dei Hospital, Msida, Malta
| |
Collapse
|
3
|
Buie RW, Rañola JM, Chen AT, Shirts BH. An Algorithm for Optimal Testing in Co‐segregation Analysis. Hum Mutat 2022; 43:547-556. [PMID: 35225377 PMCID: PMC9018554 DOI: 10.1002/humu.24363] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/13/2021] [Revised: 01/29/2022] [Accepted: 02/23/2022] [Indexed: 11/07/2022]
Abstract
Clinical genetic sequencing tests often identify variants of uncertain significance. One source of data that can help classify the pathogenicity of variants is familial cosegregation analysis. Identifying and genotyping relatives for cosegregation analysis can be time consuming and costly. We propose an algorithm that describes a single measure of expected variant information gain from genotyping a single additional relative in a family. Then we explore the performance of this algorithm by comparing actual recruitment strategies used in 35 families who had pursued cosegregation analysis with synthetic pedigrees of possible testing outcomes if the families had pursued an optimized testing strategy instead. For each actual and synthetic pedigree, we calculated the likelihood ratio of pathogenicity as each successive test was added to the pedigree. We analyzed the differences in cosegregation likelihood ratio over time resulting from actual versus optimized testing approaches. Employing the testing strategy indicated by the algorithm would have led to maximal information more rapidly in 30 of the 35 pedigrees (86%). Many clinical and research laboratories are involved in targeted cosegregation analysis. The algorithm we present can facilitate a data driven approach to optimal relative recruitment and genotyping for cosegregation analysis and more efficient variant classification.
Collapse
Affiliation(s)
- Ronald W. Buie
- Department of Bioinformatics and Medical Education, School of Medicine University of Washington Seattle WA USA
| | | | - Annie T. Chen
- Department of Bioinformatics and Medical Education, School of Medicine University of Washington Seattle WA USA
| | - Brian H. Shirts
- Department of Laboratory Medicine and Pathology, School of Medicine University of Washington Seattle WA USA
| |
Collapse
|
4
|
Saturation variant interpretation using CRISPR prime editing. Nat Biotechnol 2022; 40:885-895. [DOI: 10.1038/s41587-021-01201-1] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/18/2021] [Accepted: 12/20/2021] [Indexed: 12/13/2022]
|
5
|
Barbon L, Offord V, Radford EJ, Butler AP, Gerety SS, Adams DJ, Tan HK, Waters AJ. Variant Library Annotation Tool (VaLiAnT): an oligonucleotide library design and annotation tool for saturation genome editing and other deep mutational scanning experiments. Bioinformatics 2022; 38:892-899. [PMID: 34791067 PMCID: PMC8796380 DOI: 10.1093/bioinformatics/btab776] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/05/2021] [Revised: 07/13/2021] [Accepted: 11/10/2021] [Indexed: 02/04/2023] Open
Abstract
MOTIVATION CRISPR/Cas9-based technology allows for the functional analysis of genetic variants at single nucleotide resolution whilst maintaining genomic context. This approach, known as saturation genome editing (SGE), a form of deep mutational scanning, systematically alters each position in a target region to explore its function. SGE experiments require the design and synthesis of oligonucleotide variant libraries which are introduced into the genome. This technology is applicable to diverse fields such as disease variant identification, drug development, structure-function studies, synthetic biology, evolutionary genetics and host-pathogen interactions. Here, we present the Variant Library Annotation Tool (VaLiAnT) which can be used to generate variant libraries from user-defined genomic coordinates and standard input files. The software can accommodate user-specified species, reference sequences and transcript annotations. RESULTS Coordinates for a genomic range are provided by the user to retrieve a corresponding oligonucleotide reference sequence. A user-specified range within this sequence is then subject to systematic, nucleotide and/or amino acid saturating mutator functions. VaLiAnT provides a novel way to retrieve, mutate and annotate genomic sequences for oligonucleotide library generation. Specific features for SGE library generation can be employed. In addition, VaLiAnT is configurable, allowing for cDNA and prime editing saturation library generation, with other diverse applications possible. AVAILABILITY AND IMPLEMENTATION VaLiAnT is a command line tool written in Python. Source code, testing data, example input and output files and executables are available (https://github.com/cancerit/VaLiAnT) in addition to a detailed user manual (https://github.com/cancerit/VaLiAnT/wiki). VaLiAnT is licensed under AGPLv3. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Luca Barbon
- Cancer, Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Hinxton, Cambridge, CB10 1SA, UK
| | - Victoria Offord
- Cancer, Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Hinxton, Cambridge, CB10 1SA, UK
| | - Elizabeth J Radford
- Human Genetics Programme, Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
- Department of Paediatrics, University of Cambridge, Cambridge CB2 0QQ, UK
| | - Adam P Butler
- Cancer, Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Hinxton, Cambridge, CB10 1SA, UK
| | - Sebastian S Gerety
- Human Genetics Programme, Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
| | - David J Adams
- Cancer, Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Hinxton, Cambridge, CB10 1SA, UK
| | - Hong Kee Tan
- Human Genetics Programme, Wellcome Sanger Institute, Hinxton, Cambridge CB10 1SA, UK
| | - Andrew J Waters
- Cancer, Ageing and Somatic Mutation Programme, Wellcome Sanger Institute, Hinxton, Cambridge, CB10 1SA, UK
| |
Collapse
|
6
|
Sadikovic B, Levy MA, Kerkhof J, Aref-Eshghi E, Schenkel L, Stuart A, McConkey H, Henneman P, Venema A, Schwartz CE, Stevenson RE, Skinner SA, DuPont BR, Fletcher RS, Balci TB, Siu VM, Granadillo JL, Masters J, Kadour M, Friez MJ, van Haelst MM, Mannens MMAM, Louie RJ, Lee JA, Tedder ML, Alders M. Clinical epigenomics: genome-wide DNA methylation analysis for the diagnosis of Mendelian disorders. Genet Med 2021; 23:1065-1074. [PMID: 33547396 PMCID: PMC8187150 DOI: 10.1038/s41436-020-01096-4] [Citation(s) in RCA: 91] [Impact Index Per Article: 30.3] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/27/2020] [Revised: 12/29/2020] [Accepted: 12/31/2020] [Indexed: 01/23/2023] Open
Abstract
Purpose We describe the clinical implementation of genome-wide DNA methylation analysis in rare disorders across the EpiSign diagnostic laboratory network and the assessment of results and clinical impact in the first subjects tested. Methods We outline the logistics and data flow between an integrated network of clinical diagnostics laboratories in Europe, the United States, and Canada. We describe the clinical validation of EpiSign using 211 specimens and assess the test performance and diagnostic yield in the first 207 subjects tested involving two patient subgroups: the targeted cohort (subjects with previous ambiguous/inconclusive genetic findings including genetic variants of unknown clinical significance) and the screening cohort (subjects with clinical findings consistent with hereditary neurodevelopmental syndromes and no previous conclusive genetic findings). Results Among the 207 subjects tested, 57 (27.6%) were positive for a diagnostic episignature including 48/136 (35.3%) in the targeted cohort and 8/71 (11.3%) in the screening cohort, with 4/207 (1.9%) remaining inconclusive after EpiSign analysis. Conclusion This study describes the implementation of diagnostic clinical genomic DNA methylation testing in patients with rare disorders. It provides strong evidence of clinical utility of EpiSign analysis, including the ability to provide conclusive findings in the majority of subjects tested.
