1
|
Lin YJ, Menon AS, Hu Z, Brenner SE. Variant Impact Predictor database (VIPdb), version 2: trends from three decades of genetic variant impact predictors. Hum Genomics 2024; 18:90. [PMID: 39198917 PMCID: PMC11360829 DOI: 10.1186/s40246-024-00663-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2024] [Accepted: 08/19/2024] [Indexed: 09/01/2024] Open
Abstract
BACKGROUND Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb). RESULTS The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past three decades, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 190 VIPs, resulting in a total of 407 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods. CONCLUSIONS VIPdb version 2 summarizes 407 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. VIPdb is available at https://genomeinterpretation.org/vipdb.
Collapse
Affiliation(s)
- Yu-Jen Lin
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, 94720, USA
- Center for Computational Biology, University of California, Berkeley, CA, 94720, USA
| | - Arul S Menon
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, 94720, USA
- College of Computing, Data Science, and Society, University of California, Berkeley, CA, 94720, USA
| | - Zhiqiang Hu
- Department of Plant and Microbial Biology, University of California, 111 Koshland Hall #3102, Berkeley, CA, 94720-3102, USA
- Illumina, Foster City, CA, 94404, USA
| | - Steven E Brenner
- Department of Molecular and Cell Biology, University of California, Berkeley, CA, 94720, USA.
- Center for Computational Biology, University of California, Berkeley, CA, 94720, USA.
- College of Computing, Data Science, and Society, University of California, Berkeley, CA, 94720, USA.
- Department of Plant and Microbial Biology, University of California, 111 Koshland Hall #3102, Berkeley, CA, 94720-3102, USA.
| |
Collapse
|
2
|
Lin YJ, Menon AS, Hu Z, Brenner SE. Variant Impact Predictor database (VIPdb), version 2: Trends from 25 years of genetic variant impact predictors. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.06.25.600283. [PMID: 38979289 PMCID: PMC11230257 DOI: 10.1101/2024.06.25.600283] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 07/10/2024]
Abstract
Background Variant interpretation is essential for identifying patients' disease-causing genetic variants amongst the millions detected in their genomes. Hundreds of Variant Impact Predictors (VIPs), also known as Variant Effect Predictors (VEPs), have been developed for this purpose, with a variety of methodologies and goals. To facilitate the exploration of available VIP options, we have created the Variant Impact Predictor database (VIPdb). Results The Variant Impact Predictor database (VIPdb) version 2 presents a collection of VIPs developed over the past 25 years, summarizing their characteristics, ClinGen calibrated scores, CAGI assessment results, publication details, access information, and citation patterns. We previously summarized 217 VIPs and their features in VIPdb in 2019. Building upon this foundation, we identified and categorized an additional 186 VIPs, resulting in a total of 403 VIPs in VIPdb version 2. The majority of the VIPs have the capacity to predict the impacts of single nucleotide variants and nonsynonymous variants. More VIPs tailored to predict the impacts of insertions and deletions have been developed since the 2010s. In contrast, relatively few VIPs are dedicated to the prediction of splicing, structural, synonymous, and regulatory variants. The increasing rate of citations to VIPs reflects the ongoing growth in their use, and the evolving trends in citations reveal development in the field and individual methods. Conclusions VIPdb version 2 summarizes 403 VIPs and their features, potentially facilitating VIP exploration for various variant interpretation applications. Availability VIPdb version 2 is available at https://genomeinterpretation.org/vipdb.
Collapse
Affiliation(s)
- Yu-Jen Lin
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
- Center for Computational Biology, University of California, Berkeley, California 94720, USA
| | - Arul S. Menon
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
- College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, USA
| | - Zhiqiang Hu
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
- Currently at: Illumina, Foster City, California 94404, USA
| | - Steven E. Brenner
- Department of Molecular and Cell Biology, University of California, Berkeley, California 94720, USA
- Center for Computational Biology, University of California, Berkeley, California 94720, USA
- College of Computing, Data Science, and Society, University of California, Berkeley, California 94720, USA
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
| |
Collapse
|
3
|
Ahn SH, Park Y, Kim JH. Contradiction in Star-Allele Nomenclature of Pharmacogenes between Common Haplotypes and Rare Variants. Genes (Basel) 2024; 15:521. [PMID: 38674455 PMCID: PMC11050392 DOI: 10.3390/genes15040521] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2024] [Revised: 04/18/2024] [Accepted: 04/19/2024] [Indexed: 04/28/2024] Open
Abstract
The nomenclature of star alleles has been widely used in pharmacogenomics to enhance treatment outcomes, predict drug response variability, and reduce adverse reactions. However, the discovery of numerous rare functional variants through genome sequencing introduces complexities into the star-allele system. This study aimed to assess the nature and impact of the rapid discovery of numerous rare functional variants in the traditional haplotype-based star-allele system. We developed a new method to construct haplogroups, representing a common ancestry structure, by iteratively excluding rare and functional variants of the 25 representative pharmacogenes using the 2504 genomes from the 1000 Genomes Project. In total, 192 haplogroups and 288 star alleles were identified, with an average of 7.68 ± 4.2 cross-ethnic haplogroups per gene. Most of the haplogroups (70.8%, 136/192) were highly aligned with their corresponding classical star alleles (VI = 1.86 ± 0.78), exhibiting higher genetic diversity than the star alleles. Approximately 41.3% (N = 119) of the star alleles in the 2504 genomes did not belong to any of the haplogroups, and most of them (91.3%, 105/116) were determined by a single variant according to the allele-definition table provided by CPIC. These functional single variants had low allele frequency (MAF < 1%), high evolutionary conservation, and variant deleteriousness, which suggests significant negative selection. It is suggested that the traditional haplotype-based naming system for pharmacogenetic star alleles now needs to be adjusted by balancing both traditional haplotyping and newly emerging variant-sequencing approaches to reduce naming complexity.
Collapse
Affiliation(s)
- Se Hwan Ahn
- Department of Biomedical Sciences, Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul 03080, Republic of Korea;
| | - Yoomi Park
- Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul 03080, Republic of Korea;
- Medical Research Center, Seoul National University College of Medicine, Seoul 03080, Republic of Korea
| | - Ju Han Kim
- Department of Biomedical Sciences, Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul 03080, Republic of Korea;
- Seoul National University Biomedical Informatics (SNUBI), Seoul National University College of Medicine, Seoul 03080, Republic of Korea;
| |
Collapse
|
4
|
Zhao J, Cato LD, Arora UP, Bao EL, Bryant SC, Williams N, Jia Y, Goldman SR, Nangalia J, Erb MA, Vos SM, Armstrong SA, Sankaran VG. Inherited blood cancer predisposition through altered transcription elongation. Cell 2024; 187:642-658.e19. [PMID: 38218188 PMCID: PMC10872907 DOI: 10.1016/j.cell.2023.12.016] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/06/2023] [Revised: 11/26/2023] [Accepted: 12/08/2023] [Indexed: 01/15/2024]
Abstract
Despite advances in defining diverse somatic mutations that cause myeloid malignancies, a significant heritable component for these cancers remains largely unexplained. Here, we perform rare variant association studies in a large population cohort to identify inherited predisposition genes for these blood cancers. CTR9, which encodes a key component of the PAF1 transcription elongation complex, is among the significant genes identified. The risk variants found in the cases cause loss of function and result in a ∼10-fold increased odds of acquiring a myeloid malignancy. Partial CTR9 loss of function expands human hematopoietic stem cells (HSCs) by increased super elongation complex-mediated transcriptional activity, which thereby increases the expression of key regulators of HSC self-renewal. By following up on insights from a human genetic study examining inherited predisposition to the myeloid malignancies, we define a previously unknown antagonistic interaction between the PAF1 and super elongation complexes. These insights could enable targeted approaches for blood cancer prevention.
Collapse
Affiliation(s)
- Jiawei Zhao
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Center for Cancer Immunology, Faculty of Pharmaceutical Sciences, Shenzhen Institute of Advanced Technology, Chinese Academy of Sciences (CAS), Shenzhen, China.
| | - Liam D Cato
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Uma P Arora
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Erik L Bao
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Nicholas Williams
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK; UK and MRC-Wellcome Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Yuemeng Jia
- Harvard Stem Cell Institute, Cambridge, MA, USA; Stem Cell Program, Boston Children's Hospital, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA
| | - Seth R Goldman
- Nascent Transcriptomics Core, Department of Biological Chemistry and Molecular Pharmacology, Harvard Medical School, Boston, MA, USA
| | - Jyoti Nangalia
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK; UK and MRC-Wellcome Cambridge Stem Cell Institute, University of Cambridge, Cambridge, UK
| | - Michael A Erb
- Department of Chemistry, The Scripps Research Institute, La Jolla, CA, USA
| | - Seychelle M Vos
- Department of Biology, Massachusetts Institute of Technology, Cambridge, MA, USA; Howard Hughes Medical Institute, Massachusetts Institute of Technology, Cambridge, MA, USA
| | - Scott A Armstrong
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Vijay G Sankaran
- Division of Hematology/Oncology, Boston Children's Hospital, Harvard Medical School, Boston, MA, USA; Department of Pediatric Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard Stem Cell Institute, Cambridge, MA, USA.
| |
Collapse
|
5
|
Gomez F, Fisk B, McMichael JF, Mosior M, Foltz JA, Skidmore ZL, Duncavage EJ, Miller CA, Abel H, Li YS, Russler-Germain DA, Krysiak K, Watkins MP, Ramirez CA, Schmidt A, Martins Rodrigues F, Trani L, Khanna A, Wagner JA, Fulton RS, Fronick CC, O'Laughlin MD, Schappe T, Cashen AF, Mehta-Shah N, Kahl BS, Walker J, Bartlett NL, Griffith M, Fehniger TA, Griffith OL. Ultra-Deep Sequencing Reveals the Mutational Landscape of Classical Hodgkin Lymphoma. CANCER RESEARCH COMMUNICATIONS 2023; 3:2312-2330. [PMID: 37910143 PMCID: PMC10648575 DOI: 10.1158/2767-9764.crc-23-0140] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/30/2023] [Revised: 07/27/2023] [Accepted: 10/24/2023] [Indexed: 11/03/2023]
Abstract
The malignant Hodgkin and Reed Sternberg (HRS) cells of classical Hodgkin lymphoma (cHL) are scarce in affected lymph nodes, creating a challenge to detect driver somatic mutations. As an alternative to cell purification techniques, we hypothesized that ultra-deep exome sequencing would allow genomic study of HRS cells, thereby streamlining analysis and avoiding technical pitfalls. To test this, 31 cHL tumor/normal pairs were exome sequenced to approximately 1,000× median depth of coverage. An orthogonal error-corrected sequencing approach verified >95% of the discovered mutations. We identified mutations in genes novel to cHL including: CDH5 and PCDH7, novel stop gain mutations in IL4R, and a novel pattern of recurrent mutations in pathways regulating Hippo signaling. As a further application of our exome sequencing, we attempted to identify expressed somatic single-nucleotide variants (SNV) in single-nuclei RNA sequencing (snRNA-seq) data generated from a patient in our cohort. Our snRNA analysis identified a clear cluster of cells containing a somatic SNV identified in our deep exome data. This cluster has differentially expressed genes that are consistent with genes known to be dysregulated in HRS cells (e.g., PIM1 and PIM3). The cluster also contains cells with an expanded B-cell clonotype further supporting a malignant phenotype. This study provides proof-of-principle that ultra-deep exome sequencing can be utilized to identify recurrent mutations in HRS cells and demonstrates the feasibility of snRNA-seq in the context of cHL. These studies provide the foundation for the further analysis of genomic variants in large cohorts of patients with cHL. SIGNIFICANCE Our data demonstrate the utility of ultra-deep exome sequencing in uncovering somatic variants in Hodgkin lymphoma, creating new opportunities to define the genes that are recurrently mutated in this disease. We also show for the first time the successful application of snRNA-seq in Hodgkin lymphoma and describe the expression profile of a putative cluster of HRS cells in a single patient.
