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Garg E, Arguello-Pascualli P, Vishnyakova O, Halevy AR, Yoo S, Brooks JD, Bull SB, Gagnon F, Greenwood CMT, Hung RJ, Lawless JF, Lerner-Ellis J, Dennis JK, Abraham RJS, Garant JM, Thiruvahindrapuram B, Jones SJM, Strug LJ, Paterson AD, Sun L, Elliott LT. Canadian COVID-19 host genetics cohort replicates known severity associations. PLoS Genet 2024; 20:e1011192. [PMID: 38517939 PMCID: PMC10990181 DOI: 10.1371/journal.pgen.1011192] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2023] [Revised: 04/03/2024] [Accepted: 02/22/2024] [Indexed: 03/24/2024] Open
Abstract
The HostSeq initiative recruited 10,059 Canadians infected with SARS-CoV-2 between March 2020 and March 2023, obtained clinical information on their disease experience and whole genome sequenced (WGS) their DNA. We analyzed the WGS data for genetic contributors to severe COVID-19 (considering 3,499 hospitalized cases and 4,975 non-hospitalized after quality control). We investigated the evidence for replication of loci reported by the International Host Genetics Initiative (HGI); analyzed the X chromosome; conducted rare variant gene-based analysis and polygenic risk score testing. Population stratification was adjusted for using meta-analysis across ancestry groups. We replicated two loci identified by the HGI for COVID-19 severity: the LZTFL1/SLC6A20 locus on chromosome 3 and the FOXP4 locus on chromosome 6 (the latter with a variant significant at P < 5E-8). We found novel significant associations with MRAS and WDR89 in gene-based analyses, and constructed a polygenic risk score that explained 1.01% of the variance in severe COVID-19. This study provides independent evidence confirming the robustness of previously identified COVID-19 severity loci by the HGI and identifies novel genes for further investigation.
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Affiliation(s)
- Elika Garg
- Department of Statistics and Actuarial Science, Simon Fraser University, Vancouver, British Columbia, Canada
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Paola Arguello-Pascualli
- BC Children’s Hospital Research Institute, Vancouver, British Columbia, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Olga Vishnyakova
- Department of Statistics and Actuarial Science, Simon Fraser University, Vancouver, British Columbia, Canada
- Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, Canada
| | - Anat R. Halevy
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada
| | - Samantha Yoo
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada
- School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada
| | - Jennifer D. Brooks
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Shelley B. Bull
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada
| | - France Gagnon
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Celia M. T. Greenwood
- Gerald Bronfman Department of Oncology, Department of Epidemiology, Biostatistics and Occupational Health, Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - Rayjean J. Hung
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada
| | - Jerald F. Lawless
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, Ontario, Canada
| | - Jordan Lerner-Ellis
- Lunenfeld-Tanenbaum Research Institute, Sinai Health, Toronto, Ontario, Canada
- Mount Sinai Hospital, Toronto, Ontario, Canada
- Department of Laboratory Medicine and Pathobiology, University of Toronto, Toronto, Ontario, Canada
| | - Jessica K. Dennis
- BC Children’s Hospital Research Institute, Vancouver, British Columbia, Canada
- Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
| | - Rohan J. S. Abraham
- Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, Canada
| | - Jean-Michel Garant
- Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, Canada
| | | | - Steven J. M. Jones
- Department of Medical Genetics, University of British Columbia, Vancouver, British Columbia, Canada
- Canada’s Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, Canada
| | | | - Lisa J. Strug
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Andrew D. Paterson
- Genetics and Genome Biology Program, The Hospital for Sick Children, Toronto, Ontario, Canada
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
| | - Lei Sun
- Dalla Lana School of Public Health, University of Toronto, Toronto, Ontario, Canada
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, Canada
| | - Lloyd T. Elliott
- Department of Statistics and Actuarial Science, Simon Fraser University, Vancouver, British Columbia, Canada
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Lu T, Nakanishi T, Yoshiji S, Butler-Laporte G, Greenwood CMT, Richards JB. Dose-dependent Association of Alcohol Consumption With Obesity and Type 2 Diabetes: Mendelian Randomization Analyses. J Clin Endocrinol Metab 2023; 108:3320-3329. [PMID: 37368847 DOI: 10.1210/clinem/dgad324] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Indexed: 06/29/2023]
Abstract
CONTEXT Effects of modest alcohol consumption remain controversial. Mendelian randomization (MR) can help to mitigate biases due to confounding and reverse causation in observational studies, and evaluate the potential causal role of alcohol consumption. OBJECTIVE This work aimed to evaluate dose-dependent effect of alcohol consumption on obesity and type 2 diabetes. METHODS Assessing 408 540 participants of European ancestry in the UK Biobank, we first tested the association between self-reported alcohol intake frequency and 10 anthropometric measurements, obesity, and type 2 diabetes. We then conducted MR analyses both in the overall population and in subpopulations stratified by alcohol intake frequency. RESULTS Among individuals having more than 14 drinks per week, a 1-drink-per-week increase in genetically predicted alcohol intake frequency was associated with a 0.36-kg increase in fat mass (SD = 0.03 kg), a 1.08-fold increased odds of obesity (95% CI, 1.06-1.10), and a 1.10-fold increased odds of type 2 diabetes (95% CI, 1.06-1.13). These associations were stronger in women than in men. Furthermore, no evidence was found supporting the association between genetically increased alcohol intake frequency and improved health outcomes among individuals having 7 or fewer drinks per week, as MR estimates largely overlapped with the null. These results withstood multiple sensitivity analyses assessing the validity of MR assumptions. CONCLUSION As opposed to observational associations, MR results suggest there may not be protective effects of modest alcohol consumption on obesity traits and type 2 diabetes. Heavy alcohol consumption could lead to increased measures of obesity as well as increased risk of type 2 diabetes.
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Affiliation(s)
- Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, H3T 1E2, Canada
- Department of Statistical Sciences, University of Toronto, Toronto, Ontario, M5S 1A1, Canada
- 5 Prime Sciences Inc, Montréal, Québec, H3Y 2W4, Canada
| | - Tomoko Nakanishi
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, H3T 1E2, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, H3A 0G4, Canada
- Kyoto-McGill International Collaborative Program in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, 606-8501, Japan
- Japan Society for Promotion of Science, Tokyo, 102-0083, Japan
| | - Satoshi Yoshiji
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, H3T 1E2, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, H3A 0G4, Canada
- Kyoto-McGill International Collaborative Program in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, 606-8501, Japan
- Japan Society for Promotion of Science, Tokyo, 102-0083, Japan
| | - Guillaume Butler-Laporte
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, H3T 1E2, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec, H3A 04, Canada
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, H3T 1E2, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, H3A 0G4, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec, H3A 04, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montréal, Québec, H3A 0G4, Canada
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, H3T 1E2, Canada
- 5 Prime Sciences Inc, Montréal, Québec, H3Y 2W4, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, H3A 0G4, Canada
- Department of Twin Research, King's College London, London, WC2R 2LS, UK
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Vasileva D, Greenwood CMT, Daley D. A Review of the Epigenetic Clock: Emerging Biomarkers for Asthma and Allergic Disease. Genes (Basel) 2023; 14:1724. [PMID: 37761864 PMCID: PMC10531327 DOI: 10.3390/genes14091724] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2023] [Revised: 08/22/2023] [Accepted: 08/23/2023] [Indexed: 09/29/2023] Open
Abstract
DNA methylation (DNAm) is a dynamic, age-dependent epigenetic modification that can be used to study interactions between genetic and environmental factors. Environmental exposures during critical periods of growth and development may alter DNAm patterns, leading to increased susceptibility to diseases such as asthma and allergies. One method to study the role of DNAm is the epigenetic clock-an algorithm that uses DNAm levels at select age-informative Cytosine-phosphate-Guanine (CpG) dinucleotides to predict epigenetic age (EA). The difference between EA and calendar age (CA) is termed epigenetic age acceleration (EAA) and reveals information about the biological capacity of an individual. Associations between EAA and disease susceptibility have been demonstrated for a variety of age-related conditions and, more recently, phenotypes such as asthma and allergic diseases, which often begin in childhood and progress throughout the lifespan. In this review, we explore different epigenetic clocks and how they have been applied, particularly as related to childhood asthma. We delve into how in utero and early life exposures (e.g., smoking, air pollution, maternal BMI) result in methylation changes. Furthermore, we explore the potential for EAA to be used as a biomarker for asthma and allergic diseases and identify areas for further study.
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Affiliation(s)
- Denitsa Vasileva
- Centre for Heart Lung Innovation, University of British Columbia and Saint Paul’s Hospital, Vancouver, BC V6Z 1Y6, Canada;
| | - Celia M. T. Greenwood
- Lady Davis Institute for Medical Research, Montreal, QC H3T 1E2, Canada;
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC H3A 0G4, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montreal, QC H3A 0G4, Canada
- Department of Human Genetics, McGill University, Montreal, QC H3A 0G4, Canada
| | - Denise Daley
- Centre for Heart Lung Innovation, University of British Columbia and Saint Paul’s Hospital, Vancouver, BC V6Z 1Y6, Canada;
- Department of Medicine, Respiratory Division, University of British Columbia, Vancouver, BC V6T 1Z3, Canada
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Zhou S, Sosina OA, Bovijn J, Laurent L, Sharma V, Akbari P, Forgetta V, Jiang L, Kosmicki JA, Banerjee N, Morris JA, Oerton E, Jones M, LeBlanc MG, Idone V, Overton JD, Reid JG, Cantor M, Abecasis GR, Goltzman D, Greenwood CMT, Langenberg C, Baras A, Economides AN, Ferreira MAR, Hatsell S, Ohlsson C, Richards JB, Lotta LA. Converging evidence from exome sequencing and common variants implicates target genes for osteoporosis. Nat Genet 2023; 55:1277-1287. [PMID: 37558884 DOI: 10.1038/s41588-023-01444-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/08/2022] [Accepted: 06/09/2023] [Indexed: 08/11/2023]
Abstract
In this study, we leveraged the combined evidence of rare coding variants and common alleles to identify therapeutic targets for osteoporosis. We undertook a large-scale multiancestry exome-wide association study for estimated bone mineral density, which showed that the burden of rare coding alleles in 19 genes was associated with estimated bone mineral density (P < 3.6 × 10-7). These genes were highly enriched for a set of known causal genes for osteoporosis (65-fold; P = 2.5 × 10-5). Exome-wide significant genes had 96-fold increased odds of being the top ranked effector gene at a given GWAS locus (P = 1.8 × 10-10). By integrating proteomics Mendelian randomization evidence, we prioritized CD109 (cluster of differentiation 109) as a gene for which heterozygous loss of function is associated with higher bone density. CRISPR-Cas9 editing of CD109 in SaOS-2 osteoblast-like cell lines showed that partial CD109 knockdown led to increased mineralization. This study demonstrates that the convergence of common and rare variants, proteomics and CRISPR can highlight new bone biology to guide therapeutic development.
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Affiliation(s)
- Sirui Zhou
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Quebec, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Quebec, Canada
- Department of Human Genetics, McGill University, Montréal, Quebec, Canada
| | - Olukayode A Sosina
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Jonas Bovijn
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Laetitia Laurent
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Quebec, Canada
| | - Vasundhara Sharma
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Quebec, Canada
| | - Parsa Akbari
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Vincenzo Forgetta
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Quebec, Canada
- Five Prime Sciences Inc, Montréal, Québec, Canada
| | - Lai Jiang
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Quebec, Canada
| | - Jack A Kosmicki
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Nilanjana Banerjee
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | | | - Erin Oerton
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
| | - Marcus Jones
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Michelle G LeBlanc
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | | | - John D Overton
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Jeffrey G Reid
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Michael Cantor
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Goncalo R Abecasis
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - David Goltzman
- Research Institute of the McGill University Health Centre, Montréal, Québec, Canada
| | - Celia M T Greenwood
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Quebec, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Quebec, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montréal, Québec, Canada
| | - Claudia Langenberg
- MRC Epidemiology Unit, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health, Charité University Medicine Berlin, Berlin, Germany
| | - Aris Baras
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Aris N Economides
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | - Manuel A R Ferreira
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
| | | | - Claes Ohlsson
- Centre of Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Drug Treatment, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden
| | - J Brent Richards
- Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Quebec, Canada.
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Quebec, Canada.
- Department of Human Genetics, McGill University, Montréal, Quebec, Canada.
- Five Prime Sciences Inc, Montréal, Québec, Canada.
- Department of Twin Research, King's College London, London, UK.
| | - Luca A Lotta
- Regeneron Genetics Center, Regeneron Pharmaceuticals Inc, Tarrytown, NY, USA
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Lu T, Silveira PP, Greenwood CMT. Development of risk prediction models for depression combining genetic and early life risk factors. Front Neurosci 2023; 17:1143496. [PMID: 37534032 PMCID: PMC10390723 DOI: 10.3389/fnins.2023.1143496] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2023] [Accepted: 07/03/2023] [Indexed: 08/04/2023] Open
Abstract
Background Both genetic and early life risk factors play important roles in the pathogenesis and progression of adult depression. However, the interplay between these risk factors and their added value to risk prediction models have not been fully elucidated. Methods Leveraging a meta-analysis of major depressive disorder genome-wide association studies (N = 45,591 cases and 97,674 controls), we developed and optimized a polygenic risk score for depression using LDpred in a model selection dataset from the UK Biobank (N = 130,092 European ancestry individuals). In a UK Biobank test dataset (N = 278,730 European ancestry individuals), we tested whether the polygenic risk score and early life risk factors were associated with each other and compared their associations with depression phenotypes. Finally, we conducted joint predictive modeling to combine this polygenic risk score with early life risk factors by stepwise regression, and assessed the model performance in identifying individuals at high risk of depression. Results In the UK Biobank test dataset, the polygenic risk score for depression was moderately associated with multiple early life risk factors. For instance, a one standard deviation increase in the polygenic risk score was associated with 1.16-fold increased odds of frequent domestic violence (95% CI: 1.14-1.19) and 1.09-fold increased odds of not having access to medical care as a child (95% CI: 1.05-1.14). However, the polygenic risk score was more strongly associated with depression phenotypes than most early life risk factors. A joint predictive model integrating the polygenic risk score, early life risk factors, age and sex achieved an AUROC of 0.6766 for predicting strictly defined major depressive disorder, while a model without the polygenic risk score and a model without any early life risk factors had an AUROC of 0.6593 and 0.6318, respectively. Conclusion We have developed a polygenic risk score to partly capture the genetic liability to depression. Although genetic and early life risk factors can be correlated, joint predictive models improved risk stratification despite limited improvement in magnitude, and may be explored as tools to better identify individuals at high risk of depression.
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Affiliation(s)
- Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
| | - Patrícia Pelufo Silveira
- Department of Psychiatry, Faculty of Medicine, McGill University, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics and Mental Health, Douglas Research Center, McGill University, Montreal, QC, Canada
| | - Celia M. T. Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada
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Yu JCY, Zeng Y, Zhao K, Lu T, Oros Klein K, Colmegna I, Lora M, Bhatnagar SR, Leask A, Greenwood CMT, Hudson M. Novel insights into systemic sclerosis using a sensitive computational method to analyze whole-genome bisulfite sequencing data. Clin Epigenetics 2023; 15:96. [PMID: 37270501 DOI: 10.1186/s13148-023-01513-w] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2022] [Accepted: 05/28/2023] [Indexed: 06/05/2023] Open
Abstract
BACKGROUND Abnormal DNA methylation is thought to contribute to the onset and progression of systemic sclerosis. Currently, the most comprehensive assay for profiling DNA methylation is whole-genome bisulfite sequencing (WGBS), but its precision depends on read depth and it may be subject to sequencing errors. SOMNiBUS, a method for regional analysis, attempts to overcome some of these limitations. Using SOMNiBUS, we re-analyzed WGBS data previously analyzed using bumphunter, an approach that initially fits single CpG associations, to contrast DNA methylation estimates by both methods. METHODS Purified CD4+ T lymphocytes of 9 SSc and 4 control females were sequenced using WGBS. We separated the resulting sequencing data into regions with dense CpG data, and differentially methylated regions (DMRs) were inferred with the SOMNiBUS region-level test, adjusted for age. Pathway enrichment analysis was performed with ingenuity pathway analysis (IPA). We compared the results obtained by SOMNiBUS and bumphunter. RESULTS Of 8268 CpG regions of ≥ 60 CpGs eligible for analysis with SOMNiBUS, we identified 131 DMRs and 125 differentially methylated genes (DMGs; p-values less than Bonferroni-corrected threshold of 6.05-06 controlling family-wise error rate at 0.05; 1.6% of the regions). In comparison, bumphunter identified 821,929 CpG regions, 599 DMRs (of which none had ≥ 60 CpGs) and 340 DMGs (q-value of 0.05; 0.04% of all regions). The top ranked gene identified by SOMNiBUS was FLT4, a lymphangiogenic orchestrator, and the top ranked gene on chromosome X was CHST7, known to catalyze the sulfation of glycosaminoglycans in the extracellular matrix. The top networks identified by IPA included connective tissue disorders. CONCLUSIONS SOMNiBUS is a complementary method of analyzing WGBS data that enhances biological insights into SSc and provides novel avenues of investigation into its pathogenesis.
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Affiliation(s)
- Jeffrey C Y Yu
- McGill University, 845 Sherbrooke St W, Montreal, H3A 0G4, Canada
| | - Yixiao Zeng
- McGill University, 845 Sherbrooke St W, Montreal, H3A 0G4, Canada
| | - Kaiqiong Zhao
- McGill University, 845 Sherbrooke St W, Montreal, H3A 0G4, Canada
| | - Tianyuan Lu
- McGill University, 845 Sherbrooke St W, Montreal, H3A 0G4, Canada
| | - Kathleen Oros Klein
- Lady Davis Institute for Medical Research, Jewish General Hospital, 3755 Côte Sainte Catherine, Montreal, H3T 1E2, Canada
| | - Inés Colmegna
- McGill University, 845 Sherbrooke St W, Montreal, H3A 0G4, Canada
- Research Institute of the McGill University Health Center, Montreal, Canada
| | - Maximilien Lora
- Research Institute of the McGill University Health Center, Montreal, Canada
| | | | | | - Celia M T Greenwood
- McGill University, 845 Sherbrooke St W, Montreal, H3A 0G4, Canada
- Lady Davis Institute for Medical Research, Jewish General Hospital, 3755 Côte Sainte Catherine, Montreal, H3T 1E2, Canada
| | - Marie Hudson
- McGill University, 845 Sherbrooke St W, Montreal, H3A 0G4, Canada.
- Lady Davis Institute for Medical Research, Jewish General Hospital, 3755 Côte Sainte Catherine, Montreal, H3T 1E2, Canada.
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7
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Su CY, Zhou S, Gonzalez-Kozlova E, Butler-Laporte G, Brunet-Ratnasingham E, Nakanishi T, Jeon W, Morrison DR, Laurent L, Afilalo J, Afilalo M, Henry D, Chen Y, Carrasco-Zanini J, Farjoun Y, Pietzner M, Kimchi N, Afrasiabi Z, Rezk N, Bouab M, Petitjean L, Guzman C, Xue X, Tselios C, Vulesevic B, Adeleye O, Abdullah T, Almamlouk N, Moussa Y, DeLuca C, Duggan N, Schurr E, Brassard N, Durand M, Del Valle DM, Thompson R, Cedillo MA, Schadt E, Nie K, Simons NW, Mouskas K, Zaki N, Patel M, Xie H, Harris J, Marvin R, Cheng E, Tuballes K, Argueta K, Scott I, Greenwood CMT, Paterson C, Hinterberg MA, Langenberg C, Forgetta V, Pineau J, Mooser V, Marron T, Beckmann ND, Kim-Schulze S, Charney AW, Gnjatic S, Kaufmann DE, Merad M, Richards JB. Circulating proteins to predict COVID-19 severity. Sci Rep 2023; 13:6236. [PMID: 37069249 PMCID: PMC10107586 DOI: 10.1038/s41598-023-31850-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2022] [Accepted: 03/17/2023] [Indexed: 04/19/2023] Open
Abstract
Predicting COVID-19 severity is difficult, and the biological pathways involved are not fully understood. To approach this problem, we measured 4701 circulating human protein abundances in two independent cohorts totaling 986 individuals. We then trained prediction models including protein abundances and clinical risk factors to predict COVID-19 severity in 417 subjects and tested these models in a separate cohort of 569 individuals. For severe COVID-19, a baseline model including age and sex provided an area under the receiver operator curve (AUC) of 65% in the test cohort. Selecting 92 proteins from the 4701 unique protein abundances improved the AUC to 88% in the training cohort, which remained relatively stable in the testing cohort at 86%, suggesting good generalizability. Proteins selected from different COVID-19 severity were enriched for cytokine and cytokine receptors, but more than half of the enriched pathways were not immune-related. Taken together, these findings suggest that circulating proteins measured at early stages of disease progression are reasonably accurate predictors of COVID-19 severity. Further research is needed to understand how to incorporate protein measurement into clinical care.
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Affiliation(s)
- Chen-Yang Su
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
- Department of Computer Science, McGill University, Montréal, QC, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
| | - Sirui Zhou
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | | | - Guillaume Butler-Laporte
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | | | - Tomoko Nakanishi
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- Graduate School of Medicine, McGill International Collaborative School in Genomic Medicine, Kyoto University, Kyoto, Japan
- Japan Society for the Promotion of Science, Tokyo, Japan
| | - Wonseok Jeon
- Department of Computer Science, McGill University, Montréal, QC, Canada
| | - David R Morrison
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Laetitia Laurent
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Jonathan Afilalo
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | - Marc Afilalo
- Department of Emergency Medicine, Jewish General Hospital, McGill University, Montreal, QC, Canada
| | - Danielle Henry
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Yiheng Chen
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
- Department of Human Genetics, McGill University, Montreal, QC, Canada
| | - Julia Carrasco-Zanini
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Yossi Farjoun
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Maik Pietzner
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Nofar Kimchi
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Zaman Afrasiabi
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Nardin Rezk
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Meriem Bouab
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Louis Petitjean
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Charlotte Guzman
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Xiaoqing Xue
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Chris Tselios
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Branka Vulesevic
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Olumide Adeleye
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Tala Abdullah
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Noor Almamlouk
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Yara Moussa
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Chantal DeLuca
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Naomi Duggan
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Erwin Schurr
- Infectious Diseases and Immunity in Global Health Program, Research Institute of the McGill University Health Centre, Montréal, QC, Canada
| | - Nathalie Brassard
- Research Centre of the Centre Hospitalier de L'Université de Montréal, Montreal, QC, Canada
| | - Madeleine Durand
- Research Centre of the Centre Hospitalier de L'Université de Montréal, Montreal, QC, Canada
| | - Diane Marie Del Valle
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ryan Thompson
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mario A Cedillo
- Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Eric Schadt
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kai Nie
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nicole W Simons
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Konstantinos Mouskas
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Nicolas Zaki
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Manishkumar Patel
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Hui Xie
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jocelyn Harris
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Robert Marvin
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Esther Cheng
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kevin Tuballes
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Kimberly Argueta
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Ieisha Scott
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | | | | | - Claudia Langenberg
- MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
- Computational Medicine, Berlin Institute of Health at Charité - Universitätsmedizin Berlin, Berlin, Germany
| | - Vincenzo Forgetta
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada
| | - Joelle Pineau
- Department of Computer Science, McGill University, Montréal, QC, Canada
| | - Vincent Mooser
- Department of Human Genetics, McGill University, Montreal, QC, Canada
| | - Thomas Marron
- Immunotherapy and Phase 1 Trials, Mount Sinai Hospital, New York, NY, USA
| | - Noam D Beckmann
- Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Seunghee Kim-Schulze
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Alexander W Charney
- Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Sacha Gnjatic
- Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
- Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Daniel E Kaufmann
- Research Centre of the Centre Hospitalier de L'Université de Montréal, Montreal, QC, Canada
- Department of Medicine, Université de Montréal, Montreal, QC, Canada
- Division of Infectious Diseases, Department of Medicine, University Hospital of Lausanne and University of Lausanne, Lausanne, Switzerland
| | - Miriam Merad
- Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, Pavilion H-413, 3755 Côte-Ste-Catherine Montréal, Montreal, QC, H3T 1E2, Canada.