Collapse
Affiliation(s)
- Bekim Sadikovic
- Molecular Genetics Laboratory, Molecular Diagnostics Division, London Health Sciences Centre, London, ON, Canada. .,Department of Pathology and Laboratory Medicine, Western University, London, ON, Canada.
| | - Michael A Levy
- Molecular Genetics Laboratory, Molecular Diagnostics Division, London Health Sciences Centre, London, ON, Canada.,Department of Pathology and Laboratory Medicine, Western University, London, ON, Canada
| | - Jennifer Kerkhof
- Molecular Genetics Laboratory, Molecular Diagnostics Division, London Health Sciences Centre, London, ON, Canada.,Department of Pathology and Laboratory Medicine, Western University, London, ON, Canada
| | - Erfan Aref-Eshghi
- Molecular Genetics Laboratory, Molecular Diagnostics Division, London Health Sciences Centre, London, ON, Canada.,Department of Pathology and Laboratory Medicine, Western University, London, ON, Canada
| | - Laila Schenkel
- Molecular Genetics Laboratory, Molecular Diagnostics Division, London Health Sciences Centre, London, ON, Canada.,Department of Pathology and Laboratory Medicine, Western University, London, ON, Canada
| | - Alan Stuart
- Molecular Genetics Laboratory, Molecular Diagnostics Division, London Health Sciences Centre, London, ON, Canada.,Department of Pathology and Laboratory Medicine, Western University, London, ON, Canada
| | - Haley McConkey
- Molecular Genetics Laboratory, Molecular Diagnostics Division, London Health Sciences Centre, London, ON, Canada.,Department of Pathology and Laboratory Medicine, Western University, London, ON, Canada
| | - Peter Henneman
- Amsterdam University Medical Center, University of Amsterdam, Department of Clinical Genetics, Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands
| | - Andrea Venema
- Amsterdam University Medical Center, University of Amsterdam, Department of Clinical Genetics, Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands
| | | | | | | | | | | | - Tugce B Balci
- Department of Pediatrics, Division of Medical Genetics, Western University, London, ON, Canada.,Medical Genetics Program of Southwestern Ontario, London Health Sciences Centre, London, ON, Canada
| | - Victoria Mok Siu
- Department of Pediatrics, Division of Medical Genetics, Western University, London, ON, Canada.,Medical Genetics Program of Southwestern Ontario, London Health Sciences Centre, London, ON, Canada
| | - Jorge L Granadillo
- Division of Genetics and Genomic Medicine, Department of Pediatrics, Washington University School of Medicine in St. Louis, St. Louis, MO, USA
| | - Jennefer Masters
- Molecular Genetics Laboratory, Molecular Diagnostics Division, London Health Sciences Centre, London, ON, Canada.,Department of Pathology and Laboratory Medicine, Western University, London, ON, Canada
| | - Mike Kadour
- Molecular Genetics Laboratory, Molecular Diagnostics Division, London Health Sciences Centre, London, ON, Canada.,Department of Pathology and Laboratory Medicine, Western University, London, ON, Canada
| | | | - Mieke M van Haelst
- Amsterdam University Medical Center, University of Amsterdam, Department of Clinical Genetics, Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands
| | - Marcel M A M Mannens
- Amsterdam University Medical Center, University of Amsterdam, Department of Clinical Genetics, Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands
| | | | | | | | - Marielle Alders
- Amsterdam University Medical Center, University of Amsterdam, Department of Clinical Genetics, Amsterdam Reproduction and Development Research Institute, Amsterdam, The Netherlands.
| |
Collapse
|
7
|
Detection of aberrant gene expression events in RNA sequencing data. Nat Protoc 2021; 16:1276-1296. [PMID: 33462443 DOI: 10.1038/s41596-020-00462-5] [Citation(s) in RCA: 52] [Impact Index Per Article: 17.3] [Reference Citation Analysis] [Abstract] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2019] [Accepted: 11/06/2020] [Indexed: 12/20/2022]
Abstract
RNA sequencing (RNA-seq) has emerged as a powerful approach to discover disease-causing gene regulatory defects in individuals affected by genetically undiagnosed rare disorders. Pioneering studies have shown that RNA-seq could increase the diagnosis rates over DNA sequencing alone by 8-36%, depending on the disease entity and tissue probed. To accelerate adoption of RNA-seq by human genetics centers, detailed analysis protocols are now needed. We present a step-by-step protocol that details how to robustly detect aberrant expression levels, aberrant splicing and mono-allelic expression in RNA-seq data using dedicated statistical methods. We describe how to generate and assess quality control plots and interpret the analysis results. The protocol is based on the detection of RNA outliers pipeline (DROP), a modular computational workflow that integrates all the analysis steps, can leverage parallel computing infrastructures and generates browsable web page reports.
Collapse
|
8
|
Lambert MP. Improving interpretation of genetic testing for hereditary hemorrhagic, thrombotic, and platelet disorders. HEMATOLOGY. AMERICAN SOCIETY OF HEMATOLOGY. EDUCATION PROGRAM 2020; 2020:76-81. [PMID: 33275718 PMCID: PMC7727548 DOI: 10.1182/hematology.2020000091] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/10/2023]
Abstract
The last 10 years have seen an explosion in the amount of data available through next-generation sequencing. These data are advancing quickly, and this pace makes it difficult for most practitioners to easily keep up with all of the new information. Complicating this understanding is sometimes conflicting information about variant pathogenicity or even about the role of some genes in the pathogenesis of disease. The more widespread clinical use of sequencing has expanded phenotypes, including the identification of mild phenotypes associated with previously serious disease, such as with some variants in RUNX1, MYH9, ITG2A, and others. Several organizations have taken up the task of cataloging and systematically evaluating genes and variants using a standardized approach and making the data publicly available so that others can benefit from their gene/variant curation. The efforts in testing for hereditary hemorrhagic, thrombotic, and platelet disorders have been led by the International Society on Thrombosis and Haemostasis Scientific Standardization Committee on Genomics in Thrombosis and Hemostasis, the American Society of Hematology, and the National Institutes of Health National Human Genome Research Institute Clinical Genome Resource. This article outlines current efforts to improve the interpretation of genetic testing and the role of standardizing and disseminating information. By assessing the strength of gene-disease associations, standardizing variant curation guidelines, sharing genomic data among expert members, and incorporating data from existing disease databases, the number of variants of uncertain significance will decrease, thereby improving the value of genetic testing as a diagnostic tool.
Collapse
Affiliation(s)
- Michele P Lambert
- Division of Hematology, The Children's Hospital of Philadelphia, Philadelphia, PA; and Department of Pediatrics, Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA
| |
Collapse
|
9
|
Caldwell KA, Willicott CW, Caldwell GA. Modeling neurodegeneration in Caenorhabditis elegans. Dis Model Mech 2020; 13:13/10/dmm046110. [PMID: 33106318 PMCID: PMC7648605 DOI: 10.1242/dmm.046110] [Citation(s) in RCA: 71] [Impact Index Per Article: 17.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/15/2022] Open
Abstract
The global burden of neurodegenerative diseases underscores the urgent need for innovative strategies to define new drug targets and disease-modifying factors. The nematode Caenorhabditis elegans has served as the experimental subject for multiple transformative discoveries that have redefined our understanding of biology for ∼60 years. More recently, the considerable attributes of C. elegans have been applied to neurodegenerative diseases, including amyotrophic lateral sclerosis, Alzheimer's disease, Parkinson's disease and Huntington's disease. Transgenic nematodes with genes encoding normal and disease variants of proteins at the single- or multi-copy level under neuronal-specific promoters limits expression to select neuronal subtypes. The anatomical transparency of C. elegans affords the use of co-expressed fluorescent proteins to follow the progression of neurodegeneration as the animals age. Significantly, a completely defined connectome facilitates detailed understanding of the impact of neurodegeneration on organismal health and offers a unique capacity to accurately link cell death with behavioral dysfunction or phenotypic variation in vivo. Moreover, chemical treatments, as well as forward and reverse genetic screening, hasten the identification of modifiers that alter neurodegeneration. When combined, these chemical-genetic analyses establish critical threshold states to enhance or reduce cellular stress for dissecting associated pathways. Furthermore, C. elegans can rapidly reveal whether lifespan or healthspan factor into neurodegenerative processes. Here, we outline the methodologies employed to investigate neurodegeneration in C. elegans and highlight numerous studies that exemplify its utility as a pre-clinical intermediary to expedite and inform mammalian translational research. Summary: While unsurpassed as an experimental system for fundamental biology, Caenorhabditis elegans remains undervalued for its translational potential. Here, we highlight significant outcomes from, and resources available for, C. elegans-based research into neurodegenerative disorders.