Collapse
Affiliation(s)
- Felicia Gomez
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
- Siteman Cancer Center, Washington University School of Medicine, St Louis, Missouri
| | - Bryan Fisk
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Joshua F. McMichael
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Matthew Mosior
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Jennifer A. Foltz
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
| | - Zachary L. Skidmore
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Eric J. Duncavage
- Department of Pathology and Immunology, Washington University School of Medicine, St Louis, Missouri
| | - Christopher A. Miller
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Haley Abel
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Yi-Shan Li
- Department of Pathology and Immunology, Washington University School of Medicine, St Louis, Missouri
| | - David A. Russler-Germain
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
| | - Kilannin Krysiak
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
- Siteman Cancer Center, Washington University School of Medicine, St Louis, Missouri
- Department of Pathology and Immunology, Washington University School of Medicine, St Louis, Missouri
| | - Marcus P. Watkins
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
| | - Cody A. Ramirez
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Alina Schmidt
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Fernanda Martins Rodrigues
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Lee Trani
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Ajay Khanna
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
| | - Julia A. Wagner
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
| | - Robert S. Fulton
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Catrina C. Fronick
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Michelle D. O'Laughlin
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Timothy Schappe
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
| | - Amanda F. Cashen
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
| | - Neha Mehta-Shah
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
| | - Brad S. Kahl
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
| | - Jason Walker
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
| | - Nancy L. Bartlett
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
| | - Malachi Griffith
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
- Siteman Cancer Center, Washington University School of Medicine, St Louis, Missouri
- Department of Genetics, Washington University School of Medicine, St Louis, Missouri
| | - Todd A. Fehniger
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- Siteman Cancer Center, Washington University School of Medicine, St Louis, Missouri
| | - Obi L. Griffith
- Department of Medicine, Division of Oncology, Washington University School of Medicine, St Louis, Missouri
- McDonnell Genome Institute, Department of Medicine, Washington University School of Medicine, St Louis, Missouri
- Siteman Cancer Center, Washington University School of Medicine, St Louis, Missouri
- Department of Genetics, Washington University School of Medicine, St Louis, Missouri
| |
Collapse
|
6
|
Kotta M, Torchio M, Bayliss P, Cohen MC, Quarrell O, Wheeldon N, Marton T, Gentilini D, Crotti L, Coombs RC, Schwartz PJ. Cardiac Genetic Investigation of Sudden Infant and Early Childhood Death: A Study From Victims to Families. J Am Heart Assoc 2023; 12:e029100. [PMID: 37589201 PMCID: PMC10547337 DOI: 10.1161/jaha.122.029100] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/03/2023] [Accepted: 06/27/2023] [Indexed: 08/18/2023]
Abstract
Background Sudden infant death syndrome (SIDS) is the leading cause of death up to age 1. Sudden unexplained death in childhood (SUDC) is similar but affects mostly toddlers aged 1 to 4. SUDC is rarer than SIDS, and although cardiogenetic testing (molecular autopsy) identifies an underlying cause in a fraction of SIDS, less is known about SUDC. Methods and Results Seventy-seven SIDS and 16 SUDC cases underwent molecular autopsy with 25 definitive-evidence arrhythmia-associated genes. In 18 cases, another 76 genes with varying degrees of evidence were analyzed. Parents were offered cascade screening. Double-blind review of clinical-genetic data established genotype-phenotype correlations. The yield of likely pathogenic variants in the 25 genes was higher in SUDC than in SIDS (18.8% [3/16] versus 2.6% [2/77], respectively; P=0.03), whereas novel/ultra-rare variants of uncertain significance were comparably represented. Rare variants of uncertain significance and likely benign variants were found only in SIDS. In cases with expanded analyses, likely pathogenic/likely benign variants stemmed only from definitive-evidence genes, whereas all other genes contributed only variants of uncertain significance. Among 24 parents screened, variant status and phenotype largely agreed, and 3 cases positively correlated for cardiac channelopathies. Genotype-phenotype correlations significantly aided variant adjudication. Conclusions Genetic yield is higher in SUDC than in SIDS although, in both, it is contributed only by definitive-evidence genes. SIDS/SUDC cascade family screening facilitates establishment or dismissal of a diagnosis through definitive variant adjudication indicating that anonymity is no longer justifiable. Channelopathies may underlie a relevant fraction of SUDC. Binary classifications of genetic causality (pathogenic versus benign) could not always be adequate.
Collapse
Affiliation(s)
- Maria‐Christina Kotta
- Center for Cardiac Arrhythmias of Genetic Origin and Laboratory of Cardiovascular GeneticsIRCCS Istituto Auxologico ItalianoMilanItaly
| | - Margherita Torchio
- Center for Cardiac Arrhythmias of Genetic Origin and Laboratory of Cardiovascular GeneticsIRCCS Istituto Auxologico ItalianoMilanItaly
| | - Pauline Bayliss
- Department of Clinical GeneticsSheffield Children’s NHS Foundation TrustSheffieldUnited Kingdom
| | - Marta C. Cohen
- Department of HistopathologySheffield Children’s NHS Foundation TrustSheffieldUnited Kingdom
| | - Oliver Quarrell
- Sheffield Children’s Hospital NHS Foundation TrustSheffieldUnited Kingdom
- Department of NeurosciencesUniversity of SheffieldSheffieldUnited Kingdom
| | - Nigel Wheeldon
- Cardiothoracic CentreNorthern General Hospital, Sheffield Teaching Hospitals NHS TrustSheffieldUnited Kingdom
| | - Tamás Marton
- Cellular Pathology DepartmentBirmingham Women’s and Children’s HospitalBirminghamUnited Kingdom
| | - Davide Gentilini
- Bioinformatics and Statistical Genetics UnitIRCCS Istituto Auxologico ItalianoMilanItaly
| | - Lia Crotti
- Center for Cardiac Arrhythmias of Genetic Origin and Laboratory of Cardiovascular GeneticsIRCCS Istituto Auxologico ItalianoMilanItaly
- Department of Medicine and SurgeryUniversity of Milano‐BicoccaMilanItaly
| | - Robert C. Coombs
- Department of NeonatologySheffield Teaching Hospitals. NHS TrustSheffieldUnited Kingdom
| | - Peter J. Schwartz
- Center for Cardiac Arrhythmias of Genetic Origin and Laboratory of Cardiovascular GeneticsIRCCS Istituto Auxologico ItalianoMilanItaly
| |
Collapse
|
7
|
Klonowski J, Liang Q, Coban-Akdemir Z, Lo C, Kostka D. aenmd: annotating escape from nonsense-mediated decay for transcripts with protein-truncating variants. Bioinformatics 2023; 39:btad556. [PMID: 37688563 PMCID: PMC10534055 DOI: 10.1093/bioinformatics/btad556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Revised: 07/13/2023] [Accepted: 09/07/2023] [Indexed: 09/11/2023] Open
Abstract
SUMMARY DNA changes that cause premature termination codons (PTCs) represent a large fraction of clinically relevant pathogenic genomic variation. Typically, PTCs induce transcript degradation by nonsense-mediated mRNA decay (NMD) and render such changes loss-of-function alleles. However, certain PTC-containing transcripts escape NMD and can exert dominant-negative or gain-of-function (DN/GOF) effects. Therefore, systematic identification of human PTC-causing variants and their susceptibility to NMD contributes to the investigation of the role of DN/GOF alleles in human disease. Here we present aenmd, a software for annotating PTC-containing transcript-variant pairs for predicted escape from NMD. aenmd is user-friendly and self-contained. It offers functionality not currently available in other methods and is based on established and experimentally validated rules for NMD escape; the software is designed to work at scale, and to integrate seamlessly with existing analysis workflows. We applied aenmd to variants in the gnomAD, Clinvar, and GWAS catalog databases and report the prevalence of human PTC-causing variants in these databases, and the subset of these variants that could exert DN/GOF effects via NMD escape. AVAILABILITY AND IMPLEMENTATION aenmd is implemented in the R programming language. Code is available on GitHub as an R-package (github.com/kostkalab/aenmd.git), and as a containerized command-line interface (github.com/kostkalab/aenmd_cli.git).
Collapse
Affiliation(s)
- Jonathan Klonowski
- Department of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15201, United States
| | - Qianqian Liang
- Department of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15201, United States
| | - Zeynep Coban-Akdemir
- Department of Epidemiology, Human Genetics and Environmental Sciences, University of Texas School of Public Health, Houston, TX 77030, United States
| | - Cecilia Lo
- Department of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15201, United States
| | - Dennis Kostka
- Department of Developmental Biology, University of Pittsburgh School of Medicine, Pittsburgh, PA 15201, United States
- Department of Computational & Systems Biology and Center for Evolutionary Biology and Medicine, University of Pittsburgh School of Medicine, Pittsburgh, PA 15260,United States
| |
Collapse
|
8
|
Vellichirammal NN, Tan YD, Xiao P, Eudy J, Shats O, Kelly D, Desler M, Cowan K, Guda C. The mutational landscape of a US Midwestern breast cancer cohort reveals subtype-specific cancer drivers and prognostic markers. Hum Genomics 2023; 17:64. [PMID: 37454130 PMCID: PMC10349437 DOI: 10.1186/s40246-023-00511-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/31/2022] [Accepted: 07/11/2023] [Indexed: 07/18/2023] Open
Abstract
BACKGROUND Female breast cancer remains the second leading cause of cancer-related death in the USA. The heterogeneity in the tumor morphology across the cohort and within patients can lead to unpredictable therapy resistance, metastasis, and clinical outcome. Hence, supplementing classic pathological markers with intrinsic tumor molecular markers can help identify novel molecular subtypes and the discovery of actionable biomarkers. METHODS We conducted a large multi-institutional genomic analysis of paired normal and tumor samples from breast cancer patients to profile the complex genomic architecture of breast tumors. Long-term patient follow-up, therapeutic regimens, and treatment response for this cohort are documented using the Breast Cancer Collaborative Registry. The majority of the patients in this study were at tumor stage 1 (51.4%) and stage 2 (36.3%) at the time of diagnosis. Whole-exome sequencing data from 554 patients were used for mutational profiling and identifying cancer drivers. RESULTS We identified 54 tumors having at least 1000 mutations and 185 tumors with less than 100 mutations. Tumor mutational burden varied across the classified subtypes, and the top ten mutated genes include MUC4, MUC16, PIK3CA, TTN, TP53, NBPF10, NBPF1, CDC27, AHNAK2, and MUC2. Patients were classified based on seven biological and tumor-specific parameters, including grade, stage, hormone receptor status, histological subtype, Ki67 expression, lymph node status, race, and mutational profiles compared across different subtypes. Mutual exclusion of mutations in PIK3CA and TP53 was pronounced across different tumor grades. Cancer drivers specific to each subtype include TP53, PIK3CA, CDC27, CDH1, STK39, CBFB, MAP3K1, and GATA3, and mutations associated with patient survival were identified in our cohort. CONCLUSIONS This extensive study has revealed tumor burden, driver genes, co-occurrence, mutual exclusivity, and survival effects of mutations on a US Midwestern breast cancer cohort, paving the way for developing personalized therapeutic strategies.
Collapse
Affiliation(s)
| | - Yuan-De Tan
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Peng Xiao
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - James Eudy
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, 68198, USA
| | - Oleg Shats
- Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE, USA
- Fred and Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, USA
| | - David Kelly
- Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE, USA
- Fred and Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, USA
| | - Michelle Desler
- Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE, USA
- Fred and Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, USA
| | - Kenneth Cowan
- Eppley Institute for Research in Cancer and Allied Diseases, University of Nebraska Medical Center, Omaha, NE, USA
- Fred and Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, USA
| | - Chittibabu Guda
- Department of Genetics, Cell Biology and Anatomy, University of Nebraska Medical Center, Omaha, NE, 68198, USA.
- Center for Biomedical Informatics Research and Innovation, University of Nebraska Medical Center, Omaha, NE, 68198, USA.
- Fred and Pamela Buffett Cancer Center, University of Nebraska Medical Center, Omaha, USA.
| |
Collapse
|
9
|
Danzi MC, Dohrn MF, Fazal S, Beijer D, Rebelo AP, Cintra V, Züchner S. Deep structured learning for variant prioritization in Mendelian diseases. Nat Commun 2023; 14:4167. [PMID: 37443090 PMCID: PMC10345112 DOI: 10.1038/s41467-023-39306-7] [Citation(s) in RCA: 11] [Impact Index Per Article: 11.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/11/2022] [Accepted: 06/07/2023] [Indexed: 07/15/2023] Open
Abstract
Effective computer-aided or automated variant evaluations for monogenic diseases will expedite clinical diagnostic and research efforts of known and novel disease-causing genes. Here we introduce MAVERICK: a Mendelian Approach to Variant Effect pRedICtion built in Keras. MAVERICK is an ensemble of transformer-based neural networks that can classify a wide range of protein-altering single nucleotide variants (SNVs) and indels and assesses whether a variant would be pathogenic in the context of dominant or recessive inheritance. We demonstrate that MAVERICK outperforms all other major programs that assess pathogenicity in a Mendelian context. In a cohort of 644 previously solved patients with Mendelian diseases, MAVERICK ranks the causative pathogenic variant within the top five variants in over 95% of cases. Seventy-six percent of cases were solved by the top-ranked variant. MAVERICK ranks the causative pathogenic variant in hitherto novel disease genes within the first five candidate variants in 70% of cases. MAVERICK has already facilitated the identification of a novel disease gene causing a degenerative motor neuron disease. These results represent a significant step towards automated identification of causal variants in patients with Mendelian diseases.
