1
|
Bendapudi PK, Nazeen S, Ryu J, Söylemez O, Robbins A, Rouaisnel B, O’Neil JK, Pokhriyal R, Yang M, Colling M, Pasko B, Bouzinier M, Tomczak L, Collier L, Barrios D, Ram S, Toth-Petroczy A, Krier J, Fieg E, Dzik WH, Hudspeth JC, Pozdnyakova O, Nardi V, Knight J, Maas R, Sunyaev S, Losman JA. Low-frequency inherited complement receptor variants are associated with purpura fulminans. Blood 2024; 143:1032-1044. [PMID: 38096369 PMCID: PMC10950473 DOI: 10.1182/blood.2023021231] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Accepted: 11/15/2023] [Indexed: 03/16/2024] Open
Abstract
ABSTRACT Extreme disease phenotypes can provide key insights into the pathophysiology of common conditions, but studying such cases is challenging due to their rarity and the limited statistical power of existing methods. Herein, we used a novel approach to pathway-based mutational burden testing, the rare variant trend test (RVTT), to investigate genetic risk factors for an extreme form of sepsis-induced coagulopathy, infectious purpura fulminans (PF). In addition to prospective patient sample collection, we electronically screened over 10.4 million medical records from 4 large hospital systems and identified historical cases of PF for which archived specimens were available to perform germline whole-exome sequencing. We found a significantly increased burden of low-frequency, putatively function-altering variants in the complement system in patients with PF compared with unselected patients with sepsis (P = .01). A multivariable logistic regression analysis found that the number of complement system variants per patient was independently associated with PF after controlling for age, sex, and disease acuity (P = .01). Functional characterization of PF-associated variants in the immunomodulatory complement receptors CR3 and CR4 revealed that they result in partial or complete loss of anti-inflammatory CR3 function and/or gain of proinflammatory CR4 function. Taken together, these findings suggest that inherited defects in CR3 and CR4 predispose to the maladaptive hyperinflammation that characterizes severe sepsis with coagulopathy.
Collapse
Affiliation(s)
- Pavan K. Bendapudi
- Division of Hemostasis and Thrombosis, Beth Israel Deaconess Medical Center, Boston, MA
- Division of Hematology and Blood Transfusion Service, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - Sumaiya Nazeen
- Harvard Medical School, Boston, MA
- Division of Genomic Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Justine Ryu
- Division of Hemostasis and Thrombosis, Beth Israel Deaconess Medical Center, Boston, MA
- Harvard Medical School, Boston, MA
| | - Onuralp Söylemez
- Harvard Medical School, Boston, MA
- Division of Genomic Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Alissa Robbins
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Betty Rouaisnel
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Jillian K. O’Neil
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Ruchika Pokhriyal
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
| | - Moua Yang
- Division of Hemostasis and Thrombosis, Beth Israel Deaconess Medical Center, Boston, MA
- Harvard Medical School, Boston, MA
| | - Meaghan Colling
- Division of Hematology and Blood Transfusion Service, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - Bryce Pasko
- Department of Pathology, University of Colorado School of Medicine, Aurora, CO
| | - Michael Bouzinier
- Harvard Medical School, Boston, MA
- Division of Genomic Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Lindsay Tomczak
- Division of Hemostasis and Thrombosis, Beth Israel Deaconess Medical Center, Boston, MA
| | - Lindsay Collier
- Division of Hemostasis and Thrombosis, Beth Israel Deaconess Medical Center, Boston, MA
| | - David Barrios
- Division of Hemostasis and Thrombosis, Beth Israel Deaconess Medical Center, Boston, MA
- Harvard Medical School, Boston, MA
| | - Sanjay Ram
- Division of Infectious Diseases and Immunology, University of Massachusetts Medical School, Worcester, MA
| | - Agnes Toth-Petroczy
- Harvard Medical School, Boston, MA
- Division of Genomic Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Joel Krier
- Harvard Medical School, Boston, MA
- Division of Genomic Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Elizabeth Fieg
- Division of Genomic Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Walter H. Dzik
- Division of Hematology and Blood Transfusion Service, Massachusetts General Hospital, Boston, MA
- Harvard Medical School, Boston, MA
| | - James C. Hudspeth
- Department of Medicine, Boston Medical Center, Boston, MA
- Boston University School of Medicine, Boston, MA
| | - Olga Pozdnyakova
- Harvard Medical School, Boston, MA
- Department of Pathology, Brigham and Women’s Hospital, Boston, MA
| | - Valentina Nardi
- Harvard Medical School, Boston, MA
- Department of Pathology, Massachusetts General Hospital, Boston, MA
| | - James Knight
- Yale Center for Genome Analysis, Yale University, New Haven, CT