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.
- Department of Human Genetics, McGill University, Montreal, QC, Canada.
- Department of Twin Research, King's College London, London, UK.
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8
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Henry M, Harvey R, Chen LM, Meaney M, Nguyen TTT, Kao HT, Rosberger Z, Frenkiel S, Hier M, Zeitouni A, Kost K, Mlynarek A, Richardson K, Greenwood CMT, Melnychuk D, Gold P, Chartier G, Black M, Mascarella M, MacDonald C, Sadeghi N, Sultanem K, Shenouda G, Cury F, O'Donnell KJ. Genetic predisposition to depression and inflammation impacts symptom burden and survival in patients with head and neck cancer: A longitudinal study. J Affect Disord 2023; 331:149-157. [PMID: 36948466 DOI: 10.1016/j.jad.2023.03.007] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/17/2022] [Revised: 02/13/2023] [Accepted: 03/06/2023] [Indexed: 03/24/2023]
Abstract
OBJECTIVE The primary purpose of this study was to investigate the contribution of genetic predispositions to depression and inflammation, as measured through polygenic risk scores, on symptom burden (physical and psychological) in patients with head and neck cancer in the immediate post-treatment period (i.e., at three months post-diagnosis), as well as on 3-, 6-, 12-, 24- and 36-month survival. METHODS Prospective longitudinal study of 223 adults (72 % participation) newly diagnosed with a first occurrence of primary head and neck cancer, paired with genetic data (Illumina PsychArray), validated psychometric measures, Structured Clinical Interviews for DSM Disorders (SCID-I), and medical chart reviews. RESULTS Symptom burden at 3 months was predicted by (R2 adj. = 0.38, p < 0.001): a baseline SCID-I Anxiety Disorder (b = 1.69, B = 0.23, 95%CI = 0.43-2.94; p = 0.009), baseline levels of HADS anxiety (b = 0.20, B = 0.29, 95%CI = 0.07-0.34; p = 0.003), the polygenic risk score (PRS) for depression (b = 0.66, B = 0.18, 95%CI = 0.003-1.32; p = 0.049), and cumulated dose of radiotherapy (b = 0.002, B = 0.46, 95%CI = 0.001-0.003; p < 0.001). When controlling for factors known to be associated with cancer survival, patients with a higher PRS associated with depression and inflammation, respectively, presented higher risk of death within 36 months (b = 1.75, Exp(B) = 5.75, 95%CI = 1.55-21.27, p = 0.009 and b = 0.14, Exp(B) = 1.15, 95%CI = 1.01-1.30, p = 0.03). CONCLUSIONS Our results outline three potential pathways of symptom burden in patients with head and neck cancer: a genetic predisposition towards depression; an initial anxiety disorder upon being diagnosed with cancer or high levels of anxiety upon diagnosis; and a dose-related response to radiotherapy. One may want to investigate early interventions in these areas to alleviate symptom burden in patients faced with a life-threatening disease, as well as consider targeting genetic predisposition towards depression and inflammation implicated in survival. The high prevalence of distress in patients with head and neck cancer is an opportunity to study genetic predispositions, which could potentially be broadly generalized to other cancers and diseases.
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Affiliation(s)
- Melissa Henry
- McGill University, Canada; Jewish General Hospital, Canada; Lady-Davis Institute for Medical Research, Canada.
| | | | | | | | | | - Han-Tin Kao
- McGill University, Canada; Douglas Hospital, Canada
| | - Zeev Rosberger
- McGill University, Canada; Jewish General Hospital, Canada; Lady-Davis Institute for Medical Research, Canada
| | - Saul Frenkiel
- McGill University, Canada; Jewish General Hospital, Canada; Douglas Hospital, Canada
| | - Michael Hier
- McGill University, Canada; Jewish General Hospital, Canada; Lady-Davis Institute for Medical Research, Canada
| | - Anthony Zeitouni
- McGill University, Canada; McGill University Health Centre, Canada
| | - Karen Kost
- McGill University, Canada; McGill University Health Centre, Canada
| | - Alex Mlynarek
- McGill University, Canada; Jewish General Hospital, Canada; McGill University Health Centre, Canada
| | - Keith Richardson
- McGill University, Canada; McGill University Health Centre, Canada
| | - Celia M T Greenwood
- McGill University, Canada; Lady-Davis Institute for Medical Research, Canada
| | | | - Phil Gold
- McGill University, Canada; Jewish General Hospital, Canada
| | | | - Martin Black
- McGill University, Canada; Jewish General Hospital, Canada
| | - Marco Mascarella
- McGill University, Canada; McGill University Health Centre, Canada
| | | | - Nader Sadeghi
- McGill University, Canada; McGill University Health Centre, Canada
| | - Khalil Sultanem
- McGill University, Canada; McGill University Health Centre, Canada
| | - Georges Shenouda
- McGill University, Canada; McGill University Health Centre, Canada
| | - Fabio Cury
- McGill University, Canada; McGill University Health Centre, Canada
| | - Kieran John O'Donnell
- McGill University, Canada; Douglas Hospital, Canada; Yale Child Study Center, Yale School of Medicine, Yale University, United States of America; Department of Obstetrics Gynecology & Reproductive Sciences, Yale School of Medicine, Yale University, United States of America
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9
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Fierheller CT, Alenezi WM, Serruya C, Revil T, Amuzu S, Bedard K, Subramanian DN, Fewings E, Bruce JP, Prokopec S, Bouchard L, Provencher D, Foulkes WD, El Haffaf Z, Mes-Masson AM, Tischkowitz M, Campbell IG, Pugh TJ, Greenwood CMT, Ragoussis J, Tonin PN. Molecular Genetic Characteristics of FANCI, a Proposed New Ovarian Cancer Predisposing Gene. Genes (Basel) 2023; 14:genes14020277. [PMID: 36833203 PMCID: PMC9956348 DOI: 10.3390/genes14020277] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/01/2022] [Revised: 01/11/2023] [Accepted: 01/16/2023] [Indexed: 01/26/2023] Open
Abstract
FANCI was recently identified as a new candidate ovarian cancer (OC)-predisposing gene from the genetic analysis of carriers of FANCI c.1813C>T; p.L605F in OC families. Here, we aimed to investigate the molecular genetic characteristics of FANCI, as they have not been described in the context of cancer. We first investigated the germline genetic landscape of two sisters with OC from the discovery FANCI c.1813C>T; p.L605F family (F1528) to re-affirm the plausibility of this candidate. As we did not find other conclusive candidates, we then performed a candidate gene approach to identify other candidate variants in genes involved in the FANCI protein interactome in OC families negative for pathogenic variants in BRCA1, BRCA2, BRIP1, RAD51C, RAD51D, and FANCI, which identified four candidate variants. We then investigated FANCI in high-grade serous ovarian carcinoma (HGSC) from FANCI c.1813C>T carriers and found evidence of loss of the wild-type allele in tumour DNA from some of these cases. The somatic genetic landscape of OC tumours from FANCI c.1813C>T carriers was investigated for mutations in selected genes, copy number alterations, and mutational signatures, which determined that the profiles of tumours from carriers were characteristic of features exhibited by HGSC cases. As other OC-predisposing genes such as BRCA1 and BRCA2 are known to increase the risk of other cancers including breast cancer, we investigated the carrier frequency of germline FANCI c.1813C>T in various cancer types and found overall more carriers among cancer cases compared to cancer-free controls (p = 0.007). In these different tumour types, we also identified a spectrum of somatic variants in FANCI that were not restricted to any specific region within the gene. Collectively, these findings expand on the characteristics described for OC cases carrying FANCI c.1813C>T; p.L605F and suggest the possible involvement of FANCI in other cancer types at the germline and/or somatic level.
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Affiliation(s)
- Caitlin T. Fierheller
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada
- Cancer Research Program, The Research Institute of the McGill University Health Centre, Montreal, QC H4A 3J1, Canada
| | - Wejdan M. Alenezi
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada
- Cancer Research Program, The Research Institute of the McGill University Health Centre, Montreal, QC H4A 3J1, Canada
- Department of Medical Laboratory Technology, Taibah University, Medina 42353, Saudi Arabia
| | - Corinne Serruya
- Cancer Research Program, The Research Institute of the McGill University Health Centre, Montreal, QC H4A 3J1, Canada
| | - Timothée Revil
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada
- McGill Genome Centre, McGill University, Montreal, QC H3A 0G1, Canada
| | - Setor Amuzu
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada
- McGill Genome Centre, McGill University, Montreal, QC H3A 0G1, Canada
| | - Karine Bedard
- Laboratoire de Diagnostic Moléculaire, Centre Hospitalier de l’Université de Montréal (CHUM), Montreal, QC H2X 3E4, Canada
- Département de Pathologie et Biologie Cellulaire, Université de Montréal, Montreal, QC H3T 1J4, Canada
| | - Deepak N. Subramanian
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia
| | - Eleanor Fewings
- Department of Medical Genetics, National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge, Cambridge CB2 1TN, UK
| | - Jeffrey P. Bruce
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
| | - Stephenie Prokopec
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
| | - Luigi Bouchard
- Department of Biochemistry and Functional Genomics, Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
- Department of Medical Biology, Centres Intégrés Universitaires de Santé et de Services Sociaux du Saguenay-Lac-Saint-Jean Hôpital Universitaire de Chicoutimi, Saguenay, QC G7H 7K9, Canada
- Centre de Recherche du Centre Hospitalier l’Université de Sherbrooke, Sherbrooke, QC J1K 2R1, Canada
| | - Diane Provencher
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal and Institut du Cancer de Montréal, Montreal, QC H2X 0A9, Canada
- Division of Gynecologic Oncology, Université de Montréal, Montreal, QC H3T 1J4, Canada
| | - William D. Foulkes
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada
- Cancer Research Program, The Research Institute of the McGill University Health Centre, Montreal, QC H4A 3J1, Canada
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC H3T 1E2, Canada
- Department of Medicine, McGill University, Montreal, QC H3G 2M1, Canada
| | - Zaki El Haffaf
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal, Montreal, QC H2X 0A9, Canada
| | - Anne-Marie Mes-Masson
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal and Institut du Cancer de Montréal, Montreal, QC H2X 0A9, Canada
- Department of Medicine, Université de Montréal, Montreal, QC H3T 1J4, Canada
| | - Marc Tischkowitz
- Department of Medical Genetics, National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge, Cambridge CB2 1TN, UK
| | - Ian G. Campbell
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, VIC 3000, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC 3010, Australia
| | - Trevor J. Pugh
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON M5G 2C1, Canada
| | - Celia M. T. Greenwood
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC H3T 1E2, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montreal, QC H4A 3T2, Canada
- Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montreal, QC H3A 1Y7, Canada
| | - Jiannis Ragoussis
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada
- McGill Genome Centre, McGill University, Montreal, QC H3A 0G1, Canada
| | - Patricia N. Tonin
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada
- Cancer Research Program, The Research Institute of the McGill University Health Centre, Montreal, QC H4A 3J1, Canada
- Department of Medicine, McGill University, Montreal, QC H3G 2M1, Canada
- Correspondence:
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10
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Lu T, Forgetta V, Greenwood CMT, Zhou S, Richards JB. Circulating Proteins Influencing Psychiatric Disease: A Mendelian Randomization Study. Biol Psychiatry 2023; 93:82-91. [PMID: 36280454 DOI: 10.1016/j.biopsych.2022.08.015] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/05/2022] [Revised: 08/11/2022] [Accepted: 08/17/2022] [Indexed: 11/02/2022]
Abstract
BACKGROUND There is a pressing need for novel drug targets for psychiatric disorders. Circulating proteins are potential candidates because they are relatively easy to measure and modulate and play important roles in signaling. METHODS We performed two-sample Mendelian randomization analyses to estimate the associations between circulating protein abundances and risk of 10 psychiatric disorders. Genetic variants associated with 1611 circulating protein abundances identified in 6 large-scale proteomic studies were used as genetic instruments. Effects of the circulating proteins on psychiatric disorders were estimated by Wald ratio or inverse variance-weighted ratio tests. Horizontal pleiotropy, colocalization, and protein-altering effects were examined to validate the assumptions of Mendelian randomization. RESULTS Nine circulating protein-to-disease associations withstood multiple sensitivity analyses. Among them, 2 circulating proteins had associations replicated in 3 proteomic studies. A 1 standard deviation increase in the genetically predicted circulating TIMP4 level was associated with a reduced risk of anorexia nervosa (minimum odds ratio [OR] = 0.83; 95% CI, 0.76-0.91) and bipolar disorder (minimum OR = 0.88; 95% CI, 0.82-0.94). A 1 standard deviation increase in the genetically predicted circulating ESAM level was associated with an increased risk of schizophrenia (maximum OR = 1.32; 95% CI, 1.22-1.43). In addition, 58 suggestive protein-to-disease associations warrant validation with observational or experimental evidence. For instance, a 1 standard deviation increase in the ERLEC1-201-to-ERLEC1-202 splice variant ratio was associated with a reduced risk of schizophrenia (OR = 0.94; 95% CI, 0.90-0.97). CONCLUSIONS Prioritized circulating proteins appear to influence the risk of psychiatric disease and may be explored as intervention targets.
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Affiliation(s)
- Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
| | - Vincenzo Forgetta
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada; Gerald Bronfman Department of Oncology, McGill University, Montreal, Quebec, Canada; Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Sirui Zhou
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada; Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Department of Human Genetics, McGill University, Montreal, Quebec, Canada; Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom.
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11
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Alenezi WM, Fierheller CT, Serruya C, Revil T, Oros KK, Subramanian DN, Bruce J, Spiegelman D, Pugh T, Campbell IG, Mes-Masson AM, Provencher D, Foulkes WD, Haffaf ZE, Rouleau G, Bouchard L, Greenwood CMT, Ragoussis J, Tonin PN. Genetic analyses of DNA repair pathway associated genes implicate new candidate cancer predisposing genes in ancestrally defined ovarian cancer cases. Front Oncol 2023; 13:1111191. [PMID: 36969007 PMCID: PMC10030840 DOI: 10.3389/fonc.2023.1111191] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 02/06/2023] [Indexed: 03/29/2023] Open
Abstract
Not all familial ovarian cancer (OC) cases are explained by pathogenic germline variants in known risk genes. A candidate gene approach involving DNA repair pathway genes was applied to identify rare recurring pathogenic variants in familial OC cases not associated with known OC risk genes from a population exhibiting genetic drift. Whole exome sequencing (WES) data of 15 OC cases from 13 families tested negative for pathogenic variants in known OC risk genes were investigated for candidate variants in 468 DNA repair pathway genes. Filtering and prioritization criteria were applied to WES data to select top candidates for further analyses. Candidates were genotyped in ancestry defined study groups of 214 familial and 998 sporadic OC or breast cancer (BC) cases and 1025 population-matched controls and screened for additional carriers in 605 population-matched OC cases. The candidate genes were also analyzed in WES data from 937 familial or sporadic OC cases of diverse ancestries. Top candidate variants in ERCC5, EXO1, FANCC, NEIL1 and NTHL1 were identified in 5/13 (39%) OC families. Collectively, candidate variants were identified in 7/435 (1.6%) sporadic OC cases and 1/566 (0.2%) sporadic BC cases versus 1/1025 (0.1%) controls. Additional carriers were identified in 6/605 (0.9%) OC cases. Tumour DNA from ERCC5, NEIL1 and NTHL1 variant carriers exhibited loss of the wild-type allele. Carriers of various candidate variants in these genes were identified in 31/937 (3.3%) OC cases of diverse ancestries versus 0-0.004% in cancer-free controls. The strategy of applying a candidate gene approach in a population exhibiting genetic drift identified new candidate OC predisposition variants in DNA repair pathway genes.
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Affiliation(s)
- Wejdan M. Alenezi
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- Cancer Research Program, Centre for Translational Biology, The Research Institute of McGill University Health Centre, Montreal, QC, Canada
- Department of Medical Laboratory Technology, Taibah University, Medina, Saudi Arabia
| | - Caitlin T. Fierheller
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- Cancer Research Program, Centre for Translational Biology, The Research Institute of McGill University Health Centre, Montreal, QC, Canada
| | - Corinne Serruya
- Cancer Research Program, Centre for Translational Biology, The Research Institute of McGill University Health Centre, Montreal, QC, Canada
| | - Timothée Revil
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- McGill Genome Centre, McGill University, Montreal, QC, Canada
| | - Kathleen K. Oros
- Lady Davis Institute for Medical Research of the Jewish General Hospital, Montreal, QC, Canada
| | - Deepak N. Subramanian
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Jeffrey Bruce
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
| | - Dan Spiegelman
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Trevor Pugh
- Princess Margaret Cancer Centre, University Health Network, Toronto, ON, Canada
- Department of Medical Biophysics, University of Toronto, Toronto, ON, Canada
- Ontario Institute for Cancer Research, Toronto, ON, Canada
| | - Ian G. Campbell
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
- Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, VIC, Australia
| | - Anne-Marie Mes-Masson
- Centre de recherche du Centre hospitalier de l’Université de Montréal and Institut du cancer de Montréal, Montreal, QC, Canada
- Departement of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Diane Provencher
- Centre de recherche du Centre hospitalier de l’Université de Montréal and Institut du cancer de Montréal, Montreal, QC, Canada
- Division of Gynecologic Oncology, Université de Montréal, Montreal, QC, Canada
| | - William D. Foulkes
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- Cancer Research Program, Centre for Translational Biology, The Research Institute of McGill University Health Centre, Montreal, QC, Canada
- Lady Davis Institute for Medical Research of the Jewish General Hospital, Montreal, QC, Canada
- Department of Medical Genetics, McGill University Health Centre, Montreal, QC, Canada
- Department of Medicine, McGill University, Montreal, QC, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada
| | - Zaki El Haffaf
- Centre de recherche du Centre hospitalier de l’Université de Montréal and Institut du cancer de Montréal, Montreal, QC, Canada
- Service de Médecine Génique, Centre Hospitalier de l’Université de Montréal, Montreal, QC, Canada
| | - Guy Rouleau
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
| | - Luigi Bouchard
- Department of Biochemistry and Functional Genomics, Université de Sherbrooke, Sherbrooke, QC, Canada
- Department of Medical Biology, Centres intégrés universitaires de santé et de services sociaux du Saguenay-Lac-Saint-Jean hôpital Universitaire de Chicoutimi, Saguenay, QC, Canada
- Centre de Recherche du Centre hospitalier l’Université de Sherbrooke, Sherbrooke, QC, Canada
| | - Celia M. T. Greenwood
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- Lady Davis Institute for Medical Research of the Jewish General Hospital, Montreal, QC, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | - Jiannis Ragoussis
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- McGill Genome Centre, McGill University, Montreal, QC, Canada
| | - Patricia N. Tonin
- Department of Human Genetics, McGill University, Montreal, QC, Canada
- Cancer Research Program, Centre for Translational Biology, The Research Institute of McGill University Health Centre, Montreal, QC, Canada
- Department of Medicine, McGill University, Montreal, QC, Canada
- *Correspondence: Patricia N. Tonin,
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12
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Chen Y, Lu T, Pettersson-Kymmer U, Stewart ID, Butler-Laporte G, Nakanishi T, Cerani A, Liang KYH, Yoshiji S, Willett JDS, Su CY, Raina P, Greenwood CMT, Farjoun Y, Forgetta V, Langenberg C, Zhou S, Ohlsson C, Richards JB. Genomic atlas of the plasma metabolome prioritizes metabolites implicated in human diseases. Nat Genet 2023; 55:44-53. [PMID: 36635386 PMCID: PMC7614162 DOI: 10.1038/s41588-022-01270-1] [Citation(s) in RCA: 42] [Impact Index Per Article: 42.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/14/2022] [Accepted: 11/18/2022] [Indexed: 01/14/2023]
Abstract
Metabolic processes can influence disease risk and provide therapeutic targets. By conducting genome-wide association studies of 1,091 blood metabolites and 309 metabolite ratios, we identified associations with 690 metabolites at 248 loci and associations with 143 metabolite ratios at 69 loci. Integrating metabolite-gene and gene expression information identified 94 effector genes for 109 metabolites and 48 metabolite ratios. Using Mendelian randomization (MR), we identified 22 metabolites and 20 metabolite ratios having estimated causal effect on 12 traits and diseases, including orotate for estimated bone mineral density, α-hydroxyisovalerate for body mass index and ergothioneine for inflammatory bowel disease and asthma. We further measured the orotate level in a separate cohort and demonstrated that, consistent with MR, orotate levels were positively associated with incident hip fractures. This study provides a valuable resource describing the genetic architecture of metabolites and delivers insights into their roles in common diseases, thereby offering opportunities for therapeutic targets.
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Affiliation(s)
- Yiheng Chen
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
- 5 Prime Sciences Inc, Montreal, Quebec, Canada
| | | | - Isobel D Stewart
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Guillaume Butler-Laporte
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Tomoko Nakanishi
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Kyoto-McGill International Collaborative School in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Japan Society for the Promotion of Science, Tokyo, Japan
| | - Agustin Cerani
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Kevin Y H Liang
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
| | - Satoshi Yoshiji
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
- Kyoto-McGill International Collaborative School in Genomic Medicine, Graduate School of Medicine, Kyoto University, Kyoto, Japan
- Japan Society for the Promotion of Science, Tokyo, Japan
| | - Julian Daniel Sunday Willett
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
- McGill Genome Centre, McGill University, Montreal, Quebec, Canada
| | - Chen-Yang Su
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Computer Science, McGill University, Montreal, Quebec, Canada
| | - Parminder Raina
- Department of Health Research Methods, Evidence, and Impact, McMaster University, Hamilton, Ontario, Canada
- McMaster Institute for Research on Aging, McMaster University, Hamilton, Ontario, Canada
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montreal, Quebec, Canada
| | - Yossi Farjoun
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- 5 Prime Sciences Inc, Montreal, Quebec, Canada
- The Broad Institute of Harvard and MIT, Cambridge, MA, USA
- Fulcrum Genomics, Boulder, CO, USA
| | - Vincenzo Forgetta
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- 5 Prime Sciences Inc, Montreal, Quebec, Canada
| | - Claudia Langenberg
- MRC Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Precision Healthcare University Research Institute, Queen Mary University of London, London, UK
- Computational Medicine, Berlin Institute of Health (BIH) at Charité-Universitätsmedizin Berlin, Berlin, Germany
| | - Sirui Zhou
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Claes Ohlsson
- Sahlgrenska Osteoporosis Centre, Centre of Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Drug Treatment, Sahlgrenska University Hospital, Region Västra Götaland, Gothenburg, Sweden
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada.
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.
- 5 Prime Sciences Inc, Montreal, Quebec, Canada.
- Department of Epidemiology, Biostatistics, and Occupational Health, McGill University, Montreal, Quebec, Canada.
- Department of Medicine, McGill University, Montreal, Quebec, Canada.
- Department of Twin Research, King's College London, London, UK.
