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Vanderstichele T, Burnham KL, de Klein N, Tardaguila M, Howell B, Walter K, Kundu K, Koeppel J, Lee W, Tokolyi A, Persyn E, Nath AP, Marten J, Petrovski S, Roberts DJ, Di Angelantonio E, Danesh J, Berton A, Platt A, Butterworth AS, Soranzo N, Parts L, Inouye M, Paul DS, Davenport EE. Misexpression of inactive genes in whole blood is associated with nearby rare structural variants. Am J Hum Genet 2024; 111:1524-1543. [PMID: 39053458 PMCID: PMC11339615 DOI: 10.1016/j.ajhg.2024.06.017] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/12/2024] [Revised: 06/27/2024] [Accepted: 06/27/2024] [Indexed: 07/27/2024] Open
Abstract
Gene misexpression is the aberrant transcription of a gene in a context where it is usually inactive. Despite its known pathological consequences in specific rare diseases, we have a limited understanding of its wider prevalence and mechanisms in humans. To address this, we analyzed gene misexpression in 4,568 whole-blood bulk RNA sequencing samples from INTERVAL study blood donors. We found that while individual misexpression events occur rarely, in aggregate they were found in almost all samples and a third of inactive protein-coding genes. Using 2,821 paired whole-genome and RNA sequencing samples, we identified that misexpression events are enriched in cis for rare structural variants. We established putative mechanisms through which a subset of SVs lead to gene misexpression, including transcriptional readthrough, transcript fusions, and gene inversion. Overall, we develop misexpression as a type of transcriptomic outlier analysis and extend our understanding of the variety of mechanisms by which genetic variants can influence gene expression.
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Affiliation(s)
| | - Katie L Burnham
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Niek de Klein
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | | | - Brittany Howell
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Klaudia Walter
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Kousik Kundu
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK; Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Puddicombe Way, Cambridge, UK
| | - Jonas Koeppel
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Wanseon Lee
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Alex Tokolyi
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Elodie Persyn
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK; Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Artika P Nath
- Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia
| | - Jonathan Marten
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK
| | - Slavé Petrovski
- Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK; Department of Medicine, University of Melbourne, Austin Health, Melbourne, VIC, Australia
| | - David J Roberts
- Radcliffe Department of Medicine, John Radcliffe Hospital, Oxford, UK; Clinical Services, NHS Blood and Transplant, Oxford Centre, John Radcliffe Hospital, Oxford, UK
| | - Emanuele Di Angelantonio
- Human Technopole, Fondazione Human Technopole, Milan, Italy; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK; British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK; National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - John Danesh
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK; British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK; British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK; National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Alix Berton
- Translational Science and Experimental Medicine, Research and Early Development, Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Molndal, Sweden
| | - Adam Platt
- Translational Science and Experimental Medicine, Research and Early Development, Respiratory and Immunology, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
| | - Adam S Butterworth
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK; British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK; National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Nicole Soranzo
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK; Human Technopole, Fondazione Human Technopole, Milan, Italy; Department of Haematology, University of Cambridge, Cambridge Biomedical Campus, Puddicombe Way, Cambridge, UK; British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK; National Institute for Health and Care Research Blood and Transplant Research Unit in Donor Health and Behaviour, University of Cambridge, Cambridge, UK
| | - Leopold Parts
- Wellcome Sanger Institute, Wellcome Genome Campus, Hinxton, UK
| | - Michael Inouye
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK; Cambridge Baker Systems Genomics Initiative, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Cambridge Baker Systems Genomics Initiative, Baker Heart and Diabetes Institute, Melbourne, VIC, Australia; British Heart Foundation Centre of Research Excellence, University of Cambridge, Cambridge, UK; Health Data Research UK Cambridge, Wellcome Genome Campus and University of Cambridge, Cambridge, UK
| | - Dirk S Paul
- British Heart Foundation Cardiovascular Epidemiology Unit, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK; Victor Phillip Dahdaleh Heart and Lung Research Institute, University of Cambridge, Cambridge, UK; Centre for Genomics Research, Discovery Sciences, BioPharmaceuticals R&D, AstraZeneca, Cambridge, UK
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2
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Ohta R, Tanigawa Y, Suzuki Y, Kellis M, Morishita S. A polygenic score method boosted by non-additive models. Nat Commun 2024; 15:4433. [PMID: 38811555 DOI: 10.1038/s41467-024-48654-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/27/2023] [Accepted: 05/08/2024] [Indexed: 05/31/2024] Open
Abstract
Dominance heritability in complex traits has received increasing recognition. However, most polygenic score (PGS) approaches do not incorporate non-additive effects. Here, we present GenoBoost, a flexible PGS modeling framework capable of considering both additive and non-additive effects, specifically focusing on genetic dominance. Building on statistical boosting theory, we derive provably optimal GenoBoost scores and provide its efficient implementation for analyzing large-scale cohorts. We benchmark it against seven commonly used PGS methods and demonstrate its competitive predictive performance. GenoBoost is ranked the best for four traits and second-best for three traits among twelve tested disease outcomes in UK Biobank. We reveal that GenoBoost improves prediction for autoimmune diseases by incorporating non-additive effects localized in the MHC locus and, more broadly, works best in less polygenic traits. We further demonstrate that GenoBoost can infer the mode of genetic inheritance without requiring prior knowledge. For example, GenoBoost finds non-zero genetic dominance effects for 602 of 900 selected genetic variants, resulting in 2.5% improvements in predicting psoriasis cases. Lastly, we show that GenoBoost can prioritize genetic loci with genetic dominance not previously reported in the GWAS catalog. Our results highlight the increased accuracy and biological insights from incorporating non-additive effects in PGS models.