| | - Richard Maas
- Harvard Medical School, Boston, MA
- Division of Genomic Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Shamil Sunyaev
- Harvard Medical School, Boston, MA
- Division of Genomic Medicine, Brigham and Women’s Hospital, Boston, MA
| | - Julie-Aurore Losman
- Harvard Medical School, Boston, MA
- Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA
- Division of Hematology, Brigham and Women’s Hospital, Boston, MA
| |
Collapse
|
2
|
Tilch E, Schormair B, Zhao C, Högl B, Stefani A, Berger K, Trenkwalder C, Bachmann CG, Hornyak M, Fietze I, Müller-Nurasyid M, Peters A, Herms S, Nöthen MM, Müller-Myhsok B, Oexle K, Winkelmann J. Exomechip-based rare variant association study in restless legs syndrome. Sleep Med 2022; 94:26-30. [DOI: 10.1016/j.sleep.2022.04.001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Revised: 03/17/2022] [Accepted: 04/04/2022] [Indexed: 11/16/2022]
|
3
|
Lea-Henry TN, Chuah A, Stanley M, Athanasopoulos V, Starkey MR, Christiadi D, Kitching AR, Cook MC, Andrews TD, Vinuesa CG, Walters GD, Jiang SH. Increased burden of rare variants in genes of the endosomal Toll-like receptor pathway in patients with systemic lupus erythematosus. Lupus 2021; 30:1756-1763. [PMID: 34266320 DOI: 10.1177/09612033211033979] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/17/2022]
Abstract
OBJECTIVE To compare the frequency of rare variants in genes of the pathophysiologically relevant endosomal Toll-like receptor (eTLR) pathway and any quantifiable differences in variant rarity, predicted deleteriousness, or molecular proximity in patients with systemic lupus erythematosus (SLE) and healthy controls. PATIENTS AND METHODS 65 genes associated with the eTLR pathway were identified by literature search and pathway analysis. Using next generation sequencing techniques, these were compared in two randomised cohorts of patients with SLE (n = 114 and n = 113) with 197 healthy controls. Genetically determined ethnicity was used to normalise minor allele frequencies (MAF) for the identified genetic variants and these were then compared by their frequency: rare (MAF < 0.005), uncommon (MAF 0.005-0.02), and common (MAF >0.02). This was compared to the results for 65 randomly selected genes. RESULTS Patients with SLE are more likely to carry a rare nonsynonymous variant affecting proteins within the eTLR pathway than healthy controls. Furthermore, individuals with SLE are more likely to have multiple rare variants in this pathway. There were no differences in rarity, Combined Annotation Dependent Depletion (CADD) score, or molecular proximity for rare eTLR pathway variants. CONCLUSIONS Rare non-synonymous variants are enriched in patients with SLE in the eTLR pathway. This supports the hypothesis that SLE arises from several rare variants of relatively large effect rather than many common variants of small effect.
Collapse
Affiliation(s)
- Tom N Lea-Henry
- Department of Immunology and Infectious Disease, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Aaron Chuah
- Department of Immunology and Infectious Disease, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Maurice Stanley
- Department of Immunology and Infectious Disease, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia.,Centre for Personalised Immunology, NHMRC Centre for Research Excellence, Canberra, ACT, Australia
| | - Vicki Athanasopoulos
- Department of Immunology and Infectious Disease, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia.,Centre for Personalised Immunology, NHMRC Centre for Research Excellence, Canberra, ACT, Australia.,China Australia Centre for Personalised Immunology, Shanghai Renji Hospital, Jiao Tong University Shanghai, Huangpu Qu, China
| | - Malcolm R Starkey
- Department of Immunology and Pathology, Central Clinical School, Monash University, Melbourne, VIC, Australia
| | - Daniel Christiadi
- Department of Immunology and Infectious Disease, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia.,Centre for Personalised Immunology, NHMRC Centre for Research Excellence, Canberra, ACT, Australia
| | - A Richard Kitching
- Centre for Personalised Immunology, NHMRC Centre for Research Excellence, Canberra, ACT, Australia.,Centre for Inflammatory Diseases, 439191Monash University Department of Medicine, Monash University Department of Medicine, Clayton, VIC, Australia.,Department of Nephrology, Monash Health, Clayton, VIC, Australia.,Department of Paediatric Nephrology. Monash Health, Clayton, VIC, Australia
| | - Matthew C Cook
- Department of Immunology and Infectious Disease, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia.,Centre for Personalised Immunology, NHMRC Centre for Research Excellence, Canberra, ACT, Australia.,China Australia Centre for Personalised Immunology, Shanghai Renji Hospital, Jiao Tong University Shanghai, Huangpu Qu, China
| | - Thomas D Andrews
- Department of Immunology and Infectious Disease, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia
| | - Carola G Vinuesa