Collapse
Affiliation(s)
- Kim A Caldwell
- Department of Biological Sciences, The University of Alabama, Tuscaloosa, AL 35487, USA .,Departments of Neurobiology, Neurology, Center for Neurodegeneration and Experimental Therapeutics, and Nathan Shock Center of Excellence in the Basic Biology of Aging, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| | - Corey W Willicott
- Department of Biological Sciences, The University of Alabama, Tuscaloosa, AL 35487, USA
| | - Guy A Caldwell
- Department of Biological Sciences, The University of Alabama, Tuscaloosa, AL 35487, USA.,Departments of Neurobiology, Neurology, Center for Neurodegeneration and Experimental Therapeutics, and Nathan Shock Center of Excellence in the Basic Biology of Aging, University of Alabama at Birmingham, Birmingham, AL 35294, USA
| |
Collapse
|
10
|
Althari S, Najmi LA, Bennett AJ, Aukrust I, Rundle JK, Colclough K, Molnes J, Kaci A, Nawaz S, van der Lugt T, Hassanali N, Mahajan A, Molven A, Ellard S, McCarthy MI, Bjørkhaug L, Njølstad PR, Gloyn AL. Unsupervised Clustering of Missense Variants in HNF1A Using Multidimensional Functional Data Aids Clinical Interpretation. Am J Hum Genet 2020; 107:670-682. [PMID: 32910913 PMCID: PMC7536579 DOI: 10.1016/j.ajhg.2020.08.016] [Citation(s) in RCA: 18] [Impact Index Per Article: 4.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/09/2020] [Accepted: 08/14/2020] [Indexed: 12/30/2022] Open
Abstract
Exome sequencing in diabetes presents a diagnostic challenge because depending on frequency, functional impact, and genomic and environmental contexts, HNF1A variants can cause maturity-onset diabetes of the young (MODY), increase type 2 diabetes risk, or be benign. A correct diagnosis matters as it informs on treatment, progression, and family risk. We describe a multi-dimensional functional dataset of 73 HNF1A missense variants identified in exomes of 12,940 individuals. Our aim was to develop an analytical framework for stratifying variants along the HNF1A phenotypic continuum to facilitate diagnostic interpretation. HNF1A variant function was determined by four different molecular assays. Structure of the multi-dimensional dataset was explored using principal component analysis, k-means, and hierarchical clustering. Weights for tissue-specific isoform expression and functional domain were integrated. Functionally annotated variant subgroups were used to re-evaluate genetic diagnoses in national MODY diagnostic registries. HNF1A variants demonstrated a range of behaviors across the assays. The structure of the multi-parametric data was shaped primarily by transactivation. Using unsupervised learning methods, we obtained high-resolution functional clusters of the variants that separated known causal MODY variants from benign and type 2 diabetes risk variants and led to reclassification of 4% and 9% of HNF1A variants identified in the UK and Norway MODY diagnostic registries, respectively. Our proof-of-principle analyses facilitated informative stratification of HNF1A variants along the continuum, allowing improved evaluation of clinical significance, management, and precision medicine in diabetes clinics. Transcriptional activity appears a superior readout supporting pursuit of transactivation-centric experimental designs for high-throughput functional screens.
Collapse
Affiliation(s)
- Sara Althari
- Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Oxford OX3 7LE, UK
| | - Laeya A. Najmi
- Center for Diabetes Research, Department of Clinical Science, University of Bergen, 5020 Bergen, Norway,Department of Medical Genetics, Haukeland University Hospital, 5021 Bergen, Norway,Department of Medicine, Division of Cardiovascular Medicine, Stanford University School of Medicine, Stanford, CA 94305-5101, USA
| | - Amanda J. Bennett
- Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Oxford OX3 7LE, UK
| | - Ingvild Aukrust
- Center for Diabetes Research, Department of Clinical Science, University of Bergen, 5020 Bergen, Norway,Department of Medical Genetics, Haukeland University Hospital, 5021 Bergen, Norway
| | - Jana K. Rundle
- Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Oxford OX3 7LE, UK
| | - Kevin Colclough
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter EX1 2LU, UK,Exeter Genomics Laboratory, Royal Devon and Exeter NHS Foundation Trust, Exeter EX2 5DW, UK
| | - Janne Molnes
- Center for Diabetes Research, Department of Clinical Science, University of Bergen, 5020 Bergen, Norway,Department of Medical Genetics, Haukeland University Hospital, 5021 Bergen, Norway
| | - Alba Kaci
- Center for Diabetes Research, Department of Clinical Science, University of Bergen, 5020 Bergen, Norway,Department of Pediatrics and Adolescents, Haukeland University Hospital, 5021 Bergen, Norway
| | - Sameena Nawaz
- Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Oxford OX3 7LE, UK
| | | | - Neelam Hassanali
- Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Oxford OX3 7LE, UK
| | - Anubha Mahajan
- Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Oxford OX3 7LE, UK,Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK
| | - Anders Molven
- Center for Diabetes Research, Department of Clinical Science, University of Bergen, 5020 Bergen, Norway,Department of Clinical Medicine, University of Bergen, 5020 Bergen, Norway,Department of Pathology, Haukeland University Hospital, 5021 Bergen, Norway
| | - Sian Ellard
- Institute of Biomedical and Clinical Science, University of Exeter Medical School, Exeter EX1 2LU, UK,Exeter Genomics Laboratory, Royal Devon and Exeter NHS Foundation Trust, Exeter EX2 5DW, UK
| | - Mark I. McCarthy
- Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Oxford OX3 7LE, UK,Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK,Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford OX3 7LE, UK
| | - Lise Bjørkhaug
- Department of Safety, Chemistry, and Biomedical Laboratory Sciences, Western Norway University of Applied Sciences, 5020 Bergen, Norway
| | - Pål Rasmus Njølstad
- Center for Diabetes Research, Department of Clinical Science, University of Bergen, 5020 Bergen, Norway,Department of Pediatrics and Adolescents, Haukeland University Hospital, 5021 Bergen, Norway,Corresponding:
| | - Anna L. Gloyn
- Oxford Centre for Diabetes, Endocrinology & Metabolism, University of Oxford, Oxford OX3 7LE, UK,Wellcome Centre for Human Genetics, University of Oxford, Oxford OX3 7BN, UK,Oxford NIHR Biomedical Research Centre, Churchill Hospital, Oxford OX3 7LE, UK,Division of Endocrinology, Department of Pediatrics, Stanford School of Medicine, Stanford University, Stanford, CA 94305-5101, USA
| |
Collapse
|
11
|
Kroncke BM, Smith DK, Zuo Y, Glazer AM, Roden DM, Blume JD. A Bayesian method to estimate variant-induced disease penetrance. PLoS Genet 2020; 16:e1008862. [PMID: 32569262 PMCID: PMC7347235 DOI: 10.1371/journal.pgen.1008862] [Citation(s) in RCA: 10] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/14/2019] [Revised: 07/09/2020] [Accepted: 05/14/2020] [Indexed: 01/09/2023] Open
Abstract
A major challenge emerging in genomic medicine is how to assess best disease risk from rare or novel variants found in disease-related genes. The expanding volume of data generated by very large phenotyping efforts coupled to DNA sequence data presents an opportunity to reinterpret genetic liability of disease risk. Here we propose a framework to estimate the probability of disease given the presence of a genetic variant conditioned on features of that variant. We refer to this as the penetrance, the fraction of all variant heterozygotes that will present with disease. We demonstrate this methodology using a well-established disease-gene pair, the cardiac sodium channel gene SCN5A and the heart arrhythmia Brugada syndrome. From a review of 756 publications, we developed a pattern mixture algorithm, based on a Bayesian Beta-Binomial model, to generate SCN5A penetrance probabilities for the Brugada syndrome conditioned on variant-specific attributes. These probabilities are determined from variant-specific features (e.g. function, structural context, and sequence conservation) and from observations of affected and unaffected heterozygotes. Variant functional perturbation and structural context prove most predictive of Brugada syndrome penetrance.