Collapse
Affiliation(s)
- Matt C Danzi
- Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Maike F Dohrn
- Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
- Department of Neurology, Medical Faculty of the RWTH Aachen University, Aachen, Germany
| | - Sarah Fazal
- Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Danique Beijer
- Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Adriana P Rebelo
- Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Vivian Cintra
- Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA
| | - Stephan Züchner
- Dr. John T. Macdonald Foundation Department of Human Genetics and John P. Hussman Institute for Human Genomics, University of Miami Miller School of Medicine, Miami, FL, USA.
| |
Collapse
|
10
|
Veilleux CC, Garrett EC, Pajic P, Saitou M, Ochieng J, Dagsaan LD, Dominy NJ, Perry GH, Gokcumen O, Melin AD. Human subsistence and signatures of selection on chemosensory genes. Commun Biol 2023; 6:683. [PMID: 37400713 DOI: 10.1038/s42003-023-05047-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/23/2022] [Accepted: 06/15/2023] [Indexed: 07/05/2023] Open
Abstract
Chemosensation (olfaction, taste) is essential for detecting and assessing foods, such that dietary shifts elicit evolutionary changes in vertebrate chemosensory genes. The transition from hunting and gathering to agriculture dramatically altered how humans acquire food. Recent genetic and linguistic studies suggest agriculture may have precipitated olfactory degeneration. Here, we explore the effects of subsistence behaviors on olfactory (OR) and taste (TASR) receptor genes among rainforest foragers and neighboring agriculturalists in Africa and Southeast Asia. We analyze 378 functional OR and 26 functional TASR genes in 133 individuals across populations in Uganda (Twa, Sua, BaKiga) and the Philippines (Agta, Mamanwa, Manobo) with differing subsistence histories. We find no evidence of relaxed selection on chemosensory genes in agricultural populations. However, we identify subsistence-related signatures of local adaptation on chemosensory genes within each geographic region. Our results highlight the importance of culture, subsistence economy, and drift in human chemosensory perception.
Collapse
Affiliation(s)
- Carrie C Veilleux
- Department of Anatomy, Midwestern University, 19555 N 59th Ave, Glendale, AZ, 85308, USA.
- Department of Anthropology & Archaeology, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada.
| | - Eva C Garrett
- Department of Anthropology & Archaeology, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada
- Department of Anthropology, Boston University, 232 Bay State Road, Boston, MA, 02215, USA
| | - Petar Pajic
- Department of Biological Sciences, University at Buffalo, 109 Cooke Hall, Buffalo, NY, 14260, USA
| | - Marie Saitou
- Department of Biological Sciences, University at Buffalo, 109 Cooke Hall, Buffalo, NY, 14260, USA
| | - Joseph Ochieng
- Department of Anatomy, Makerere University College of Health Sciences, Kampala, Uganda
| | - Lilia D Dagsaan
- National Commission for Indigenous Peoples, Botolan, Philippines
| | - Nathaniel J Dominy
- Department of Anthropology, Dartmouth College, 6047 Silsby Hall, Hanover, NH, 03755, USA
| | - George H Perry
- Departments of Anthropology and Biology, The Pennsylvania State University, 410 Carpenter Building, University Park, PA, 16802, USA
| | - Omer Gokcumen
- Department of Biological Sciences, University at Buffalo, 109 Cooke Hall, Buffalo, NY, 14260, USA
| | - Amanda D Melin
- Department of Anthropology & Archaeology, University of Calgary, 2500 University Drive NW, Calgary, AB, T2N 1N4, Canada.
- Department of Medical Genetics, University of Calgary, 3330 Hospital Drive NW, Calgary, AB, T2N 4N1, Canada.
- Alberta Children's Hospital Research Institute, 3330 Hospital Dr. NW, Calgary, AB, T2N 4N1, Canada.
| |
Collapse
|
11
|
Kumar S, Gerstein M. Unified views on variant impact across many diseases. Trends Genet 2023; 39:442-450. [PMID: 36858880 PMCID: PMC10192142 DOI: 10.1016/j.tig.2023.02.002] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/14/2022] [Revised: 02/02/2023] [Accepted: 02/02/2023] [Indexed: 03/03/2023]
Abstract
Genomic studies of human disorders are often performed by distinct research communities (i.e., focused on rare diseases, common diseases, or cancer). Despite underlying differences in the mechanistic origin of different disease categories, these studies share the goal of identifying causal genomic events that are critical for the clinical manifestation of the disease phenotype. Moreover, these studies face common challenges, including understanding the complex genetic architecture of the disease, deciphering the impact of variants on multiple scales, and interpreting noncoding mutations. Here, we highlight these challenges in depth and argue that properly addressing them will require a more unified vocabulary and approach across disease communities. Toward this goal, we present a unified perspective on relating variant impact to various genomic disorders.
Collapse
Affiliation(s)
- Sushant Kumar
- Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada; Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.
| | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA; Department of Computer Science, Yale University, New Haven, CT 06520, USA; Department of Statistics & Data Science, Yale University, New Haven, CT 06520, USA.
| |
Collapse
|
12
|
Quan L, Chu X, Sun X, Wu T, Lyu Q. How Deepbics Quantifies Intensities of Transcription Factor-DNA Binding and Facilitates Prediction of Single Nucleotide Variant Pathogenicity With a Deep Learning Model Trained On ChIP-Seq Data Sets. IEEE/ACM TRANSACTIONS ON COMPUTATIONAL BIOLOGY AND BIOINFORMATICS 2023; 20:1594-1599. [PMID: 35471887 DOI: 10.1109/tcbb.2022.3170343] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 05/04/2023]
Abstract
The binding of DNA sequences to cell type-specific transcription factors is essential for regulating gene expression in all organisms. Many variants occurring in these binding regions play crucial roles in human disease by disrupting the cis-regulation of gene expression. We first implemented a sequence-based deep learning model called deepBICS to quantify the intensity of transcription factors-DNA binding. The experimental results not only showed the superiority of deepBICS on ChIP-seq data sets but also suggested deepBICS as a language model could help the classification of disease-related and neutral variants. We then built a language model-based method called deepBICS4SNV to predict the pathogenicity of single nucleotide variants. The good performance of deepBICS4SNV on 2 tests related to Mendelian disorders and viral diseases shows the sequence contextual information derived from language models can improve prediction accuracy and generalization capability.
Collapse
|
13
|
Alasiri A, Karczewski KJ, Cole B, Loza BL, Moore JH, van der Laan SW, Asselbergs FW, Keating BJ, van Setten J. LoFTK: a framework for fully automated calculation of predicted Loss-of-Function variants and genes. BioData Min 2023; 16:3. [PMID: 36732776 PMCID: PMC9893534 DOI: 10.1186/s13040-023-00321-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/04/2022] [Accepted: 01/04/2023] [Indexed: 02/04/2023] Open
Abstract
BACKGROUND Loss-of-Function (LoF) variants in human genes are important due to their impact on clinical phenotypes and frequent occurrence in the genomes of healthy individuals. The association of LoF variants with complex diseases and traits may lead to the discovery and validation of novel therapeutic targets. Current approaches predict high-confidence LoF variants without identifying the specific genes or the number of copies they affect. Moreover, there is a lack of methods for detecting knockout genes caused by compound heterozygous (CH) LoF variants. RESULTS We have developed the Loss-of-Function ToolKit (LoFTK), which allows efficient and automated prediction of LoF variants from genotyped, imputed and sequenced genomes. LoFTK enables the identification of genes that are inactive in one or two copies and provides summary statistics for downstream analyses. LoFTK can identify CH LoF variants, which result in LoF genes with two copies lost. Using data from parents and offspring we show that 96% of CH LoF genes predicted by LoFTK in the offspring have the respective alleles donated by each parent. CONCLUSIONS LoFTK is a command-line based tool that provides a reliable computational workflow for predicting LoF variants from genotyped and sequenced genomes, identifying genes that are inactive in 1 or 2 copies. LoFTK is an open software and is freely available to non-commercial users at https://github.com/CirculatoryHealth/LoFTK .
Collapse
Affiliation(s)
- Abdulrahman Alasiri
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, Netherlands
- Medical Genomics Research Department, King Abdullah International Medical Research Center, King Saud Bin Abdulaziz University for Health Sciences, Ministry of National Guard Health Affairs, Riyadh, Saudi Arabia
| | - Konrad J Karczewski
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Brian Cole
- Bioinformatics Core, Harvard Medical School, Boston, MA, USA
| | - Bao-Li Loza
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jason H Moore
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, Los Angeles, CA, USA
| | - Sander W van der Laan
- Central Diagnostic Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, University of Utrecht, Utrecht, Netherlands
| | - Folkert W Asselbergs
- Department of Cardiology, Amsterdam University Medical Centers, University of Amsterdam, Amsterdam, Netherlands
- Health Data Research UK and Institute of Health Informatics, University College London, London, UK
| | - Brendan J Keating
- Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Jessica van Setten
- Department of Cardiology, Division Heart and Lungs, University Medical Center Utrecht, University of Utrecht, Heidelberglaan 100, 3584 CX, Utrecht, Netherlands.
| |
Collapse
|
14
|
Zhou H, Arapoglou T, Li X, Li Z, Zheng X, Moore J, Asok A, Kumar S, Blue E, Buyske S, Cox N, Felsenfeld A, Gerstein M, Kenny E, Li B, Matise T, Philippakis A, Rehm HL, Sofia HJ, Snyder G, Weng Z, Neale B, Sunyaev S, Lin X. FAVOR: functional annotation of variants online resource and annotator for variation across the human genome. Nucleic Acids Res 2023; 51:D1300-D1311. [PMID: 36350676 PMCID: PMC9825437 DOI: 10.1093/nar/gkac966] [Citation(s) in RCA: 36] [Impact Index Per Article: 36.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/15/2022] [Revised: 09/25/2022] [Accepted: 10/14/2022] [Indexed: 11/11/2022] Open
Abstract
Large biobank-scale whole genome sequencing (WGS) studies are rapidly identifying a multitude of coding and non-coding variants. They provide an unprecedented resource for illuminating the genetic basis of human diseases. Variant functional annotations play a critical role in WGS analysis, result interpretation, and prioritization of disease- or trait-associated causal variants. Existing functional annotation databases have limited scope to perform online queries and functionally annotate the genotype data of large biobank-scale WGS studies. We develop the Functional Annotation of Variants Online Resources (FAVOR) to meet these pressing needs. FAVOR provides a comprehensive multi-faceted variant functional annotation online portal that summarizes and visualizes findings of all possible nine billion single nucleotide variants (SNVs) across the genome. It allows for rapid variant-, gene- and region-level queries of variant functional annotations. FAVOR integrates variant functional information from multiple sources to describe the functional characteristics of variants and facilitates prioritizing plausible causal variants influencing human phenotypes. Furthermore, we provide a scalable annotation tool, FAVORannotator, to functionally annotate large-scale WGS studies and efficiently store the genotype and their variant functional annotation data in a single file using the annotated Genomic Data Structure (aGDS) format, making downstream analysis more convenient. FAVOR and FAVORannotator are available at https://favor.genohub.org.
Collapse
Affiliation(s)
- Hufeng Zhou
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Theodore Arapoglou
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Xihao Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Zilin Li
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Department of Biostatistics and Health Data Science, Indiana University School of Medicine, Indianapolis, IN, USA
| | - Xiuwen Zheng
- Department of Biostatistics, University of Washington, Seattle, WA 98195, USA
| | - Jill Moore
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | | | - Sushant Kumar
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Princess Margaret Cancer Centre, Toronto, ON, Canada
| | - Elizabeth E Blue
- Division of Medical Genetics, University of Washington, Seattle, WA, USA
- Brotman Baty Institute for Precision Medicine, Seattle, WA, USA
| | - Steven Buyske
- Department of Statistics, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Nancy Cox
- Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | | | - Mark Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT, USA
| | - Eimear Kenny
- Department of Genetics and Genomic Science, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Bingshan Li
- Department of Molecular Physiology and Biophysics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Tara Matise
- Department of Genetics, Rutgers, The State University of New Jersey, Piscataway, NJ, USA
| | - Anthony Philippakis
- Data Science Platform, Broad Institute of Harvard and MIT, Cambridge, MA, USA
| | - Heidi L Rehm
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Heidi J Sofia
- National Human Genome Research Institute, Bethesda, DC, USA
| | - Grace Snyder
- National Human Genome Research Institute, Bethesda, DC, USA
| | | | - Zhiping Weng
- Program in Bioinformatics and Integrative Biology, University of Massachusetts Chan Medical School, Worcester, MA, USA
| | - Benjamin Neale
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
| | - Shamil R Sunyaev
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
| | - Xihong Lin
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Department of Statistics, Harvard University, Cambridge, MA, USA
| |
Collapse
|
15
|
Schaflinger E, Blatterer J, Khan AS, Kaufmann L, Auinger L, Tatrai B, Abbasi SW, Zeeshan Ali M, Abbasi AA, Al Kaissi A, Petek E, Wagner K, Ahmad Khan M, Windpassinger C. An exceptional biallelic N-terminal frame shift mutation in ZMPSTE24 leads to non-lethal progeria due to possible utilization of a downstream alternative start codon. Gene 2022; 833:146582. [PMID: 35597529 DOI: 10.1016/j.gene.2022.146582] [Citation(s) in RCA: 2] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/09/2021] [Revised: 03/29/2022] [Accepted: 05/16/2022] [Indexed: 11/16/2022]
Abstract
Biallelic mutations in ZMPSTE24 are known to be associated with autosomal recessive mandibuloacral dysplasia with type B lipodystrophy (MADB) and lethal restrictive dermopathy (RD), respectively. Disease manifestation is depending on the remaining enzyme activity of the mutated ZMPSTE24 protein. To date, complete loss of function has exclusively been reported in RD cases. In this study, we identified a novel N-terminal homozygous frameshift mutation (c.28_29insA) in a consanguineous family segregating with MADB. An in-depth analysis of the mutated sequence revealed, that the one base pair insertion creates a novel downstream in-frame start codon, which supposedly serves as an alternative translation initiation site (TIS). This possible rescue mechanism would explain the relatively mild clinical outcome in the studied individuals. Our findings demonstrate the necessity for careful interpretation of N-terminal variants potentially effecting translation initiation.