- Department of Immunology and Infectious Disease, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia.,Centre for Personalised Immunology, NHMRC Centre for Research Excellence, Canberra, ACT, Australia.,China Australia Centre for Personalised Immunology, Shanghai Renji Hospital, Jiao Tong University Shanghai, Huangpu Qu, China
| | - Giles D Walters
- Department of Renal Medicine, 34381Canberra Hospital, The Canberra Hospital, Garran, ACT, Australia
| | - Simon H Jiang
- Department of Immunology and Infectious Disease, John Curtin School of Medical Research, Australian National University, Canberra, ACT, Australia.,Centre for Personalised Immunology, NHMRC Centre for Research Excellence, Canberra, ACT, Australia.,China Australia Centre for Personalised Immunology, Shanghai Renji Hospital, Jiao Tong University Shanghai, Huangpu Qu, China.,Department of Renal Medicine, 34381Canberra Hospital, The Canberra Hospital, Garran, ACT, Australia
| |
Collapse
|
4
|
Shivakumar M, Miller JE, Dasari VR, Zhang Y, Lee MTM, Carey DJ, Gogoi R, Kim D. Genetic Analysis of Functional Rare Germline Variants across Nine Cancer Types from an Electronic Health Record Linked Biobank. Cancer Epidemiol Biomarkers Prev 2021; 30:1681-1688. [PMID: 34244158 DOI: 10.1158/1055-9965.epi-21-0082] [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: 01/18/2021] [Revised: 02/15/2021] [Accepted: 06/17/2021] [Indexed: 11/16/2022] Open
Abstract
BACKGROUND Rare variants play an essential role in the etiology of cancer. In this study, we aim to characterize rare germline variants that impact the risk of cancer. METHODS We performed a genome-wide rare variant analysis using germline whole exome sequencing (WES) data derived from the Geisinger MyCode initiative to discover cancer predisposition variants. The case-control association analysis was conducted by binning variants in 5,538 patients with cancer and 7,286 matched controls in a discovery set and 1,991 patients with cancer and 2,504 matched controls in a validation set across nine cancer types. Further, The Cancer Genome Atlas (TCGA) germline data were used to replicate the findings. RESULTS We identified 133 significant pathway-cancer pairs (85 replicated) and 90 significant gene-cancer pairs (12 replicated). In addition, we identified 18 genes and 3 pathways that were associated with survival outcome across cancers (Bonferroni P < 0.05). CONCLUSIONS In this study, we identified potential predisposition genes and pathways based on rare variants in nine cancers. IMPACT This work adds to the knowledge base and progress being made in precision medicine.
Collapse
Affiliation(s)
- Manu Shivakumar
- Biomedical & Translational Informatics Institute, Geisinger, Danville, Pennsylvania
- Department of Biostatistics, Epidemiology and Informatics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Jason E Miller
- Department of Genetics, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania
- Institute for Biomedical Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | | | - Yanfei Zhang
- Genomic Medicine Institute, Geisinger, Danville, Pennsylvania
| | | | - David J Carey
- Department of Molecular and Functional Genomics, Geisinger, Danville, Pennsylvania
| | - Radhika Gogoi
- Weis Center for Research, Geisinger Clinic, Danville, Pennsylvania.
| | | |
Collapse
|
5
|
Pang H, Xia Y, Luo S, Huang G, Li X, Xie Z, Zhou Z. Emerging roles of rare and low-frequency genetic variants in type 1 diabetes mellitus. J Med Genet 2021; 58:289-296. [PMID: 33753534 PMCID: PMC8086251 DOI: 10.1136/jmedgenet-2020-107350] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/18/2020] [Revised: 01/06/2021] [Accepted: 01/10/2021] [Indexed: 12/12/2022]
Abstract
Type 1 diabetes mellitus (T1DM) is defined as an autoimmune disorder and has enormous complexity and heterogeneity. Although its precise pathogenic mechanisms are obscure, this disease is widely acknowledged to be precipitated by environmental factors in individuals with genetic susceptibility. To date, the known susceptibility loci, which have mostly been identified by genome-wide association studies, can explain 80%–85% of the heritability of T1DM. Researchers believe that at least a part of its missing genetic component is caused by undetected rare and low-frequency variants. Most common variants have only small to modest effect sizes, which increases the difficulty of dissecting their functions and restricts their potential clinical application. Intriguingly, many studies have indicated that rare and low-frequency variants have larger effect sizes and play more significant roles in susceptibility to common diseases, including T1DM, than common variants do. Therefore, better recognition of rare and low-frequency variants is beneficial for revealing the genetic architecture of T1DM and for providing new and potent therapeutic targets for this disease. Here, we will discuss existing challenges as well as the great significance of this field and review current knowledge of the contributions of rare and low-frequency variants to T1DM.