Collapse
Affiliation(s)
- Brett M. Kroncke
- Department of Medicine Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Vanderbilt Center for Arrhythmia Research and Therapeutics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Department of Pharmacology Vanderbilt University, Nashville, Tennessee, United States of America
| | - Derek K. Smith
- Department of Biostatistics Vanderbilt University, Nashville, Tennessee, United States of America
| | - Yi Zuo
- Department of Biostatistics Vanderbilt University, Nashville, Tennessee, United States of America
| | - Andrew M. Glazer
- Department of Medicine Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Vanderbilt Center for Arrhythmia Research and Therapeutics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Dan M. Roden
- Department of Medicine Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Vanderbilt Center for Arrhythmia Research and Therapeutics, Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
- Department of Pharmacology Vanderbilt University, Nashville, Tennessee, United States of America
- Department of Biomedical Informatics Vanderbilt University Medical Center, Nashville, Tennessee, United States of America
| | - Jeffrey D. Blume
- Department of Biostatistics Vanderbilt University, Nashville, Tennessee, United States of America
| |
Collapse
|
12
|
Abstract
PURPOSE OF REVIEW Identifying pathogenic variation underlying pediatric developmental disease is critical for medical management, therapeutic development, and family planning. This review summarizes current genetic testing options along with their potential benefits and limitations. We also describe results from large-scale genomic sequencing projects in pediatric and neonatal populations with a focus on clinical utility. RECENT FINDINGS Recent advances in DNA sequencing technology have made genomic sequencing a feasible and effective testing option in a variety of clinical settings. These cutting-edge tests offer much promise to both medical providers and patients as it has been demonstrated to detect causal genetic variation in ∼25% or more of previously unresolved cases. Efforts aimed at promoting data sharing across clinical genetics laboratories and systematic reanalysis of existing genomic sequencing data have further improved diagnostic rates and reduced the number of unsolved cases. SUMMARY Genomic sequencing is a powerful and increasingly cost-effective alternative to current genetic tests and will continue to grow in clinical utility as more of the genome is understood and as analytical methods are improved. The evolution of genomic sequencing is changing the landscape of clinical testing and requires medical professionals who are adept at understanding and returning genomic results to patients.
Collapse
Affiliation(s)
- Matthew B. Neu
- HudsonAlpha Institute for Biotechnology, Huntsville, AL, USA
- Department of Genetics, University of Alabama at Birmingham, Birmingham, AL, USA
- University of Alabama at Birmingham Medical Scientist Training Program, Birmingham, AL, USA
| | | | | |
Collapse
|
13
|
Fuselli S. Beyond drugs: the evolution of genes involved in human response to medications. Proc Biol Sci 2019; 286:20191716. [PMID: 31640517 DOI: 10.1098/rspb.2019.1716] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/29/2022] Open
Abstract
The genetic variation of our species reflects human demographic history and adaptation to diverse local environments. Part of this genetic variation affects individual responses to exogenous substances, such as food, pollutants and drugs, and plays an important role in drug efficacy and safety. This review provides a synthesis of the evolution of loci implicated in human pharmacological response and metabolism, interpreted within the theoretical framework of population genetics and molecular evolution. In particular, I review and discuss key evolutionary aspects of different pharmacogenes in humans and other species, such as the relationship between the type of substrates and rate of evolution; the selective pressure exerted by landscape variables or dietary habits; expected and observed patterns of rare genetic variation. Finally, I discuss how this knowledge can be translated directly or after the implementation of specific studies, into practical guidelines.
Collapse
Affiliation(s)
- Silvia Fuselli
- Department of Life Sciences and Biotechnology, University of Ferrara, Ferrara, Italy
| |
Collapse
|
14
|
Next-Generation Sequencing Improves Diagnosis, Prognosis and Clinical Management of Myeloid Neoplasms. Cancers (Basel) 2019; 11:cancers11091364. [PMID: 31540291 PMCID: PMC6770229 DOI: 10.3390/cancers11091364] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/21/2019] [Revised: 09/04/2019] [Accepted: 09/10/2019] [Indexed: 12/19/2022] Open
Abstract
Molecular diagnosis of myeloid neoplasms (MN) is based on the detection of multiple genetic alterations using various techniques. Next-generation sequencing (NGS) has been proved as a useful method for analyzing many genes simultaneously. In this context, we analyzed diagnostic samples from 121 patients affected by MN and ten relapse samples from a subset of acute myeloid leukemia patients using two enrichment-capture NGS gene panels. Pathogenicity classification of variants was enhanced by the development and application of a custom onco-hematology score. A total of 278 pathogenic variants were detected in 84% of patients. For structural alterations, 82% of those identified by cytogenetics were detected by NGS, 25 of 31 copy number variants and three out of three translocations. The detection of variants using NGS changed the diagnosis of seven patients and the prognosis of 15 patients and enabled us to identify 44 suitable candidates for clinical trials. Regarding AML, six of the ten relapsed patients lost or gained variants, comparing with diagnostic samples. In conclusion, the use of NGS panels in MN improves genetic characterization of the disease compared with conventional methods, thus demonstrating its potential clinical utility in routine clinical testing. This approach leads to better-adjusted treatments for each patient.