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13
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Galan A, Papaluca A, Nejatie A, Matanes E, Brahimi F, Tong W, Hachim IY, Yasmeen A, Carmona E, Klein KO, Billes S, Dawod AE, Gawande P, Jeter AM, Mes-Masson AM, Greenwood CMT, Gotlieb WH, Saragovi HU. GD2 and GD3 gangliosides as diagnostic biomarkers for all stages and subtypes of epithelial ovarian cancer. Front Oncol 2023; 13:1134763. [PMID: 37124505 PMCID: PMC10145910 DOI: 10.3389/fonc.2023.1134763] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/30/2022] [Accepted: 03/24/2023] [Indexed: 05/02/2023] Open
Abstract
Background Ovarian cancer (OC) is the deadliest gynecological cancer, often diagnosed at advanced stages. A fast and accurate diagnostic method for early-stage OC is needed. The tumor marker gangliosides, GD2 and GD3, exhibit properties that make them ideal potential diagnostic biomarkers, but they have never before been quantified in OC. We investigated the diagnostic utility of GD2 and GD3 for diagnosis of all subtypes and stages of OC. Methods This retrospective study evaluated GD2 and GD3 expression in biobanked tissue and serum samples from patients with invasive epithelial OC, healthy donors, non-malignant gynecological conditions, and other cancers. GD2 and GD3 levels were evaluated in tissue samples by immunohistochemistry (n=299) and in two cohorts of serum samples by quantitative ELISA. A discovery cohort (n=379) showed feasibility of GD2 and GD3 quantitative ELISA for diagnosing OC, and a subsequent model cohort (n=200) was used to train and cross-validate a diagnostic model. Results GD2 and GD3 were expressed in tissues of all OC subtypes and FIGO stages but not in surrounding healthy tissue or other controls. In serum, GD2 and GD3 were elevated in patients with OC. A diagnostic model that included serum levels of GD2+GD3+age was superior to the standard of care (CA125, p<0.001) in diagnosing OC and early-stage (I/II) OC. Conclusion GD2 and GD3 expression was associated with high rates of selectivity and specificity for OC. A diagnostic model combining GD2 and GD3 quantification in serum had diagnostic power for all subtypes and all stages of OC, including early stage. Further research exploring the utility of GD2 and GD3 for diagnosis of OC is warranted.
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Affiliation(s)
- Alba Galan
- Translational Cancer Center, Lady Davis Institute-Jewish General Hospital, McGill University, Montreal, QC, Canada
| | - Arturo Papaluca
- Translational Cancer Center, Lady Davis Institute-Jewish General Hospital, McGill University, Montreal, QC, Canada
- Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Ali Nejatie
- Translational Cancer Center, Lady Davis Institute-Jewish General Hospital, McGill University, Montreal, QC, Canada
- Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Emad Matanes
- Translational Cancer Center, Lady Davis Institute-Jewish General Hospital, McGill University, Montreal, QC, Canada
- Department of Ob-Gyn, Jewish General Hospital, McGill University and Segal Cancer Center, Lady Davis Institute of Medical Research, Montreal, QC, Canada
| | - Fouad Brahimi
- Translational Cancer Center, Lady Davis Institute-Jewish General Hospital, McGill University, Montreal, QC, Canada
| | - Wenyong Tong
- Translational Cancer Center, Lady Davis Institute-Jewish General Hospital, McGill University, Montreal, QC, Canada
- Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
| | - Ibrahim Yaseen Hachim
- Clinical Sciences Department, College of Medicine, University of Sharjah, Sharjah, United Arab Emirates
| | - Amber Yasmeen
- Department of Ob-Gyn, Jewish General Hospital, McGill University and Segal Cancer Center, Lady Davis Institute of Medical Research, Montreal, QC, Canada
| | - Euridice Carmona
- Centre de recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM) and Institut du Cancer de Montréal, Montreal, QC, Canada
| | - Kathleen Oros Klein
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Université de Montréal, Montreal, QC, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, and Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | - Sonja Billes
- R&D Department, AOA Dx Inc, Cambridge, MA, United States
| | - Ahmed E. Dawod
- R&D Department, AOA Dx Inc, Cambridge, MA, United States
| | - Prasad Gawande
- R&D Department, AOA Dx Inc, Cambridge, MA, United States
| | | | - Anne-Marie Mes-Masson
- Centre de recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM) and Institut du Cancer de Montréal, Montreal, QC, Canada
- Department of Medicine, Université de Montréal, Montreal, QC, Canada
| | - Celia M. T. Greenwood
- Division of Gynecologic Oncology, Department of Obstetrics and Gynecology, Université de Montréal, Montreal, QC, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, and Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | - Walter H. Gotlieb
- Translational Cancer Center, Lady Davis Institute-Jewish General Hospital, McGill University, Montreal, QC, Canada
- Department of Ob-Gyn, Jewish General Hospital, McGill University and Segal Cancer Center, Lady Davis Institute of Medical Research, Montreal, QC, Canada
| | - H. Uri Saragovi
- Translational Cancer Center, Lady Davis Institute-Jewish General Hospital, McGill University, Montreal, QC, Canada
- Pharmacology and Therapeutics, McGill University, Montreal, QC, Canada
- Ophthalmology and Vision Science. McGill University, Montreal, QC, Canada
- *Correspondence: H. Uri Saragovi,
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14
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Lu T, Forgetta V, Richards JB, Greenwood CMT. Genetic determinants of polygenic prediction accuracy within a population. Genetics 2022; 222:6762086. [PMID: 36250789 PMCID: PMC9713421 DOI: 10.1093/genetics/iyac158] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/30/2022] [Accepted: 10/10/2022] [Indexed: 11/15/2022] Open
Abstract
Genomic risk prediction is on the emerging path toward personalized medicine. However, the accuracy of polygenic prediction varies strongly in different individuals. Based on up to 352,277 European ancestry participants in the UK Biobank, we constructed polygenic risk scores for 15 physiological and biochemical quantitative traits. We identified a total of 185 polygenic prediction variability quantitative trait loci for 11 traits by Levene's test among 254,376 unrelated individuals. We validated the effects of prediction variability quantitative trait loci using an independent test set of 58,927 individuals. For instance, a score aggregating 51 prediction variability quantitative trait locus variants for triglycerides had the strongest Spearman correlation of 0.185 (P-value <1.0 × 10-300) with the squared prediction errors. We found a strong enrichment of complex genetic effects conferred by prediction variability quantitative trait loci compared to risk loci identified in genome-wide association studies, including 89 prediction variability quantitative trait loci exhibiting dominance effects. Incorporation of dominance effects into polygenic risk scores significantly improved polygenic prediction for triglycerides, low-density lipoprotein cholesterol, vitamin D, and platelet. In conclusion, we have discovered and profiled genetic determinants of polygenic prediction variability for 11 quantitative biomarkers. These findings may assist interpretation of genomic risk prediction in various contexts and encourage novel approaches for constructing polygenic risk scores with complex genetic effects.
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Affiliation(s)
- Tianyuan Lu
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC H3T 1E2, Canada.,Quantitative Life Sciences Program, McGill University, Montreal, QC H3A 0G4, Canada
| | - Vincenzo Forgetta
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC H3T 1E2, Canada
| | - John Brent Richards
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC H3T 1E2, Canada.,Department of Human Genetics, McGill University, Montreal, QC H3A 0G4, Canada.,Department of Twin Research and Genetic Epidemiology, King's College London, London WC2R 2LS, UK
| | - Celia M T Greenwood
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC H3T 1E2, Canada.,Department of Human Genetics, McGill University, Montreal, QC H3A 0G4, Canada.,Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC H3A 0G4, Canada.,Gerald Bronfman Department of Oncology, McGill University, Montreal, QC H3A 0G4, Canada
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15
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Shao X, Vishweswaraiah S, Čuperlović-Culf M, Yilmaz A, Greenwood CMT, Surendra A, McGuinness B, Passmore P, Kehoe PG, Maddens ME, Bennett SAL, Green BD, Radhakrishna U, Graham SF. Dementia with Lewy bodies post-mortem brains reveal differentially methylated CpG sites with biomarker potential. Commun Biol 2022; 5:1279. [PMID: 36418427 PMCID: PMC9684551 DOI: 10.1038/s42003-022-03965-x] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/12/2021] [Accepted: 09/08/2022] [Indexed: 11/25/2022] Open
Abstract
Dementia with Lewy bodies (DLB) is a common form of dementia with known genetic and environmental interactions. However, the underlying epigenetic mechanisms which reflect these gene-environment interactions are poorly studied. Herein, we measure genome-wide DNA methylation profiles of post-mortem brain tissue (Broadmann area 7) from 15 pathologically confirmed DLB brains and compare them with 16 cognitively normal controls using Illumina MethylationEPIC arrays. We identify 17 significantly differentially methylated CpGs (DMCs) and 17 differentially methylated regions (DMRs) between the groups. The DMCs are mainly located at the CpG islands, promoter and first exon regions. Genes associated with the DMCs are linked to "Parkinson's disease" and "metabolic pathway", as well as the diseases of "severe intellectual disability" and "mood disorders". Overall, our study highlights previously unreported DMCs offering insights into DLB pathogenesis with the possibility that some of these could be used as biomarkers of DLB in the future.
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Affiliation(s)
- Xiaojian Shao
- grid.24433.320000 0004 0449 7958National Research Council of Canada, Digital Technologies Research Centre, Ottawa, Canada
| | - Sangeetha Vishweswaraiah
- grid.261277.70000 0001 2219 916XOakland University-William Beaumont School of Medicine, Rochester, MI 48309 USA
| | - Miroslava Čuperlović-Culf
- grid.24433.320000 0004 0449 7958National Research Council of Canada, Digital Technologies Research Centre, Ottawa, Canada ,grid.28046.380000 0001 2182 2255Ottawa Institute of Systems Biology, Ottawa, Ontario Canada ,grid.28046.380000 0001 2182 2255Department of Biochemistry, Microbiology, sand Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario Canada
| | - Ali Yilmaz
- grid.261277.70000 0001 2219 916XOakland University-William Beaumont School of Medicine, Rochester, MI 48309 USA ,Beaumont Research Institute, Royal Oak, MI 48073 USA
| | - Celia M. T. Greenwood
- grid.414980.00000 0000 9401 2774Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Canada ,grid.14709.3b0000 0004 1936 8649Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Canada ,grid.14709.3b0000 0004 1936 8649Gerald Bronfman Department of Oncology, McGill University, Montréal, Canada
| | - Anuradha Surendra
- grid.24433.320000 0004 0449 7958National Research Council of Canada, Digital Technologies Research Centre, Ottawa, Canada
| | - Bernadette McGuinness
- grid.4777.30000 0004 0374 7521Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Belfast, UK
| | - Peter Passmore
- grid.4777.30000 0004 0374 7521Centre for Public Health, School of Medicine, Dentistry and Biomedical Sciences, Queen’s University Belfast, Belfast, UK
| | - Patrick G. Kehoe
- grid.5337.20000 0004 1936 7603Dementia Research Group, Translational Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Michael E. Maddens
- grid.261277.70000 0001 2219 916XOakland University-William Beaumont School of Medicine, Rochester, MI 48309 USA ,Beaumont Research Institute, Royal Oak, MI 48073 USA
| | - Steffany A. L. Bennett
- grid.28046.380000 0001 2182 2255Ottawa Institute of Systems Biology, Ottawa, Ontario Canada ,grid.28046.380000 0001 2182 2255Department of Biochemistry, Microbiology, sand Immunology, Faculty of Medicine, University of Ottawa, Ottawa, Ontario Canada
| | - Brian D. Green
- grid.4777.30000 0004 0374 7521Institute for Global Food Security, School of Biological Sciences, Faculty of Medicine, Health and Life Sciences, Queen’s University Belfast, Northern Ireland, UK
| | - Uppala Radhakrishna
- grid.261277.70000 0001 2219 916XOakland University-William Beaumont School of Medicine, Rochester, MI 48309 USA ,Beaumont Research Institute, Royal Oak, MI 48073 USA
| | - Stewart F. Graham
- grid.261277.70000 0001 2219 916XOakland University-William Beaumont School of Medicine, Rochester, MI 48309 USA ,Beaumont Research Institute, Royal Oak, MI 48073 USA
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16
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St.-Pierre J, Zhang X, Lu T, Jiang L, Loffree X, Wang L, Bhatnagar S, Greenwood CMT. Considering strategies for SNP selection in genetic and polygenic risk scores. Front Genet 2022; 13:900595. [PMID: 36819922 PMCID: PMC9930898 DOI: 10.3389/fgene.2022.900595] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2022] [Accepted: 10/05/2022] [Indexed: 02/04/2023] Open
Abstract
Genetic risk scores (GRS) and polygenic risk scores (PRS) are weighted sums of, respectively, several or many genetic variant indicator variables. Although they are being increasingly proposed for clinical use, the best ways to construct them are still actively debated. In this commentary, we present several case studies illustrating practical challenges associated with building or attempting to improve score performance when there is expected to be heterogeneity of disease risk between cohorts or between subgroups of individuals. Specifically, we contrast performance associated with several ways of selecting single nucleotide polymorphisms (SNPs) for inclusion in these scores. By considering GRS and PRS as predictors that are measured with error, insights into their strengths and weaknesses may be obtained, and SNP selection approaches play an important role in defining such errors.
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Affiliation(s)
- Julien St.-Pierre
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada
| | - Xinyi Zhang
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
| | - Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, Canada,Quantitative Life Sciences, McGill University, Montréal, QC, Canada
| | - Lai Jiang
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, Canada
| | - Xavier Loffree
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, Canada,Department of Statistics and Actuarial Sciences, University of Waterloo, Waterloo, ON, Canada
| | - Linbo Wang
- Department of Statistical Sciences, University of Toronto, Toronto, ON, Canada
| | - Sahir Bhatnagar
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada
| | - Celia M. T. Greenwood
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada,Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC, Canada,Quantitative Life Sciences, McGill University, Montréal, QC, Canada,Gerald Bronfman Department of Oncology, McGill University, Montréal, QC, Canada,*Correspondence: Celia M. T. Greenwood,
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17
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Chen OJ, Castellsagué E, Moustafa-Kamal M, Nadaf J, Rivera B, Fahiminiya S, Wang Y, Gamache I, Pacifico C, Jiang L, Carrot-Zhang J, Witkowski L, Berghuis AM, Schönberger S, Schneider D, Hillmer M, Bens S, Siebert R, Stewart CJR, Zhang Z, Chao WCH, Greenwood CMT, Barford D, Tischkowitz M, Majewski J, Foulkes WD, Teodoro JG. Germline Missense Variants in CDC20 Result in Aberrant Mitotic Progression and Familial Cancer. Cancer Res 2022; 82:3499-3515. [PMID: 35913887 DOI: 10.1158/0008-5472.can-21-3956] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/17/2021] [Revised: 04/12/2022] [Accepted: 07/26/2022] [Indexed: 11/16/2022]
Abstract
CDC20 is a coactivator of the anaphase promoting complex/cyclosome (APC/C) and is essential for mitotic progression. APC/CCDC20 is inhibited by the spindle assembly checkpoint (SAC), which prevents premature separation of sister chromatids and aneuploidy in daughter cells. Although overexpression of CDC20 is common in many cancers, oncogenic mutations have never been identified in humans. Using whole-exome sequencing, we identified heterozygous missense CDC20 variants (L151R and N331K) that segregate with ovarian germ cell tumors in two families. Functional characterization showed these mutants retain APC/C activation activity but have impaired binding to BUBR1, a component of the SAC. Expression of L151R and N331K variants promoted mitotic slippage in HeLa cells and primary skin fibroblasts derived from carriers. Generation of mice carrying the N331K variant using CRISPR-Cas9 showed that, although homozygous N331K mice were nonviable, heterozygotes displayed accelerated oncogenicity of Myc-driven cancers. These findings highlight an unappreciated role for CDC20 variants as tumor-promoting genes. SIGNIFICANCE Two germline CDC20 missense variants that segregate with cancer in two families compromise the spindle assembly checkpoint and lead to aberrant mitotic progression, which could predispose cells to transformation. See related commentary by Villarroya-Beltri and Malumbres, p. 3432.
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Affiliation(s)
- Owen J Chen
- Goodman Cancer Institute, McGill University, Montréal, Québec, Canada
- Department of Biochemistry, McGill University, Montréal, Québec, Canada
| | - Ester Castellsagué
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Division of Medical Genetics and Cancer Axis, Lady Davis Institute, Segal Cancer Centre, Jewish General Hospital, Montréal, Québec, Canada
- Translational Research Laboratory, Catalan Institute of Oncology, Bellvitge Institute for Biomedical Research, L'Hospitalet de Llobregat, Barcelona, Spain
| | - Mohamed Moustafa-Kamal
- Goodman Cancer Institute, McGill University, Montréal, Québec, Canada
- Department of Biochemistry, McGill University, Montréal, Québec, Canada
| | - Javad Nadaf
- McGill University and Génome Québec Innovation Centre, Montréal, Québec, Canada
| | - Barbara Rivera
- Cancer Axis, Lady Davis Institute, Jewish General Hospital, Montréal, Québec, Canada
- Hereditary Cancer Programme, Catalan Institute of Oncology, Bellvitge Institute for Biomedical Research, L'Hospitalet de Llobregat, Barcelona, Spain
- Gerald Bronfman Department of Oncology, McGill University, Montréal, Québec, Canada
| | - Somayyeh Fahiminiya
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Cancer Research Program, Research Institute of the McGill University Health Centre, Montréal, Québec, Canada
| | - Yilin Wang
- Goodman Cancer Institute, McGill University, Montréal, Québec, Canada
- Department of Biochemistry, McGill University, Montréal, Québec, Canada
| | - Isabelle Gamache
- Goodman Cancer Institute, McGill University, Montréal, Québec, Canada
| | - Caterina Pacifico
- Goodman Cancer Institute, McGill University, Montréal, Québec, Canada
- Department of Biology, McGill University, Montréal, Québec, Canada
| | - Lai Jiang
- Cancer Axis, Lady Davis Institute, Jewish General Hospital, Montréal, Québec, Canada
- Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montréal, Québec, Canada
| | - Jian Carrot-Zhang
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Cancer Research Program, Research Institute of the McGill University Health Centre, Montréal, Québec, Canada
| | - Leora Witkowski
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Division of Medical Genetics and Cancer Axis, Lady Davis Institute, Segal Cancer Centre, Jewish General Hospital, Montréal, Québec, Canada
| | - Albert M Berghuis
- Department of Biochemistry, McGill University, Montréal, Québec, Canada
- Centre de Recherche en Biologie Structurale, McGill University, Montréal, Québec, Canada
- Department of Microbiology and Immunology, Montréal, Québec, Canada
| | - Stefan Schönberger
- Department of Pediatric Hematology and Oncology, Pediatrics III, University Hospital of Essen, University of Duisburg-Essen, Essen, Germany
| | - Dominik Schneider
- Clinic of Pediatrics, Dortmund Municipal Hospital, Dortmund, Germany
| | - Morten Hillmer
- Institute of Human Genetics, University of Ulm & Ulm University Medical Center, Ulm, Germany
| | - Susanne Bens
- Institute of Human Genetics, University of Ulm & Ulm University Medical Center, Ulm, Germany
| | - Reiner Siebert
- Institute of Human Genetics, University of Ulm & Ulm University Medical Center, Ulm, Germany
| | - Colin J R Stewart
- Department of Histopathology, King Edward Memorial Hospital, and School for Women's and Infants' Health, University of Western Australia, Perth, Australia
| | - Ziguo Zhang
- Institute of Cancer Research, London, United Kingdom
| | - William C H Chao
- Faculty of Health Sciences, University of Macau, Macau SAR, China
| | - Celia M T Greenwood
- Cancer Axis, Lady Davis Institute, Jewish General Hospital, Montréal, Québec, Canada
- Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montréal, Québec, Canada
- Departments of Oncology and Human Genetics, McGill University, Montréal, Québec, Canada
| | - David Barford
- Institute of Cancer Research, London, United Kingdom
| | - Marc Tischkowitz
- Department of Medical Genetics, National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, United Kingdom
| | - Jacek Majewski
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Cancer Research Program, Research Institute of the McGill University Health Centre, Montréal, Québec, Canada
| | - William D Foulkes
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Division of Medical Genetics and Cancer Axis, Lady Davis Institute, Segal Cancer Centre, Jewish General Hospital, Montréal, Québec, Canada
- Program in Cancer Genetics, Department of Oncology and Human Genetics, McGill University, Montréal, Québec, Canada
- Division of Medical Genetics and Cancer Research Program, Research Institute of the McGill University Health Centre, Montréal, Québec, Canada
| | - Jose G Teodoro
- Goodman Cancer Institute, McGill University, Montréal, Québec, Canada
- Department of Biochemistry, McGill University, Montréal, Québec, Canada
- Department of Microbiology and Immunology, Montréal, Québec, Canada
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18
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Butler-Laporte G, Gonzalez-Kozlova E, Su CY, Zhou S, Nakanishi T, Brunet-Ratnasingham E, Morrison D, Laurent L, Afilalo J, Afilalo M, Henry D, Chen Y, Carrasco-Zanini J, Farjoun Y, Pietzner M, Kimchi N, Afrasiabi Z, Rezk N, Bouab M, Petitjean L, Guzman C, Xue X, Tselios C, Vulesevic B, Adeleye O, Abdullah T, Almamlouk N, Moussa Y, DeLuca C, Duggan N, Schurr E, Brassard N, Durand M, Del Valle DM, Thompson R, Cedillo MA, Schadt E, Nie K, Simons NW, Mouskas K, Zaki N, Patel M, Xie H, Harris J, Marvin R, Cheng E, Tuballes K, Argueta K, Scott I, Greenwood CMT, Paterson C, Hinterberg M, Langenberg C, Forgetta V, Mooser V, Marron T, Beckmann N, Kenigsberg E, Charney AW, Kim-schulze S, Merad M, Kaufmann DE, Gnjatic S, Richards JB. The dynamic changes and sex differences of 147 immune-related proteins during acute COVID-19 in 580 individuals. Clin Proteomics 2022; 19:34. [PMID: 36171541 PMCID: PMC9516500 DOI: 10.1186/s12014-022-09371-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/15/2021] [Accepted: 08/21/2022] [Indexed: 02/02/2023] Open
Abstract
INTRODUCTION Severe COVID-19 leads to important changes in circulating immune-related proteins. To date it has been difficult to understand their temporal relationship and identify cytokines that are drivers of severe COVID-19 outcomes and underlie differences in outcomes between sexes. Here, we measured 147 immune-related proteins during acute COVID-19 to investigate these questions. METHODS We measured circulating protein abundances using the SOMAscan nucleic acid aptamer panel in two large independent hospital-based COVID-19 cohorts in Canada and the United States. We fit generalized additive models with cubic splines from the start of symptom onset to identify protein levels over the first 14 days of infection which were different between severe cases and controls, adjusting for age and sex. Severe cases were defined as individuals with COVID-19 requiring invasive or non-invasive mechanical respiratory support. RESULTS 580 individuals were included in the analysis. Mean subject age was 64.3 (sd 18.1), and 47% were male. Of the 147 proteins, 69 showed a significant difference between cases and controls (p < 3.4 × 10-4). Three clusters were formed by 108 highly correlated proteins that replicated in both cohorts, making it difficult to determine which proteins have a true causal effect on severe COVID-19. Six proteins showed sex differences in levels over time, of which 3 were also associated with severe COVID-19: CCL26, IL1RL2, and IL3RA, providing insights to better understand the marked differences in outcomes by sex. CONCLUSIONS Severe COVID-19 is associated with large changes in 69 immune-related proteins. Further, five proteins were associated with sex differences in outcomes. These results provide direct insights into immune-related proteins that are strongly influenced by severe COVID-19 infection.