Collapse
Affiliation(s)
- Haipeng Pang
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Ying Xia
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Shuoming Luo
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Gan Huang
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Xia Li
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Zhiguo Xie
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| | - Zhiguang Zhou
- National Clinical Research Center for Metabolic Diseases, Key Laboratory of Diabetes Immunology (Central South University), Ministry of Education, and Department of Metabolism and Endocrinology, The Second Xiangya Hospital of Central South University, Changsha, Hunan, China
| |
Collapse
|
6
|
Scelsi MA, Napolioni V, Greicius MD, Altmann A. Network propagation of rare variants in Alzheimer's disease reveals tissue-specific hub genes and communities. PLoS Comput Biol 2021; 17:e1008517. [PMID: 33411734 PMCID: PMC7817020 DOI: 10.1371/journal.pcbi.1008517] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2020] [Revised: 01/20/2021] [Accepted: 11/10/2020] [Indexed: 11/18/2022] Open
Abstract
State-of-the-art rare variant association testing methods aggregate the contribution of rare variants in biologically relevant genomic regions to boost statistical power. However, testing single genes separately does not consider the complex interaction landscape of genes, nor the downstream effects of non-synonymous variants on protein structure and function. Here we present the NETwork Propagation-based Assessment of Genetic Events (NETPAGE), an integrative approach aimed at investigating the biological pathways through which rare variation results in complex disease phenotypes. We applied NETPAGE to sporadic, late-onset Alzheimer's disease (AD), using whole-genome sequencing from the AD Neuroimaging Initiative (ADNI) cohort, as well as whole-exome sequencing from the AD Sequencing Project (ADSP). NETPAGE is based on network propagation, a framework that models information flow on a graph and simulates the percolation of genetic variation through tissue-specific gene interaction networks. The result of network propagation is a set of smoothed gene scores that can be tested for association with disease status through sparse regression. The application of NETPAGE to AD enabled the identification of a set of connected genes whose smoothed variation profile was robustly associated to case-control status, based on gene interactions in the hippocampus. Additionally, smoothed scores significantly correlated with risk of conversion to AD in Mild Cognitive Impairment (MCI) subjects. Lastly, we investigated tissue-specific transcriptional dysregulation of the core genes in two independent RNA-seq datasets, as well as significant enrichments in terms of gene sets with known connections to AD. We present a framework that enables enhanced genetic association testing for a wide range of traits, diseases, and sample sizes.
Collapse
Affiliation(s)
- Marzia Antonella Scelsi
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | - Valerio Napolioni
- Functional Imaging in Neuropsychiatric Disorders (FIND) Lab, Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, United States of America
| | - Michael D Greicius
- Functional Imaging in Neuropsychiatric Disorders (FIND) Lab, Department of Neurology and Neurological Sciences, Stanford University School of Medicine, Stanford, California, United States of America
| | - Andre Altmann
- Centre for Medical Image Computing, Department of Medical Physics and Biomedical Engineering, University College London, London, United Kingdom
| | | |
Collapse
|
7
|
Fore R, Boehme J, Li K, Westra J, Tintle N. Multi-Set Testing Strategies Show Good Behavior When Applied to Very Large Sets of Rare Variants. Front Genet 2020; 11:591606. [PMID: 33240333 PMCID: PMC7680887 DOI: 10.3389/fgene.2020.591606] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/05/2020] [Accepted: 10/05/2020] [Indexed: 12/22/2022] Open
Abstract
Gene-based tests of association (e.g., variance components and burden tests) are now common practice for analyses attempting to elucidate the contribution of rare genetic variants on common disease. As sequencing datasets continue to grow in size, the number of variants within each set (e.g., gene) being tested is also continuing to grow. Pathway-based methods have been used to allow for the initial aggregation of gene-based statistical evidence and then the subsequent aggregation of evidence across the pathway. This “multi-set” approach (first gene-based test, followed by pathway-based) lacks thorough exploration in regard to evaluating genotype–phenotype associations in the age of large, sequenced datasets. In particular, we wonder whether there are statistical and biological characteristics that make the multi-set approach optimal vs. simply doing all gene-based tests? In this paper, we provide an intuitive framework for evaluating these questions and use simulated data to affirm us this intuition. A real data application is provided demonstrating how our insights manifest themselves in practice. Ultimately, we find that when initial subsets are biologically informative (e.g., tending to aggregate causal genetic variants within one or more subsets, often genes), multi-set strategies can improve statistical power, with particular gains in cases where causal variants are aggregated in subsets with less variants overall (high proportion of causal variants in the subset). However, we find that there is little advantage when the sets are non-informative (similar proportion of causal variants in the subsets). Our application to real data further demonstrates this intuition. In practice, we recommend wider use of pathway-based methods and further exploration of optimal ways of aggregating variants into subsets based on emerging biological evidence of the genetic architecture of complex disease.