Collapse
|
15
|
Wu Y, Weile J, Cote AG, Sun S, Knapp J, Verby M, Roth FP. A web application and service for imputing and visualizing missense variant effect maps. Bioinformatics 2019; 35:3191-3193. [PMID: 30649215 PMCID: PMC6735881 DOI: 10.1093/bioinformatics/btz012] [Citation(s) in RCA: 8] [Impact Index Per Article: 1.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2018] [Revised: 12/04/2018] [Accepted: 01/07/2019] [Indexed: 11/24/2022] Open
Abstract
SUMMARY The promise of personalized genomic medicine depends on our ability to assess the functional impact of rare sequence variation. Multiplexed assays can experimentally measure the functional impact of missense variants on a massive scale. However, even after such assays, many missense variants remain poorly measured. Here we describe a software pipeline and application to impute missing information in experimentally determined variant effect maps. AVAILABILITY AND IMPLEMENTATION http://impute.varianteffect.org source code: https://github.com/joewuca/imputation. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
Collapse
Affiliation(s)
- Yingzhou Wu
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Jochen Weile
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Atina G Cote
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Song Sun
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Jennifer Knapp
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Marta Verby
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - Frederick P Roth
- Donnelly Centre, University of Toronto, Toronto, ON, Canada
- Department of Molecular Genetics, University of Toronto, Toronto, ON, Canada
- Department of Computer Science, University of Toronto, Toronto, ON, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
- Center for Cancer Systems Biology, Dana Farber Cancer Institute, Boston, MA, USA
- Canadian Institute for Advanced Research, Toronto, ON, Canada
| |
Collapse
|
16
|
Gonorazky HD, Naumenko S, Ramani AK, Nelakuditi V, Mashouri P, Wang P, Kao D, Ohri K, Viththiyapaskaran S, Tarnopolsky MA, Mathews KD, Moore SA, Osorio AN, Villanova D, Kemaladewi DU, Cohn RD, Brudno M, Dowling JJ. Expanding the Boundaries of RNA Sequencing as a Diagnostic Tool for Rare Mendelian Disease. Am J Hum Genet 2019; 104:466-483. [PMID: 30827497 PMCID: PMC6407525 DOI: 10.1016/j.ajhg.2019.01.012] [Citation(s) in RCA: 138] [Impact Index Per Article: 27.6] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/02/2018] [Accepted: 01/22/2019] [Indexed: 02/06/2023] Open
Abstract
Gene-panel and whole-exome analyses are now standard methodologies for mutation detection in Mendelian disease. However, the diagnostic yield achieved is at best 50%, leaving the genetic basis for disease unsolved in many individuals. New approaches are thus needed to narrow the diagnostic gap. Whole-genome sequencing is one potential strategy, but it currently has variant-interpretation challenges, particularly for non-coding changes. In this study we focus on transcriptome analysis, specifically total RNA sequencing (RNA-seq), by using monogenetic neuromuscular disorders as proof of principle. We examined a cohort of 25 exome and/or panel "negative" cases and provided genetic resolution in 36% (9/25). Causative mutations were identified in coding and non-coding exons, as well as in intronic regions, and the mutational pathomechanisms included transcriptional repression, exon skipping, and intron inclusion. We address a key barrier of transcriptome-based diagnostics: the need for source material with disease-representative expression patterns. We establish that blood-based RNA-seq is not adequate for neuromuscular diagnostics, whereas myotubes generated by transdifferentiation from an individual's fibroblasts accurately reflect the muscle transcriptome and faithfully reveal disease-causing mutations. Our work confirms that RNA-seq can greatly improve diagnostic yield in genetically unresolved cases of Mendelian disease, defines strengths and challenges of the technology, and demonstrates the suitability of cell models for RNA-based diagnostics. Our data set the stage for development of RNA-seq as a powerful clinical diagnostic tool that can be applied to the large population of individuals with undiagnosed, rare diseases and provide a framework for establishing minimally invasive strategies for doing so.
Collapse
Affiliation(s)
- Hernan D Gonorazky
- Division of Neurology, the Hospital for Sick Children, Toronto, ON M5G 1X8, Canada; Program in Genetics and Genome Biology, Research Institute, the Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Sergey Naumenko
- Centre for Computational Medicine, the Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Arun K Ramani
- Centre for Computational Medicine, the Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Viswateja Nelakuditi
- Centre for Computational Medicine, the Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Pouria Mashouri
- Centre for Computational Medicine, the Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Peiqui Wang
- Centre for Computational Medicine, the Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Dennis Kao
- Centre for Computational Medicine, the Hospital for Sick Children, Toronto, ON M5G 1X8, Canada
| | - Krish Ohri
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1X8, Canada
| | | | - Mark A Tarnopolsky
- Department of Pediatrics, McMaster University, Hamilton, ON L8S 4L8, Canada
| | - Katherine D Mathews
- Departments of Pediatrics and Neurology, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USA
| | - Steven A Moore
- Department of Pathology, University of Iowa Carver College of Medicine, Iowa City, IA 52242, USA
| | - Andres N Osorio
- Neuromuscular Unit, Neuropaediatrics Department, Institut de Recerca Hospital Universitari Sant Joan de Deu, Barcelona 08950, Spain; Center for the Biomedical Research on Rare Diseases (CIBERER), Instituto de Salud Carlos III (ISCIII), Barcelona 08950, Spain
| | - David Villanova
- GenomicTales Parc de la Mola, 10, AD700 Escaldes-Engordany, Andorra
| | - Dwi U Kemaladewi
- Program in Genetics and Genome Biology, Research Institute, the Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Ronald D Cohn
- Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1X8, Canada; Program in Genetics and Genome Biology, Research Institute, the Hospital for Sick Children, Toronto, ON M5G 0A4, Canada
| | - Michael Brudno
- Centre for Computational Medicine, the Hospital for Sick Children, Toronto, ON M5G 1X8, Canada; Program in Genetics and Genome Biology, Research Institute, the Hospital for Sick Children, Toronto, ON M5G 0A4, Canada; Department of Computer Science, University of Toronto, Toronto, ON M5G 0A4, Canada.
| | - James J Dowling
- Division of Neurology, the Hospital for Sick Children, Toronto, ON M5G 1X8, Canada; Department of Molecular Genetics, University of Toronto, Toronto, ON M5G 1X8, Canada; Program in Genetics and Genome Biology, Research Institute, the Hospital for Sick Children, Toronto, ON M5G 0A4, Canada.
| |
Collapse
|
17
|
Daly AK, Rettie AE, Fowler DM, Miners JO. Pharmacogenomics of CYP2C9: Functional and Clinical Considerations. J Pers Med 2017; 8:E1. [PMID: 29283396 PMCID: PMC5872075 DOI: 10.3390/jpm8010001] [Citation(s) in RCA: 113] [Impact Index Per Article: 16.1] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/31/2017] [Revised: 12/18/2017] [Accepted: 12/20/2017] [Indexed: 02/07/2023] Open
Abstract
CYP2C9 is the most abundant CYP2C subfamily enzyme in human liver and the most important contributor from this subfamily to drug metabolism. Polymorphisms resulting in decreased enzyme activity are common in the CYP2C9 gene and this, combined with narrow therapeutic indices for several key drug substrates, results in some important issues relating to drug safety and efficacy. CYP2C9 substrate selectivity is detailed and, based on crystal structures for the enzyme, we describe how CYP2C9 catalyzes these reactions. Factors relevant to clinical response to CYP2C9 substrates including inhibition, induction and genetic polymorphism are discussed in detail. In particular, we consider the issue of ethnic variation in pattern and frequency of genetic polymorphisms and clinical implications. Warfarin is the most well studied CYP2C9 substrate; recent work on use of dosing algorithms that include CYP2C9 genotype to improve patient safety during initiation of warfarin dosing are reviewed and prospects for their clinical implementation considered. Finally, we discuss a novel approach to cataloging the functional capabilities of rare 'variants of uncertain significance', which are increasingly detected as more exome and genome sequencing of diverse populations is conducted.