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Affiliation(s)
- Guillaume Butler-Laporte
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada ,grid.14709.3b0000 0004 1936 8649Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec Canada
| | - Edgar Gonzalez-Kozlova
- grid.59734.3c0000 0001 0670 2351Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Chen-Yang Su
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada ,grid.14709.3b0000 0004 1936 8649Department of Computer Science, McGill University, Montréal, Québec Canada
| | - Sirui Zhou
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada ,grid.14709.3b0000 0004 1936 8649Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec Canada
| | - Tomoko Nakanishi
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada ,grid.14709.3b0000 0004 1936 8649Department of Human Genetics, McGill University, Montréal, Québec Canada ,grid.258799.80000 0004 0372 2033Graduate School of Medicine, McGill International Collaborative School in Genomic Medicine, Kyoto University, KyotoKyoto, Japan ,grid.54432.340000 0001 0860 6072Japan Society for the Promotion of Science, Tokyo, Japan
| | - Elsa Brunet-Ratnasingham
- grid.410559.c0000 0001 0743 2111Research Centre of the Centre Hospitalier de L’Université de Montréal, Montréal, Québec Canada
| | - David Morrison
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada
| | - Laetitia Laurent
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada
| | - Jonathan Afilalo
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada ,grid.14709.3b0000 0004 1936 8649Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec Canada
| | - Marc Afilalo
- grid.414980.00000 0000 9401 2774Department of Emergency Medicine, Jewish General Hospital, McGill University, Montréal, Québec Canada
| | - Danielle Henry
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada
| | - Yiheng Chen
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada ,grid.14709.3b0000 0004 1936 8649Department of Human Genetics, McGill University, Montréal, Québec Canada
| | - Julia Carrasco-Zanini
- grid.5335.00000000121885934MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK
| | - Yossi Farjoun
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada
| | - Maik Pietzner
- grid.5335.00000000121885934MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK ,grid.484013.a0000 0004 6879 971XComputational Medicine, Berlin Institute of Health at Charité—Universitätsmedizin Berlin, Berlin, Germany
| | - Nofar Kimchi
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada
| | - Zaman Afrasiabi
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada
| | - Nardin Rezk
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada
| | - Meriem Bouab
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada
| | - Louis Petitjean
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada
| | - Charlotte Guzman
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada
| | - Xiaoqing Xue
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada
| | - Chris Tselios
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada
| | - Branka Vulesevic
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada
| | - Olumide Adeleye
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada
| | - Tala Abdullah
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada
| | - Noor Almamlouk
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada
| | - Yara Moussa
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada
| | - Chantal DeLuca
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada
| | - Naomi Duggan
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada
| | - Erwin Schurr
- grid.63984.300000 0000 9064 4811Infectious Diseases and Immunity in Global Health Program, Research Institute of the McGill University Health Centre, Montréal, Québec Canada
| | - Nathalie Brassard
- grid.410559.c0000 0001 0743 2111Research Centre of the Centre Hospitalier de L’Université de Montréal, Montréal, Québec Canada
| | - Madeleine Durand
- grid.410559.c0000 0001 0743 2111Research Centre of the Centre Hospitalier de L’Université de Montréal, Montréal, Québec Canada
| | - Diane Marie Del Valle
- grid.59734.3c0000 0001 0670 2351Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Ryan Thompson
- grid.59734.3c0000 0001 0670 2351Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Mario A. Cedillo
- grid.59734.3c0000 0001 0670 2351Department of Radiology, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Eric Schadt
- grid.59734.3c0000 0001 0670 2351Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Kai Nie
- grid.59734.3c0000 0001 0670 2351Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Nicole W. Simons
- grid.59734.3c0000 0001 0670 2351Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Konstantinos Mouskas
- grid.59734.3c0000 0001 0670 2351Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Nicolas Zaki
- grid.59734.3c0000 0001 0670 2351Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Manishkumar Patel
- grid.59734.3c0000 0001 0670 2351Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Hui Xie
- grid.59734.3c0000 0001 0670 2351Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Jocelyn Harris
- grid.59734.3c0000 0001 0670 2351Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Robert Marvin
- grid.59734.3c0000 0001 0670 2351Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Esther Cheng
- grid.59734.3c0000 0001 0670 2351Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Kevin Tuballes
- grid.59734.3c0000 0001 0670 2351Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Kimberly Argueta
- grid.59734.3c0000 0001 0670 2351Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Ieisha Scott
- grid.59734.3c0000 0001 0670 2351Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | | | - Celia M. T. Greenwood
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada ,grid.14709.3b0000 0004 1936 8649Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec Canada
| | - Clare Paterson
- grid.437866.80000 0004 0625 700XSomaLogic Inc, Boulder, CO USA
| | | | - Claudia Langenberg
- grid.5335.00000000121885934MRC Epidemiology Unit, School of Clinical Medicine, University of Cambridge, Cambridge, UK ,grid.437866.80000 0004 0625 700XSomaLogic Inc, Boulder, CO USA
| | - Vincenzo Forgetta
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada
| | - Vincent Mooser
- grid.14709.3b0000 0004 1936 8649Department of Human Genetics, McGill University, Montréal, Québec Canada
| | - Thomas Marron
- grid.59734.3c0000 0001 0670 2351Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY USA ,grid.59734.3c0000 0001 0670 2351Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA ,grid.416167.30000 0004 0442 1996Early Phase Trials Unit, Mount Sinai Hospital, New York, NY USA
| | - Noam Beckmann
- grid.59734.3c0000 0001 0670 2351Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Ephraim Kenigsberg
- grid.59734.3c0000 0001 0670 2351Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Alexander W. Charney
- grid.59734.3c0000 0001 0670 2351Mount Sinai Clinical Intelligence Center, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Seunghee Kim-schulze
- grid.59734.3c0000 0001 0670 2351Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Miriam Merad
- grid.59734.3c0000 0001 0670 2351Precision Immunology Institute, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - Daniel E. Kaufmann
- grid.410559.c0000 0001 0743 2111Research Centre of the Centre Hospitalier de L’Université de Montréal, Montréal, Québec Canada ,grid.14848.310000 0001 2292 3357Department of Medicine, Université de Montréal, Montréal, Québec Canada
| | - Sacha Gnjatic
- grid.59734.3c0000 0001 0670 2351Human Immune Monitoring Center, Icahn School of Medicine at Mount Sinai, New York, NY USA
| | - J Brent Richards
- grid.14709.3b0000 0004 1936 8649Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec Canada ,grid.14709.3b0000 0004 1936 8649Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec Canada ,grid.14709.3b0000 0004 1936 8649Department of Human Genetics, McGill University, Montréal, Québec Canada ,grid.13097.3c0000 0001 2322 6764Department of Twin Research, King’s College London, London, UK ,5 Prime Sciences, Montreal, Québec Canada ,grid.14709.3b0000 0004 1936 8649McGill University, King’s College London (Honorary), Jewish General Hospital, Pavilion H-4133755 Côte-Ste-Catherine, Montréal, Québec H3T 1E2 Canada
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Barry A, Bhagwat N, Misic B, Poline JB, Greenwood CMT. Asymmetric influence measure for high dimensional regression. COMMUN STAT-THEOR M 2022. [DOI: 10.1080/03610926.2020.1841793] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 10/23/2022]
Affiliation(s)
- Amadou Barry
- Departments of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada
- Lady Davis Institute, Jewish General Hospital, Montreal, Québec, Canada
| | - Nikhil Bhagwat
- Faculty of Medicine, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McConnell Brain Imaging Centre, McGill University, Montreal, Québec, Canada
| | - Bratislav Misic
- Faculty of Medicine, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McConnell Brain Imaging Centre, McGill University, Montreal, Québec, Canada
| | - Jean-Baptiste Poline
- Faculty of Medicine, Department of Neurology and Neurosurgery, Montreal Neurological Institute and Hospital, McConnell Brain Imaging Centre, McGill University, Montreal, Québec, Canada
- Henry H. Wheeler Jr. Brain Imaging Center, Helen Wills Neuroscience Institute, University of California, Berkeley, California, USA
- Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montreal, Québec, Canada
| | - Celia M. T. Greenwood
- Departments of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada
- Lady Davis Institute, Jewish General Hospital, Montreal, Québec, Canada
- Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montreal, Québec, Canada
- Departments of Oncology and Human Genetics, McGill University, Montreal, Québec, Canada
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20
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Lebeau B, Zhao K, Jangal M, Zhao T, Guerra M, Greenwood CMT, Witcher M. Single base-pair resolution analysis of DNA binding motif with MoMotif reveals an oncogenic function of CTCF zinc-finger 1 mutation. Nucleic Acids Res 2022; 50:8441-8458. [PMID: 35947648 PMCID: PMC9410893 DOI: 10.1093/nar/gkac658] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/17/2022] [Accepted: 07/21/2022] [Indexed: 12/24/2022] Open
Abstract
Defining the impact of missense mutations on the recognition of DNA motifs is highly dependent on bioinformatic tools that define DNA binding elements. However, classical motif analysis tools remain limited in their capacity to identify subtle changes in complex binding motifs between distinct conditions. To overcome this limitation, we developed a new tool, MoMotif, that facilitates a sensitive identification, at the single base-pair resolution, of complex, or subtle, alterations to core binding motifs, discerned from ChIP-seq data. We employed MoMotif to define the previously uncharacterized recognition motif of CTCF zinc-finger 1 (ZF1), and to further define the impact of CTCF ZF1 mutation on its association with chromatin. Mutations of CTCF ZF1 are exclusive to breast cancer and are associated with metastasis and therapeutic resistance, but the underlying mechanisms are unclear. Using MoMotif, we identified an extension of the CTCF core binding motif, necessitating a functional ZF1 to bind appropriately. Using a combination of ChIP-Seq and RNA-Seq, we discover that the inability to bind this extended motif drives an altered transcriptional program associated with the oncogenic phenotypes observed clinically. Our study demonstrates that MoMotif is a powerful new tool for comparative ChIP-seq analysis and characterising DNA-protein contacts.
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Affiliation(s)
| | | | - Maika Jangal
- Lady Davis Institute, Jewish General Hospital, Montréal, Québec H3T 1E2, Canada
| | - Tiejun Zhao
- Lady Davis Institute, Jewish General Hospital, Montréal, Québec H3T 1E2, Canada
| | - Maria Guerra
- Lady Davis Institute, Jewish General Hospital, Montréal, Québec H3T 1E2, Canada
| | - Celia M T Greenwood
- Correspondence may also be addressed to Celia Greenwood. Tel: +1 514 340 8222 (Ext 28397);
| | - Michael Witcher
- To whom correspondence should be addressed. Tel: +1 514 340 8222 (Ext 23363);
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Lu T, Forgetta V, Greenwood CMT, Richards JB. Identifying Causes of Fracture Beyond Bone Mineral Density: Evidence From Human Genetics. J Bone Miner Res 2022; 37:1592-1602. [PMID: 35689460 DOI: 10.1002/jbmr.4632] [Citation(s) in RCA: 4] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 09/07/2021] [Revised: 05/28/2022] [Accepted: 06/04/2022] [Indexed: 11/10/2022]
Abstract
New therapies may help to prevent osteoporotic fractures other than through increasing bone mineral density (BMD). Because fracture risk has an important genetic component, we aim to identify loci increasing fracture risk that do not decrease BMD, using a recently-proposed structural equation model adapted to remove genetic influences of BMD on fracture risk. We used summary statistics of the largest genome-wide association studies (GWASs) for BMD and for fracture in these analyses. We next estimated the genetic correlation between the non-BMD or BMD-related genetic effects and other clinical risk factors for fracture. Last, based on white British participants in the UK Biobank, we conducted genetic risk score analyses to assess whether the aggregated genetic effects conferred increased major osteoporotic fracture risk. We found that only three loci affecting fracture risk exhibited genetic effects not mediated by BMD: SOST, CPED1-WNT16, and RSPO3, while these three loci simultaneously conferred BMD-related effects. No strong genetic associations between non-BMD or BMD-related effects and 16 clinical risk factors were observed. However, non-BMD effects might be genetic correlated with hip bone size. In the UK Biobank, a 1 standard deviation (1-SD) increase in the non-BMD genetic risk score conferred an odds ratio of 1.17 for incident major osteoporotic fracture, compared to 1.29 by a BMD-related genetic risk score. Our study suggests that the majority of common genetic predisposition toward fracture risk acts upon BMD. Although non-BMD genetic effects may exist, they are not strongly correlated with most traditional clinical risk factors. Risk loci harboring non-BMD genetic effects may influence other perspectives of bone quality, or confer effects that existing GWASs fail to capture, but they demonstrate weaker impact on fracture risk than BMD-related genetic effects. These findings suggest that most successful drug development programs for osteoporosis should focus on pathways identified through BMD-associated loci. © 2022 American Society for Bone and Mineral Research (ASBMR).
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Affiliation(s)
- Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada.,Quantitative Life Sciences Program, McGill University, Montreal, Canada
| | - Vincenzo Forgetta
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada.,Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada.,Gerald Bronfman Department of Oncology, McGill University, Montreal, Canada.,Department of Human Genetics, McGill University, Montreal, Canada
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada.,Department of Human Genetics, McGill University, Montreal, Canada.,Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
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22
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Lu T, Forgetta V, Richards JB, Greenwood CMT. Polygenic risk score as a possible tool for identifying familial monogenic causes of complex diseases. Genet Med 2022; 24:1545-1555. [PMID: 35460399 DOI: 10.1016/j.gim.2022.03.022] [Citation(s) in RCA: 10] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/08/2021] [Revised: 03/29/2022] [Accepted: 03/30/2022] [Indexed: 01/13/2023] Open
Abstract
PURPOSE The study aimed to evaluate whether polygenic risk scores could be helpful in addition to family history for triaging individuals to undergo deep-depth diagnostic sequencing for identifying monogenic causes of complex diseases. METHODS Among 44,550 exome-sequenced European ancestry UK Biobank participants, we identified individuals with a clinically reported or computationally predicted monogenic pathogenic variant for breast cancer, bowel cancer, heart disease, diabetes, or Alzheimer disease. We derived polygenic risk scores for these diseases. We tested whether a polygenic risk score could identify rare pathogenic variant heterozygotes among individuals with a parental disease history. RESULTS Monogenic causes of complex diseases were more prevalent among individuals with a parental disease history than in the rest of the population. Polygenic risk scores showed moderate discriminative power to identify familial monogenic causes. For instance, we showed that prescreening the patients with a polygenic risk score for type 2 diabetes can prioritize individuals to undergo diagnostic sequencing for monogenic diabetes variants and reduce needs for such sequencing by up to 37%. CONCLUSION Among individuals with a family history of complex diseases, those with a low polygenic risk score are more likely to have monogenic causes of the disease and could be prioritized to undergo genetic testing.
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Affiliation(s)
- Tianyuan Lu
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Quantitative Life Sciences Program, McGill University, Montreal, Quebec, Canada
| | - Vincenzo Forgetta
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - John Brent Richards
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada; Department of Twin Research and Genetic Epidemiology, School of Life Course & Population Sciences, Faculty of Life Sciences & Medicine, King's College London, London, United Kingdom
| | - Celia M T Greenwood
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Department of Human Genetics, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada; Department of Epidemiology, Biostatistics and Occupational Health, Faculty of Medicine and Health Sciences, McGill University, Montreal, Quebec, Canada; Gerald Bronfman Department of Oncology, Gerald Bronfman Department of Oncology, McGill University, McGill University, Montreal, Quebec, Canada.
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Jiang L, Greenlaw K, Ciampi A, Canty AJ, Gross J, Turecki G, Greenwood CMT. A Bayesian hierarchical model for improving measurement of 5mC and 5hmC levels: Toward revealing associations between phenotypes and methylation states. Genet Epidemiol 2022; 46:446-462. [PMID: 35753057 DOI: 10.1002/gepi.22489] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/30/2021] [Revised: 04/20/2022] [Accepted: 05/04/2022] [Indexed: 11/09/2022]
Abstract
5-hydroxymethylcytosine (5hmC) is a methylation state linked with gene regulation, commonly found in cells of the central nervous system. 5hmC is associated with demethylation of cytosines from 5-methylcytosine (5mC) to the unmethylated state. The presence of 5hmC can be inferred by a paired experiment involving bisulfite and oxidation-bisulfite treatments on the same sample, followed by a methylation assay using a platform such as the Illumina Infinium MethylationEPIC BeadChip (EPIC). Existing methods for analysis of the resulting EPIC data are not ideal. Most approaches ignore the correlation between the two experiments and any imprecision associated with DNA damage from the additional treatment. Estimates of 5mC/5hmC levels free from these limitations are desirable to reveal associations between methylation states and phenotypes. We propose a hierarchical Bayesian method called Constrained HYdroxy Methylation Estimation (CHYME) to simultaneously estimate 5mC/5hmC signals as well as any associations between these signals and covariates or phenotypes, while accounting for the potential impact of DNA damage and dependencies induced by the experimental design. Simulations show that CHYME has valid type 1 error and better power than a range of alternative methods, including the popular OxyBS method and linear models on transformed proportions. Other methods we examined suffer from hugely inflated type 1 error for inference on 5hmC proportions. We use CHYME to explore genome-wide associations between 5mC/5hmC levels and cause of death in postmortem prefrontal cortex brain tissue samples. These analyses indicate that CHYME is a useful tool to reveal phenotypic associations with 5mC/5hmC levels.
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Affiliation(s)
- Lai Jiang
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada
| | - Keelin Greenlaw
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada
| | - Antonio Ciampi
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec, Canada
| | - Angelo J Canty
- Department of Mathematics and Statistics, McMaster University, Hamilton, Ontario, Canada
| | - Jeffrey Gross
- Department of Psychiatry, Douglas Institute, McGill University, Montréal, Québec, Canada
| | - Gustavo Turecki
- Department of Psychiatry, Douglas Institute, McGill University, Montréal, Québec, Canada
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Québec, Canada.,Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec, Canada.,Gerald Bronfman Department of Oncology, McGill University, Montréal, Québec, Canada.,Department of Human Genetics, McGill University, Montréal, Québec, Canada
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24
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Lu T, Forgetta V, Richards JB, Greenwood CMT. Capturing additional genetic risk from family history for improved polygenic risk prediction. Commun Biol 2022; 5:595. [PMID: 35710731 PMCID: PMC9203758 DOI: 10.1038/s42003-022-03532-4] [Citation(s) in RCA: 5] [Impact Index Per Article: 2.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/17/2022] [Accepted: 05/24/2022] [Indexed: 12/01/2022] Open
Abstract
Family history of complex traits may reflect transmitted rare pathogenic variants, intra-familial shared exposures to environmental and lifestyle factors, as well as a common genetic predisposition. We developed a latent factor model to quantify trait heritability in excess of that captured by a common variant-based polygenic risk score, but inferable from family history. For 941 children in the Avon Longitudinal Study of Parents and Children cohort, a joint predictor combining a polygenic risk score for height and mid-parental height was able to explain ~55% of the total variance in sex-adjusted adult height z-scores, close to the estimated heritability. Marginal yet consistent risk prediction improvements were also achieved among ~400,000 European ancestry participants for 11 complex diseases in the UK Biobank. Our work showcases a paradigm for risk calculation, and supports incorporation of family history into polygenic risk score-based genetic risk prediction models.
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Affiliation(s)
- Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada. .,Quantitative Life Sciences Program, McGill University, Montreal, QC, Canada.
| | - Vincenzo Forgetta
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.,Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.,Department of Human Genetics, McGill University, Montreal, QC, Canada.,Department of Twin Research and Genetic Epidemiology, King's College London, London, UK
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada. .,Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada. .,Department of Human Genetics, McGill University, Montreal, QC, Canada. .,Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada.
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25
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Fierheller CT, Guitton-Sert L, Alenezi WM, Revil T, Oros KK, Gao Y, Bedard K, Arcand SL, Serruya C, Behl S, Meunier L, Fleury H, Fewings E, Subramanian DN, Nadaf J, Bruce JP, Bell R, Provencher D, Foulkes WD, El Haffaf Z, Mes-Masson AM, Majewski J, Pugh TJ, Tischkowitz M, James PA, Campbell IG, Greenwood CMT, Ragoussis J, Masson JY, Tonin PN. A functionally impaired missense variant identified in French Canadian families implicates FANCI as a candidate ovarian cancer-predisposing gene. Genome Med 2021; 13:186. [PMID: 34861889 PMCID: PMC8642877 DOI: 10.1186/s13073-021-00998-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/02/2020] [Accepted: 10/27/2021] [Indexed: 12/14/2022] Open
Abstract
Background Familial ovarian cancer (OC) cases not harbouring pathogenic variants in either of the BRCA1 and BRCA2 OC-predisposing genes, which function in homologous recombination (HR) of DNA, could involve pathogenic variants in other DNA repair pathway genes. Methods Whole exome sequencing was used to identify rare variants in HR genes in a BRCA1 and BRCA2 pathogenic variant negative OC family of French Canadian (FC) ancestry, a population exhibiting genetic drift. OC cases and cancer-free individuals from FC and non-FC populations were investigated for carrier frequency of FANCI c.1813C>T; p.L605F, the top-ranking candidate. Gene and protein expression were investigated in cancer cell lines and tissue microarrays, respectively. Results In FC subjects, c.1813C>T was more common in familial (7.1%, 3/42) than sporadic (1.6%, 7/439) OC cases (P = 0.048). Carriers were detected in 2.5% (74/2950) of cancer-free females though female/male carriers were more likely to have a first-degree relative with OC (121/5249, 2.3%; Spearman correlation = 0.037; P = 0.011), suggesting a role in risk. Many of the cancer-free females had host factors known to reduce risk to OC which could influence cancer risk in this population. There was an increased carrier frequency of FANCI c.1813C>T in BRCA1 and BRCA2 pathogenic variant negative OC families, when including the discovery family, compared to cancer-free females (3/23, 13%; OR = 5.8; 95%CI = 1.7–19; P = 0.005). In non-FC subjects, 10 candidate FANCI variants were identified in 4.1% (21/516) of Australian OC cases negative for pathogenic variants in BRCA1 and BRCA2, including 10 carriers of FANCI c.1813C>T. Candidate variants were significantly more common in familial OC than in sporadic OC (P = 0.04). Localization of FANCD2, part of the FANCI-FANCD2 (ID2) binding complex in the Fanconi anaemia (FA) pathway, to sites of induced DNA damage was severely impeded in cells expressing the p.L605F isoform. This isoform was expressed at a reduced level, destabilized by DNA damaging agent treatment in both HeLa and OC cell lines, and exhibited sensitivity to cisplatin but not to a poly (ADP-ribose) polymerase inhibitor. By tissue microarray analyses, FANCI protein was consistently expressed in fallopian tube epithelial cells and only expressed at low-to-moderate levels in 88% (83/94) of OC samples. Conclusions This is the first study to describe candidate OC variants in FANCI, a member of the ID2 complex of the FA DNA repair pathway. Our data suggest that pathogenic FANCI variants may modify OC risk in cancer families. Supplementary Information The online version contains supplementary material available at 10.1186/s13073-021-00998-5.