Collapse
Affiliation(s)
- Erich Schaflinger
- Diagnostic and Research Institute of Human Genetics, Medical University of Graz, Graz 8010, Austria
| | - Jasmin Blatterer
- Diagnostic and Research Institute of Human Genetics, Medical University of Graz, Graz 8010, Austria
| | - Aiman Saeed Khan
- Gomal Centre of Biochemistry and Biotechnology, Gomal University, D.I.Khan, Khyber Pakhtunkhwa, Pakistan
| | - Lukas Kaufmann
- Diagnostic and Research Institute of Human Genetics, Medical University of Graz, Graz 8010, Austria
| | - Lisa Auinger
- Diagnostic and Research Institute of Human Genetics, Medical University of Graz, Graz 8010, Austria
| | - Benjamin Tatrai
- Diagnostic and Research Institute of Human Genetics, Medical University of Graz, Graz 8010, Austria
| | - Sumra Wajid Abbasi
- NUMS Department of Biological Sciences, National University of Medical Sciences, The Mall, Abid Majeed Road, Rawalpindi, Punjab, Pakistan
| | - Muhammad Zeeshan Ali
- Gomal Centre of Biochemistry and Biotechnology, Gomal University, D.I.Khan, Khyber Pakhtunkhwa, Pakistan
| | - Ansar Ahmad Abbasi
- Department of Zoology, Mirpur University of Science and Technology, Mirpur, AJK 10250, Pakistan
| | - Ali Al Kaissi
- Ludwig Boltzmann Institute of Osteology at Hanusch Hospital of OEGK and AUVA Trauma Centre Meidling, 1st Med. Dept. Hanusch Hospital, Vienna, Austria
| | - Erwin Petek
- Diagnostic and Research Institute of Human Genetics, Medical University of Graz, Graz 8010, Austria
| | - Klaus Wagner
- Diagnostic and Research Institute of Human Genetics, Medical University of Graz, Graz 8010, Austria
| | - Muzammil Ahmad Khan
- Gomal Centre of Biochemistry and Biotechnology, Gomal University, D.I.Khan, Khyber Pakhtunkhwa, Pakistan.
| | - Christian Windpassinger
- Diagnostic and Research Institute of Human Genetics, Medical University of Graz, Graz 8010, Austria.
| |
Collapse
|
16
|
Computational Resources for the Interpretation of Variations in Cancer. ADVANCES IN EXPERIMENTAL MEDICINE AND BIOLOGY 2022; 1361:177-198. [DOI: 10.1007/978-3-030-91836-1_10] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/19/2022]
|
17
|
Barbosa-Gouveia S, Vázquez-Mosquera ME, González-Vioque E, Álvarez JV, Chans R, Laranjeira F, Martins E, Ferreira AC, Avila-Alvarez A, Couce ML. Utility of Gene Panels for the Diagnosis of Inborn Errors of Metabolism in a Metabolic Reference Center. Genes (Basel) 2021; 12:1262. [PMID: 34440436 PMCID: PMC8391361 DOI: 10.3390/genes12081262] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/15/2021] [Revised: 08/04/2021] [Accepted: 08/16/2021] [Indexed: 11/28/2022] Open
Abstract
Next-generation sequencing (NGS) technologies have been proposed as a first-line test for the diagnosis of inborn errors of metabolism (IEM), a group of genetically heterogeneous disorders with overlapping or nonspecific phenotypes. Over a 3-year period, we prospectively analyzed 311 pediatric patients with a suspected IEM using four targeted gene panels. The rate of positive diagnosis was 61.86% for intermediary metabolism defects, 32.84% for complex molecular defects, 19% for hypoglycemic/hyperglycemic events, and 17% for mitochondrial diseases, and a conclusive molecular diagnosis was established in 2-4 weeks. Forty-one patients for whom negative results were obtained with the mitochondrial diseases panel underwent subsequent analyses using the NeuroSeq panel, which groups all genes from the individual panels together with genes associated with neurological disorders (1870 genes in total). This achieved a diagnostic rate of 32%. We next evaluated the utility of a tool, Phenomizer, for differential diagnosis, and established a correlation between phenotype and molecular findings in 39.3% of patients. Finally, we evaluated the mutational architecture of the genes analyzed by determining z-scores, loss-of-function observed/expected upper bound fraction (LOEUF), and haploinsufficiency (HI) scores. In summary, targeted gene panels for specific groups of IEMs enabled rapid and effective diagnosis, which is critical for the therapeutic management of IEM patients.
Collapse
Affiliation(s)
- Sofia Barbosa-Gouveia
- Unit of Diagnosis and Treatment of Congenital Metabolic Diseases, Department of Paediatrics, IDIS-Health Research Institute of Santiago de Compostela, Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), European Reference Network for Hereditary Metabolic Disorders (MetabERN), Santiago de Compostela University Clinical Hospital, 15704 Santiago de Compostela, Spain; (S.B.-G.); (M.E.V.-M.); (J.V.Á.); (R.C.)
| | - María E. Vázquez-Mosquera
- Unit of Diagnosis and Treatment of Congenital Metabolic Diseases, Department of Paediatrics, IDIS-Health Research Institute of Santiago de Compostela, Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), European Reference Network for Hereditary Metabolic Disorders (MetabERN), Santiago de Compostela University Clinical Hospital, 15704 Santiago de Compostela, Spain; (S.B.-G.); (M.E.V.-M.); (J.V.Á.); (R.C.)
| | - Emiliano González-Vioque
- Department of Clinical Biochemistry, Puerta de Hierro-Majadahonda University Hospital, 28222 Majadahonda, Spain;
| | - José V. Álvarez
- Unit of Diagnosis and Treatment of Congenital Metabolic Diseases, Department of Paediatrics, IDIS-Health Research Institute of Santiago de Compostela, Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), European Reference Network for Hereditary Metabolic Disorders (MetabERN), Santiago de Compostela University Clinical Hospital, 15704 Santiago de Compostela, Spain; (S.B.-G.); (M.E.V.-M.); (J.V.Á.); (R.C.)
| | - Roi Chans
- Unit of Diagnosis and Treatment of Congenital Metabolic Diseases, Department of Paediatrics, IDIS-Health Research Institute of Santiago de Compostela, Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), European Reference Network for Hereditary Metabolic Disorders (MetabERN), Santiago de Compostela University Clinical Hospital, 15704 Santiago de Compostela, Spain; (S.B.-G.); (M.E.V.-M.); (J.V.Á.); (R.C.)
| | - Francisco Laranjeira
- Biochemical Genetics Unit, Centro de Genética Médica Doutor Jacinto Magalhães, 4050-466 Porto, Portugal;
| | - Esmeralda Martins
- Centro Materno-Infantil do Norte, Centro Hospitalar Universitário do Porto (CHUP), Coordinator of the Centro de Referência de Doenças Hereditárias do Metabolismo do CHUP, 4050-466 Porto, Portugal;
| | - Ana Cristina Ferreira
- Hospital D. Estefânia, Centro Hospitalar de Lisboa Central (CHLC), Coordinator of the Centro de Referência de Doenças Hereditárias do Metabolismo do CHLC, 1169-050 Lisboa, Portugal;
| | - Alejandro Avila-Alvarez
- Neonatology Unit, Pediatrics Department, Complexo Hospitalario Universitario de A Coruña, SERGAS, 15006 A Coruña, Spain;
| | - María L. Couce
- Unit of Diagnosis and Treatment of Congenital Metabolic Diseases, Department of Paediatrics, IDIS-Health Research Institute of Santiago de Compostela, Centro de Investigación Biomédica en Red de Enfermedades Raras (CIBERER), European Reference Network for Hereditary Metabolic Disorders (MetabERN), Santiago de Compostela University Clinical Hospital, 15704 Santiago de Compostela, Spain; (S.B.-G.); (M.E.V.-M.); (J.V.Á.); (R.C.)
| |
Collapse
|
18
|
Supek F, Lehner B, Lindeboom RG. To NMD or Not To NMD: Nonsense-Mediated mRNA Decay in Cancer and Other Genetic Diseases. Trends Genet 2021; 37:657-668. [DOI: 10.1016/j.tig.2020.11.002] [Citation(s) in RCA: 48] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/07/2020] [Revised: 11/02/2020] [Accepted: 11/03/2020] [Indexed: 02/07/2023]
|
19
|
Computational prediction of protein subdomain stability in MYBPC3 enables clinical risk stratification in hypertrophic cardiomyopathy and enhances variant interpretation. Genet Med 2021; 23:1281-1287. [PMID: 33782553 PMCID: PMC8257482 DOI: 10.1038/s41436-021-01134-9] [Citation(s) in RCA: 6] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/04/2020] [Revised: 02/15/2021] [Accepted: 02/16/2021] [Indexed: 12/13/2022] Open
Abstract
Purpose Variants in MYBPC3 causing loss of function are the most common cause of hypertrophic cardiomyopathy (HCM). However, a substantial number of patients carry missense variants of uncertain significance (VUS) in MYBPC3. We hypothesize that a structural-based algorithm, STRUM, which estimates the effect of missense variants on protein folding, will identify a subgroup of HCM patients with a MYBPC3 VUS associated with increased clinical risk. Methods Among 7,963 patients in the multicenter Sarcomeric Human Cardiomyopathy Registry (SHaRe), 120 unique missense VUS in MYBPC3 were identified. Variants were evaluated for their effect on subdomain folding and a stratified time-to-event analysis for an overall composite endpoint (first occurrence of ventricular arrhythmia, heart failure, all-cause mortality, atrial fibrillation, and stroke) was performed for patients with HCM and a MYBPC3 missense VUS. Results We demonstrated that patients carrying a MYBPC3 VUS predicted to cause subdomain misfolding (STRUM+, ΔΔG ≤ −1.2 kcal/mol) exhibited a higher rate of adverse events compared with those with a STRUM- VUS (hazard ratio = 2.29, P = 0.0282). In silico saturation mutagenesis of MYBPC3 identified 4,943/23,427 (21%) missense variants that were predicted to cause subdomain misfolding. Conclusion STRUM identifies patients with HCM and a MYBPC3 VUS who may be at higher clinical risk and provides supportive evidence for pathogenicity.
Collapse
|
20
|
Liu X, Li C, Mou C, Dong Y, Tu Y. dbNSFP v4: a comprehensive database of transcript-specific functional predictions and annotations for human nonsynonymous and splice-site SNVs. Genome Med 2020; 12:103. [PMID: 33261662 PMCID: PMC7709417 DOI: 10.1186/s13073-020-00803-9] [Citation(s) in RCA: 277] [Impact Index Per Article: 69.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/17/2020] [Accepted: 11/09/2020] [Indexed: 12/12/2022] Open
Abstract
Whole exome sequencing has been increasingly used in human disease studies. Prioritization based on appropriate functional annotations has been used as an indispensable step to select candidate variants. Here we present the latest updates to dbNSFP (version 4.1), a database designed to facilitate this step by providing deleteriousness prediction and functional annotation for all potential nonsynonymous and splice-site SNVs (a total of 84,013,093) in the human genome. The current version compiled 36 deleteriousness prediction scores, including 12 transcript-specific scores, and other variant and gene-level functional annotations. The database is available at http://database.liulab.science/dbNSFP with a downloadable version and a web-service.
Collapse
Affiliation(s)
- Xiaoming Liu
- USF Genomics & College of Public Health, University of South Florida, Tampa, FL, USA.
| | - Chang Li
- USF Genomics & College of Public Health, University of South Florida, Tampa, FL, USA
| | - Chengcheng Mou
- Department of Computer Science and Engineering, College of Engineering, University of South Florida, Tampa, FL, USA
| | - Yibo Dong
- USF Genomics & College of Public Health, University of South Florida, Tampa, FL, USA
| | - Yicheng Tu
- Department of Computer Science and Engineering, College of Engineering, University of South Florida, Tampa, FL, USA
| |
Collapse
|
21
|
A comparative genomics multitool for scientific discovery and conservation. Nature 2020; 587:240-245. [PMID: 33177664 PMCID: PMC7759459 DOI: 10.1038/s41586-020-2876-6] [Citation(s) in RCA: 162] [Impact Index Per Article: 40.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/17/2019] [Accepted: 07/27/2020] [Indexed: 12/11/2022]
Abstract
The Zoonomia Project is investigating the genomics of shared and specialized traits in eutherian mammals. Here we provide genome assemblies for 131 species, of which all but 9 are previously uncharacterized, and describe a whole-genome alignment of 240 species of considerable phylogenetic diversity, comprising representatives from more than 80% of mammalian families. We find that regions of reduced genetic diversity are more abundant in species at a high risk of extinction, discern signals of evolutionary selection at high resolution and provide insights from individual reference genomes. By prioritizing phylogenetic diversity and making data available quickly and without restriction, the Zoonomia Project aims to support biological discovery, medical research and the conservation of biodiversity. A whole-genome alignment of 240 phylogenetically diverse species of eutherian mammal—including 131 previously uncharacterized species—from the Zoonomia Project provides data that support biological discovery, medical research and conservation.