Collapse
Affiliation(s)
- Ann K Daly
- Institute of Cellular Medicine, Newcastle University, Framlington Place, Newcastle upon Tyne NE2 4HH, UK.
| | - Allan E Rettie
- Department of Medicinal Chemistry, University of Washington, Seattle, WA 98195, USA.
| | - Douglas M Fowler
- Department of Genome Sciences and Department of Bioengineering, University of Washington, Seattle, WA 98195, USA.
| | - John O Miners
- Department of Clinical Pharmacology, Flinders University School of Medicine, Adelaide 5042, Australia.
| |
Collapse
|
18
|
Renaud M, Tranchant C, Martin JVT, Mochel F, Synofzik M, van de Warrenburg B, Pandolfo M, Koenig M, Kolb SA, Anheim M, Alonso I, Azzedine H, Barbot C, Bereau M, Berkovic S, Bernard G, Bindoff LA, Bompaire F, Bonneau D, Bonneau P, Boycott KM, Bras J, Brais B, Brigatti KW, Cameron J, Chamova T, Choquet K, Delague V, Denizeau P, Dotti MT, El‐Euch G, Elmalik SA, Federico A, Fiskerstrand T, Gagnon C, Guerreiro R, Guissart C, Hassin‐Baer S, Heimdal KR, Héron B, Isohanni P, Kalaydijeva L, Kawarai T, Koht JA, Lai S, Piana RL, Lecocq C, Linnankivi T, Lönnqvist T, Lu C, Maas R, Mahlaoui N, Mallaret M, Marelli C, Mariotti C, Mathieu J, Méneret A, Mignarri A, Monin ML, Montaut S, Nanetti L, Nadjar Y, Poujois A, Salih MA, Sousa S, Stanier P, Stoppa‐Lyonnet D, Strauss K, Tallaksen C, Tarnopolsky M, Tinant N, Tournev I, Topaloglu H, Varhaug KN, Woimant F, Wolf NI, Yahalom G, Yoon G, Young M. A recessive ataxia diagnosis algorithm for the next generation sequencing era. Ann Neurol 2017; 82:892-899. [DOI: 10.1002/ana.25084] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/26/2017] [Revised: 10/12/2017] [Accepted: 10/17/2017] [Indexed: 02/06/2023]
Affiliation(s)
- Mathilde Renaud
- Department of Neurology, Hautepierre HospitalUniversity Hospitals of StrasbourgStrasbourg France
- Institute of Genetics and Molecular and Cellular Biology, INSERM‐U964/CNRS‐UMR7104University of StrasbourgIllkirch France
- Strasbourg Federation of Translational MedicineUniversity of StrasbourgStrasbourg France
| | - Christine Tranchant
- Department of Neurology, Hautepierre HospitalUniversity Hospitals of StrasbourgStrasbourg France
- Institute of Genetics and Molecular and Cellular Biology, INSERM‐U964/CNRS‐UMR7104University of StrasbourgIllkirch France
- Strasbourg Federation of Translational MedicineUniversity of StrasbourgStrasbourg France
| | | | - Fanny Mochel
- Department of GeneticsPitié‐Salpêtrière University HospitalParis France
- Neurometabolic GRCPierre and Marie Curie UniversityParis France
- Neurometabolic Research GroupPierre and Marie Curie UniversityParis France
| | - Matthis Synofzik
- Department of Neurodegenerative Diseases, Hertie Institute for Clinical Brain ResearchUniversity of TübingenTübingen Germany
- German Center for Neurodegenerative DiseasesTübingen Germany
| | - Bart van de Warrenburg
- Department of NeurologyRadboud University Medical Center, Donders Institute for Brain, Cognition, and BehaviorNijmegen the Netherlands
| | - Massimo Pandolfo
- Department of NeurologyFree University of Brussels, Erasme HospitalBrussels Belgium
| | - Michel Koenig
- Rare Disease Genetics LaboratoryUniversity Institute of Clinical Research, University of Montpellier, Montpellier University Hospital CenterMontpellier France
| | | | - Mathieu Anheim
- Department of Neurology, Hautepierre HospitalUniversity Hospitals of StrasbourgStrasbourg France
- Institute of Genetics and Molecular and Cellular Biology, INSERM‐U964/CNRS‐UMR7104University of StrasbourgIllkirch France
- Strasbourg Federation of Translational MedicineUniversity of StrasbourgStrasbourg France
| | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | | |
Collapse
|
19
|
Leroy B, Ballinger ML, Baran-Marszak F, Bond GL, Braithwaite A, Concin N, Donehower LA, El-Deiry WS, Fenaux P, Gaidano G, Langerød A, Hellstrom-Lindberg E, Iggo R, Lehmann-Che J, Mai PL, Malkin D, Moll UM, Myers JN, Nichols KE, Pospisilova S, Ashton-Prolla P, Rossi D, Savage SA, Strong LC, Tonin PN, Zeillinger R, Zenz T, Fraumeni JF, Taschner PEM, Hainaut P, Soussi T. Recommended Guidelines for Validation, Quality Control, and Reporting of TP53 Variants in Clinical Practice. Cancer Res 2017; 77:1250-1260. [PMID: 28254861 DOI: 10.1158/0008-5472.can-16-2179] [Citation(s) in RCA: 55] [Impact Index Per Article: 7.9] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/08/2016] [Revised: 11/12/2016] [Accepted: 11/16/2016] [Indexed: 12/21/2022]
Abstract
Accurate assessment of TP53 gene status in sporadic tumors and in the germline of individuals at high risk of cancer due to Li-Fraumeni Syndrome (LFS) has important clinical implications for diagnosis, surveillance, and therapy. Genomic data from more than 20,000 cancer genomes provide a wealth of information on cancer gene alterations and have confirmed TP53 as the most commonly mutated gene in human cancer. Analysis of a database of 70,000 TP53 variants reveals that the two newly discovered exons of the gene, exons 9β and 9γ, generated by alternative splicing, are the targets of inactivating mutation events in breast, liver, and head and neck tumors. Furthermore, germline rearrange-ments in intron 1 of TP53 are associated with LFS and are frequently observed in sporadic osteosarcoma. In this context of constantly growing genomic data, we discuss how screening strategies must be improved when assessing TP53 status in clinical samples. Finally, we discuss how TP53 alterations should be described by using accurate nomenclature to avoid confusion in scientific and clinical reports. Cancer Res; 77(6); 1250-60. ©2017 AACR.