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Affiliation(s)
- Caitlin T Fierheller
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.,Cancer Research Program, Centre for Translational Biology, The Research Institute of the McGill University Health Centre, 1001 Decarie Boulevard, Montreal, Quebec, H4A 3 J1, Canada
| | - Laure Guitton-Sert
- Genome Stability Laboratory, CHU de Québec-Université Laval Research Center, Oncology Division, Quebec City, Quebec, Canada.,Department of Molecular Biology, Medical Biochemistry and Pathology, Laval University Cancer Research Center, Quebec City, Quebec, Canada
| | - Wejdan M Alenezi
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.,Cancer Research Program, Centre for Translational Biology, The Research Institute of the McGill University Health Centre, 1001 Decarie Boulevard, Montreal, Quebec, H4A 3 J1, Canada.,Department of Medical Laboratory Technology, Taibah University, Medina, Saudi Arabia
| | - Timothée Revil
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.,McGill Genome Centre, McGill University, Montreal, Quebec, Canada
| | - Kathleen K Oros
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - Yuandi Gao
- Genome Stability Laboratory, CHU de Québec-Université Laval Research Center, Oncology Division, Quebec City, Quebec, Canada.,Department of Molecular Biology, Medical Biochemistry and Pathology, Laval University Cancer Research Center, Quebec City, Quebec, Canada
| | - Karine Bedard
- Laboratoire de Diagnostic Moléculaire, Centre Hospitalier de l'Université de Montréal (CHUM), Montreal, Quebec, Canada.,Département de pathologie et biologie cellulaire, Université de Montréal, Montreal, Quebec, Canada
| | - Suzanna L Arcand
- Cancer Research Program, Centre for Translational Biology, The Research Institute of the McGill University Health Centre, 1001 Decarie Boulevard, Montreal, Quebec, H4A 3 J1, Canada
| | - Corinne Serruya
- Cancer Research Program, Centre for Translational Biology, The Research Institute of the McGill University Health Centre, 1001 Decarie Boulevard, Montreal, Quebec, H4A 3 J1, Canada
| | - Supriya Behl
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Liliane Meunier
- Centre de recherche du Centre hospitalier de l'Université de Montréal and Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - Hubert Fleury
- Centre de recherche du Centre hospitalier de l'Université de Montréal and Institut du cancer de Montréal, Montreal, Quebec, Canada
| | - Eleanor Fewings
- Department of Medical Genetics, National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK
| | - Deepak N Subramanian
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia
| | - Javad Nadaf
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.,McGill Genome Centre, McGill University, Montreal, Quebec, Canada
| | - Jeffrey P Bruce
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Rachel Bell
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada
| | - Diane Provencher
- Centre de recherche du Centre hospitalier de l'Université de Montréal and Institut du cancer de Montréal, Montreal, Quebec, Canada.,Division of Gynecologic Oncology, Université de Montréal, Montreal, Quebec, Canada
| | - William D Foulkes
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.,Cancer Research Program, Centre for Translational Biology, The Research Institute of the McGill University Health Centre, 1001 Decarie Boulevard, Montreal, Quebec, H4A 3 J1, Canada.,Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada.,Department of Medicine, McGill University, Montreal, Quebec, Canada
| | - Zaki El Haffaf
- Centre de recherche du Centre Hospitalier de l'Université de Montréal, Montreal, Quebec, Canada
| | - Anne-Marie Mes-Masson
- Centre de recherche du Centre hospitalier de l'Université de Montréal and Institut du cancer de Montréal, Montreal, Quebec, Canada.,Department of Medicine, Université de Montréal, Montreal, Quebec, Canada
| | - Jacek Majewski
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada
| | - Trevor J Pugh
- Princess Margaret Cancer Centre, University Health Network, Toronto, Ontario, Canada.,Department of Medical Biophysics, University of Toronto, Toronto, Ontario, Canada.,Ontario Institute for Cancer Research, Toronto, Ontario, Canada
| | - Marc Tischkowitz
- Department of Medical Genetics, National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, UK
| | - Paul A James
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia.,The Parkville Familial Cancer Centre, Peter MacCallum Cancer Centre and The Royal Melbourne Hospital, Melbourne, Victoria, Australia
| | - Ian G Campbell
- Cancer Genetics Laboratory, Peter MacCallum Cancer Centre, Melbourne, Victoria, Australia.,Sir Peter MacCallum Department of Oncology, University of Melbourne, Melbourne, Victoria, Australia
| | - Celia M T Greenwood
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.,Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada.,Gerald Bronfman Department of Oncology, McGill University, Montreal, Quebec, Canada.,Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montreal, Quebec, Canada
| | - Jiannis Ragoussis
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada.,McGill Genome Centre, McGill University, Montreal, Quebec, Canada
| | - Jean-Yves Masson
- Genome Stability Laboratory, CHU de Québec-Université Laval Research Center, Oncology Division, Quebec City, Quebec, Canada.,Department of Molecular Biology, Medical Biochemistry and Pathology, Laval University Cancer Research Center, Quebec City, Quebec, Canada
| | - Patricia N Tonin
- Department of Human Genetics, McGill University, Montreal, Quebec, Canada. .,Cancer Research Program, Centre for Translational Biology, The Research Institute of the McGill University Health Centre, 1001 Decarie Boulevard, Montreal, Quebec, H4A 3 J1, Canada. .,Department of Medicine, McGill University, Montreal, Quebec, Canada.
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26
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Escribe C, Lu T, Keller-Baruch J, Forgetta V, Xiao B, Richards JB, Bhatnagar S, Oualkacha K, Greenwood CMT. Block coordinate descent algorithm improves variable selection and estimation in error-in-variables regression. Genet Epidemiol 2021; 45:874-890. [PMID: 34468045 PMCID: PMC9292988 DOI: 10.1002/gepi.22430] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/22/2021] [Revised: 07/19/2021] [Accepted: 08/12/2021] [Indexed: 11/13/2022]
Abstract
Medical research increasingly includes high‐dimensional regression modeling with a need for error‐in‐variables methods. The Convex Conditioned Lasso (CoCoLasso) utilizes a reformulated Lasso objective function and an error‐corrected cross‐validation to enable error‐in‐variables regression, but requires heavy computations. Here, we develop a Block coordinate Descent Convex Conditioned Lasso (BDCoCoLasso) algorithm for modeling high‐dimensional data that are only partially corrupted by measurement error. This algorithm separately optimizes the estimation of the uncorrupted and corrupted features in an iterative manner to reduce computational cost, with a specially calibrated formulation of cross‐validation error. Through simulations, we show that the BDCoCoLasso algorithm successfully copes with much larger feature sets than CoCoLasso, and as expected, outperforms the naïve Lasso with enhanced estimation accuracy and consistency, as the intensity and complexity of measurement errors increase. Also, a new smoothly clipped absolute deviation penalization option is added that may be appropriate for some data sets. We apply the BDCoCoLasso algorithm to data selected from the UK Biobank. We develop and showcase the utility of covariate‐adjusted genetic risk scores for body mass index, bone mineral density, and lifespan. We demonstrate that by leveraging more information than the naïve Lasso in partially corrupted data, the BDCoCoLasso may achieve higher prediction accuracy. These innovations, together with an R package, BDCoCoLasso, make error‐in‐variables adjustments more accessible for high‐dimensional data sets. We posit the BDCoCoLasso algorithm has the potential to be widely applied in various fields, including genomics‐facilitated personalized medicine research.
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Affiliation(s)
- Célia Escribe
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada.,Operations Research Center, Massachusetts Institute of Technology, Cambridge, MA, United States
| | - Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada.,Quantitative Life Sciences Program, McGill University, Montreal, Québec, Canada
| | - Julyan Keller-Baruch
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada.,Department of Human Genetics, McGill University, Montreal, Québec, Canada
| | - Vincenzo Forgetta
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada
| | - Bowei Xiao
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada.,Quantitative Life Sciences Program, McGill University, Montreal, Québec, Canada
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada.,Department of Human Genetics, McGill University, Montreal, Québec, Canada.,Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada.,Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom
| | - Sahir Bhatnagar
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada.,Department of Diagnostic Radiology, McGill University, Montreal, Québec, Canada
| | - Karim Oualkacha
- Département de Mathématiques, Université du Québec à Montréal, Montreal, Québec, Canada
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Québec, Canada.,Department of Human Genetics, McGill University, Montreal, Québec, Canada.,Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Québec, Canada.,Gerald Bronfman Department of Oncology, McGill University, Montreal, Québec, Canada
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27
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Guma E, Bordignon PDC, Devenyi GA, Gallino D, Anastassiadis C, Cvetkovska V, Barry AD, Snook E, Germann J, Greenwood CMT, Misic B, Bagot RC, Chakravarty MM. Early or Late Gestational Exposure to Maternal Immune Activation Alters Neurodevelopmental Trajectories in Mice: An Integrated Neuroimaging, Behavioral, and Transcriptional Study. Biol Psychiatry 2021; 90:328-341. [PMID: 34053674 DOI: 10.1016/j.biopsych.2021.03.017] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/04/2021] [Revised: 02/23/2021] [Accepted: 03/15/2021] [Indexed: 02/06/2023]
Abstract
BACKGROUND Exposure to maternal immune activation (MIA) in utero is a risk factor for neurodevelopmental disorders later in life. The impact of the gestational timing of MIA exposure on downstream development remains unclear. METHODS We characterized neurodevelopmental trajectories of mice exposed to the viral mimetic poly I:C (polyinosinic:polycytidylic acid) either on gestational day 9 (early) or on day 17 (late) using longitudinal structural magnetic resonance imaging from weaning to adulthood. Using multivariate methods, we related neuroimaging and behavioral variables for the time of greatest alteration (adolescence/early adulthood) and identified regions for further investigation using RNA sequencing. RESULTS Early MIA exposure was associated with accelerated brain volume increases in adolescence/early adulthood that normalized in later adulthood in the striatum, hippocampus, and cingulate cortex. Similarly, alterations in anxiety-like, stereotypic, and sensorimotor gating behaviors observed in adolescence normalized in adulthood. MIA exposure in late gestation had less impact on anatomical and behavioral profiles. Multivariate maps associated anxiety-like, social, and sensorimotor gating deficits with volume of the dorsal and ventral hippocampus and anterior cingulate cortex, among others. The most transcriptional changes were observed in the dorsal hippocampus, with genes enriched for fibroblast growth factor regulation, autistic behaviors, inflammatory pathways, and microRNA regulation. CONCLUSIONS Leveraging an integrated hypothesis- and data-driven approach linking brain-behavior alterations to the transcriptome, we found that MIA timing differentially affects offspring development. Exposure in late gestation leads to subthreshold deficits, whereas exposure in early gestation perturbs brain development mechanisms implicated in neurodevelopmental disorders.
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Affiliation(s)
- Elisa Guma
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada; Computational Brain Imaging Lab, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada.
| | - Pedro do Couto Bordignon
- Department of Psychology, McGill University, Montreal, Quebec, Canada; Ludmer Center for Neuroinformatics and Mental Health, Montreal, Quebec, Canada
| | - Gabriel A Devenyi
- Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Computational Brain Imaging Lab, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Daniel Gallino
- Computational Brain Imaging Lab, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada
| | - Chloe Anastassiadis
- Computational Brain Imaging Lab, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Institute of Medical Science & Collaborative Program in Neuroscience, University of Toronto, Toronto, Ontario, Canada
| | | | - Amadou D Barry
- Departments of Human Genetics and Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada; Ludmer Center for Neuroinformatics and Mental Health, Montreal, Quebec, Canada
| | - Emily Snook
- Computational Brain Imaging Lab, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada; Faculty of Medicine, University of Toronto, Toronto, Ontario, Canada
| | - Jurgen Germann
- Computational Brain Imaging Lab, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada; University Health Network, Toronto, Ontario, Canada
| | - Celia M T Greenwood
- Gerald Bronfman Department of Oncology, McGill University, Montreal, Quebec, Canada; Lady Davis Institute for Medical Research, Jewish General Hospital, McGill University, Montreal, Quebec, Canada; Departments of Human Genetics and Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada; Ludmer Center for Neuroinformatics and Mental Health, Montreal, Quebec, Canada
| | - Bratislav Misic
- Montreal Neurological Institute, McGill University, Montreal, Quebec, Canada
| | - Rosemary C Bagot
- Department of Psychology, McGill University, Montreal, Quebec, Canada; Ludmer Center for Neuroinformatics and Mental Health, Montreal, Quebec, Canada
| | - M Mallar Chakravarty
- Integrated Program in Neuroscience, McGill University, Montreal, Quebec, Canada; Department of Psychiatry, McGill University, Montreal, Quebec, Canada; Department of Biological and Biomedical Engineering, McGill University, Montreal, Quebec, Canada; Computational Brain Imaging Lab, Cerebral Imaging Center, Douglas Mental Health University Institute, Montreal, Quebec, Canada.
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28
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Zeng Y, Zhao K, Oros Klein K, Shao X, Fritzler MJ, Hudson M, Colmegna I, Pastinen T, Bernatsky S, Greenwood CMT. Thousands of CpGs Show DNA Methylation Differences in ACPA-Positive Individuals. Genes (Basel) 2021; 12:1349. [PMID: 34573331 PMCID: PMC8472734 DOI: 10.3390/genes12091349] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/24/2021] [Revised: 08/25/2021] [Accepted: 08/26/2021] [Indexed: 11/27/2022] Open
Abstract
High levels of anti-citrullinated protein antibodies (ACPA) are often observed prior to a diagnosis of rheumatoid arthritis (RA). We undertook a replication study to confirm CpG sites showing evidence of differential methylation in subjects positive vs. negative for ACPA, in a new subset of 112 individuals sampled from the population cohort and biobank CARTaGENE in Quebec, Canada. Targeted custom capture bisulfite sequencing was conducted at approximately 5.3 million CpGs located in regulatory or hypomethylated regions from whole blood; library and protocol improvements had been instituted between the original and this replication study, enabling better coverage and additional identification of differentially methylated regions (DMRs). Using binomial regression models, we identified 19,472 ACPA-associated differentially methylated cytosines (DMCs), of which 430 overlapped with the 1909 DMCs reported by the original study; 814 DMRs of relevance were clustered by grouping adjacent DMCs into regions. Furthermore, we performed an additional integrative analysis by looking at the DMRs that overlap with RA related loci published in the GWAS Catalog, and protein-coding genes associated with these DMRs were enriched in the biological process of cell adhesion and involved in immune-related pathways.
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Affiliation(s)
- Yixiao Zeng
- PhD Program in Quantitative Life Sciences, Interfaculty Studies, McGill University, Montréal, QC H3A 1E3, Canada;
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC H3T 1E2, Canada; (K.Z.); (K.O.K.); (M.H.)
| | - Kaiqiong Zhao
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC H3T 1E2, Canada; (K.Z.); (K.O.K.); (M.H.)
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC H3A 1A2, Canada
| | - Kathleen Oros Klein
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC H3T 1E2, Canada; (K.Z.); (K.O.K.); (M.H.)
| | - Xiaojian Shao
- Digital Technologies Research Centre, National Research Council Canada, Ottawa, ON K1A 0R6, Canada;
| | - Marvin J. Fritzler
- Cumming School of Medicine, University of Calgary, Calgary, AB T2N 1N4, Canada;
| | - Marie Hudson
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC H3T 1E2, Canada; (K.Z.); (K.O.K.); (M.H.)
- Department of Medicine, McGill University, Montréal, QC H4A 3J1, Canada; (I.C.); (S.B.)
- Division of Rheumatology, Jewish General Hospital, Montréal, QC H3T 1E2, Canada
| | - Inés Colmegna
- Department of Medicine, McGill University, Montréal, QC H4A 3J1, Canada; (I.C.); (S.B.)
- Division of Rheumatology, McGill University, Montréal, QC H3G 1A4, Canada
| | - Tomi Pastinen
- Department of Human Genetics, McGill University, Montréal, QC H3A 0C7, Canada;
- Center for Pediatric Genomic Medicine, Children’s Mercy, Kansas City, MO 64108, USA
| | - Sasha Bernatsky
- Department of Medicine, McGill University, Montréal, QC H4A 3J1, Canada; (I.C.); (S.B.)
- Division of Rheumatology, McGill University, Montréal, QC H3G 1A4, Canada
| | - Celia M. T. Greenwood
- PhD Program in Quantitative Life Sciences, Interfaculty Studies, McGill University, Montréal, QC H3A 1E3, Canada;
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, QC H3T 1E2, Canada; (K.Z.); (K.O.K.); (M.H.)
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC H3A 1A2, Canada
- Department of Human Genetics, McGill University, Montréal, QC H3A 0C7, Canada;
- Gerald Bronfman Department of Oncology, McGill University, Montréal, QC H4A 3T2, Canada
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29
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Fierheller C, Alenezi WM, Serruya C, Revil T, Nadaf J, Mes-Masson AM, Provencher D, Foulkes WD, Haffaf ZE, Greenwood CMT, Masson JY, Ragoussis J, Tonin PN. Abstract 2056: The genomic landscape of carriers of rare variants in FANCI, a new candidate ovarian cancer predisposing gene. Cancer Res 2021. [DOI: 10.1158/1538-7445.am2021-2056] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/16/2022]
Abstract
Abstract
The potentially pathogenic variant (PPV), FANCI c.1813C>T; p.L605F, in a new candidate ovarian cancer (OC) predisposing gene was discovered by whole exome sequencing (WES) of familial OC cases from the founder French Canadian (FC) population for discovering new OC predisposing genes. Modeling this variant in cellulo suggested this variant encodes an unstable protein. FANCI is an essential member upstream of the homologous recombination DNA repair pathway and intersects BRCA1 and BRCA2 function, proteins encoded by genes involved in hereditary OC. Investigating the FC founder population facilitates the discovery of PPVs as they are more likely to harbor recurrent variants due to common ancestors, increasing the likelihood of identifying candidate genes in cancer families.
To further support the candidacy of FANCI as a new OC predisposing gene, we investigated the germline landscape of c.1813C>T carrier FC OC cases for co-occurring PPVs in known or proposed new (emerging) OC predisposing genes or other genes involved in similar pathways (DNA repair).
Using WES of peripheral blood lymphocyte DNA, the genomic landscape of 10 FANCI carriers were investigated for heterozygous PPVs in 276 DNA repair pathways genes, which included known (BRCA1, BRCA2, MSH2, MLH1, MSH6, PMS2) and emerging (BRIP1, RAD51C, RAD51D) OC predisposing genes. Top ranking candidate variants (minor allele frequency <1%) were identified using 11 different in silico tools that assessed amino acid conversation or potential pathogenicity. Pathogenicity of known and emerging OC predisposing genes was assessed using BRCAExchange (www.brcaexchange.org) and ClinVar (www.ncbi.nlm.nih.gov/clinvar/). A similar analysis was done with WES data from 13 FC OC cases harboring pathogenic BRCA1/BRCA2 variants.
We identified 31 variants in 27 genes in FANCI c.1813C>T carriers. A previously known carrier of a pathogenic BRCA1 variant (c.2836_2837del; p.Ile946GlnfsTer5) was also identified in a familial case. No carriers of other pathogenic variants in known or emerging OC predisposing genes were found. However, FANCI carriers were found to carry at least one other PPV in a DNA repair pathway gene (range 2-9; average=4.1). There were no other carriers of variants in common among all OC cases, though at least 2 cases carried a variant in the same gene. FC OC cases harboring BRCA1/BRCA2 variants carried at least one other PPV in a DNA repair pathway gene (range 2-7; average 4).
It is possible that the identified variants influence or modify risk in conjunction with FANCI, though no PPV was identified in all carriers. As new cancer predisposing genes are identified it will become increasingly important to characterize the genetic context in which variants are identified. This will allow for further insight to clinical translatability once penetrance has been established.
Citation Format: Caitlin Fierheller, Wejdan M Alenezi, Corinne Serruya, Timothée Revil, Javad Nadaf, Anne-Marie Mes-Masson, Diane Provencher, William D Foulkes, Zaki El Haffaf, Celia M T Greenwood, Jean-Yves Masson, Jiannis Ragoussis, Patricia N Tonin. The genomic landscape of carriers of rare variants in FANCI, a new candidate ovarian cancer predisposing gene [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2021; 2021 Apr 10-15 and May 17-21. Philadelphia (PA): AACR; Cancer Res 2021;81(13_Suppl):Abstract nr 2056.
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Affiliation(s)
| | | | - Corinne Serruya
- 2The Research Institute of the McGill University Health Centre, Montreal, Quebec, Canada
| | | | - Javad Nadaf
- 1McGill University, Montreal, Quebec, Canada
| | | | | | | | - Zaki El Haffaf
- 5Centre de recherche du Centre hospitalier de l'Université de Montréal, Montreal, Quebec, Canada
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Lu T, Cardenas A, Perron P, Hivert MF, Bouchard L, Greenwood CMT. Detecting cord blood cell type-specific epigenetic associations with gestational diabetes mellitus and early childhood growth. Clin Epigenetics 2021; 13:131. [PMID: 34174944 PMCID: PMC8236204 DOI: 10.1186/s13148-021-01114-5] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/06/2021] [Accepted: 06/14/2021] [Indexed: 11/30/2022] Open
Abstract
BACKGROUND Epigenome-wide association studies (EWAS) have provided opportunities to understand the role of epigenetic mechanisms in development and pathophysiology of many chronic diseases. However, an important limitation of conventional EWAS is that profiles of epigenetic variability are often obtained in samples of mixed cell types. Here, we aim to assess whether changes in cord blood DNA methylation (DNAm) associated with gestational diabetes mellitus (GDM) exposure and early childhood growth markers occur in a cell type-specific manner. RESULTS We analyzed 275 cord blood samples collected at delivery from a prospective pre-birth cohort with genome-wide DNAm profiled by the Illumina MethylationEPIC array. We estimated proportions of seven common cell types in each sample using a cord blood-specific DNAm reference panel. Leveraging a recently developed approach named CellDMC, we performed cell type-specific EWAS to identify CpG loci significantly associated with GDM, or 3-year-old body mass index (BMI) z-score. A total of 1410 CpG loci displayed significant cell type-specific differences in methylation level between 23 GDM cases and 252 controls with a false discovery rate < 0.05. Gene Ontology enrichment analysis indicated that LDL transportation emerged from CpG specifically identified from B-cells DNAm analyses and the mitogen-activated protein kinase pathway emerged from CpG specifically identified from natural killer cells DNAm analyses. In addition, we identified four and six loci associated with 3-year-old BMI z-score that were specific to CD8+ T-cells and monocytes, respectively. By performing genome-wide permutation tests, we validated that most of our detected signals had low false positive rates. CONCLUSION Compared to conventional EWAS adjusting for the effects of cell type heterogeneity, the proposed approach based on cell type-specific EWAS could provide additional biologically meaningful associations between CpG methylation, prenatal maternal GDM or 3-year-old BMI. With careful validation, these findings may provide new insights into the pathogenesis, programming, and consequences of related childhood metabolic dysregulation. Therefore, we propose that cell type-specific analyses are worth cautious explorations.
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Affiliation(s)
- Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, 3755 Chemin de La Côte-Sainte-Catherine, Montréal, QC, H3T 1E2, Canada
- Quantitative Life Sciences Program, McGill University, Montréal, QC, Canada
| | - Andres Cardenas
- Division of Environmental Health Sciences, School of Public Health and Center for Computational Biology, University of California, Berkeley, CA, USA
| | - Patrice Perron
- Department of Medicine, Université de Sherbrooke, Sherbrooke, QC, Canada
- Centre de Recherche du Centre Hospitalier, Universitaire de Sherbrooke, Sherbrooke, QC, Canada
| | - Marie-France Hivert
- Department of Medicine, Université de Sherbrooke, Sherbrooke, QC, Canada
- Diabetes Unit, Massachusetts General Hospital, Boston, MA, USA
- Department of Population Medicine, Harvard Pilgrim Health Care Institute, Harvard Medical School, Boston, MA, USA
| | - Luigi Bouchard
- Centre de Recherche du Centre Hospitalier, Universitaire de Sherbrooke, Sherbrooke, QC, Canada
- Department of Biochemistry and Functional Genomics, Université de Sherbrooke, Sherbrooke, QC, Canada
- Department of Medical Biology, Centre Intégré Universitaire de Santé et de Services Sociaux Saguenay-Lac-Saint-Jean - Hôpital Universitaire de Chicoutimi, Saguenay, QC, Canada
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, 3755 Chemin de La Côte-Sainte-Catherine, Montréal, QC, H3T 1E2, Canada.
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada.
- Department of Human Genetics, McGill University, Montréal, QC, Canada.
- Gerald Bronfman Department of Oncology, McGill University, Montréal, QC, Canada.
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Lu T, Forgetta V, Wu H, Perry JRB, Ong KK, Greenwood CMT, Timpson NJ, Manousaki D, Richards JB. A Polygenic Risk Score to Predict Future Adult Short Stature Among Children. J Clin Endocrinol Metab 2021; 106:1918-1928. [PMID: 33788949 PMCID: PMC8266463 DOI: 10.1210/clinem/dgab215] [Citation(s) in RCA: 18] [Impact Index Per Article: 6.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/02/2021] [Indexed: 11/30/2022]
Abstract
CONTEXT Adult height is highly heritable, yet no genetic predictor has demonstrated clinical utility compared to mid-parental height. OBJECTIVE To develop a polygenic risk score for adult height and evaluate its clinical utility. DESIGN A polygenic risk score was constructed based on meta-analysis of genomewide association studies and evaluated on the Avon Longitudinal Study of Parents and Children (ALSPAC) cohort. SUBJECTS Participants included 442 599 genotyped White British individuals in the UK Biobank and 941 genotyped child-parent trios of European ancestry in the ALSPAC cohort. INTERVENTIONS None. MAIN OUTCOME MEASURES Standing height was measured using stadiometer; Standing height 2 SDs below the sex-specific population average was considered as short stature. RESULTS Combined with sex, a polygenic risk score captured 71.1% of the total variance in adult height in the UK Biobank. In the ALSPAC cohort, the polygenic risk score was able to identify children who developed adulthood short stature with an area under the receiver operating characteristic curve (AUROC) of 0.84, which is close to that of mid-parental height. Combining this polygenic risk score with mid-parental height or only one of the child's parent's height could improve the AUROC to at most 0.90. The polygenic risk score could also substitute mid-parental height in age-specific Khamis-Roche height predictors and achieve an equally strong discriminative power in identifying children with a short stature in adulthood. CONCLUSIONS A polygenic risk score could be considered as an alternative or adjunct to mid-parental height to improve screening for children at risk of developing short stature in adulthood in European ancestry populations.