Collapse
Affiliation(s)
- Ruby Fore
- Department of Biostatistics, Brown University, Providence, RI, United States
| | - Jaden Boehme
- Department of Mathematics, Oregon State University, Corvallis, OR, United States
| | - Kevin Li
- Department of Mathematics, School of Arts and Sciences, Columbia University, New York, NY, United States
| | - Jason Westra
- Department of Mathematics and Statistics, Dordt University, Sioux Center, IA, United States
| | - Nathan Tintle
- Department of Mathematics and Statistics, Dordt University, Sioux Center, IA, United States
| |
Collapse
|
8
|
Sandling JK, Pucholt P, Hultin Rosenberg L, Farias FHG, Kozyrev SV, Eloranta ML, Alexsson A, Bianchi M, Padyukov L, Bengtsson C, Jonsson R, Omdal R, Lie BA, Massarenti L, Steffensen R, Jakobsen MA, Lillevang ST, Lerang K, Molberg Ø, Voss A, Troldborg A, Jacobsen S, Syvänen AC, Jönsen A, Gunnarsson I, Svenungsson E, Rantapää-Dahlqvist S, Bengtsson AA, Sjöwall C, Leonard D, Lindblad-Toh K, Rönnblom L. Molecular pathways in patients with systemic lupus erythematosus revealed by gene-centred DNA sequencing. Ann Rheum Dis 2020; 80:109-117. [PMID: 33037003 PMCID: PMC7788061 DOI: 10.1136/annrheumdis-2020-218636] [Citation(s) in RCA: 27] [Impact Index Per Article: 6.8] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/20/2020] [Revised: 09/15/2020] [Accepted: 09/16/2020] [Indexed: 01/02/2023]
Abstract
Objectives Systemic lupus erythematosus (SLE) is an autoimmune disease with extensive heterogeneity in disease presentation between patients, which is likely due to an underlying molecular diversity. Here, we aimed at elucidating the genetic aetiology of SLE from the immunity pathway level to the single variant level, and stratify patients with SLE into distinguishable molecular subgroups, which could inform treatment choices in SLE. Methods We undertook a pathway-centred approach, using sequencing of immunological pathway genes. Altogether 1832 candidate genes were analysed in 958 Swedish patients with SLE and 1026 healthy individuals. Aggregate and single variant association testing was performed, and we generated pathway polygenic risk scores (PRS). Results We identified two main independent pathways involved in SLE susceptibility: T lymphocyte differentiation and innate immunity, characterised by HLA and interferon, respectively. Pathway PRS defined pathways in individual patients, who on average were positive for seven pathways. We found that SLE organ damage was more pronounced in patients positive for the T or B cell receptor signalling pathways. Further, pathway PRS-based clustering allowed stratification of patients into four groups with different risk score profiles. Studying sets of genes with priors for involvement in SLE, we observed an aggregate common variant contribution to SLE at genes previously reported for monogenic SLE as well as at interferonopathy genes. Conclusions Our results show that pathway risk scores have the potential to stratify patients with SLE beyond clinical manifestations into molecular subsets, which may have implications for clinical follow-up and therapy selection.