Collapse
|
22
|
Choudhury A, Aron S, Botigué LR, Sengupta D, Botha G, Bensellak T, Wells G, Kumuthini J, Shriner D, Fakim YJ, Ghoorah AW, Dareng E, Odia T, Falola O, Adebiyi E, Hazelhurst S, Mazandu G, Nyangiri OA, Mbiyavanga M, Benkahla A, Kassim SK, Mulder N, Adebamowo SN, Chimusa ER, Muzny D, Metcalf G, Gibbs RA, Rotimi C, Ramsay M, Adeyemo AA, Lombard Z, Hanchard NA. High-depth African genomes inform human migration and health. Nature 2020; 586:741-748. [PMID: 33116287 PMCID: PMC7759466 DOI: 10.1038/s41586-020-2859-7] [Citation(s) in RCA: 168] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/10/2019] [Accepted: 08/07/2020] [Indexed: 01/05/2023]
Abstract
The African continent is regarded as the cradle of modern humans and African genomes contain more genetic variation than those from any other continent, yet only a fraction of the genetic diversity among African individuals has been surveyed1. Here we performed whole-genome sequencing analyses of 426 individuals-comprising 50 ethnolinguistic groups, including previously unsampled populations-to explore the breadth of genomic diversity across Africa. We uncovered more than 3 million previously undescribed variants, most of which were found among individuals from newly sampled ethnolinguistic groups, as well as 62 previously unreported loci that are under strong selection, which were predominantly found in genes that are involved in viral immunity, DNA repair and metabolism. We observed complex patterns of ancestral admixture and putative-damaging and novel variation, both within and between populations, alongside evidence that Zambia was a likely intermediate site along the routes of expansion of Bantu-speaking populations. Pathogenic variants in genes that are currently characterized as medically relevant were uncommon-but in other genes, variants denoted as 'likely pathogenic' in the ClinVar database were commonly observed. Collectively, these findings refine our current understanding of continental migration, identify gene flow and the response to human disease as strong drivers of genome-level population variation, and underscore the scientific imperative for a broader characterization of the genomic diversity of African individuals to understand human ancestry and improve health.
Collapse
Affiliation(s)
- Ananyo Choudhury
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Shaun Aron
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Laura R Botigué
- Center for Research in Agricultural Genomics (CRAG), Plant and Animal Genomics Program, CSIC-IRTA-UAB-UB, Barcelona, Spain
| | - Dhriti Sengupta
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Gerrit Botha
- Computational Biology Division and H3ABioNet, Department of Integrative Biomedical Sciences, IDM, University of Cape Town, Cape Town, South Africa
| | - Taoufik Bensellak
- System and Data Engineering Team, Abdelmalek Essaadi University, ENSA, Tangier, Morocco
| | - Gordon Wells
- Centre for Proteomic and Genomic Research (CPGR), Cape Town, South Africa.,South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa.,Africa Health Research Institute, Durban, South Africa
| | - Judit Kumuthini
- Centre for Proteomic and Genomic Research (CPGR), Cape Town, South Africa.,South African National Bioinformatics Institute, University of the Western Cape, Bellville, South Africa
| | - Daniel Shriner
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Yasmina J Fakim
- Department of Agriculture and Food Science, Faculty of Agriculture, University of Mauritius, Reduit, Mauritius.,Department of Digital Technologies,Faculty of Information, Communication & Digital Technologies, University of Mauritius, Reduit, Mauritius
| | - Anisah W Ghoorah
- Department of Digital Technologies,Faculty of Information, Communication & Digital Technologies, University of Mauritius, Reduit, Mauritius
| | - Eileen Dareng
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.,Institute of Human Virology Nigeria, Abuja, Nigeria
| | - Trust Odia
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Nigeria
| | - Oluwadamilare Falola
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Nigeria
| | - Ezekiel Adebiyi
- Covenant University Bioinformatics Research (CUBRe), Covenant University, Ota, Nigeria.,Department of Computer and Information Sciences, Covenant University, Ota, Nigeria
| | - Scott Hazelhurst
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa
| | - Gaston Mazandu
- Computational Biology Division and H3ABioNet, Department of Integrative Biomedical Sciences, IDM, University of Cape Town, Cape Town, South Africa
| | - Oscar A Nyangiri
- College of Veterinary Medicine, Animal Resources and Biosecurity, Makerere University, Kampala, Uganda
| | - Mamana Mbiyavanga
- Computational Biology Division and H3ABioNet, Department of Integrative Biomedical Sciences, IDM, University of Cape Town, Cape Town, South Africa
| | - Alia Benkahla
- Laboratory of Bioinformatics, Biomathematics and Biostatistics (BIMS), Institute Pasteur of Tunis, Tunis, Tunisia
| | - Samar K Kassim
- Medical Biochemistry and Molecular Biology Department, Faculty of Medicine, Ain Shams University, Abbaseya, Cairo, Egypt
| | - Nicola Mulder
- Computational Biology Division and H3ABioNet, Department of Integrative Biomedical Sciences, IDM, University of Cape Town, Cape Town, South Africa
| | - Sally N Adebamowo
- Department of Epidemiology and Public Health, University of Maryland School of Medicine, University of Maryland Baltimore, Baltimore, MD, USA.,University of Maryland Greenebaum Comprehensive Cancer Center, University of Maryland School of Medicine, University of Maryland Baltimore, Baltimore, MD, USA
| | - Emile R Chimusa
- Division of Human Genetics, Department of Pathology, Faculty of Health Sciences, Institute for Infectious, Disease and Molecular Medicine, University of Cape Town, Cape Town, South Africa
| | - Donna Muzny
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Ginger Metcalf
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Richard A Gibbs
- Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.,Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA
| | | | - Charles Rotimi
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Michèle Ramsay
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.,Division of Human Genetics, National Health Laboratory Service, and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | | | - Adebowale A Adeyemo
- Center for Research on Genomics and Global Health, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA.
| | - Zané Lombard
- Division of Human Genetics, National Health Laboratory Service, and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa.
| | - Neil A Hanchard
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, Texas, USA.
| |
Collapse
|
23
|
Lee YR, Yehia L, Kishikawa T, Ni Y, Leach B, Zhang J, Panch N, Liu J, Wei W, Eng C, Pandolfi PP. WWP1 Gain-of-Function Inactivation of PTEN in Cancer Predisposition. N Engl J Med 2020; 382:2103-2116. [PMID: 32459922 PMCID: PMC7839065 DOI: 10.1056/nejmoa1914919] [Citation(s) in RCA: 49] [Impact Index Per Article: 12.3] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 12/14/2022]
Abstract
BACKGROUND Patients with PTEN hamartoma tumor syndrome (PHTS) have germline mutations in the tumor-suppressor gene encoding phosphatase and tensin homologue (PTEN). Such mutations have been associated with a hereditary predisposition to multiple types of cancer, including the Cowden syndrome. However, a majority of patients who have PHTS-related phenotypes have tested negative for PTEN mutations. In a previous study, we found that the E3 ubiquitin ligase WWP1 negatively regulates the function of PTEN. METHODS In a prospective cohort study conducted from 2005 through 2015, we enrolled 431 patients with wild-type PTEN who met at least the relaxed diagnostic criteria of the International Cowden Consortium. Patients were scanned for WWP1 germline variants. We used the Cancer Genome Atlas (TCGA) data set as representative of apparently sporadic cancers and the Exome Aggregation Consortium data set excluding TCGA (non-TCGA ExAC) and the noncancer Genome Aggregation Database (gnomAD) as representative of population controls without a reported cancer diagnosis. We established both in vitro and murine in vivo models to functionally characterize representative WWP1 variants. RESULTS The existence of germline WWP1 variants was first established in a family with wild-type PTEN who had oligopolyposis and early-onset colon cancers. A validation series indicated that WWP1 germline variants occurred in 5 of 126 unrelated patients (4%) with oligopolyposis as a predominant phenotype. Germline WWP1 variants, particularly the WWP1 K740N and N745S alleles, were enriched in patients who did not have PHTS but had prevalent sporadic cancers, including PTEN-related cancer types in TCGA (odds ratio, 1.5; 95% confidence interval, 1.1 to 2.1; P = 0.01). The prioritized WWP1 variants resulted in gain-of-function effects, which led to aberrant enzymatic activation with consequent PTEN inactivation, thereby triggering hyperactive growth-promoting PI3K signaling in cellular and murine models. CONCLUSIONS In this study involving patients with disorders resulting in a predisposition to the development of multiple malignant neoplasms without PTEN germline mutations, we confirmed the function of WWP1 as a cancer-susceptibility gene through direct aberrant regulation of the PTEN-PI3K signaling axis. (Funded by the National Institutes of Health and others.).
Collapse
Affiliation(s)
- Yu-Ru Lee
- From the Cancer Research Institute, Beth Israel Deaconess Cancer Center (Y.-R.L., T.K., J.Z., N.P., J.L., W.W., P.P.P.), and the Departments of Medicine (Y.-R.L., T.K., N.P., P.P.P.) and Pathology (J.Z., J.L., W.W., P.P.P.), Beth Israel Deaconess Medical Center, Harvard Medical School, Boston; the Genomic Medicine Institute (L.Y., Y.N., B.L., C.E.) and the Department of Quantitative Health Sciences (Y.N.), Lerner Research Institute, Cleveland Clinic, the Taussig Cancer Institute (C.E.), the Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine (C.E.), and the Germline High Risk Cancer Focus Group, Case Comprehensive Cancer Center, Case Western Reserve University (C.E.) - all in Cleveland; the Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University (J.Z.), and the Medical Research Institute, Wuhan University (J.Z.) - both in Wuhan, China; and the Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy (P.P.P.)
| | - Lamis Yehia
- From the Cancer Research Institute, Beth Israel Deaconess Cancer Center (Y.-R.L., T.K., J.Z., N.P., J.L., W.W., P.P.P.), and the Departments of Medicine (Y.-R.L., T.K., N.P., P.P.P.) and Pathology (J.Z., J.L., W.W., P.P.P.), Beth Israel Deaconess Medical Center, Harvard Medical School, Boston; the Genomic Medicine Institute (L.Y., Y.N., B.L., C.E.) and the Department of Quantitative Health Sciences (Y.N.), Lerner Research Institute, Cleveland Clinic, the Taussig Cancer Institute (C.E.), the Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine (C.E.), and the Germline High Risk Cancer Focus Group, Case Comprehensive Cancer Center, Case Western Reserve University (C.E.) - all in Cleveland; the Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University (J.Z.), and the Medical Research Institute, Wuhan University (J.Z.) - both in Wuhan, China; and the Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy (P.P.P.)
| | - Takahiro Kishikawa
- From the Cancer Research Institute, Beth Israel Deaconess Cancer Center (Y.-R.L., T.K., J.Z., N.P., J.L., W.W., P.P.P.), and the Departments of Medicine (Y.-R.L., T.K., N.P., P.P.P.) and Pathology (J.Z., J.L., W.W., P.P.P.), Beth Israel Deaconess Medical Center, Harvard Medical School, Boston; the Genomic Medicine Institute (L.Y., Y.N., B.L., C.E.) and the Department of Quantitative Health Sciences (Y.N.), Lerner Research Institute, Cleveland Clinic, the Taussig Cancer Institute (C.E.), the Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine (C.E.), and the Germline High Risk Cancer Focus Group, Case Comprehensive Cancer Center, Case Western Reserve University (C.E.) - all in Cleveland; the Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University (J.Z.), and the Medical Research Institute, Wuhan University (J.Z.) - both in Wuhan, China; and the Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy (P.P.P.)
| | - Ying Ni
- From the Cancer Research Institute, Beth Israel Deaconess Cancer Center (Y.-R.L., T.K., J.Z., N.P., J.L., W.W., P.P.P.), and the Departments of Medicine (Y.-R.L., T.K., N.P., P.P.P.) and Pathology (J.Z., J.L., W.W., P.P.P.), Beth Israel Deaconess Medical Center, Harvard Medical School, Boston; the Genomic Medicine Institute (L.Y., Y.N., B.L., C.E.) and the Department of Quantitative Health Sciences (Y.N.), Lerner Research Institute, Cleveland Clinic, the Taussig Cancer Institute (C.E.), the Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine (C.E.), and the Germline High Risk Cancer Focus Group, Case Comprehensive Cancer Center, Case Western Reserve University (C.E.) - all in Cleveland; the Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University (J.Z.), and the Medical Research Institute, Wuhan University (J.Z.) - both in Wuhan, China; and the Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy (P.P.P.)