Collapse
Affiliation(s)
- Bernard Leroy
- Sorbonne Université, UPMC Univ Paris 06, Paris, France
| | - Mandy L Ballinger
- Cancer Division, Garvan Institute of Medical Research, Darlinghurst, NSW, Australia
| | - Fanny Baran-Marszak
- Hôpital Avicenne, Assistance Publique-Hôpitaux De Paris, Bobigny, Service D'H ematologie Biologique, France
| | - Gareth L Bond
- Ludwig Institute for Cancer Research, University of Oxford, Nuffield Department of Clinical Medicine, Old Road Campus Research Building, Oxford, United Kingdom
| | - Antony Braithwaite
- Dept of Pathology, School of Medicine, University of Otago, Dunedin, New Zealand.,Children's Medical Research Institute, University of Sydney, Westmead NSW, Australia
| | - Nicole Concin
- Department of Gynecology and Obstetrics, Innsbruck Medical University, Innsbruck, Austria
| | | | - Wafik S El-Deiry
- Department of Hematology/Oncology and Molecular Therapeutics Program, Fox Chase Cancer Center, Philadelphia, Pennsylvania
| | - Pierre Fenaux
- Service d'hématologie séniors, Hôpital St Louis/Université Paris 7, 1 avenue Claude Vellefaux, Paris, France
| | - Gianluca Gaidano
- Division of Hematology, Department of Translational Medicine, Amedeo Avogadro University of Eastern Piedmont, Novara, Italy
| | - Anita Langerød
- Department of Genetics, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway
| | - Eva Hellstrom-Lindberg
- Karolinska Institute, Department of Medicine, Center for Hematology and Regenerative Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - Richard Iggo
- Bergonié Cancer Institute University of Bordeaux 229 cours de l'Argonne 33076 Bordeaux, France
| | | | - Phuong L Mai
- Cancer Genetics Program, Magee Womens Hospital, University of Pittsburgh Medical Center, Pittsburgh, Pennsylvania
| | - David Malkin
- Division of Hematology/Oncology, Department of Pediatrics, The Hospital for Sick Children, University of Toronto, Toronto, Ontario, Canada
| | - Ute M Moll
- Department of Pathology, Stony Brook University, Stony Brook, New York
| | - Jeffrey N Myers
- The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Kim E Nichols
- Department of Oncology, Division of Cancer Predisposition, St. Jude Children's Research Hospital, Memphis, Tennessee
| | - Sarka Pospisilova
- Masaryk University, CEITEC - Molecular Medicine and University Hospital Brno, Department of Internal Medicine - Hematology and Oncology, Brno, Czech Republic
| | - Patricia Ashton-Prolla
- Universidade Federal do Rio Grande do Sul (UFRGS) e Serviço deGenética Médica-HCPA, Rua Ramiro Barcelos, Porto Alegre, Brasil
| | - Davide Rossi
- Division of Hematology, Department of Translational Medicine, Amedeo Avogadro University of Eastern Piedmont, Novara, Italy
| | - Sharon A Savage
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Louise C Strong
- The University of Texas MD Anderson Cancer Center, Houston, Texas
| | - Patricia N Tonin
- Departments of Medicine and Human Genetics, McGill University and Cancer Research Program, The Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
| | - Robert Zeillinger
- Molecular Oncology Group, Department of Obstetrics and Gynaecology, Medical University of Vienna, Vienna, Austria
| | - Thorsten Zenz
- University of Heidelberg, Department of Medicine V, Heidelberg, Germany; Department of Translational Oncology, National Center for Tumor Diseases and German Cancer Research Center (dkfz), Heidelberg, Germany
| | - Joseph F Fraumeni
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, Maryland
| | - Peter E M Taschner
- Generade Centre of Expertise Genomics and University of Applied Sciences Leiden, Leiden, the Netherlands
| | - Pierre Hainaut
- Institut Albert Bonniot, Inserm 823, Université Grenoble Alpes, Rond Point de la Chantourne, La Tronche, France
| | - Thierry Soussi
- Sorbonne Université, UPMC Univ Paris 06, Paris, France. .,Department of Oncology-Pathology, Karolinska Institutet, Cancer Center Karolinska, Stockholm, Sweden.,INSERM, U1138, Centre de Recherche des Cordeliers, Paris, France
| |
Collapse
|
20
|
Starita LM, Ahituv N, Dunham MJ, Kitzman JO, Roth FP, Seelig G, Shendure J, Fowler DM. Variant Interpretation: Functional Assays to the Rescue. Am J Hum Genet 2017; 101:315-325. [PMID: 28886340 PMCID: PMC5590843 DOI: 10.1016/j.ajhg.2017.07.014] [Citation(s) in RCA: 214] [Impact Index Per Article: 30.6] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2022] Open
Abstract
Classical genetic approaches for interpreting variants, such as case-control or co-segregation studies, require finding many individuals with each variant. Because the overwhelming majority of variants are present in only a few living humans, this strategy has clear limits. Fully realizing the clinical potential of genetics requires that we accurately infer pathogenicity even for rare or private variation. Many computational approaches to predicting variant effects have been developed, but they can identify only a small fraction of pathogenic variants with the high confidence that is required in the clinic. Experimentally measuring a variant's functional consequences can provide clearer guidance, but individual assays performed only after the discovery of the variant are both time and resource intensive. Here, we discuss how multiplex assays of variant effect (MAVEs) can be used to measure the functional consequences of all possible variants in disease-relevant loci for a variety of molecular and cellular phenotypes. The resulting large-scale functional data can be combined with machine learning and clinical knowledge for the development of "lookup tables" of accurate pathogenicity predictions. A coordinated effort to produce, analyze, and disseminate large-scale functional data generated by multiplex assays could be essential to addressing the variant-interpretation crisis.
Collapse
Affiliation(s)
- Lea M Starita
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA.
| | - Nadav Ahituv
- Department of Bioengineering and Therapeutic Sciences, University of California, San Francisco, San Francisco, CA 94158, USA; Institute for Human Genetics, University of California, San Francisco, San Francisco, CA 94158, USA
| | - Maitreya J Dunham
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Jacob O Kitzman
- Department of Human Genetics, University of Michigan, Ann Arbor, MI 48109, USA; Department of Bioinformatics & Computational Medicine, University of Michigan, Ann Arbor, MI 48109, USA
| | - Frederick P Roth
- Donnelly Centre and Departments of Molecular Genetics and Computer Science, University of Toronto, Toronto, ON M5S 3E1, Canada; Lunenfeld-Tanenbaum Research Institute, Mt. Sinai Hospital, Toronto, ON M5G 1X5, Canada; Center for Cancer Systems Biology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Canadian Institute for Advanced Research, Toronto, ON M5G 1Z8, Canada
| | - Georg Seelig
- Department of Electrical Engineering, University of Washington, Seattle, WA 98195, USA; Department of Computer Science & Engineering, University of Washington, Seattle, WA 98195, USA
| | - Jay Shendure
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA; Howard Hughes Medical Institute, Seattle, WA 98195, USA
| | - Douglas M Fowler
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA; Department of Bioengineering, University of Washington, Seattle, WA 98195, USA.
| |
Collapse
|
21
|
Genomic Testing in The Paediatric Population: Ethical Considerations in Light of Recent Policy Statements. Mol Diagn Ther 2017; 20:407-14. [PMID: 27251403 DOI: 10.1007/s40291-016-0210-7] [Citation(s) in RCA: 7] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/03/2023]
Abstract
Genomic testing is rapidly becoming established in clinical practice and research. In this paper we consider the ethical issues arising in genomic testing in children, focusing on the clinical context. After describing how genomics can be said to depart from genetics in ethically relevant ways, we introduce two case studies involving ethical issues in paediatric genomic testing. We then summarise and critically engage with recent professional statements regarding genomic testing in children; notably the 2015 position statement from the American Society for Human Genetics. To provide a conceptual framework for these debates, we then synthesise recent bioethics literature regarding appropriate concepts to use when making decisions regarding children, and applying them to genomic testing. We recommend an approach to genomic testing in children that is child-focused and flexibly applied, driven by a core clinical question. Finally, we examine the role that age and emerging capacity should play in these debates.
Collapse
|
22
|
Manolio TA, Fowler DM, Starita LM, Haendel MA, MacArthur DG, Biesecker LG, Worthey E, Chisholm RL, Green ED, Jacob HJ, McLeod HL, Roden D, Rodriguez LL, Williams MS, Cooper GM, Cox NJ, Herman GE, Kingsmore S, Lo C, Lutz C, MacRae CA, Nussbaum RL, Ordovas JM, Ramos EM, Robinson PN, Rubinstein WS, Seidman C, Stranger BE, Wang H, Westerfield M, Bult C. Bedside Back to Bench: Building Bridges between Basic and Clinical Genomic Research. Cell 2017; 169:6-12. [PMID: 28340351 DOI: 10.1016/j.cell.2017.03.005] [Citation(s) in RCA: 77] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/11/2022]
Abstract
Genome sequencing has revolutionized the diagnosis of genetic diseases. Close collaborations between basic scientists and clinical genomicists are now needed to link genetic variants with disease causation. To facilitate such collaborations, we recommend prioritizing clinically relevant genes for functional studies, developing reference variant-phenotype databases, adopting phenotype description standards, and promoting data sharing.