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Affiliation(s)
- Tianyuan Lu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Canada
- Quantitative Life Sciences Program, McGill University, Montréal, Canada
| | - Vincenzo Forgetta
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Canada
| | - Haoyu Wu
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Canada
| | - John R B Perry
- Medical Research Council Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
| | - Ken K Ong
- Medical Research Council Epidemiology Unit, Institute of Metabolic Science, University of Cambridge, Cambridge, UK
- Department of Pediatrics, University of Cambridge School of Clinical Medicine, Cambridge, UK
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Canada
- Department of Human Genetics, McGill University, Montréal, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montréal, Canada
| | - Nicholas J Timpson
- Medical Research Council Integrative Epidemiology Unit, Department of Population Health Sciences, University of Bristol, Bristol, United Kingdom
| | - Despoina Manousaki
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Canada
- Department of Pediatrics, Université de Montréal, Montréal, Canada
| | - J Brent Richards
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montréal, Canada
- Department of Human Genetics, McGill University, Montréal, Canada
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, UK
- Correspondence: J. Brent Richards, Jewish General Hospital, Room H-413, 3755 Chemin de la Côte-Sainte-Catherine, Montréal, Québec, H3T 1E2, Canada. E-mail:
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Huguet G, Schramm C, Douard E, Tamer P, Main A, Monin P, England J, Jizi K, Renne T, Poirier M, Nowak S, Martin CO, Younis N, Knoth IS, Jean-Louis M, Saci Z, Auger M, Tihy F, Mathonnet G, Maftei C, Léveillé F, Porteous D, Davies G, Redmond P, Harris SE, Hill WD, Lemyre E, Schumann G, Bourgeron T, Pausova Z, Paus T, Karama S, Lippe S, Deary IJ, Almasy L, Labbe A, Glahn D, Greenwood CMT, Jacquemont S. Genome-wide analysis of gene dosage in 24,092 individuals estimates that 10,000 genes modulate cognitive ability. Mol Psychiatry 2021; 26:2663-2676. [PMID: 33414497 PMCID: PMC8953148 DOI: 10.1038/s41380-020-00985-z] [Citation(s) in RCA: 27] [Impact Index Per Article: 9.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 07/01/2020] [Revised: 10/30/2020] [Accepted: 11/30/2020] [Indexed: 11/09/2022]
Abstract
Genomic copy number variants (CNVs) are routinely identified and reported back to patients with neuropsychiatric disorders, but their quantitative effects on essential traits such as cognitive ability are poorly documented. We have recently shown that the effect size of deletions on cognitive ability can be statistically predicted using measures of intolerance to haploinsufficiency. However, the effect sizes of duplications remain unknown. It is also unknown if the effect of multigenic CNVs are driven by a few genes intolerant to haploinsufficiency or distributed across tolerant genes as well. Here, we identified all CNVs > 50 kilobases in 24,092 individuals from unselected and autism cohorts with assessments of general intelligence. Statistical models used measures of intolerance to haploinsufficiency of genes included in CNVs to predict their effect size on intelligence. Intolerant genes decrease general intelligence by 0.8 and 2.6 points of intelligence quotient when duplicated or deleted, respectively. Effect sizes showed no heterogeneity across cohorts. Validation analyses demonstrated that models could predict CNV effect sizes with 78% accuracy. Data on the inheritance of 27,766 CNVs showed that deletions and duplications with the same effect size on intelligence occur de novo at the same frequency. We estimated that around 10,000 intolerant and tolerant genes negatively affect intelligence when deleted, and less than 2% have large effect sizes. Genes encompassed in CNVs were not enriched in any GOterms but gene regulation and brain expression were GOterms overrepresented in the intolerant subgroup. Such pervasive effects on cognition may be related to emergent properties of the genome not restricted to a limited number of biological pathways.
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Affiliation(s)
- Guillaume Huguet
- Department of Pediatrics, Université de Montréal, Montreal, QC, Canada.
- Centre de recherche et Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada.
| | - Catherine Schramm
- Department of Pediatrics, Université de Montréal, Montreal, QC, Canada
- Centre de recherche et Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
| | - Elise Douard
- Department of Pediatrics, Université de Montréal, Montreal, QC, Canada
- Centre de recherche et Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
| | - Petra Tamer
- Department of Pediatrics, Université de Montréal, Montreal, QC, Canada
- Centre de recherche et Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
| | - Antoine Main
- Centre de recherche et Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
- Département de Sciences de la Décision, HEC Montreal, Montreal, QC, Canada
| | - Pauline Monin
- Centre de recherche et Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
- Human Genetics and Cognitive Functions, University Paris Diderot, Sorbonne Paris Cité, Paris, France
| | - Jade England
- Department of Pediatrics, Université de Montréal, Montreal, QC, Canada
- Centre de recherche et Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
| | - Khadije Jizi
- Department of Pediatrics, Université de Montréal, Montreal, QC, Canada
- Centre de recherche et Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
| | - Thomas Renne
- Centre de recherche et Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
- Universite de Rouen Normandie, UFR des Sciences et Techniques, Rouen, France
| | - Myriam Poirier
- Department of Pediatrics, Université de Montréal, Montreal, QC, Canada
- Centre de recherche et Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
| | - Sabrina Nowak
- Department of Pediatrics, Université de Montréal, Montreal, QC, Canada
- Centre de recherche et Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
| | - Charles-Olivier Martin
- Department of Pediatrics, Université de Montréal, Montreal, QC, Canada
- Centre de recherche et Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
| | - Nadine Younis
- Department of Pediatrics, Université de Montréal, Montreal, QC, Canada
- Centre de recherche et Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
| | - Inga Sophia Knoth
- Department of Pediatrics, Université de Montréal, Montreal, QC, Canada
- Centre de recherche et Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
| | - Martineau Jean-Louis
- Department of Pediatrics, Université de Montréal, Montreal, QC, Canada
- Centre de recherche et Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
| | - Zohra Saci
- Department of Pediatrics, Université de Montréal, Montreal, QC, Canada
- Centre de recherche et Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
| | - Maude Auger
- Department of Pediatrics, Université de Montréal, Montreal, QC, Canada
- Centre de recherche et Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
| | - Frédérique Tihy
- Department of Pediatrics, Université de Montréal, Montreal, QC, Canada
- Centre de recherche et Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
| | - Géraldine Mathonnet
- Department of Pediatrics, Université de Montréal, Montreal, QC, Canada
- Centre de recherche et Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
| | - Catalina Maftei
- Department of Pediatrics, Université de Montréal, Montreal, QC, Canada
- Centre de recherche et Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
| | - France Léveillé
- Department of Pediatrics, Université de Montréal, Montreal, QC, Canada
- Centre de recherche et Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
| | - David Porteous
- Lothian Birth Cohorts Group, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
- Medical Genetics Section, Centre for Genomic & Experimental Medicine, MRC Institute of Genetics & Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, EH4 2XU, UK
- Generation Scotland, Centre for Genomic and Experimental Medicine, University of Edinburgh, Edinburgh, EH4 2XU, UK
| | - Gail Davies
- Lothian Birth Cohorts Group, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Paul Redmond
- Lothian Birth Cohorts Group, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Sarah E Harris
- Lothian Birth Cohorts Group, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - W David Hill
- Lothian Birth Cohorts Group, Department of Psychology, School of Philosophy, Psychology and Language Sciences, The University of Edinburgh, Edinburgh, EH8 9JZ, UK
| | - Emmanuelle Lemyre
- Department of Pediatrics, Université de Montréal, Montreal, QC, Canada
- Centre de recherche et Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
| | - Gunter Schumann
- Institute of Psychiatry, Psychology, and Neuroscience, King's College London, London, England
| | - Thomas Bourgeron
- Department of Neurosciences, Human Genetics and Cognitive Functions, Institut Pasteur, Paris, France
- Centre National de la Recherche Scientifique Genes, Synapses and Cognition Laboratory, Institut Pasteur, Paris, France
- Human Genetics and Cognitive Functions, University Paris Diderot, Sorbonne Paris Cité, Paris, France
| | - Zdenka Pausova
- The Hospital for Sick Children, University of Toronto, Toronto, ON, Canada
| | - Tomas Paus
- Departments of Psychology and Psychiatry, University of Toronto, Toronto, ON, Canada
| | - Sherif Karama
- Montreal Neurological Institute, McGill University, Montreal, QC, Canada
- McConnell Brain Imaging Center, McGill University, Montreal, QC, Canada
- Douglas Mental Health University Institute, Montreal, QC, Canada
| | - Sarah Lippe
- Centre de recherche et Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada
- Psychology, Université de Montréal, Montreal, QC, Canada
| | - Ian J Deary
- Medical Genetics Section, Centre for Genomic & Experimental Medicine, MRC Institute of Genetics & Molecular Medicine, University of Edinburgh, Western General Hospital, Edinburgh, EH4 2XU, UK
| | - Laura Almasy
- Department of Biomedical and Health Informatics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Aurélie Labbe
- Human Genetics and Cognitive Functions, University Paris Diderot, Sorbonne Paris Cité, Paris, France
| | - David Glahn
- Department of Psychiatry, Boston Children's Hospital and Harvard Medical School, Boston, MA 02115, USA
- Olin Neuropsychiatric Research Center, Institute of Living, Hartford Hospital, Hartford, CT, USA
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
- Gerald Bronfman Department of Oncology, Departments of Epidemiology, Biostatistics & Occupational Health and Human Genetics, McGill University, Montreal, QC, Canada
| | - Sébastien Jacquemont
- Department of Pediatrics, Université de Montréal, Montreal, QC, Canada.
- Centre de recherche et Centre Hospitalier Universitaire Sainte-Justine, Montreal, QC, Canada.
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Bazinet A, Rys RN, Barry A, Greenwood CMT, Young YK, Mendoza A, LaPorta I, Wever CM, Mercier FE, Johnson NA. A 10-color flow cytometry panel for diagnosis and minimal residual disease in chronic lymphocytic leukemia. Leuk Lymphoma 2021; 62:2352-2359. [PMID: 34020575 DOI: 10.1080/10428194.2021.1919658] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/31/2023]
Abstract
Diagnosis and minimal residual disease (MRD) monitoring of chronic lymphocytic leukemia (CLL) by flow cytometry currently requires multiple antibody panels. We added CD23 and CD200 to the EuroFlowTM lymphoid screening tube (LST) to create a 10-color modified LST (mLST) capable of diagnosing typical CLL in a single tube. We then explored if the mLST could be used for MRD by comparing its performance to the European Research Initiative on CLL (ERIC) panel using spiked cryopreserved and fresh patient samples. Over 1 year of use in our clinical laboratory, the mLST diagnosed CLL without further immunophenotyping in 56% of samples with an abnormal clone. There was good agreement in MRD results between the mLST and ERIC panels. Therefore, the mLST can streamline CLL diagnosis by reducing technician time and the number of panels required. It may have the potential to screen for MRD in laboratories without access to dedicated panels (ERIC).
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Affiliation(s)
- Alexandre Bazinet
- Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Canada.,Department of Medicine, Jewish General Hospital, McGill University, Montreal, Canada
| | - Ryan N Rys
- Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Canada
| | - Amadou Barry
- Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Canada.,Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montreal, Canada
| | - Celia M T Greenwood
- Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Canada.,Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montreal, Canada.,Gerald Bronfman Department of Oncology, McGill University, Montreal, Canada.,Department of Human Genetics, McGill University, Montreal, Canada
| | - Yoon Kow Young
- Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Canada
| | - Alma Mendoza
- Clinical Flow Cytometry, Jewish General Hospital, Montreal, Canada
| | - Ida LaPorta
- Clinical Flow Cytometry, Jewish General Hospital, Montreal, Canada
| | | | - François E Mercier
- Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Canada.,Department of Medicine, Jewish General Hospital, McGill University, Montreal, Canada
| | - Nathalie A Johnson
- Lady Davis Institute, Jewish General Hospital, McGill University, Montreal, Canada.,Department of Medicine, Jewish General Hospital, McGill University, Montreal, Canada
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Caillon A, Zhao K, Klein KO, Greenwood CMT, Lu Z, Paradis P, Schiffrin EL. High Systolic Blood Pressure at Hospital Admission Is an Important Risk Factor in Models Predicting Outcome of COVID-19 Patients. Am J Hypertens 2021; 34:282-290. [PMID: 33386395 PMCID: PMC7799245 DOI: 10.1093/ajh/hpaa225] [Citation(s) in RCA: 29] [Impact Index Per Article: 9.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/25/2020] [Revised: 12/23/2020] [Accepted: 12/28/2020] [Indexed: 12/27/2022] Open
Abstract
BACKGROUND The risk that coronavirus disease 2019 (COVID-19) patients develop critical illness that can be fatal depends on their age and immune status and may also be affected by comorbidities like hypertension. The goal of this study was to develop models that predict outcome using parameters collected at admission to the hospital. METHODS AND RESULTS This is a retrospective single-center cohort study of COVID-19 patients at the Seventh Hospital of Wuhan City, China. Forty-three demographic, clinical, and laboratory parameters collected at admission plus discharge/death status, days from COVID-19 symptoms onset, and days of hospitalization were analyzed. From 157 patients, 120 were discharged and 37 died. Pearson correlations showed that hypertension and systolic blood pressure (SBP) were associated with death and respiratory distress parameters. A penalized logistic regression model efficiently predicts the probability of death with 13 of 43 variables. A regularized Cox regression model predicts the probability of survival with 7 of above 13 variables. SBP but not hypertension was a covariate in both mortality and survival prediction models. SBP was elevated in deceased compared with discharged COVID-19 patients. CONCLUSIONS Using an unbiased approach, we developed models predicting outcome of COVID-19 patients based on data available at hospital admission. This can contribute to evidence-based risk prediction and appropriate decision-making at hospital triage to provide the most appropriate care and ensure the best patient outcome. High SBP, a cause of end-organ damage and an important comorbid factor, was identified as a covariate in both mortality and survival prediction models.
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Affiliation(s)
- Antoine Caillon
- Lady Davis Institute for Medical Research, Sir Mortimer B. Davis-Jewish General Hospital, McGill University, Montréal, Québec, Canada
| | - Kaiqiong Zhao
- Lady Davis Institute for Medical Research, Sir Mortimer B. Davis-Jewish General Hospital, McGill University, Montréal, Québec, Canada
| | - Kathleen Oros Klein
- Lady Davis Institute for Medical Research, Sir Mortimer B. Davis-Jewish General Hospital, McGill University, Montréal, Québec, Canada
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Sir Mortimer B. Davis-Jewish General Hospital, McGill University, Montréal, Québec, Canada
- Department of Oncology, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec, Canada
| | - Zhibing Lu
- Department of Cardiology, Zhongnan Hospital of Wuhan University, Hubei, China
| | - Pierre Paradis
- Lady Davis Institute for Medical Research, Sir Mortimer B. Davis-Jewish General Hospital, McGill University, Montréal, Québec, Canada
| | - Ernesto L Schiffrin
- Lady Davis Institute for Medical Research, Sir Mortimer B. Davis-Jewish General Hospital, McGill University, Montréal, Québec, Canada
- Department of Medicine, Sir Mortimer B. Davis-Jewish General Hospital, McGill University, Montréal, Québec, Canada
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Amuzu S, Carmona E, Mes-Masson AM, Greenwood CMT, Tonin PN, Ragoussis J. Candidate Markers of Olaparib Response from Genomic Data Analyses of Human Cancer Cell Lines. Cancers (Basel) 2021; 13:1296. [PMID: 33803939 PMCID: PMC7998846 DOI: 10.3390/cancers13061296] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/31/2021] [Revised: 03/04/2021] [Accepted: 03/09/2021] [Indexed: 12/16/2022] Open
Abstract
The benefit of PARP inhibitor olaparib in relapsed and advanced high-grade serous ovarian carcinoma (HGSOC) is well established especially in BRCA1/2 mutation carriers. Identification of additional biomarkers can help expand the population of patients most likely to benefit from olaparib treatment. To identify candidate markers of olaparib response we analyzed genomic and in vitro olaparib response data from two independent groups of cancer cell lines. Using pan-cancer cell lines (n = 896) from the Genomics of Drug Sensitivity in Cancer database, we applied linear regression methods to identify statistically significant gene predictors of olaparib response based on mRNA expression. We then analyzed whole exome sequencing and mRNA gene expression data from our collection of 18 HGSOC cell lines previously classified as sensitive, intermediate, or resistant based on in vitro olaparib response for mutations, copy number variation and differential expression of candidate olaparib response genes. We identify genes previously associated with olaparib response (SLFN11, ABCB1), and discover novel candidate olaparib sensitivity genes with known functions including interaction with PARP1 (PUM3, EEF1A1) and involvement in homologous recombination DNA repair (ELP4). Further investigations at experimental and clinical levels are required to validate novel candidates, and ultimately determine their efficacy as potential biomarkers of olaparib sensitivity.
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Affiliation(s)
- Setor Amuzu
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada; (C.M.T.G.); (P.N.T.); (J.R.)
- McGill Genome Centre, McGill University, Montreal, QC H3A 0G1, Canada
| | - Euridice Carmona
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), Montreal, QC H2X 0A9, Canada; (E.C.); (A.-M.M.-M.)
- Institut du Cancer de Montréal, Montreal, QC H2X 0A9, Canada
| | - Anne-Marie Mes-Masson
- Centre de Recherche du Centre Hospitalier de l’Université de Montréal (CRCHUM), Montreal, QC H2X 0A9, Canada; (E.C.); (A.-M.M.-M.)
- Institut du Cancer de Montréal, Montreal, QC H2X 0A9, Canada
- Department of Medicine, Université de Montréal, Montreal, QC H3C 3J7, Canada
| | - Celia M. T. Greenwood
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada; (C.M.T.G.); (P.N.T.); (J.R.)
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC H3T 1E2, Canada
- Departments of Oncology and Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC H3A 1A2, Canada
| | - Patricia N. Tonin
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada; (C.M.T.G.); (P.N.T.); (J.R.)
- Cancer Research Program, Centre for Translational Biology, The Research Institute of the McGill University Health Centre, Montreal, QC H4A 3J1, Canada
- Department of Medicine, McGill University, Montreal, QC H3A 0G4, Canada
| | - Jiannis Ragoussis
- Department of Human Genetics, McGill University, Montreal, QC H3A 0C7, Canada; (C.M.T.G.); (P.N.T.); (J.R.)
- McGill Genome Centre, McGill University, Montreal, QC H3A 0G1, Canada
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Lu T, Forgetta V, Keller-Baruch J, Nethander M, Bennett D, Forest M, Bhatnagar S, Walters RG, Lin K, Chen Z, Li L, Karlsson M, Mellström D, Orwoll E, McCloskey EV, Kanis JA, Leslie WD, Clarke RJ, Ohlsson C, Greenwood CMT, Richards JB. Improved prediction of fracture risk leveraging a genome-wide polygenic risk score. Genome Med 2021; 13:16. [PMID: 33536041 PMCID: PMC7860212 DOI: 10.1186/s13073-021-00838-6] [Citation(s) in RCA: 30] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/29/2020] [Accepted: 01/22/2021] [Indexed: 11/24/2022] Open
Abstract
BACKGROUND Accurately quantifying the risk of osteoporotic fracture is important for directing appropriate clinical interventions. While skeletal measures such as heel quantitative speed of sound (SOS) and dual-energy X-ray absorptiometry bone mineral density are able to predict the risk of osteoporotic fracture, the utility of such measurements is subject to the availability of equipment and human resources. Using data from 341,449 individuals of white British ancestry, we previously developed a genome-wide polygenic risk score (PRS), called gSOS, that captured 25.0% of the total variance in SOS. Here, we test whether gSOS can improve fracture risk prediction. METHODS We examined the predictive power of gSOS in five genome-wide genotyped cohorts, including 90,172 individuals of European ancestry and 25,034 individuals of Asian ancestry. We calculated gSOS for each individual and tested for the association between gSOS and incident major osteoporotic fracture and hip fracture. We tested whether adding gSOS to the risk prediction models had added value over models using other commonly used clinical risk factors. RESULTS A standard deviation decrease in gSOS was associated with an increased odds of incident major osteoporotic fracture in populations of European ancestry, with odds ratios ranging from 1.35 to 1.46 in four cohorts. It was also associated with a 1.26-fold (95% confidence interval (CI) 1.13-1.41) increased odds of incident major osteoporotic fracture in the Asian population. We demonstrated that gSOS was more predictive of incident major osteoporotic fracture (area under the receiver operating characteristic curve (AUROC) = 0.734; 95% CI 0.727-0.740) and incident hip fracture (AUROC = 0.798; 95% CI 0.791-0.805) than most traditional clinical risk factors, including prior fracture, use of corticosteroids, rheumatoid arthritis, and smoking. We also showed that adding gSOS to the Fracture Risk Assessment Tool (FRAX) could refine the risk prediction with a positive net reclassification index ranging from 0.024 to 0.072. CONCLUSIONS We generated and validated a PRS for SOS which was associated with the risk of fracture. This score was more strongly associated with the risk of fracture than many clinical risk factors and provided an improvement in risk prediction. gSOS should be explored as a tool to improve risk stratification to identify individuals at high risk of fracture.
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Affiliation(s)
- Tianyuan Lu
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Room H-413, 3755 Chemin de la Côte-Sainte-Catherine, Montreal, Quebec, H3T 1E2, Canada
- Quantitative Life Sciences Program, McGill University, Montreal, Canada
| | - Vincenzo Forgetta
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Room H-413, 3755 Chemin de la Côte-Sainte-Catherine, Montreal, Quebec, H3T 1E2, Canada
| | - Julyan Keller-Baruch
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Room H-413, 3755 Chemin de la Côte-Sainte-Catherine, Montreal, Quebec, H3T 1E2, Canada
| | - Maria Nethander
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Bioinformatics Core Facility, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Derrick Bennett
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- National Institute for Health Research Oxford Biomedical Research Centre, Oxford University Hospitals NHS Foundation Trust, Oxford, UK
| | - Marie Forest
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Room H-413, 3755 Chemin de la Côte-Sainte-Catherine, Montreal, Quebec, H3T 1E2, Canada
| | - Sahir Bhatnagar
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada
- Department of Diagnostic Radiology, McGill University, Montreal, Canada
| | - Robin G Walters
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Kuang Lin
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Zhengming Chen
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
- MRC Population Health Research Unit, University of Oxford, Oxford, UK
| | - Liming Li
- School of Public Health, Peking University Health Science Centre, Beijing, China
| | - Magnus Karlsson
- Clinical and Molecular Osteoporosis Research Unit, Department of Orthopedics and Clinical Sciences, Lund University, Lund, Sweden
- Skåne University Hospital, Malmö, Sweden
| | - Dan Mellström
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Eric Orwoll
- Bone & Mineral Unit, Oregon Health & Science University, Portland, USA
- Department of Medicine, Oregon Health & Science University, Portland, USA
| | - Eugene V McCloskey
- Mellanby Centre for Bone Research, Centre for Integrated Research in Musculoskeletal Ageing, University of Sheffield, Sheffield, UK
| | - John A Kanis
- Centre for Metabolic Bone Diseases, University of Sheffield, Sheffield, UK
- Mary Mackillop Institute for Health Research, Australian Catholic University, Melbourne, Australia
| | - William D Leslie
- Department of Medicine, University of Manitoba, Winnipeg, Canada
| | - Robert J Clarke
- Clinical Trial Service Unit and Epidemiological Studies Unit, Nuffield Department of Population Health, University of Oxford, Oxford, UK
| | - Claes Ohlsson
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute of Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Department of Drug Treatment, Sahlgrenska University Hospital, Gothenburg, Sweden
| | - Celia M T Greenwood
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Room H-413, 3755 Chemin de la Côte-Sainte-Catherine, Montreal, Quebec, H3T 1E2, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada
- Department of Human Genetics, McGill University, Montreal, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montreal, Canada
| | - J Brent Richards
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Room H-413, 3755 Chemin de la Côte-Sainte-Catherine, Montreal, Quebec, H3T 1E2, Canada.
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada.
- Department of Human Genetics, McGill University, Montreal, Canada.