| | - Brandie Leach
- From the Cancer Research Institute, Beth Israel Deaconess Cancer Center (Y.-R.L., T.K., J.Z., N.P., J.L., W.W., P.P.P.), and the Departments of Medicine (Y.-R.L., T.K., N.P., P.P.P.) and Pathology (J.Z., J.L., W.W., P.P.P.), Beth Israel Deaconess Medical Center, Harvard Medical School, Boston; the Genomic Medicine Institute (L.Y., Y.N., B.L., C.E.) and the Department of Quantitative Health Sciences (Y.N.), Lerner Research Institute, Cleveland Clinic, the Taussig Cancer Institute (C.E.), the Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine (C.E.), and the Germline High Risk Cancer Focus Group, Case Comprehensive Cancer Center, Case Western Reserve University (C.E.) - all in Cleveland; the Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University (J.Z.), and the Medical Research Institute, Wuhan University (J.Z.) - both in Wuhan, China; and the Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy (P.P.P.)
| | - Jinfang Zhang
- From the Cancer Research Institute, Beth Israel Deaconess Cancer Center (Y.-R.L., T.K., J.Z., N.P., J.L., W.W., P.P.P.), and the Departments of Medicine (Y.-R.L., T.K., N.P., P.P.P.) and Pathology (J.Z., J.L., W.W., P.P.P.), Beth Israel Deaconess Medical Center, Harvard Medical School, Boston; the Genomic Medicine Institute (L.Y., Y.N., B.L., C.E.) and the Department of Quantitative Health Sciences (Y.N.), Lerner Research Institute, Cleveland Clinic, the Taussig Cancer Institute (C.E.), the Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine (C.E.), and the Germline High Risk Cancer Focus Group, Case Comprehensive Cancer Center, Case Western Reserve University (C.E.) - all in Cleveland; the Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University (J.Z.), and the Medical Research Institute, Wuhan University (J.Z.) - both in Wuhan, China; and the Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy (P.P.P.)
| | - Nivedita Panch
- From the Cancer Research Institute, Beth Israel Deaconess Cancer Center (Y.-R.L., T.K., J.Z., N.P., J.L., W.W., P.P.P.), and the Departments of Medicine (Y.-R.L., T.K., N.P., P.P.P.) and Pathology (J.Z., J.L., W.W., P.P.P.), Beth Israel Deaconess Medical Center, Harvard Medical School, Boston; the Genomic Medicine Institute (L.Y., Y.N., B.L., C.E.) and the Department of Quantitative Health Sciences (Y.N.), Lerner Research Institute, Cleveland Clinic, the Taussig Cancer Institute (C.E.), the Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine (C.E.), and the Germline High Risk Cancer Focus Group, Case Comprehensive Cancer Center, Case Western Reserve University (C.E.) - all in Cleveland; the Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University (J.Z.), and the Medical Research Institute, Wuhan University (J.Z.) - both in Wuhan, China; and the Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy (P.P.P.)
| | - Jing Liu
- From the Cancer Research Institute, Beth Israel Deaconess Cancer Center (Y.-R.L., T.K., J.Z., N.P., J.L., W.W., P.P.P.), and the Departments of Medicine (Y.-R.L., T.K., N.P., P.P.P.) and Pathology (J.Z., J.L., W.W., P.P.P.), Beth Israel Deaconess Medical Center, Harvard Medical School, Boston; the Genomic Medicine Institute (L.Y., Y.N., B.L., C.E.) and the Department of Quantitative Health Sciences (Y.N.), Lerner Research Institute, Cleveland Clinic, the Taussig Cancer Institute (C.E.), the Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine (C.E.), and the Germline High Risk Cancer Focus Group, Case Comprehensive Cancer Center, Case Western Reserve University (C.E.) - all in Cleveland; the Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University (J.Z.), and the Medical Research Institute, Wuhan University (J.Z.) - both in Wuhan, China; and the Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy (P.P.P.)
| | - Wenyi Wei
- From the Cancer Research Institute, Beth Israel Deaconess Cancer Center (Y.-R.L., T.K., J.Z., N.P., J.L., W.W., P.P.P.), and the Departments of Medicine (Y.-R.L., T.K., N.P., P.P.P.) and Pathology (J.Z., J.L., W.W., P.P.P.), Beth Israel Deaconess Medical Center, Harvard Medical School, Boston; the Genomic Medicine Institute (L.Y., Y.N., B.L., C.E.) and the Department of Quantitative Health Sciences (Y.N.), Lerner Research Institute, Cleveland Clinic, the Taussig Cancer Institute (C.E.), the Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine (C.E.), and the Germline High Risk Cancer Focus Group, Case Comprehensive Cancer Center, Case Western Reserve University (C.E.) - all in Cleveland; the Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University (J.Z.), and the Medical Research Institute, Wuhan University (J.Z.) - both in Wuhan, China; and the Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy (P.P.P.)
| | - Charis Eng
- From the Cancer Research Institute, Beth Israel Deaconess Cancer Center (Y.-R.L., T.K., J.Z., N.P., J.L., W.W., P.P.P.), and the Departments of Medicine (Y.-R.L., T.K., N.P., P.P.P.) and Pathology (J.Z., J.L., W.W., P.P.P.), Beth Israel Deaconess Medical Center, Harvard Medical School, Boston; the Genomic Medicine Institute (L.Y., Y.N., B.L., C.E.) and the Department of Quantitative Health Sciences (Y.N.), Lerner Research Institute, Cleveland Clinic, the Taussig Cancer Institute (C.E.), the Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine (C.E.), and the Germline High Risk Cancer Focus Group, Case Comprehensive Cancer Center, Case Western Reserve University (C.E.) - all in Cleveland; the Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University (J.Z.), and the Medical Research Institute, Wuhan University (J.Z.) - both in Wuhan, China; and the Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy (P.P.P.)
| | - Pier Paolo Pandolfi
- From the Cancer Research Institute, Beth Israel Deaconess Cancer Center (Y.-R.L., T.K., J.Z., N.P., J.L., W.W., P.P.P.), and the Departments of Medicine (Y.-R.L., T.K., N.P., P.P.P.) and Pathology (J.Z., J.L., W.W., P.P.P.), Beth Israel Deaconess Medical Center, Harvard Medical School, Boston; the Genomic Medicine Institute (L.Y., Y.N., B.L., C.E.) and the Department of Quantitative Health Sciences (Y.N.), Lerner Research Institute, Cleveland Clinic, the Taussig Cancer Institute (C.E.), the Department of Genetics and Genome Sciences, Case Western Reserve University School of Medicine (C.E.), and the Germline High Risk Cancer Focus Group, Case Comprehensive Cancer Center, Case Western Reserve University (C.E.) - all in Cleveland; the Department of Radiation and Medical Oncology, Zhongnan Hospital of Wuhan University (J.Z.), and the Medical Research Institute, Wuhan University (J.Z.) - both in Wuhan, China; and the Molecular Biotechnology Center, Department of Molecular Biotechnology and Health Sciences, University of Turin, Turin, Italy (P.P.P.)
| |
Collapse
|
24
|
Kumar S, Warrell J, Li S, McGillivray PD, Meyerson W, Salichos L, Harmanci A, Martinez-Fundichely A, Chan CWY, Nielsen MM, Lochovsky L, Zhang Y, Li X, Lou S, Pedersen JS, Herrmann C, Getz G, Khurana E, Gerstein MB. Passenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences. Cell 2020; 180:915-927.e16. [PMID: 32084333 PMCID: PMC7210002 DOI: 10.1016/j.cell.2020.01.032] [Citation(s) in RCA: 75] [Impact Index Per Article: 18.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/01/2018] [Revised: 08/23/2019] [Accepted: 01/29/2020] [Indexed: 01/23/2023]
Abstract
The dichotomous model of "drivers" and "passengers" in cancer posits that only a few mutations in a tumor strongly affect its progression, with the remaining ones being inconsequential. Here, we leveraged the comprehensive variant dataset from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) project to demonstrate that-in addition to the dichotomy of high- and low-impact variants-there is a third group of medium-impact putative passengers. Moreover, we also found that molecular impact correlates with subclonal architecture (i.e., early versus late mutations), and different signatures encode for mutations with divergent impact. Furthermore, we adapted an additive-effects model from complex-trait studies to show that the aggregated effect of putative passengers, including undetected weak drivers, provides significant additional power (∼12% additive variance) for predicting cancerous phenotypes, beyond PCAWG-identified driver mutations. Finally, this framework allowed us to estimate the frequency of potential weak-driver mutations in PCAWG samples lacking any well-characterized driver alterations.
Collapse
Affiliation(s)
- Sushant Kumar
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Jonathan Warrell
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Shantao Li
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Patrick D McGillivray
- Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA; Yale School of Medicine, Yale University, New Haven, CT 06510, USA
| | - William Meyerson
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Yale School of Medicine, Yale University, New Haven, CT 06510, USA
| | - Leonidas Salichos
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Arif Harmanci
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Center for Precision Health, School of Biomedical Informatics, University of Texas Health Sciences Center, Houston, TX 77030, USA
| | - Alexander Martinez-Fundichely
- Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10021, USA; Department of Physiology and Biophysics, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA
| | - Calvin W Y Chan
- Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany; Faculty of Biosciences, Heidelberg University, Heidelberg 69120, Germany
| | - Morten Muhlig Nielsen
- Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark
| | - Lucas Lochovsky
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Yan Zhang
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Biomedical Informatics, College of Medicine, Ohio State University, Columbus, OH 43210, USA; The Ohio State University Comprehensive Cancer Center (OSUCCC-James), Columbus, OH 43210, USA
| | - Xiaotong Li
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA
| | - Shaoke Lou
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA
| | - Jakob Skou Pedersen
- Department of Molecular Medicine (MOMA), Aarhus University Hospital, Aarhus, Denmark; Bioinformatics Research Centre (BiRC), Aarhus University, Aarhus, Denmark
| | - Carl Herrmann
- Division of Theoretical Bioinformatics, German Cancer Research Center (DKFZ), Heidelberg 69120, Germany; Health Data Science Unit, Medical Faculty Heidelberg and BioQuant, Heidelberg 69120, Germany
| | - Gad Getz
- The Broad Institute of MIT and Harvard, Cambridge, MA 02124, USA; Massachusetts General Hospital Center for Cancer Research, Charlestown, MA 02129, USA; Harvard Medical School, 250 Longwood Avenue, Boston, MA 02115, USA
| | - Ekta Khurana
- Institute for Computational Biomedicine, Weill Cornell Medical College, New York, NY 10021, USA; Department of Physiology and Biophysics, Weill Cornell Medicine, 1300 York Avenue, New York, NY 10065, USA; Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, New York, NY, USA; Meyer Cancer Center, Weill Cornell Medicine, New York, NY, USA
| | - Mark B Gerstein
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT 06520, USA; Department of Molecular Biophysics and Biochemistry, Yale University, New Haven, CT 06520, USA; Department of Computer Science, Yale University, New Haven, CT 06511, USA.
| |
Collapse
|
25
|
Groß C, Derks M, Megens HJ, Bosse M, Groenen MAM, Reinders M, de Ridder D. pCADD: SNV prioritisation in Sus scrofa. Genet Sel Evol 2020; 52:4. [PMID: 32033531 PMCID: PMC7006094 DOI: 10.1186/s12711-020-0528-9] [Citation(s) in RCA: 14] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2019] [Accepted: 01/28/2020] [Indexed: 01/22/2023] Open
Abstract
BACKGROUND In animal breeding, identification of causative genetic variants is of major importance and high economical value. Usually, the number of candidate variants exceeds the number of variants that can be validated. One way of prioritizing probable candidates is by evaluating their potential to have a deleterious effect, e.g. by predicting their consequence. Due to experimental difficulties to evaluate variants that do not cause an amino-acid substitution, other prioritization methods are needed. For human genomes, the prediction of deleterious genomic variants has taken a step forward with the introduction of the combined annotation dependent depletion (CADD) method. In theory, this approach can be applied to any species. Here, we present pCADD (p for pig), a model to score single nucleotide variants (SNVs) in pig genomes. RESULTS To evaluate whether pCADD captures sites with biological meaning, we used transcripts from miRNAs and introns, sequences from genes that are specific for a particular tissue, and the different sites of codons, to test how well pCADD scores differentiate between functional and non-functional elements. Furthermore, we conducted an assessment of examples of non-coding and coding SNVs, which are causal for changes in phenotypes. Our results show that pCADD scores discriminate between functional and non-functional sequences and prioritize functional SNVs, and that pCADD is able to score the different positions in a codon relative to their redundancy. Taken together, these results indicate that based on pCADD scores, regions with biological relevance can be identified and distinguished according to their rate of adaptation. CONCLUSIONS We present the ability of pCADD to prioritize SNVs in the pig genome with respect to their putative deleteriousness, in accordance to the biological significance of the region in which they are located. We created scores for all possible SNVs, coding and non-coding, for all autosomes and the X chromosome of the pig reference sequence Sscrofa11.1, proposing a toolbox to prioritize variants and evaluate sequences to highlight new sites of interest to explain biological functions that are relevant to animal breeding.