Collapse
Affiliation(s)
- Teri A Manolio
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA.
| | - Douglas M Fowler
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Lea M Starita
- Department of Genome Sciences, University of Washington, Seattle, WA 98195, USA
| | - Melissa A Haendel
- Department of Medical Informatics and Clinical Epidemiology, Oregon Health and Science University, Portland, OR 97239, USA
| | - Daniel G MacArthur
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA; Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
| | - Leslie G Biesecker
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | | | - Rex L Chisholm
- Center for Genetic Medicine, Feinberg School of Medicine, Northwestern University, Chicago, IL 60611, USA
| | - Eric D Green
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Howard J Jacob
- HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA
| | - Howard L McLeod
- DeBartolo Family Personalized Medicine Institute, Moffitt Cancer Center, Tampa, FL 33612, USA
| | - Dan Roden
- Department of Medicine, Pharmacology, and Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Laura Lyman Rodriguez
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Marc S Williams
- Genomic Medicine Institute, Geisinger Health System, Danville, PA 17822, USA
| | - Gregory M Cooper
- HudsonAlpha Institute for Biotechnology, Huntsville, AL 35806, USA
| | - Nancy J Cox
- Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN 37203, USA
| | - Gail E Herman
- Institute for Genomic Medicine, The Research Institute at Nationwide Children's Hospital, Columbus, OH 43205, USA
| | - Stephen Kingsmore
- Rady Children's Institute for Genomic Medicine, San Diego, CA 92123, USA
| | - Cecilia Lo
- Department of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 1526, USA
| | - Cathleen Lutz
- Rare and Orphan Disease Center, Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME 04609, USA
| | - Calum A MacRae
- Divisions of Cardiovascular Medicine, Network Medicine and Genetics, Brigham and Women's Hospital, Boston, MA 02115, USA
| | - Robert L Nussbaum
- Invitae Genetics Information and Testing Company, San Francisco, CA 94107, USA
| | - Jose M Ordovas
- JM-USDA-Human Nutrition Research Center on Aging, Tufts University, Boston, MA 02111, USA
| | - Erin M Ramos
- National Human Genome Research Institute, National Institutes of Health, Bethesda, MD 20892, USA
| | - Peter N Robinson
- The Jackson Laboratory for Genomic Medicine, Farmington, CT 06032, USA
| | - Wendy S Rubinstein
- National Center for Biotechnology Information, National Library of Medicine, NIH, Bethesda, MD 20892, USA
| | - Christine Seidman
- Department of Medicine, Brigham and Women's Hospital, Boston, MA 02115, USA; Department of Genetics, Harvard Medical School, Boston, MA 02115, USA; Howard Hughes Medical Institute, Chevy Chase, MD 20815, USA
| | - Barbara E Stranger
- Section of Genetic Medicine, Department of Medicine, Institute for Genomics and Systems Biology, Center for Data Intensive Science, University of Chicago, Chicago, IL 60637, USA
| | - Haoyi Wang
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME 04609, USA
| | - Monte Westerfield
- Department of Biology, University of Oregon, Portland, OR 97403, USA
| | - Carol Bult
- The Jackson Laboratory for Mammalian Genetics, Bar Harbor, ME 04609, USA
| |
Collapse
|
23
|
Newson AJ, Leonard SJ, Hall A, Gaff CL. Known unknowns: building an ethics of uncertainty into genomic medicine. BMC Med Genomics 2016; 9:57. [PMID: 27586379 PMCID: PMC5009566 DOI: 10.1186/s12920-016-0219-0] [Citation(s) in RCA: 61] [Impact Index Per Article: 7.6] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/23/2015] [Accepted: 08/26/2016] [Indexed: 12/02/2022] Open
Abstract
Background Genomic testing has reached the point where, technically at least, it can be cheaper to undertake panel-, exome- or whole genome testing than it is to sequence a single gene. An attribute of these approaches is that information gleaned will often have uncertain significance. In addition to the challenges this presents for pre-test counseling and informed consent, a further consideration emerges over how - ethically - we should conceive of and respond to this uncertainty. To date, the ethical aspects of uncertainty in genomics have remained under-explored. Discussion In this paper, we draft a conceptual and ethical response to the question of how to conceive of and respond to uncertainty in genomic medicine. After introducing the problem, we articulate a concept of ‘genomic uncertainty’. Drawing on this, together with exemplar clinical cases and related empirical literature, we then critique the presumption that uncertainty is always problematic and something to be avoided, or eradicated. We conclude by outlining an ‘ethics of genomic uncertainty’; describing how we might handle uncertainty in genomic medicine. This involves fostering resilience, welfare, autonomy and solidarity. Conclusions Uncertainty will be an inherent aspect of clinical practice in genomics for some time to come. Genomic testing should not be offered with the explicit aim to reduce uncertainty. Rather, uncertainty should be appraised, adapted to and communicated about as part of the process of offering and providing genomic information.
Collapse
Affiliation(s)
- Ainsley J Newson
- Centre for Values, Ethics and the Law in Medicine, Sydney School of Public Health, University of Sydney, Level 1, Medical Foundation Building K25, 92-94, Parramatta Road, Camperdown, NSW, 2006, Australia.
| | - Samantha J Leonard
- Inserm, Unite 1027, University Toulouse III, Paul Sabatier, France.,Service de Genetique Medicale CHU Toulouse, Toulouse, France
| | | | - Clara L Gaff
- Melbourne Genomics Health Alliance, Melbourne, Australia.,Departments of Paediatrics and Medicine, University of Melbourne, Melbourne, Australia
| |
Collapse
|
24
|
Flannick J, Johansson S, Njølstad PR. Common and rare forms of diabetes mellitus: towards a continuum of diabetes subtypes. Nat Rev Endocrinol 2016; 12:394-406. [PMID: 27080136 DOI: 10.1038/nrendo.2016.50] [Citation(s) in RCA: 91] [Impact Index Per Article: 11.4] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/29/2022]
Abstract
Insights into the genetic basis of type 2 diabetes mellitus (T2DM) have been difficult to discern, despite substantial research. More is known about rare forms of diabetes mellitus, several of which share clinical and genetic features with the common form of T2DM. In this Review, we discuss the extent to which the study of rare and low-frequency mutations in large populations has begun to bridge the gap between rare and common forms of diabetes mellitus. We hypothesize that the perceived division between these diseases might be due, in part, to the historical ascertainment bias of genetic studies, rather than a clear distinction between disease pathophysiologies. We also discuss possible implications of a new model for the genetic basis of diabetes mellitus subtypes, where the boundary between subtypes becomes blurred.
Collapse
Affiliation(s)
- Jason Flannick
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, 415 Main Street, Cambridge, Massachusetts 02142, USA
- Center for Human Genetic Research, Massachusetts General Hospital, 185 Cambridge Street, Boston, Massachusetts 02114, USA
| | - Stefan Johansson
- K.G. Jebsen Center for Diabetes Research, The Department of Clinical Science, University of Bergen, Jonas Lies veg 87, N-5020 Bergen, Norway
- Center for Medical Genetics and Molecular Medicine, Haukeland University Hospital, Jonas Lies veg 65, N-5021 Bergen, Norway
| | - Pål R Njølstad
- K.G. Jebsen Center for Diabetes Research, The Department of Clinical Science, University of Bergen, Jonas Lies veg 87, N-5020 Bergen, Norway
- Department of Pediatrics, Haukeland University Hospital, Jonas Lies veg 65, N-5021 Bergen, Norway
| |
Collapse
|