- Department of Twin Research and Genetic Epidemiology, King's College London, London, UK.
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Bhagwat N, Barry A, Dickie EW, Brown ST, Devenyi GA, Hatano K, DuPre E, Dagher A, Chakravarty M, Greenwood CMT, Misic B, Kennedy DN, Poline JB. Understanding the impact of preprocessing pipelines on neuroimaging cortical surface analyses. Gigascience 2021; 10:giaa155. [PMID: 33481004 PMCID: PMC7821710 DOI: 10.1093/gigascience/giaa155] [Citation(s) in RCA: 14] [Impact Index Per Article: 4.7] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Revised: 11/01/2020] [Indexed: 11/14/2022] Open
Abstract
BACKGROUND The choice of preprocessing pipeline introduces variability in neuroimaging analyses that affects the reproducibility of scientific findings. Features derived from structural and functional MRI data are sensitive to the algorithmic or parametric differences of preprocessing tasks, such as image normalization, registration, and segmentation to name a few. Therefore it is critical to understand and potentially mitigate the cumulative biases of pipelines in order to distinguish biological effects from methodological variance. METHODS Here we use an open structural MRI dataset (ABIDE), supplemented with the Human Connectome Project, to highlight the impact of pipeline selection on cortical thickness measures. Specifically, we investigate the effect of (i) software tool (e.g., ANTS, CIVET, FreeSurfer), (ii) cortical parcellation (Desikan-Killiany-Tourville, Destrieux, Glasser), and (iii) quality control procedure (manual, automatic). We divide our statistical analyses by (i) method type, i.e., task-free (unsupervised) versus task-driven (supervised); and (ii) inference objective, i.e., neurobiological group differences versus individual prediction. RESULTS Results show that software, parcellation, and quality control significantly affect task-driven neurobiological inference. Additionally, software selection strongly affects neurobiological (i.e. group) and individual task-free analyses, and quality control alters the performance for the individual-centric prediction tasks. CONCLUSIONS This comparative performance evaluation partially explains the source of inconsistencies in neuroimaging findings. Furthermore, it underscores the need for more rigorous scientific workflows and accessible informatics resources to replicate and compare preprocessing pipelines to address the compounding problem of reproducibility in the age of large-scale, data-driven computational neuroscience.
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Affiliation(s)
- Nikhil Bhagwat
- Montreal Neurological Institute & Hospital, McGill University, Neurology and Neurosurgery, 3801 University Street, Montreal, H3A 2B4H3A 2B4, Montreal, QC, Canada
| | - Amadou Barry
- Lady Davis Institute for Medical Research, McGill University, Montreal, QC, Canada
| | - Erin W Dickie
- Kimel Family Translational Imaging-Genetics Research Lab, Centre for Addiction and Mental Health, Toronto, ON, Canada
| | - Shawn T Brown
- Montreal Neurological Institute & Hospital, McGill University, Neurology and Neurosurgery, 3801 University Street, Montreal, H3A 2B4H3A 2B4, Montreal, QC, Canada
| | - Gabriel A Devenyi
- Computational Brain Anatomy Laboratory, Douglas Mental Health Institute, Verdun, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
| | - Koji Hatano
- Montreal Neurological Institute & Hospital, McGill University, Neurology and Neurosurgery, 3801 University Street, Montreal, H3A 2B4H3A 2B4, Montreal, QC, Canada
| | - Elizabeth DuPre
- Montreal Neurological Institute & Hospital, McGill University, Neurology and Neurosurgery, 3801 University Street, Montreal, H3A 2B4H3A 2B4, Montreal, QC, Canada
| | - Alain Dagher
- Montreal Neurological Institute & Hospital, McGill University, Neurology and Neurosurgery, 3801 University Street, Montreal, H3A 2B4H3A 2B4, Montreal, QC, Canada
| | - Mallar Chakravarty
- Computational Brain Anatomy Laboratory, Douglas Mental Health Institute, Verdun, QC, Canada
- Department of Psychiatry, McGill University, Montreal, QC, Canada
- Department of Biomedical Engineering, McGill University, Montreal, QC, Canada
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, McGill University, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montreal, QC, Canada
- Gerald Bronfman Department of Oncology; Department of Epidemiology, Biostatistics & Occupational Health Department of Human Genetics, McGill University, Montreal, QC, Canada
| | - Bratislav Misic
- Montreal Neurological Institute & Hospital, McGill University, Neurology and Neurosurgery, 3801 University Street, Montreal, H3A 2B4H3A 2B4, Montreal, QC, Canada
| | - David N Kennedy
- Child and Adolescent Neurodevelopment Initiative, University of Massachusetts, Worcester, MA, USA
| | - Jean-Baptiste Poline
- Montreal Neurological Institute & Hospital, McGill University, Neurology and Neurosurgery, 3801 University Street, Montreal, H3A 2B4H3A 2B4, Montreal, QC, Canada
- Ludmer Centre for Neuroinformatics & Mental Health, McGill University, Montreal, QC, Canada
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38
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Ng KP, Pascoal TA, Mathotaarachchi S, Chan YH, Jiang L, Therriault J, Benedet AL, Shin M, Kandiah N, Greenwood CMT, Rosa-Neto P, Gauthier S. Neuropsychiatric symptoms are early indicators of an upcoming metabolic decline in Alzheimer's disease. Transl Neurodegener 2021; 10:1. [PMID: 33390174 PMCID: PMC7780680 DOI: 10.1186/s40035-020-00225-y] [Citation(s) in RCA: 13] [Impact Index Per Article: 4.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/03/2020] [Accepted: 12/01/2020] [Indexed: 11/26/2022] Open
Abstract
Background Neuropsychiatric symptoms (NPS) are increasingly recognized as early non-cognitive manifestations in the Alzheimer’s disease (AD) continuum. However, the role of NPS as an early marker of pathophysiological progression in AD remains unclear. Dominantly inherited AD (DIAD) mutation carriers are young individuals who are destined to develop AD in future due to the full penetrance of the genetic mutation. Hence, the study of DIAD mutation carriers enables the evaluation of the associations between pure AD pathophysiology and metabolic correlates of NPS without the confounding effects of co-existing pathologies. In this longitudinal study, we aimed to identify regional brain metabolic dysfunctions associated with NPS in cognitively intact DIAD mutation carriers. Methods We stratified 221 cognitively intact participants from the Dominantly Inherited Alzheimer’s Network according to their mutation carrier status. The interactions of NPS measured by the Neuropsychiatric Inventory-Questionnaire (NPI-Q), age, and estimated years to symptom onset (EYO) as a function of metabolism measured by [18F]flurodeoxyglucose ([18F]FDG) positron emission tomography, were evaluated by the mixed-effects regression model with family-level random effects in DIAD mutation carriers and non-carriers. Exploratory factor analysis was performed to identify the neuropsychiatric subsyndromes in DIAD mutation carriers using the NPI-Q sub-components. Then the effects of interactions between specific neuropsychiatric subsyndromes and EYO on metabolism were evaluated with the mixed-effects regression model. Results A total of 119 mutation carriers and 102 non-carriers were studied. The interaction of higher NPI-Q and shorter EYO was associated with more rapid declines of global and regional [18F]FDG uptake in the posterior cingulate and ventromedial prefrontal cortices, the bilateral parietal lobes and the right insula in DIAD mutation carriers. The neuropsychiatric subsyndromes of agitation, disinhibition, irritability and depression interacted with the EYO to drive the [18F]FDG uptake decline in the DIAD mutation carriers. The interaction of NPI and EYO was not associated with [18F]FDG uptake in DIAD mutation non-carriers. Conclusions The NPS in cognitively intact DIAD mutation carriers may be a clinical indicator of subsequent metabolic decline in brain networks vulnerable to AD, which supports the emerging conceptual framework that NPS represent early manifestations of neuronal injury in AD. Further studies using different methodological approaches to identify NPS in preclinical AD are needed to validate our findings.
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Affiliation(s)
- Kok Pin Ng
- Alzheimer's Disease Research Unit, McGill Centre for Studies in Aging, McGill University, Montréal, Québec, Canada.,Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Montreal, Canada.,Department of Neurology, National Neuroscience Institute, Singapore City, Singapore
| | - Tharick A Pascoal
- Alzheimer's Disease Research Unit, McGill Centre for Studies in Aging, McGill University, Montréal, Québec, Canada.,Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Montreal, Canada
| | - Sulantha Mathotaarachchi
- Alzheimer's Disease Research Unit, McGill Centre for Studies in Aging, McGill University, Montréal, Québec, Canada.,Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Montreal, Canada
| | - Yiong Huak Chan
- Biostatistics Unit, Yong Loo Lin School of Medicine, National University of Singapore, Singapore City, Singapore
| | - Lai Jiang
- Lady Davis Institute, McGill University, Montreal, Canada.,Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada
| | - Joseph Therriault
- Alzheimer's Disease Research Unit, McGill Centre for Studies in Aging, McGill University, Montréal, Québec, Canada.,Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Montreal, Canada
| | - Andrea L Benedet
- Alzheimer's Disease Research Unit, McGill Centre for Studies in Aging, McGill University, Montréal, Québec, Canada.,Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Montreal, Canada
| | - Monica Shin
- Alzheimer's Disease Research Unit, McGill Centre for Studies in Aging, McGill University, Montréal, Québec, Canada.,Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Montreal, Canada
| | - Nagaendran Kandiah
- Department of Neurology, National Neuroscience Institute, Singapore City, Singapore
| | - Celia M T Greenwood
- Lady Davis Institute, McGill University, Montreal, Canada.,Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada
| | - Pedro Rosa-Neto
- Alzheimer's Disease Research Unit, McGill Centre for Studies in Aging, McGill University, Montréal, Québec, Canada.,Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Montreal, Canada
| | - Serge Gauthier
- Alzheimer's Disease Research Unit, McGill Centre for Studies in Aging, McGill University, Montréal, Québec, Canada. .,Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Montreal, Canada.
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Szekely E, Neumann A, Sallis H, Jolicoeur-Martineau A, Verhulst FC, Meaney MJ, Pearson RM, Levitan RD, Kennedy JL, Lydon JE, Steiner M, Greenwood CMT, Tiemeier H, Evans J, Wazana A. Maternal Prenatal Mood, Pregnancy-Specific Worries, and Early Child Psychopathology: Findings From the DREAM BIG Consortium. J Am Acad Child Adolesc Psychiatry 2021; 60:186-197. [PMID: 32278003 DOI: 10.1016/j.jaac.2020.02.017] [Citation(s) in RCA: 22] [Impact Index Per Article: 7.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 08/19/2019] [Revised: 02/06/2020] [Accepted: 03/11/2020] [Indexed: 10/24/2022]
Abstract
OBJECTIVE Few studies have attempted to identify how distinct dimensions of maternal prenatal affective symptoms relate to offspring psychopathology. We defined latent dimensions of women's prenatal affective symptoms and pregnancy-specific worries to examine their association with early offspring psychopathology in three prenatal cohorts. METHOD Data were used from three cohorts of the DREAM-BIG consortium: Avon Longitudinal Study of Parents and Children (ALSPAC [N = 12,515]), Generation R (N = 6,803), and the Canadian prenatal cohort Maternal Adversity, Vulnerability, and Neurodevelopment (MAVAN [N = 578]). Maternal prenatal affective symptoms and pregnancy-specific worries were assessed using different measures in each cohort. Through confirmatory factor analyses, we determined whether comparable latent dimensions of prenatal maternal affective symptoms existed across the cohorts. We used structural equation models to examine cohort-specific associations between these dimensions and offspring psychopathology at 4 to 8 years of age (general psychopathology, specific internalizing and externalizing previously derived using confirmatory factor analyses). Cohort-based estimates were meta-analyzed using inverse variance-weighing. RESULTS Four prenatal maternal factors were similar in all cohorts: a general affective symptoms factor and three specific factors-an anxiety/depression factor, a somatic factor, and a pregnancy-specific worries factor. In meta-analyses, both the general affective symptoms factor and pregnancy-specific worries factor were independently associated with offspring general psychopathology. The general affective symptoms factor was further associated with offspring specific internalizing problems. There were no associations with specific externalizing problems. CONCLUSION These replicated findings of independent and adverse effects for prenatal general affective symptoms and pregnancy-specific worries on child mental health support the need for specific interventions in pregnancy.
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Affiliation(s)
- Eszter Szekely
- McGill University Faculty of Medicine, Montreal, Quebec, Canada; Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - Alexander Neumann
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Erasmus University Medical Center-Sophia Children's Hospital, Rotterdam, The Netherlands
| | - Hannah Sallis
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom; MRC Integrative Epidemiology Unit, University of Bristol, Bristol, United Kingdom
| | | | - Frank C Verhulst
- Erasmus University Medical Center-Sophia Children's Hospital, Rotterdam, The Netherlands; Child and Adolescent Mental Health Center, Mental Health Services, Capital Region of Denmark, Copenhagen, Denmark, and the Faculty of Health and Medical Sciences, University of Copenhagen, Denmark
| | - Michael J Meaney
- McGill University Faculty of Medicine, Montreal, Quebec, Canada; Douglas Mental Health Institute, Montreal, Quebec, Canada, and Singapore Institute for Clinical Sciences, Singapore City, Singapore
| | - Rebecca M Pearson
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Robert D Levitan
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada; University of Toronto, Toronto, Ontario, Canada
| | - James L Kennedy
- Centre for Addiction and Mental Health, Toronto, Ontario, Canada
| | - John E Lydon
- McGill University Faculty of Medicine, Montreal, Quebec, Canada
| | | | - Celia M T Greenwood
- McGill University Faculty of Medicine, Montreal, Quebec, Canada; Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada
| | - Henning Tiemeier
- Erasmus University Medical Center-Sophia Children's Hospital, Rotterdam, The Netherlands; Harvard T. H. Chan School of Public Health, Boston, Massachusetts
| | - Jonathan Evans
- Centre for Academic Mental Health, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Ashley Wazana
- McGill University Faculty of Medicine, Montreal, Quebec, Canada; Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Quebec, Canada; Centre for Child Development and Mental Health, Jewish General Hospital, Montreal, Quebec, Canada.
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40
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Lin YC, Brooks JD, Bull SB, Gagnon F, Greenwood CMT, Hung RJ, Lawless J, Paterson AD, Sun L, Strug LJ. Statistical power in COVID-19 case-control host genomic study design. Genome Med 2020; 12:115. [PMID: 33371892 PMCID: PMC7768597 DOI: 10.1186/s13073-020-00818-2] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/01/2020] [Accepted: 12/07/2020] [Indexed: 12/21/2022] Open
Abstract
The identification of genetic variation that directly impacts infection susceptibility to SARS-CoV-2 and disease severity of COVID-19 is an important step towards risk stratification, personalized treatment plans, therapeutic, and vaccine development and deployment. Given the importance of study design in infectious disease genetic epidemiology, we use simulation and draw on current estimates of exposure, infectivity, and test accuracy of COVID-19 to demonstrate the feasibility of detecting host genetic factors associated with susceptibility and severity in published COVID-19 study designs. We demonstrate that limited phenotypic data and exposure/infection information in the early stages of the pandemic significantly impact the ability to detect most genetic variants with moderate effect sizes, especially when studying susceptibility to SARS-CoV-2 infection. Our insights can aid in the interpretation of genetic findings emerging in the literature and guide the design of future host genetic studies.
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Affiliation(s)
- Yu-Chung Lin
- Dalla Lana School of Public Health, University of Toronto, Room 500, 155 College St, Toronto, ON, M5T3M7, Canada
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Room 12.9801, 686 Bay Street, Toronto, ON, M5G0A4, Canada
| | - Jennifer D Brooks
- Dalla Lana School of Public Health, University of Toronto, Room 500, 155 College St, Toronto, ON, M5T3M7, Canada
| | - Shelley B Bull
- Dalla Lana School of Public Health, University of Toronto, Room 500, 155 College St, Toronto, ON, M5T3M7, Canada
- Prosserman Centre for Population Health Research, Lunenfeld-Tanenbaum Research Institute, Sinai Health System, Toronto, ON, Canada
| | - France Gagnon
- Dalla Lana School of Public Health, University of Toronto, Room 500, 155 College St, Toronto, ON, M5T3M7, Canada
| | - Celia M T Greenwood
- Gerald Bronfman Department of Oncology, Department of Epidemiology, Biostatistics & Occupational Health, Department of Human Genetics, McGill University, Montreal, QC, Canada
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
| | - Rayjean J Hung
- Dalla Lana School of Public Health, University of Toronto, Room 500, 155 College St, Toronto, ON, M5T3M7, Canada
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Room 12.9801, 686 Bay Street, Toronto, ON, M5G0A4, Canada
| | - Jerald Lawless
- Department of Statistics and Actuarial Science, University of Waterloo, Waterloo, ON, Canada
| | - Andrew D Paterson
- Dalla Lana School of Public Health, University of Toronto, Room 500, 155 College St, Toronto, ON, M5T3M7, Canada
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Room 12.9801, 686 Bay Street, Toronto, ON, M5G0A4, Canada
| | - Lei Sun
- Dalla Lana School of Public Health, University of Toronto, Room 500, 155 College St, Toronto, ON, M5T3M7, Canada
- Department of Statistical Sciences, University of Toronto, 9th Floor, Ontario Power Building 700 University Ave, Toronto, ON, M5G 1Z5, Canada
| | - Lisa J Strug
- Dalla Lana School of Public Health, University of Toronto, Room 500, 155 College St, Toronto, ON, M5T3M7, Canada.
- Program in Genetics and Genome Biology, The Hospital for Sick Children, Room 12.9801, 686 Bay Street, Toronto, ON, M5G0A4, Canada.
- Department of Statistical Sciences, University of Toronto, 9th Floor, Ontario Power Building 700 University Ave, Toronto, ON, M5G 1Z5, Canada.
- The Centre for Applied Genomics, The Hospital for Sick Children, Toronto, ON, Canada.
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41
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Lu T, Zhou S, Wu H, Forgetta V, Greenwood CMT, Richards JB. Individuals with common diseases but with a low polygenic risk score could be prioritized for rare variant screening. Genet Med 2020; 23:508-515. [PMID: 33110269 DOI: 10.1038/s41436-020-01007-7] [Citation(s) in RCA: 31] [Impact Index Per Article: 7.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/02/2020] [Accepted: 10/05/2020] [Indexed: 02/06/2023] Open
Abstract
PURPOSE Identifying rare genetic causes of common diseases can improve diagnostic and treatment strategies, but incurs high costs. We tested whether individuals with common disease and low polygenic risk score (PRS) for that disease generated from less expensive genome-wide genotyping data are more likely to carry rare pathogenic variants. METHODS We identified patients with one of five common complex diseases among 44,550 individuals who underwent exome sequencing in the UK Biobank. We derived PRS for these five diseases, and identified pathogenic rare variant heterozygotes. We tested whether individuals with disease and low PRS were more likely to carry rare pathogenic variants. RESULTS While rare pathogenic variants conferred, at most, 5.18-fold (95% confidence interval [CI]: 2.32-10.13) increased odds of disease, a standard deviation increase in PRS, at most, increased the odds of disease by 5.25-fold (95% CI: 5.06-5.45). Among diseased patients, a standard deviation decrease in the PRS was associated with, at most, 2.82-fold (95% CI: 1.14-7.46) increased odds of identifying rare variant heterozygotes. CONCLUSION Rare pathogenic variants were more prevalent among affected patients with a low PRS. Therefore, prioritizing individuals for sequencing who have disease but low PRS may increase the yield of sequencing studies to identify rare variant heterozygotes.
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Affiliation(s)
- Tianyuan Lu
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.,Quantitative Life Sciences Program, McGill University, Montreal, QC, Canada
| | - Sirui Zhou
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
| | - Haoyu Wu
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.,Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada
| | - Vincenzo Forgetta
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
| | - Celia M T Greenwood
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.,Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.,Department of Human Genetics, McGill University, Montreal, QC, Canada.,Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada
| | - J Brent Richards
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada. .,Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada. .,Department of Human Genetics, McGill University, Montreal, QC, Canada. .,Department of Twin Research and Genetic Epidemiology, King's College London, London, United Kingdom.
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42
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Jiang L, Huguet G, Schramm C, Ciampi A, Main A, Passo C, Jean‐Louis M, Auger M, Schumann G, Porteous D, Jacquemont S, Greenwood CMT. Estimating the effects of copy‐number variants on intelligence using hierarchical Bayesian models. Genet Epidemiol 2020; 44:825-840. [DOI: 10.1002/gepi.22344] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Revised: 06/24/2020] [Accepted: 07/21/2020] [Indexed: 01/01/2023]
Affiliation(s)
- Lai Jiang
- Lady Davis Institute Jewish General Hospital Montreal Canada
- Department of Epidemiology, Biostatistics and Occupational Health McGill University Montreal Canada
- Centre Hospitalier Universitaire (CHU) Sainte‐Justine Montreal Canada
| | - Guillaume Huguet
- Centre Hospitalier Universitaire (CHU) Sainte‐Justine Montreal Canada
- Universite de Montreal Montreal Canada
| | - Catherine Schramm
- Lady Davis Institute Jewish General Hospital Montreal Canada
- Centre Hospitalier Universitaire (CHU) Sainte‐Justine Montreal Canada
- Universite de Montreal Montreal Canada
| | - Antonio Ciampi
- Department of Epidemiology, Biostatistics and Occupational Health McGill University Montreal Canada
| | - Antoine Main
- Centre Hospitalier Universitaire (CHU) Sainte‐Justine Montreal Canada
- Universite de Montreal Montreal Canada
- Department of Decision Sciences Hautes etudes commerciales de Montreal (HEC) Montreal Canada
| | - Claudine Passo
- Centre Hospitalier Universitaire (CHU) Sainte‐Justine Montreal Canada
- Universite de Montreal Montreal Canada
| | - Martineau Jean‐Louis
- Centre Hospitalier Universitaire (CHU) Sainte‐Justine Montreal Canada
- Universite de Montreal Montreal Canada
| | - Maude Auger
- Centre Hospitalier Universitaire (CHU) Sainte‐Justine Montreal Canada
- Universite de Montreal Montreal Canada
| | - Gunter Schumann
- Institute of Psychiatry, Psychology, and Neuroscience King's College London London UK
| | - David Porteous
- Department of Psychology, Lothian Birth Cohorts Group, School of Philosophy, Psychology and Language Sciences The University of Edinburgh Edinburgh UK
- Medical Genetics Section, Centre for Genomic Experimental Medicine, MRC Institute of Genetics Molecular Medicine, Western General Hospital The University of Edinburgh Edinburgh UK
- Generation Scotland, Centre for Genomic and Experimental Medicine University of Edinburgh Edinburgh UK
| | - Sébastien Jacquemont
- Centre Hospitalier Universitaire (CHU) Sainte‐Justine Montreal Canada
- Universite de Montreal Montreal Canada
| | - Celia M. T. Greenwood
- Lady Davis Institute Jewish General Hospital Montreal Canada
- Department of Epidemiology, Biostatistics and Occupational Health McGill University Montreal Canada
- Gerald Bronfman Department of Oncology McGill University Montreal Canada
- Department of Human Genetics McGill University Montreal Canada
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43
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Forgetta V, Keller-Baruch J, Forest M, Durand A, Bhatnagar S, Kemp JP, Nethander M, Evans D, Morris JA, Kiel DP, Rivadeneira F, Johansson H, Harvey NC, Mellström D, Karlsson M, Cooper C, Evans DM, Clarke R, Kanis JA, Orwoll E, McCloskey EV, Ohlsson C, Pineau J, Leslie WD, Greenwood CMT, Richards JB. Development of a polygenic risk score to improve screening for fracture risk: A genetic risk prediction study. PLoS Med 2020; 17:e1003152. [PMID: 32614825 PMCID: PMC7331983 DOI: 10.1371/journal.pmed.1003152] [Citation(s) in RCA: 41] [Impact Index Per Article: 10.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 12/05/2019] [Accepted: 06/03/2020] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Since screening programs identify only a small proportion of the population as eligible for an intervention, genomic prediction of heritable risk factors could decrease the number needing to be screened by removing individuals at low genetic risk. We therefore tested whether a polygenic risk score for heel quantitative ultrasound speed of sound (SOS)-a heritable risk factor for osteoporotic fracture-can identify low-risk individuals who can safely be excluded from a fracture risk screening program. METHODS AND FINDINGS A polygenic risk score for SOS was trained and selected in 2 separate subsets of UK Biobank (comprising 341,449 and 5,335 individuals). The top-performing prediction model was termed "gSOS", and its utility in fracture risk screening was tested in 5 validation cohorts using the National Osteoporosis Guideline Group clinical guidelines (N = 10,522 eligible participants). All individuals were genome-wide genotyped and had measured fracture risk factors. Across the 5 cohorts, the average age ranged from 57 to 75 years, and 54% of studied individuals were women. The main outcomes were the sensitivity and specificity to correctly identify individuals requiring treatment with and without genetic prescreening. The reference standard was a bone mineral density (BMD)-based Fracture Risk Assessment Tool (FRAX) score. The secondary outcomes were the proportions of the screened population requiring clinical-risk-factor-based FRAX (CRF-FRAX) screening and BMD-based FRAX (BMD-FRAX) screening. gSOS was strongly correlated with measured SOS (r2 = 23.2%, 95% CI 22.7% to 23.7%). Without genetic prescreening, guideline recommendations achieved a sensitivity and specificity for correct treatment assignment of 99.6% and 97.1%, respectively, in the validation cohorts. However, 81% of the population required CRF-FRAX tests, and 37% required BMD-FRAX tests to achieve this accuracy. Using gSOS in prescreening and limiting further assessment to those with a low gSOS resulted in small changes to the sensitivity and specificity (93.4% and 98.5%, respectively), but the proportions of individuals requiring CRF-FRAX tests and BMD-FRAX tests were reduced by 37% and 41%, respectively. Study limitations include a reliance on cohorts of predominantly European ethnicity and use of a proxy of fracture risk. CONCLUSIONS Our results suggest that the use of a polygenic risk score in fracture risk screening could decrease the number of individuals requiring screening tests, including BMD measurement, while maintaining a high sensitivity and specificity to identify individuals who should be recommended an intervention.