Collapse
Affiliation(s)
- Christian Groß
- Delft Bioinformatics Lab, University of Technology Delft, 2600GA, Delft, The Netherlands. .,Bioinformatics Group, Wageningen University & Research, 6708 PB, Wageningen, The Netherlands.
| | - Martijn Derks
- Animal Breeding and Genomics, Wageningen University & Research, Wageningen, The Netherlands
| | - Hendrik-Jan Megens
- Animal Breeding and Genomics, Wageningen University & Research, Wageningen, The Netherlands
| | - Mirte Bosse
- Animal Breeding and Genomics, Wageningen University & Research, Wageningen, The Netherlands
| | - Martien A M Groenen
- Animal Breeding and Genomics, Wageningen University & Research, Wageningen, The Netherlands
| | - Marcel Reinders
- Delft Bioinformatics Lab, University of Technology Delft, 2600GA, Delft, The Netherlands
| | - Dick de Ridder
- Bioinformatics Group, Wageningen University & Research, 6708 PB, Wageningen, The Netherlands
| |
Collapse
|
26
|
Multimodal genomic features predict outcome of immune checkpoint blockade in non-small-cell lung cancer. ACTA ACUST UNITED AC 2020; 1:99-111. [PMID: 32984843 DOI: 10.1038/s43018-019-0008-8] [Citation(s) in RCA: 119] [Impact Index Per Article: 29.8] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/31/2022]
Abstract
Despite progress in immunotherapy, identifying patients that respond has remained a challenge. Through analysis of whole-exome and targeted sequence data from 5,449 tumors, we found a significant correlation between tumor mutation burden (TMB) and tumor purity, suggesting that low tumor purity tumors are likely to have inaccurate TMB estimates. We developed a new method to estimate a corrected TMB (cTMB) that was adjusted for tumor purity and more accurately predicted outcome to immune checkpoint blockade (ICB). To identify improved predictive markers together with cTMB, we performed whole-exome sequencing for 104 lung tumors treated with ICB. Through comprehensive analyses of sequence and structural alterations, we discovered a significant enrichment in activating mutations in receptor tyrosine kinase (RTK) genes in nonresponding tumors in three immunotherapy treated cohorts. An integrated multivariable model incorporating cTMB, RTK mutations, smoking-related mutational signature and human leukocyte antigen status provided an improved predictor of response to immunotherapy that was independently validated.
Collapse
|
27
|
Lindeboom RGH, Vermeulen M, Lehner B, Supek F. The impact of nonsense-mediated mRNA decay on genetic disease, gene editing and cancer immunotherapy. Nat Genet 2019; 51:1645-1651. [PMID: 31659324 PMCID: PMC6858879 DOI: 10.1038/s41588-019-0517-5] [Citation(s) in RCA: 155] [Impact Index Per Article: 31.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/26/2019] [Accepted: 09/23/2019] [Indexed: 12/21/2022]
Abstract
Premature termination codons (PTCs) can result in the production of truncated proteins or the degradation of messenger RNAs by nonsense-mediated mRNA decay (NMD). Which of these outcomes occurs can alter the effect of a mutation, with the engagement of NMD being dependent on a series of rules. Here, by applying these rules genome-wide to obtain a resource called NMDetective, we explore the impact of NMD on genetic disease and approaches to therapy. First, human genetic diseases differ in whether NMD typically aggravates or alleviates the effects of PTCs. Second, failure to trigger NMD is a cause of ineffective gene inactivation by CRISPR-Cas9 gene editing. Finally, NMD is a determinant of the efficacy of cancer immunotherapy, with only frameshifted transcripts that escape NMD predicting a response. These results demonstrate the importance of incorporating the rules of NMD into clinical decision-making. Moreover, they suggest that inhibiting NMD may be effective in enhancing cancer immunotherapy.
Collapse
Affiliation(s)
- Rik G H Lindeboom
- Department of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Oncode Institute, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Michiel Vermeulen
- Department of Molecular Biology, Faculty of Science, Radboud Institute for Molecular Life Sciences, Oncode Institute, Radboud University Nijmegen, Nijmegen, the Netherlands
| | - Ben Lehner
- Systems Biology Program, Centre for Genomic Regulation, The Barcelona Institute of Science and Technology, Barcelona, Spain. .,Universitat Pompeu Fabra, Barcelona, Spain. .,Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain.
| | - Fran Supek
- Institució Catalana de Recerca i Estudis Avançats, Barcelona, Spain. .,Institut de Recerca Biomedica Barcelona, The Barcelona Institute of Science and Technology, Barcelona, Spain.
| |
Collapse
|
28
|
Hu Z, Yu C, Furutsuki M, Andreoletti G, Ly M, Hoskins R, Adhikari AN, Brenner SE. VIPdb, a genetic Variant Impact Predictor Database. Hum Mutat 2019; 40:1202-1214. [PMID: 31283070 PMCID: PMC7288905 DOI: 10.1002/humu.23858] [Citation(s) in RCA: 24] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2019] [Accepted: 06/27/2019] [Indexed: 12/30/2022]
Abstract
Genome sequencing identifies vast number of genetic variants. Predicting these variants' molecular and clinical effects is one of the preeminent challenges in human genetics. Accurate prediction of the impact of genetic variants improves our understanding of how genetic information is conveyed to molecular and cellular functions, and is an essential step towards precision medicine. Over one hundred tools/resources have been developed specifically for this purpose. We summarize these tools as well as their characteristics, in the genetic Variant Impact Predictor Database (VIPdb). This database will help researchers and clinicians explore appropriate tools, and inform the development of improved methods. VIPdb can be browsed and downloaded at https://genomeinterpretation.org/vipdb.
Collapse
Affiliation(s)
- Zhiqiang Hu
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
| | - Changhua Yu
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
- Department of Bioengineering, University of California, Berkeley, California 94720, USA
| | - Mabel Furutsuki
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
- Department of Electrical Engineering and Computer Sciences, University of California, Berkeley, California 94720, USA
| | - Gaia Andreoletti
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
| | - Melissa Ly
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
- Division of Data Sciences, University of California, Berkeley, California 94720, USA
| | - Roger Hoskins
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
| | - Aashish N. Adhikari
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
| | - Steven E. Brenner
- Department of Plant and Microbial Biology, University of California, Berkeley, California 94720, USA
| |
Collapse
|
29
|
Lake NJ, Formosa LE, Stroud DA, Ryan MT, Calvo SE, Mootha VK, Morar B, Procopis PG, Christodoulou J, Compton AG, Thorburn DR. A patient with homozygous nonsense variants in two Leigh syndrome disease genes: Distinguishing a dual diagnosis from a hypomorphic protein-truncating variant. Hum Mutat 2019; 40:893-898. [PMID: 30981218 DOI: 10.1002/humu.23753] [Citation(s) in RCA: 4] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/02/2018] [Revised: 03/19/2019] [Accepted: 03/24/2019] [Indexed: 01/04/2023]
Abstract
Leigh syndrome is a mitochondrial disease caused by pathogenic variants in over 85 genes. Whole exome sequencing of a patient with Leigh-like syndrome identified homozygous protein-truncating variants in two genes associated with Leigh syndrome; a reported pathogenic variant in PDHX (NP_003468.2:p.(Arg446*)), and an uncharacterized variant in complex I (CI) assembly factor TIMMDC1 (NP_057673.2:p.(Arg225*)). The TIMMDC1 variant was predicted to truncate 61 amino acids at the C-terminus and functional studies demonstrated a hypomorphic impact of the variant on CI assembly. However, the mutant protein could still rescue CI assembly in TIMMDC1 knockout cells and the patient's clinical phenotype was not clearly distinct from that of other patients with the same PDHX defect. Our data suggest that the hypomorphic effect of the TIMMDC1 protein-truncating variant does not constitute a dual diagnosis in this individual. We recommend cautious assessment of variants in the C-terminus of TIMMDC1 and emphasize the need to consider the caveats detailed within the American College of Medical Genetics and Genomics (ACMG) criteria when assessing variants.
Collapse
Affiliation(s)
- Nicole J Lake
- Brain and Mitochondrial Research, Murdoch Children's Research Institute, Royal Children's Hospital, Melbourne, Victoria, Australia.,Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
| | - Luke E Formosa
- Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton Campus, Melbourne, Victoria, Australia
| | - David A Stroud
- Department of Biochemistry and Molecular Biology, The Bio21 Molecular Science and Biotechnology Institute, The University of Melbourne, Parkville, Victoria, Australia
| | - Michael T Ryan
- Department of Biochemistry and Molecular Biology, Monash Biomedicine Discovery Institute, Monash University, Clayton Campus, Melbourne, Victoria, Australia
| | - Sarah E Calvo
- Department of Molecular Biology, Howard Hughes Medical Institute, Massachusetts General Hospital, Boston, Massachusetts.,Department of Systems Biology, Harvard Medical School, Boston, Massachusetts.,Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Vamsi K Mootha
- Department of Molecular Biology, Howard Hughes Medical Institute, Massachusetts General Hospital, Boston, Massachusetts.,Department of Systems Biology, Harvard Medical School, Boston, Massachusetts.,Metabolism Program, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Bharti Morar
- Centre for Clinical Research in Neuropsychiatry, School of Psychiatry and Clinical Neurosciences, University of Western Australia, Perth, Western Australia, Australia.,Mitochondrial Medicine and Biology, Harry Perkins Institute of Medical Research and Centre for Medical Research, University of Western Australia, Nedlands, Western Australia, Australia
| | - Peter G Procopis
- Department of Neurology, Children's Hospital at Westmead, Sydney, New South Wales, Australia.,Discipline of Child and Adolescent Health, Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia
| | - John Christodoulou
- Brain and Mitochondrial Research, Murdoch Children's Research Institute, Royal Children's Hospital, Melbourne, Victoria, Australia.,Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia.,Discipline of Child and Adolescent Health, Sydney Medical School, University of Sydney, Sydney, New South Wales, Australia.,Mitochondrial Laboratory, Victorian Clinical Genetics Services, Royal Children's Hospital, Melbourne, Victoria, Australia
| | - Alison G Compton
- Brain and Mitochondrial Research, Murdoch Children's Research Institute, Royal Children's Hospital, Melbourne, Victoria, Australia.,Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia
| | - David R Thorburn
- Brain and Mitochondrial Research, Murdoch Children's Research Institute, Royal Children's Hospital, Melbourne, Victoria, Australia.,Department of Paediatrics, University of Melbourne, Melbourne, Victoria, Australia.,Mitochondrial Laboratory, Victorian Clinical Genetics Services, Royal Children's Hospital, Melbourne, Victoria, Australia
| |
Collapse
|
30
|
Posey JE, O'Donnell-Luria AH, Chong JX, Harel T, Jhangiani SN, Coban Akdemir ZH, Buyske S, Pehlivan D, Carvalho CMB, Baxter S, Sobreira N, Liu P, Wu N, Rosenfeld JA, Kumar S, Avramopoulos D, White JJ, Doheny KF, Witmer PD, Boehm C, Sutton VR, Muzny DM, Boerwinkle E, Günel M, Nickerson DA, Mane S, MacArthur DG, Gibbs RA, Hamosh A, Lifton RP, Matise TC, Rehm HL, Gerstein M, Bamshad MJ, Valle D, Lupski JR. Insights into genetics, human biology and disease gleaned from family based genomic studies. Genet Med 2019; 21:798-812. [PMID: 30655598 PMCID: PMC6691975 DOI: 10.1038/s41436-018-0408-7] [Citation(s) in RCA: 122] [Impact Index Per Article: 24.4] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/12/2018] [Accepted: 12/05/2018] [Indexed: 12/16/2022] Open
Abstract
Identifying genes and variants contributing to rare disease phenotypes and Mendelian conditions informs biology and medicine, yet potential phenotypic consequences for variation of >75% of the ~20,000 annotated genes in the human genome are lacking. Technical advances to assess rare variation genome-wide, particularly exome sequencing (ES), enabled establishment in the United States of the National Institutes of Health (NIH)-supported Centers for Mendelian Genomics (CMGs) and have facilitated collaborative studies resulting in novel "disease gene" discoveries. Pedigree-based genomic studies and rare variant analyses in families with suspected Mendelian conditions have led to the elucidation of hundreds of novel disease genes and highlighted the impact of de novo mutational events, somatic variation underlying nononcologic traits, incompletely penetrant alleles, phenotypes with high locus heterogeneity, and multilocus pathogenic variation. Herein, we highlight CMG collaborative discoveries that have contributed to understanding both rare and common diseases and discuss opportunities for future discovery in single-locus Mendelian disorder genomics. Phenotypic annotation of all human genes; development of bioinformatic tools and analytic methods; exploration of non-Mendelian modes of inheritance including reduced penetrance, multilocus variation, and oligogenic inheritance; construction of allelic series at a locus; enhanced data sharing worldwide; and integration with clinical genomics are explored. Realizing the full contribution of rare disease research to functional annotation of the human genome, and further illuminating human biology and health, will lay the foundation for the Precision Medicine Initiative.