Collapse
Affiliation(s)
- Johanna K Sandling
- Department of Medical Sciences, Rheumatology, Uppsala University, Uppsala, Sweden
| | - Pascal Pucholt
- Department of Medical Sciences, Rheumatology, Uppsala University, Uppsala, Sweden
| | - Lina Hultin Rosenberg
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Fabiana H G Farias
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.,Department of Psychiatry, Washington University, St. Louis, Missouri, USA
| | - Sergey V Kozyrev
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Maija-Leena Eloranta
- Department of Medical Sciences, Rheumatology, Uppsala University, Uppsala, Sweden
| | - Andrei Alexsson
- Department of Medical Sciences, Rheumatology, Uppsala University, Uppsala, Sweden
| | - Matteo Bianchi
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden
| | - Leonid Padyukov
- Division of Rheumatology, Department of Medicine, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Christine Bengtsson
- Department of Public Health and Clinical Medicine/Rheumatology, Umeå University, Umeå, Sweden
| | - Roland Jonsson
- Broegelmann Research Laboratory, Department of Clinical Science, University of Bergen, Bergen, Norway
| | - Roald Omdal
- Broegelmann Research Laboratory, Department of Clinical Science, University of Bergen, Bergen, Norway.,Clinical Immunology unit, Department of Internal Medicine, Stavanger University Hospital, Stavanger, Norway
| | - Benedicte A Lie
- Department of Medical Genetics, University of Oslo, Oslo, Norway
| | - Laura Massarenti
- Institute for Inflammation Research, Center for Rheumatology and Spine Diseases, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark
| | - Rudi Steffensen
- Department of Clinical Immunology, Aalborg University, Aalborg, Denmark
| | - Marianne A Jakobsen
- Department of Clinical Immunology, Odense University Hospital, Odense, Denmark
| | - Søren T Lillevang
- Department of Clinical Immunology, Odense University Hospital, Odense, Denmark
| | | | - Karoline Lerang
- Department of Rheumatology, Oslo University Hospital, Oslo, Norway
| | - Øyvind Molberg
- Department of Rheumatology, Oslo University Hospital, Oslo, Norway.,Institute of Clinical Medicine, University of Oslo, Oslo, Norway
| | - Anne Voss
- Department of Rheumatology, Odense University Hospital, Odense, Denmark
| | - Anne Troldborg
- Department of Rheumatology, Aarhus University Hospital, Aarhus, Denmark.,Institute of Clinical Medicine, Aarhus University, Aarhus, Denmark
| | - Søren Jacobsen
- Center for Rheumatology and Spine Diseases, Copenhagen University Hospital Rigshospitalet, Copenhagen, Denmark.,Institute of Clinical Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Ann-Christine Syvänen
- Department of Medical Sciences, Molecular Medicine and Science for Life Laboratory, Uppsala University, Uppsala, Sweden
| | - Andreas Jönsen
- Department of Clinical Sciences Lund, Rheumatology, Lund University, Skane University Hospital, Lund, Sweden
| | - Iva Gunnarsson
- Division of Rheumatology, Department of Medicine, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | - Elisabet Svenungsson
- Division of Rheumatology, Department of Medicine, Karolinska Institutet and Karolinska University Hospital, Stockholm, Sweden
| | | | - Anders A Bengtsson
- Department of Clinical Sciences Lund, Rheumatology, Lund University, Skane University Hospital, Lund, Sweden
| | - Christopher Sjöwall
- Department of Biomedical and Clinical Sciences, Division of Inflammation and Infection, Linköping University, Linköping, Sweden
| | - Dag Leonard
- Department of Medical Sciences, Rheumatology, Uppsala University, Uppsala, Sweden
| | - Kerstin Lindblad-Toh
- Science for Life Laboratory, Department of Medical Biochemistry and Microbiology, Uppsala University, Uppsala, Sweden.,Broad Institute of MIT and Harvard, Cambridge, Massachusetts, USA
| | - Lars Rönnblom
- Department of Medical Sciences, Rheumatology, Uppsala University, Uppsala, Sweden
| |
Collapse
|
9
|
Benefits and Challenges of Rare Genetic Variation in Alzheimer’s Disease. CURRENT GENETIC MEDICINE REPORTS 2019. [DOI: 10.1007/s40142-019-0161-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.4] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/20/2023]
|
10
|
A weighted burden test using logistic regression for integrated analysis of sequence variants, copy number variants and polygenic risk score. Eur J Hum Genet 2018; 27:114-124. [PMID: 30258123 DOI: 10.1038/s41431-018-0272-6] [Citation(s) in RCA: 19] [Impact Index Per Article: 3.2] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/22/2018] [Revised: 08/15/2018] [Accepted: 08/30/2018] [Indexed: 12/16/2022] Open
Abstract
Previously described methods of analysis allow variants in a gene to be weighted more highly according to rarity and/or predicted function and then for the variant contributions to be summed into a gene-wise risk score, which can be compared between cases and controls using a t-test. However, this does not allow incorporating covariates into the analysis. Schizophrenia is an example of an illness where there is evidence that different kinds of genetic variation can contribute to risk, including common variants contributing to a polygenic risk score (PRS), very rare copy number variants (CNVs) and sequence variants. A logistic regression approach has been implemented to compare the gene-wise risk scores between cases and controls, while incorporating as covariates population principal components, the PRS and the presence of pathogenic CNVs and sequence variants. A likelihood ratio test is performed, comparing the likelihoods of logistic regression models with and without this score. The method was applied to an ethnically heterogeneous exome-sequenced sample of 6000 controls and 5000 schizophrenia cases. In the raw analysis, the test statistic is inflated but inclusion of principal components satisfactorily controls for this. In this dataset, the inclusion of the PRS and effect from CNVs and sequence variants had only small effects. The set of genes which are FMRP targets showed some evidence for enrichment of rare, functional variants among cases (p = 0.0005). This approach can be applied to any disease in which different kinds of genetic and non-genetic risk factors make contributions to risk.