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Affiliation(s)
- Vincenzo Forgetta
- Centre for Clinical Epidemiology, Department of Medicine, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
| | | | - Marie Forest
- Centre for Clinical Epidemiology, Department of Medicine, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
| | - Audrey Durand
- School of Computer Science, McGill University, Montréal, Québec, Canada
| | - Sahir Bhatnagar
- Centre for Clinical Epidemiology, Department of Medicine, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
| | - John P. Kemp
- University of Queensland Diamantina Institute, University of Queensland, Woolloongabba, Queensland, Australia
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Maria Nethander
- Bioinformatics Core Facility, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute for Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Daniel Evans
- California Pacific Medical Center Research Institute, San Francisco, California, United States of America
| | - John A. Morris
- Centre for Clinical Epidemiology, Department of Medicine, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
| | - Douglas P. Kiel
- Institute for Aging Research, Hebrew SeniorLife, Department of Medicine, Beth Israel Deaconess Medical Center and Harvard Medical School, Broad Institute of MIT & Harvard University, Boston, Massachusetts, United States of America
| | | | - Helena Johansson
- Centre for Metabolic Bone Diseases, University of Sheffield, Sheffield, United Kingdom
- Australian Catholic University, Melbourne, Victoria, Australia
| | - Nicholas C. Harvey
- Medical Research Council Lifecourse Epidemiology Unit, University of Southampton, Southampton, United Kingdom
- National Institute for Health Research Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom
| | - Dan Mellström
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute for Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Magnus Karlsson
- Department of Orthopaedics and Clinical Sciences, Lund University, Skane University Hospital, Malmö, Sweden
| | - Cyrus Cooper
- Medical Research Council Lifecourse Epidemiology Unit, University of Southampton, Southampton, United Kingdom
- National Institute for Health Research Southampton Biomedical Research Centre, University of Southampton and University Hospital Southampton NHS Foundation Trust, Southampton, United Kingdom
- National Institute for Health Research Oxford Biomedical Research Centre, University of Oxford, Oxford, United Kingdom
| | - David M. Evans
- University of Queensland Diamantina Institute, University of Queensland, Woolloongabba, Queensland, Australia
- Medical Research Council Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, United Kingdom
| | - Robert Clarke
- Clinical Trial Service Unit and Epidemiological Studies Unit, University of Oxford, Oxford, United Kingdom
| | - John A. Kanis
- Centre for Metabolic Bone Diseases, University of Sheffield, Sheffield, United Kingdom
- Australian Catholic University, Melbourne, Victoria, Australia
| | - Eric Orwoll
- Bone and Mineral Unit, Oregon Health & Science University, Portland, Oregon, United States of America
- Department of Medicine, Oregon Health & Science University, Portland, Oregon, United States of America
| | - Eugene V. McCloskey
- Mellanby Centre for Bone Research, Centre for Integrated Research in Musculoskeletal Ageing, University of Sheffield and Sheffield Teaching Hospitals Foundation Trust, Sheffield, United Kingdom
| | - Claes Ohlsson
- Centre for Bone and Arthritis Research, Department of Internal Medicine and Clinical Nutrition, Institute for Medicine, Sahlgrenska Academy, University of Gothenburg, Gothenburg, Sweden
| | - Joelle Pineau
- School of Computer Science, McGill University, Montréal, Québec, Canada
| | - William D. Leslie
- Department of Medicine, University of Manitoba, Winnipeg, Manitoba, Canada
| | - Celia M. T. Greenwood
- Centre for Clinical Epidemiology, Department of Medicine, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montréal, Québec, Canada
- Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montréal, Québec, Canada
| | - J. Brent Richards
- Centre for Clinical Epidemiology, Department of Medicine, Lady Davis Institute, Jewish General Hospital, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
- Department of Twin Research and Genetic Epidemiology, King’s College London, London, United Kingdom
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44
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Jiang L, Greenwood CMT, Yao W, Li L. Bayesian Hyper-LASSO Classification for Feature Selection with Application to Endometrial Cancer RNA-seq Data. Sci Rep 2020; 10:9747. [PMID: 32546735 PMCID: PMC7297975 DOI: 10.1038/s41598-020-66466-z] [Citation(s) in RCA: 5] [Impact Index Per Article: 1.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/18/2019] [Accepted: 04/29/2020] [Indexed: 11/30/2022] Open
Abstract
Feature selection is demanded in many modern scientific research problems that use high-dimensional data. A typical example is to identify gene signatures that are related to a certain disease from high-dimensional gene expression data. The expression of genes may have grouping structures, for example, a group of co-regulated genes that have similar biological functions tend to have similar expressions. Thus it is preferable to take the grouping structure into consideration to select features. In this paper, we propose a Bayesian Robit regression method with Hyper-LASSO priors (shortened by BayesHL) for feature selection in high dimensional genomic data with grouping structure. The main features of BayesHL include that it discards more aggressively unrelated features than LASSO, and it makes feature selection within groups automatically without a pre-specified grouping structure. We apply BayesHL in gene expression analysis to identify subsets of genes that contribute to the 5-year survival outcome of endometrial cancer (EC) patients. Results show that BayesHL outperforms alternative methods (including LASSO, group LASSO, supervised group LASSO, penalized logistic regression, random forest, neural network, XGBoost and knockoff) in terms of predictive power, sparsity and the ability to uncover grouping structure, and provides insight into the mechanisms of multiple genetic pathways leading to differentiated EC survival outcome.
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Affiliation(s)
- Lai Jiang
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada.
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada.
| | - Celia M T Greenwood
- Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montreal, Canada
| | - Weixin Yao
- Department of Statistics, University of California, Riverside, US
| | - Longhai Li
- Department of Mathematics and Statistics, University of Saskatchewan, Saskatoon, Canada.
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45
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Zhao K, Oualkacha K, Lakhal-Chaieb L, Labbe A, Klein K, Ciampi A, Hudson M, Colmegna I, Pastinen T, Zhang T, Daley D, Greenwood CMT. A novel statistical method for modeling covariate effects in bisulfite sequencing derived measures of DNA methylation. Biometrics 2020; 77:424-438. [PMID: 32438470 PMCID: PMC8359306 DOI: 10.1111/biom.13307] [Citation(s) in RCA: 2] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 05/15/2019] [Revised: 02/28/2020] [Accepted: 05/08/2020] [Indexed: 01/24/2023]
Abstract
Identifying disease-associated changes in DNA methylation can help us gain a better understanding of disease etiology. Bisulfite sequencing allows the generation of high-throughput methylation profiles at single-base resolution of DNA. However, optimally modeling and analyzing these sparse and discrete sequencing data is still very challenging due to variable read depth, missing data patterns, long-range correlations, data errors, and confounding from cell type mixtures. We propose a regression-based hierarchical model that allows covariate effects to vary smoothly along genomic positions and we have built a specialized EM algorithm, which explicitly allows for experimental errors and cell type mixtures, to make inference about smooth covariate effects in the model. Simulations show that the proposed method provides accurate estimates of covariate effects and captures the major underlying methylation patterns with excellent power. We also apply our method to analyze data from rheumatoid arthritis patients and controls. The method has been implemented in R package SOMNiBUS.
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Affiliation(s)
- Kaiqiong Zhao
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.,Lady Davis Institute for Medical Research, Montreal, QC, Canada
| | - Karim Oualkacha
- Département de Mathématiques, Université du Québec à Montrèal, Montreal, QC, Canada
| | - Lajmi Lakhal-Chaieb
- Département de Mathématiques et de Statistique, Université Laval, Quebec City, QC, Canada
| | - Aurélie Labbe
- Département des Sciences de la Décision, HEC Montrèal, Montreal, QC, Canada
| | - Kathleen Klein
- Lady Davis Institute for Medical Research, Montreal, QC, Canada
| | - Antonio Ciampi
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.,Lady Davis Institute for Medical Research, Montreal, QC, Canada
| | - Marie Hudson
- Lady Davis Institute for Medical Research, Montreal, QC, Canada.,Department of Medicine, McGill University, Montreal, QC, Canada
| | - Inés Colmegna
- Department of Medicine, McGill University, Montreal, QC, Canada.,The Research Institute of the McGill University Health Centre, Montreal, QC, Canada
| | - Tomi Pastinen
- Center for Pediatric Genomic Medicine, Children's Mercy Kansas City, Kansas City, MO, USA
| | - Tieyuan Zhang
- Department of Psychiatry, Douglas Mental Health University Institute, McGill University, Montreal, QC, Canada
| | - Denise Daley
- The Centre for Heart Lung Innovation, and Department of Medicine, University of British Columbia, Vancouver, BC, Canada
| | - Celia M T Greenwood
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, QC, Canada.,Lady Davis Institute for Medical Research, Montreal, QC, Canada.,Department of Human Genetics and Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada
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46
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Bhatnagar SR, Yang Y, Lu T, Schurr E, Loredo-Osti JC, Forest M, Oualkacha K, Greenwood CMT. Simultaneous SNP selection and adjustment for population structure in high dimensional prediction models. PLoS Genet 2020; 16:e1008766. [PMID: 32365090 PMCID: PMC7224575 DOI: 10.1371/journal.pgen.1008766] [Citation(s) in RCA: 3] [Impact Index Per Article: 0.8] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/12/2019] [Revised: 05/14/2020] [Accepted: 04/08/2020] [Indexed: 12/23/2022] Open
Abstract
Complex traits are known to be influenced by a combination of environmental factors and rare and common genetic variants. However, detection of such multivariate associations can be compromised by low statistical power and confounding by population structure. Linear mixed effects models (LMM) can account for correlations due to relatedness but have not been applicable in high-dimensional (HD) settings where the number of fixed effect predictors greatly exceeds the number of samples. False positives or false negatives can result from two-stage approaches, where the residuals estimated from a null model adjusted for the subjects' relationship structure are subsequently used as the response in a standard penalized regression model. To overcome these challenges, we develop a general penalized LMM with a single random effect called ggmix for simultaneous SNP selection and adjustment for population structure in high dimensional prediction models. We develop a blockwise coordinate descent algorithm with automatic tuning parameter selection which is highly scalable, computationally efficient and has theoretical guarantees of convergence. Through simulations and three real data examples, we show that ggmix leads to more parsimonious models compared to the two-stage approach or principal component adjustment with better prediction accuracy. Our method performs well even in the presence of highly correlated markers, and when the causal SNPs are included in the kinship matrix. ggmix can be used to construct polygenic risk scores and select instrumental variables in Mendelian randomization studies. Our algorithms are available in an R package available on CRAN (https://cran.r-project.org/package=ggmix).
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Affiliation(s)
- Sahir R. Bhatnagar
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec, Canada
- Department of Diagnostic Radiology, McGill University, Montréal, Québec, Canada
| | - Yi Yang
- Department of Mathematics and Statistics, McGill University, Montréal, Québec, Canada
| | - Tianyuan Lu
- Quantitative Life Sciences, McGill University, Montréal, Québec, Canada
- Lady Davis Institute, Jewish General Hospital, Montréal, Québec, Canada
| | - Erwin Schurr
- Department of Medicine, McGill University, Montréal, Québec, Canada
| | - JC Loredo-Osti
- Department of Mathematics and Statistics, Memorial University, St. John’s, Newfoundland and Labrador, Canada
| | - Marie Forest
- École de Technologie Supérieure, Montréal, Québec, Canada
| | - Karim Oualkacha
- Département de Mathématiques, Université du Québec à Montréal, Montréal, Québec, Canada
| | - Celia M. T. Greenwood
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, Québec, Canada
- Quantitative Life Sciences, McGill University, Montréal, Québec, Canada
- Lady Davis Institute, Jewish General Hospital, Montréal, Québec, Canada
- Gerald Bronfman Department of Oncology, McGill University, Montréal, Québec, Canada
- Department of Human Genetics, McGill University, Montréal, Québec, Canada
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47
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McGregor K, Labbe A, Greenwood CMT. MDiNE: a model to estimate differential co-occurrence networks in microbiome studies. Bioinformatics 2020; 36:1840-1847. [PMID: 31697315 PMCID: PMC7075537 DOI: 10.1093/bioinformatics/btz824] [Citation(s) in RCA: 21] [Impact Index Per Article: 5.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/07/2019] [Revised: 10/26/2019] [Accepted: 11/05/2019] [Indexed: 01/26/2023] Open
Abstract
MOTIVATION The human microbiota is the collection of microorganisms colonizing the human body, and plays an integral part in human health. A growing trend in microbiome analysis is to construct a network to estimate the co-occurrence patterns among taxa through precision matrices. Existing methods do not facilitate investigation into how these networks change with respect to covariates. RESULTS We propose a new model called Microbiome Differential Network Estimation (MDiNE) to estimate network changes with respect to a binary covariate. The counts of individual taxa in the samples are modeled through a multinomial distribution whose probabilities depend on a latent Gaussian random variable. A sparse precision matrix over all the latent terms determines the co-occurrence network among taxa. The model fit is obtained and evaluated using Hamiltonian Monte Carlo methods. The performance of our model is evaluated through an extensive simulation study and is shown to outperform existing methods in terms of estimation of network parameters. We also demonstrate an application of the model to estimate changes in the intestinal microbial network topology with respect to Crohn's disease. AVAILABILITY AND IMPLEMENTATION MDiNE is implemented in a freely available R package: https://github.com/kevinmcgregor/mdine. SUPPLEMENTARY INFORMATION Supplementary data are available at Bioinformatics online.
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Affiliation(s)
- Kevin McGregor
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada
- Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, Montréal, QC, Canada
| | - Aurélie Labbe
- Département de sciences de la décision, HEC Montréal, Montréal, QC, Canada
| | - Celia M T Greenwood
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montréal, QC, Canada
- Centre for Clinical Epidemiology, Lady Davis Institute, Jewish General Hospital, Montréal, QC, Canada
- Gerald Bronfman Department of Oncology, McGill Department of Human Genetics, McGill University, Montréal, QC, Canada
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48
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Lu T, Forgetta V, Yu OHY, Mokry L, Gregory M, Thanassoulis G, Greenwood CMT, Richards JB. Polygenic risk for coronary heart disease acts through atherosclerosis in type 2 diabetes. Cardiovasc Diabetol 2020; 19:12. [PMID: 32000781 PMCID: PMC6993460 DOI: 10.1186/s12933-020-0988-9] [Citation(s) in RCA: 20] [Impact Index Per Article: 5.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 11/05/2019] [Accepted: 01/14/2020] [Indexed: 12/25/2022] Open
Abstract
Background Type 2 diabetes increases the risk of coronary heart disease (CHD), yet the mechanisms involved remain poorly described. Polygenic risk scores (PRS) provide an opportunity to understand risk factors since they reflect etiologic pathways from the entire genome. We therefore tested whether a PRS for CHD influenced risk of CHD in individuals with type 2 diabetes and which risk factors were associated with this PRS. Methods We tested the association of a CHD PRS with CHD and its traditional clinical risk factors amongst individuals with type 2 diabetes in UK Biobank (N = 21,102). We next tested the association of the CHD PRS with atherosclerotic burden in a cohort of 352 genome-wide genotyped participants with type 2 diabetes who had undergone coronary angiograms. Results In the UK Biobank we found that the CHD PRS was strongly associated with CHD amongst individuals with type 2 diabetes (OR per standard deviation increase = 1.50; p = 1.5 × 10− 59). But this CHD PRS was, at best, only weakly associated with traditional clinical risk factors, such as hypertension, hyperlipidemia, glycemic control, obesity and smoking. Conversely, in the angiographic cohort, the CHD PRS was strongly associated with multivessel stenosis (OR = 1.65; p = 4.9 × 10− 4) and increased number of major stenotic lesions (OR = 1.35; p = 9.4 × 10− 3). Conclusions Polygenic predisposition to CHD is strongly associated with atherosclerotic burden in individuals with type 2 diabetes and this effect is largely independent of traditional clinical risk factors. This suggests that genetic risk for CHD acts through atherosclerosis with little effect on most traditional risk factors, providing the opportunity to explore new biological pathways.
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Affiliation(s)
- Tianyuan Lu
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.,Quantitative Life Sciences Program, McGill University, Montreal, QC, Canada
| | - Vincenzo Forgetta
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
| | - Oriana H Y Yu
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.,Division of Endocrinology, Jewish General Hospital, Montreal, QC, Canada
| | - Lauren Mokry
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
| | - Madeline Gregory
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada
| | - George Thanassoulis
- Department of Medicine, McGill University, Montreal, QC, Canada.,Preventive and Genomic Cardiology, McGill University Health Centre and Research Institute, Montreal, QC, Canada
| | - Celia M T Greenwood
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada.,Department of Human Genetics, McGill University, Montreal, QC, Canada.,Gerald Bronfman Department of Oncology, McGill University, Montreal, QC, Canada.,Department of Epidemiology, Biostatistics & Occupational Health, McGill University, Montreal, QC, Canada
| | - J Brent Richards
- Centre for Clinical Epidemiology, Lady Davis Institute for Medical Research, Jewish General Hospital, Montreal, QC, Canada. .,Department of Human Genetics, McGill University, Montreal, QC, Canada. .,Department of Twin Research and Genetic Epidemiology, King's College London, Strand, London, UK. .,Jewish General Hospital, Room H-413, 3755 Côte Sainte-Catherine Road, Montreal, QC, H3T 1E2, Canada.
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Greenwood CMT. At the interface. Genet Epidemiol 2020; 44:119-124. [PMID: 31922290 DOI: 10.1002/gepi.22277] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/09/2019] [Accepted: 12/23/2019] [Indexed: 11/11/2022]
Affiliation(s)
- Celia M T Greenwood
- Department of Clinical Epidemiology, Lady Davis Institute for Medical Research, Montreal, Quebec, Canada.,Gerald Bronfman Department of Oncology, McGill University, Montreal, Quebec, Canada.,Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Montreal, Quebec, Canada.,Department of Human Genetics, McGill University, Montreal, Quebec, Canada
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Ng KP, Richard-Devantoy S, Bertrand JA, Jiang L, Pascoal TA, Mathotaarachchi S, Therriault J, Yatawara C, Kandiah N, Greenwood CMT, Rosa-Neto P, Gauthier S. Suicidal ideation is common in autosomal dominant Alzheimer's disease at-risk persons. Int J Geriatr Psychiatry 2020; 35:60-68. [PMID: 31642105 PMCID: PMC7232741 DOI: 10.1002/gps.5215] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/06/2019] [Accepted: 09/15/2019] [Indexed: 11/08/2022]
Abstract
OBJECTIVES To study the frequency of suicidal ideation and its association with clinical and neurobiological correlates among cognitively intact autosomal dominant Alzheimer's disease (ADAD) at-risk individuals. METHODS/DESIGN In a cross-sectional study of 183 ADAD at-risk individuals (91 mutation carriers and 92 noncarriers), we compared the frequency of suicidal ideation among carriers and noncarriers. Linear mixed-effects models with family-level random effects evaluated the relationships between geriatric depression scale (GDS), neuropsychiatric inventory-questionnaire (NPI-Q), and suicidal ideation scores among all ADAD at-risk individuals. An interaction term was added to the regression models to evaluate the interactions of suicidal ideation and mutation status on neuropsychiatric symptoms. RESULTS Twenty-six (14.20%) ADAD at-risk individuals (13 [14.28%] carriers and 13 [14.13%] noncarriers) had suicidal ideation. The frequency of suicidal ideation did not differ between carriers and noncarriers. Suicidal ideation was associated with higher GDS among all ADAD at-risk individuals. When stratified into mutation carrier status, noncarriers with suicidal ideation had higher GDS than carriers. There was no statistically significant association between suicidal ideation and NPI-Q among ADAD at-risk individuals. Awareness of mutation status, neuropsychological performances, and cerebrospinal fluid AD biomarkers were not associated with suicidal ideation among carriers and noncarriers. CONCLUSIONS Suicidal ideation is common among cognitively intact ADAD at-risk individuals. While ADAD at-risk individuals with suicidal ideation have greater depressive symptoms, noncarriers with suicidal ideation have higher GDS scores than carriers. Interestingly, awareness of the mutation status was not associated with suicidal ideation in our study. Early identification of suicidal thoughts can facilitate timely interventions to prevent suicidal behaviours. Keywords autosomal dominant Alzheimer's diseasedominantly inherited Alzheimer's networkneuropsychiatric symptomssuicidal ideation.
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Affiliation(s)
- Kok Pin Ng
- Alzheimer's Disease Research Unit, McGill Centre for Studies in Aging, McGill University, Québec, Canada
- Department of Neurology, National Neuroscience Institute, Singapore
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Québec, Canada
| | - Stéphane Richard-Devantoy
- Department of Psychiatry & Douglas Mental Health University Institute, McGill Group for Suicide Studies, McGill University, Québec, Montréal, Canada
- CISSS des Laurentides, Quebec, Canada
| | - Josie-Anne Bertrand
- Douglas Research Center, Douglas Mental Health University Institute, Québec, Canada
| | - Lai Jiang
- Lady Davis Institute, McGill University, Québec, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Québec, Canada
| | - Tharick A Pascoal
- Alzheimer's Disease Research Unit, McGill Centre for Studies in Aging, McGill University, Québec, Canada
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Québec, Canada
| | - Sulantha Mathotaarachchi
- Alzheimer's Disease Research Unit, McGill Centre for Studies in Aging, McGill University, Québec, Canada
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Québec, Canada
| | - Joseph Therriault
- Alzheimer's Disease Research Unit, McGill Centre for Studies in Aging, McGill University, Québec, Canada
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Québec, Canada
| | | | | | - Celia M T Greenwood
- Lady Davis Institute, McGill University, Québec, Canada
- Department of Epidemiology, Biostatistics and Occupational Health, McGill University, Québec, Canada
| | - Pedro Rosa-Neto
- Alzheimer's Disease Research Unit, McGill Centre for Studies in Aging, McGill University, Québec, Canada
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Québec, Canada
| | - Serge Gauthier
- Alzheimer's Disease Research Unit, McGill Centre for Studies in Aging, McGill University, Québec, Canada
- Translational Neuroimaging Laboratory, The McGill University Research Centre for Studies in Aging, Québec, Canada
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