Collapse
Affiliation(s)
- Jennifer E Posey
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
| | - Anne H O'Donnell-Luria
- Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Boston Children's Hospital, Boston, MA, USA
| | - Jessica X Chong
- Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Tamar Harel
- Department of Genetic and Metabolic Diseases, Hadassah-Hebrew University Medical Center, Jerusalem, Israel
| | - Shalini N Jhangiani
- The Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Zeynep H Coban Akdemir
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Steven Buyske
- Department of Genetics, Rutgers University, Piscataway, NJ, USA
- Department of Statistics, Rutgers University, Piscataway, NJ, USA
| | - Davut Pehlivan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Claudia M B Carvalho
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Samantha Baxter
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Nara Sobreira
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Pengfei Liu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Baylor Genetics Laboratory, Houston, TX, USA
| | - Nan Wu
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Department of Orthopedic Surgery, Peking Union Medical College Hospital, Peking Union Medical College and Chinese Academy of Medical Sciences, Beijing, China
| | - Jill A Rosenfeld
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Sushant Kumar
- Computational Biology and Bioinformatics Program, Yale University Medical School, New Haven, CT, USA
| | - Dimitri Avramopoulos
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Janson J White
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Department of Pediatrics, University of Washington, Seattle, WA, USA
| | - Kimberly F Doheny
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
- Center for Inherited Disease Research, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - P Dane Witmer
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
- Center for Inherited Disease Research, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Corinne Boehm
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - V Reid Sutton
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Donna M Muzny
- The Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Eric Boerwinkle
- The Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
- Human Genetics Center, University of Texas Health Science Center, Houston, TX, USA
| | - Murat Günel
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
- Department of Genetics, Yale School of Medicine, New Haven, CT, USA
| | | | - Shrikant Mane
- Yale Center for Genome Analysis, Yale School of Medicine, Yale University, New Haven, CT, USA
| | - Daniel G MacArthur
- Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Richard A Gibbs
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- The Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA
| | - Ada Hamosh
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - Richard P Lifton
- Department of Neurosurgery, Yale School of Medicine, New Haven, CT, USA
- Department of Internal Medicine, Yale University School of Medicine, New Haven, CT, USA
- Laboratory of Human Genetics and Genomics, The Rockefeller University, New York, NY, USA
| | - Tara C Matise
- Department of Genetics, Rutgers University, Piscataway, NJ, USA
| | - Heidi L Rehm
- Analytic and Translational Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Mark Gerstein
- Computational Biology and Bioinformatics Program, Yale University Medical School, New Haven, CT, USA
| | - Michael J Bamshad
- Department of Pediatrics, University of Washington, Seattle, WA, USA
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - David Valle
- McKusick-Nathans Institute of Genetic Medicine, Johns Hopkins University, Baltimore, MD, USA
| | - James R Lupski
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA.
- The Human Genome Sequencing Center, Baylor College of Medicine, Houston, TX, USA.
- Department of Pediatrics, Baylor College of Medicine, Houston, TX, USA.
- Texas Children's Hospital, Baylor College of Medicine, Houston, TX, USA.
| |
Collapse
|
31
|
Zhou Y, Fujikura K, Mkrtchian S, Lauschke VM. Computational Methods for the Pharmacogenetic Interpretation of Next Generation Sequencing Data. Front Pharmacol 2018; 9:1437. [PMID: 30564131 PMCID: PMC6288784 DOI: 10.3389/fphar.2018.01437] [Citation(s) in RCA: 48] [Impact Index Per Article: 8.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2018] [Accepted: 11/20/2018] [Indexed: 12/21/2022] Open
Abstract
Up to half of all patients do not respond to pharmacological treatment as intended. A substantial fraction of these inter-individual differences is due to heritable factors and a growing number of associations between genetic variations and drug response phenotypes have been identified. Importantly, the rapid progress in Next Generation Sequencing technologies in recent years unveiled the true complexity of the genetic landscape in pharmacogenes with tens of thousands of rare genetic variants. As each individual was found to harbor numerous such rare variants they are anticipated to be important contributors to the genetically encoded inter-individual variability in drug effects. The fundamental challenge however is their functional interpretation due to the sheer scale of the problem that renders systematic experimental characterization of these variants currently unfeasible. Here, we review concepts and important progress in the development of computational prediction methods that allow to evaluate the effect of amino acid sequence alterations in drug metabolizing enzymes and transporters. In addition, we discuss recent advances in the interpretation of functional effects of non-coding variants, such as variations in splice sites, regulatory regions and miRNA binding sites. We anticipate that these methodologies will provide a useful toolkit to facilitate the integration of the vast extent of rare genetic variability into drug response predictions in a precision medicine framework.
Collapse
Affiliation(s)
- Yitian Zhou
- Section of Pharmacogenetics, Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Kohei Fujikura
- Department of Diagnostic Pathology, Kobe University Graduate School of Medicine, Kobe, Japan
| | - Souren Mkrtchian
- Section of Pharmacogenetics, Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| | - Volker M. Lauschke
- Section of Pharmacogenetics, Department of Physiology and Pharmacology, Karolinska Institutet, Stockholm, Sweden
| |
Collapse
|
32
|
Groß C, de Ridder D, Reinders M. Predicting variant deleteriousness in non-human species: applying the CADD approach in mouse. BMC Bioinformatics 2018; 19:373. [PMID: 30314430 PMCID: PMC6186050 DOI: 10.1186/s12859-018-2337-5] [Citation(s) in RCA: 9] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2018] [Accepted: 08/23/2018] [Indexed: 12/30/2022] Open
Abstract
Background Predicting the deleteriousness of observed genomic variants has taken a step forward with the introduction of the Combined Annotation Dependent Depletion (CADD) approach, which trains a classifier on the wealth of available human genomic information. This raises the question whether it can be done with less data for non-human species. Here, we investigate the prerequisites to construct a CADD-based model for a non-human species. Results Performance of the mouse model is competitive with that of the human CADD model and better than established methods like PhastCons conservation scores and SIFT. Like in the human case, performance varies for different genomic regions and is best for coding regions. We also show the benefits of generating a species-specific model over lifting variants to a different species or applying a generic model. With fewer genomic annotations, performance on the test set as well as on the three validation sets is still good. Conclusions It is feasible to construct species-specific CADD models even when annotations such as epigenetic markers are not available. The minimal requirement for these models is the availability of a set of genomes of closely related species that can be used to infer an ancestor genome and substitution rates for the data generation. Electronic supplementary material The online version of this article (10.1186/s12859-018-2337-5) contains supplementary material, which is available to authorized users.
Collapse
Affiliation(s)
- Christian Groß
- Delft Bioinformatics Lab, University of Technology Delft, Van Mourik Broekmanweg 6, Delft, 2600GA, The Netherlands.,Bioinformatics Group, Wageningen University & Research, Wageningen, 6708 PB, The Netherlands
| | - Dick de Ridder
- Bioinformatics Group, Wageningen University & Research, Wageningen, 6708 PB, The Netherlands
| | - Marcel Reinders
- Delft Bioinformatics Lab, University of Technology Delft, Van Mourik Broekmanweg 6, Delft, 2600GA, The Netherlands.
| |
Collapse
|
33
|
Muñoz-Fuentes V, Cacheiro P, Meehan TF, Aguilar-Pimentel JA, Brown SDM, Flenniken AM, Flicek P, Galli A, Mashhadi HH, Hrabě de Angelis M, Kim JK, Lloyd KCK, McKerlie C, Morgan H, Murray SA, Nutter LMJ, Reilly PT, Seavitt JR, Seong JK, Simon M, Wardle-Jones H, Mallon AM, Smedley D, Parkinson HE. The International Mouse Phenotyping Consortium (IMPC): a functional catalogue of the mammalian genome that informs conservation. CONSERV GENET 2018; 19:995-1005. [PMID: 30100824 PMCID: PMC6061128 DOI: 10.1007/s10592-018-1072-9] [Citation(s) in RCA: 68] [Impact Index Per Article: 11.3] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2017] [Accepted: 05/03/2018] [Indexed: 01/08/2023]
Abstract
The International Mouse Phenotyping Consortium (IMPC) is building a catalogue of mammalian gene function by producing and phenotyping a knockout mouse line for every protein-coding gene. To date, the IMPC has generated and characterised 5186 mutant lines. One-third of the lines have been found to be non-viable and over 300 new mouse models of human disease have been identified thus far. While current bioinformatics efforts are focused on translating results to better understand human disease processes, IMPC data also aids understanding genetic function and processes in other species. Here we show, using gorilla genomic data, how genes essential to development in mice can be used to help assess the potentially deleterious impact of gene variants in other species. This type of analyses could be used to select optimal breeders in endangered species to maintain or increase fitness and avoid variants associated to impaired-health phenotypes or loss-of-function mutations in genes of critical importance. We also show, using selected examples from various mammal species, how IMPC data can aid in the identification of candidate genes for studying a condition of interest, deliver information about the mechanisms involved, or support predictions for the function of genes that may play a role in adaptation. With genotyping costs decreasing and the continued improvements of bioinformatics tools, the analyses we demonstrate can be routinely applied.
Collapse
Affiliation(s)
- Violeta Muñoz-Fuentes
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD UK
| | - Pilar Cacheiro
- Clinical Pharmacology, William Harvey Research Institute, School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
| | - Terrence F. Meehan
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD UK
| | - Juan Antonio Aguilar-Pimentel
- German Mouse Clinic, Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany
| | - Steve D. M. Brown
- Medical Research Council Harwell Institute (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD UK
| | - Ann M. Flenniken
- The Centre for Phenogenomics, Toronto, ON M5T 3H7 Canada
- Mount Sinai Hospital, Toronto, ON M5G 1X5 Canada
| | - Paul Flicek
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD UK
| | | | - Hamed Haseli Mashhadi
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD UK
| | - Martin Hrabě de Angelis
- German Mouse Clinic, Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany
- German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
- School of Life Science Weihenstephan, Technische Universität München, Alte Akademie 8, 85354 Freising, Germany
| | - Jong Kyoung Kim
- Department of New Biology, DGIST, Daegu, 42988 Republic of Korea
| | - K. C. Kent Lloyd
- Mouse Biology Program, University of California, Davis, CA 95618 USA
| | - Colin McKerlie
- The Centre for Phenogenomics, Toronto, ON M5T 3H7 Canada
- Mount Sinai Hospital, Toronto, ON M5G 1X5 Canada
- The Hospital for Sick Children, Toronto, ON M5G 1X84 Canada
| | - Hugh Morgan
- Medical Research Council Harwell Institute (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD UK
| | | | - Lauryl M. J. Nutter
- The Centre for Phenogenomics, Toronto, ON M5T 3H7 Canada
- The Hospital for Sick Children, Toronto, ON M5G 1X84 Canada
| | - Patrick T. Reilly
- PHENOMIN-iCS, 1 Rue Laurent Fries, 67404 Illkirch Cedex, Alsace France
| | - John R. Seavitt
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030 USA
| | - Je Kyung Seong
- Laboratory of Developmental Biology and Genomics, College of Veterinary Medicine, Interdisciplinary Program for Bioinformatics and Program for Cancer Biology, Seoul National University, Seoul, Republic of Korea
| | - Michelle Simon
- Medical Research Council Harwell Institute (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD UK
| | | | - Ann-Marie Mallon
- Medical Research Council Harwell Institute (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD UK
| | - Damian Smedley
- Clinical Pharmacology, William Harvey Research Institute, School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
| | - Helen E. Parkinson
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD UK
| | - the IMPC consortium
- European Molecular Biology Laboratory, European Bioinformatics Institute (EMBL-EBI), Wellcome Genome Campus, Hinxton, Cambridge, CB10 1SD UK
- Clinical Pharmacology, William Harvey Research Institute, School of Medicine and Dentistry, Queen Mary University of London, Charterhouse Square, London, EC1M 6BQ UK
- German Mouse Clinic, Institute of Experimental Genetics, Helmholtz Zentrum München, German Research Center for Environmental Health, Ingolstädter Landstrasse 1, 85764 Neuherberg, Germany
- Medical Research Council Harwell Institute (Mammalian Genetics Unit and Mary Lyon Centre), Harwell, Oxfordshire OX11 0RD UK
- The Centre for Phenogenomics, Toronto, ON M5T 3H7 Canada
- Mount Sinai Hospital, Toronto, ON M5G 1X5 Canada
- Wellcome Trust Sanger Institute, Cambridge, CB10 1SA UK
- German Center for Diabetes Research (DZD), Ingolstädter Landstr. 1, 85764 Neuherberg, Germany
- School of Life Science Weihenstephan, Technische Universität München, Alte Akademie 8, 85354 Freising, Germany
- Department of New Biology, DGIST, Daegu, 42988 Republic of Korea
- Mouse Biology Program, University of California, Davis, CA 95618 USA
- The Hospital for Sick Children, Toronto, ON M5G 1X84 Canada
- The Jackson Laboratory, Bar Harbor, ME 04609 USA
- PHENOMIN-iCS, 1 Rue Laurent Fries, 67404 Illkirch Cedex, Alsace France
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX 77030 USA
- Laboratory of Developmental Biology and Genomics, College of Veterinary Medicine, Interdisciplinary Program for Bioinformatics and Program for Cancer Biology, Seoul National University, Seoul, Republic of Korea
| |
Collapse
|