Collapse
|
11
|
Momozawa Y, Dmitrieva J, Théâtre E, Deffontaine V, Rahmouni S, Charloteaux B, Crins F, Docampo E, Elansary M, Gori AS, Lecut C, Mariman R, Mni M, Oury C, Altukhov I, Alexeev D, Aulchenko Y, Amininejad L, Bouma G, Hoentjen F, Löwenberg M, Oldenburg B, Pierik MJ, Vander Meulen-de Jong AE, Janneke van der Woude C, Visschedijk MC, Lathrop M, Hugot JP, Weersma RK, De Vos M, Franchimont D, Vermeire S, Kubo M, Louis E, Georges M. IBD risk loci are enriched in multigenic regulatory modules encompassing putative causative genes. Nat Commun 2018; 9:2427. [PMID: 29930244 PMCID: PMC6013502 DOI: 10.1038/s41467-018-04365-8] [Citation(s) in RCA: 130] [Impact Index Per Article: 21.7] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2017] [Accepted: 04/24/2018] [Indexed: 02/08/2023] Open
Abstract
GWAS have identified >200 risk loci for Inflammatory Bowel Disease (IBD). The majority of disease associations are known to be driven by regulatory variants. To identify the putative causative genes that are perturbed by these variants, we generate a large transcriptome data set (nine disease-relevant cell types) and identify 23,650 cis-eQTL. We show that these are determined by ∼9720 regulatory modules, of which ∼3000 operate in multiple tissues and ∼970 on multiple genes. We identify regulatory modules that drive the disease association for 63 of the 200 risk loci, and show that these are enriched in multigenic modules. Based on these analyses, we resequence 45 of the corresponding 100 candidate genes in 6600 Crohn disease (CD) cases and 5500 controls, and show with burden tests that they include likely causative genes. Our analyses indicate that ≥10-fold larger sample sizes will be required to demonstrate the causality of individual genes using this approach.
Collapse
Affiliation(s)
- Yukihide Momozawa
- Unit of Animal Genomics, WELBIO, GIGA-R & Faculty of Veterinary Medicine, University of Liège (B34), 1 Avenue de l'Hôpital, Liège, 4000, Belgium
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Science, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
| | - Julia Dmitrieva
- Unit of Animal Genomics, WELBIO, GIGA-R & Faculty of Veterinary Medicine, University of Liège (B34), 1 Avenue de l'Hôpital, Liège, 4000, Belgium
| | - Emilie Théâtre
- Unit of Animal Genomics, WELBIO, GIGA-R & Faculty of Veterinary Medicine, University of Liège (B34), 1 Avenue de l'Hôpital, Liège, 4000, Belgium
| | - Valérie Deffontaine
- Unit of Animal Genomics, WELBIO, GIGA-R & Faculty of Veterinary Medicine, University of Liège (B34), 1 Avenue de l'Hôpital, Liège, 4000, Belgium
| | - Souad Rahmouni
- Unit of Animal Genomics, WELBIO, GIGA-R & Faculty of Veterinary Medicine, University of Liège (B34), 1 Avenue de l'Hôpital, Liège, 4000, Belgium
| | - Benoît Charloteaux
- Unit of Animal Genomics, WELBIO, GIGA-R & Faculty of Veterinary Medicine, University of Liège (B34), 1 Avenue de l'Hôpital, Liège, 4000, Belgium
| | - François Crins
- Unit of Animal Genomics, WELBIO, GIGA-R & Faculty of Veterinary Medicine, University of Liège (B34), 1 Avenue de l'Hôpital, Liège, 4000, Belgium
| | - Elisa Docampo
- Unit of Animal Genomics, WELBIO, GIGA-R & Faculty of Veterinary Medicine, University of Liège (B34), 1 Avenue de l'Hôpital, Liège, 4000, Belgium
| | - Mahmoud Elansary
- Unit of Animal Genomics, WELBIO, GIGA-R & Faculty of Veterinary Medicine, University of Liège (B34), 1 Avenue de l'Hôpital, Liège, 4000, Belgium
| | - Ann-Stephan Gori
- Unit of Animal Genomics, WELBIO, GIGA-R & Faculty of Veterinary Medicine, University of Liège (B34), 1 Avenue de l'Hôpital, Liège, 4000, Belgium
| | - Christelle Lecut
- Laboratory of Thrombosis and Hemostasis, GIGA-R, University of Liège (B34), 1 Avenue de l'Hôpital, 4000, Liège, Belgium
| | - Rob Mariman
- Unit of Animal Genomics, WELBIO, GIGA-R & Faculty of Veterinary Medicine, University of Liège (B34), 1 Avenue de l'Hôpital, Liège, 4000, Belgium
| | - Myriam Mni
- Unit of Animal Genomics, WELBIO, GIGA-R & Faculty of Veterinary Medicine, University of Liège (B34), 1 Avenue de l'Hôpital, Liège, 4000, Belgium
| | - Cécile Oury
- Laboratory of Thrombosis and Hemostasis, GIGA-R, University of Liège (B34), 1 Avenue de l'Hôpital, 4000, Liège, Belgium
| | - Ilya Altukhov
- Moscow Institute of Physics and Technology, Institutskiy Pereulok 9, Dolgoprudny, 141700, Russian Federation
| | - Dmitry Alexeev
- Novosibirsk State University, Pirogova ave. 2, Novosibirsk, 630090, Russian Federation
| | - Yuri Aulchenko
- PolyOmica, Het Vlaggeschip 61, 's-Hertogenbosch, 5237 PA, The Netherlands
- Institute of Cytology and Genetics SD RAS, Lavrentyeva ave. 10, 630090, Novosibirsk, Russia
- Centre for Global Health Research, Usher Institute of Population Health Sciences and Informatics, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, UK
| | - Leila Amininejad
- Gastroentérologie Médicale, Faculté de Médicine, Université Libre de Bruxelles, Route de Lennik 808, Anderlecht, 1070, Belgium
| | - Gerd Bouma
- Department of Gastroenterology and Hepatology, VU University Medical Centre, Amsterdam, 1081 HV, The Netherlands
| | - Frank Hoentjen
- Department of Gastroenterology and Hepatology, University Medical Centre St. Radboud, Nijmegen, 6525 GA, The Netherlands
| | - Mark Löwenberg
- Department of Gastroenterology and Hepatology, Amsterdam Medical Centre, Amsterdam, 1105 AZ, The Netherlands
| | - Bas Oldenburg
- Department of Gastroenterology and Hepatology, University Medical Centre Utrecht, 3584 cX, Utrecht, The Netherlands
| | - Marieke J Pierik
- Department of Gastroenterology and Hepatology, University Medical Centre Maastricht, Maastricht, 6229 HX, The Netherlands
| | | | - C Janneke van der Woude
- Department of Gastroenterology and Hepatology, Erasmus Medical Centre, Rotterdam, 3015 CE, The Netherlands
| | - Marijn C Visschedijk
- Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Hanzeplein 1, Groningen, 9713 GZ, The Netherlands
| | - Mark Lathrop
- McGill University Centre for Molecular and Computational Genomics, 740 Dr. Penfield Avenue, Montreal, H3A 0G1, QC, Canada
| | - Jean-Pierre Hugot
- UMR 1149 INSERM/Université Paris-Diderot Sorbonne Paris-Cité, Assistance Publique Hôpitaux de Paris, 48 Bd Sérurier, Paris, 75019, France
| | - Rinse K Weersma
- Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Hanzeplein 1, Groningen, 9713 GZ, The Netherlands
| | - Martine De Vos
- Department of Gastroenterology, University Hospital, De Pintelaan 185, Gent, 9000, Belgium
| | - Denis Franchimont
- Gastroentérologie Médicale, Faculté de Médicine, Université Libre de Bruxelles, Route de Lennik 808, Anderlecht, 1070, Belgium
| | - Severine Vermeire
- Translational Research in Gastrointestinal Disorders, Department of Clinical and Experimental Medicine, KU Leuven, UZ Herestraat 49, Leuven, 3000, Belgium
| | - Michiaki Kubo
- Laboratory for Genotyping Development, RIKEN Center for Integrative Medical Science, 1-7-22, Suehiro-cho, Tsurumi-ku, Yokohama, Kanagawa, 230-0045, Japan
| | - Edouard Louis
- CHU-Liège and Unit of Gastroenterology, GIGA-R & Faculty of Medicine, University of Liège, 1 Avenue de l'Hôpital, Liège, 4000, Belgium
| | - Michel Georges
- Unit of Animal Genomics, WELBIO, GIGA-R & Faculty of Veterinary Medicine, University of Liège (B34), 1 Avenue de l'Hôpital, Liège, 4000, Belgium.
| |
Collapse
|