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Affiliation(s)
- Rikifumi Ohta
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan.
| | - Yosuke Tanigawa
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Yuta Suzuki
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan
| | - Manolis Kellis
- Computer Science and Artificial Intelligence Laboratory, Massachusetts Institute of Technology, Cambridge, MA, USA.
- Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Shinichi Morishita
- Department of Computational Biology and Medical Sciences, Graduate School of Frontier Sciences, The University of Tokyo, Kashiwa, Chiba, Japan.
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3
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Shaked O, Loza BL, Olthoff KM, Reddy KR, Keating BJ, Testa G, Asrani SK, Shaked A. Donor and recipient genetics: implications for the development of post-transplant diabetes mellitus. Am J Transplant 2024:S1600-6135(24)00342-3. [PMID: 38782187 DOI: 10.1016/j.ajt.2024.05.014] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/22/2023] [Revised: 04/16/2024] [Accepted: 05/16/2024] [Indexed: 05/25/2024]
Abstract
Post-transplant diabetes mellitus (PTDM) is a prevalent complication of liver transplantation and is associated with cardiometabolic complications. We studied the consequences of genetic effects of liver donors and recipients on PTDM outcomes, focusing on the diverse genetic pathways related to insulin that play a role in the development of PTDM. 1115 liver transplant recipients without a pre-transplant diagnosis of type 2 diabetes mellitus (T2D) and their paired donors recruited from two transplant centers had polygenic risk scores (PRS) for T2D, insulin secretion, and insulin sensitivity calculated. Among recipients in the highest T2D-PRS quintile, donor T2D-PRS did not contribute significantly to PTDM. However, in recipients with the lowest T2D genetic risk, donor livers with the highest T2D-PRS contributed to the development of PTDM (OR (95% CI)=3.79 (1.10-13.1), p=0.035). Recipient risk was linked to factors associated with insulin secretion (OR (95% CI) = 0.85 (0.74-0.98), p=0.02), while donor livers contributed to PTDM via gene pathways involved in insulin sensitivity (OR (95% CI)=0.86 (0.75-0.99), p=0.03). Recipient and donor PRS independently and collectively serve as predictors of PTDM onset. The genetically influenced biological pathways in recipients primarily pertain to insulin secretion, whereas the genetic makeup of donors exerts an influence on insulin sensitivity.
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Affiliation(s)
- Oren Shaked
- Penn Transplant Institute, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA.
| | - Bao-Li Loza
- Penn Transplant Institute, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Kim M Olthoff
- Penn Transplant Institute, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - K Rajender Reddy
- Penn Transplant Institute, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Brendan J Keating
- Penn Transplant Institute, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | | | | | - Abraham Shaked
- Penn Transplant Institute, Department of Surgery, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
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4
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Kingdom R, Beaumont RN, Wood AR, Weedon MN, Wright CF. Genetic modifiers of rare variants in monogenic developmental disorder loci. Nat Genet 2024; 56:861-868. [PMID: 38637616 PMCID: PMC11096126 DOI: 10.1038/s41588-024-01710-0] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/07/2022] [Accepted: 03/06/2024] [Indexed: 04/20/2024]
Abstract
Rare damaging variants in a large number of genes are known to cause monogenic developmental disorders (DDs) and have also been shown to cause milder subclinical phenotypes in population cohorts. Here, we show that carrying multiple (2-5) rare damaging variants across 599 dominant DD genes has an additive adverse effect on numerous cognitive and socioeconomic traits in UK Biobank, which can be partially counterbalanced by a higher educational attainment polygenic score (EA-PGS). Phenotypic deviators from expected EA-PGS could be partly explained by the enrichment or depletion of rare DD variants. Among carriers of rare DD variants, those with a DD-related clinical diagnosis had a substantially lower EA-PGS and more severe phenotype than those without a clinical diagnosis. Our results suggest that the overall burden of both rare and common variants can modify the expressivity of a phenotype, which may then influence whether an individual reaches the threshold for clinical disease.
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Affiliation(s)
- Rebecca Kingdom
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, UK
| | - Robin N Beaumont
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, UK
| | - Andrew R Wood
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, UK
| | - Michael N Weedon
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, UK
| | - Caroline F Wright
- Department of Clinical and Biomedical Sciences, University of Exeter Medical School, Royal Devon & Exeter Hospital, Exeter, UK.
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5
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Covill LE, Sendel A, Campbell TM, Piiroinen I, Enoksson SL, Borgström EW, Hansen S, Ma K, Marits P, Norlin AC, Smith CIE, Kåhlin J, Eriksson LI, Bergman P, Bryceson YT. Evaluation of Genetic or Cellular Impairments in Type I IFN Immunity in a Cohort of Young Adults with Critical COVID-19. J Clin Immunol 2024; 44:50. [PMID: 38231281 PMCID: PMC10794435 DOI: 10.1007/s10875-023-01641-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/23/2023] [Accepted: 12/13/2023] [Indexed: 01/18/2024]
Abstract
Several genetic and immunological risk factors for severe COVID-19 have been identified, with monogenic conditions relating to 13 genes of type I interferon (IFN) immunity proposed to explain 4.8% of critical cases. However, previous cohorts have been clinically heterogeneous and were not subjected to thorough genetic and immunological analyses. We therefore aimed to systematically investigate the prevalence of rare genetic variants causing inborn errors of immunity (IEI) and functionally interrogate the type I IFN pathway in young adults that suffered from critical COVID-19 yet lacked comorbidities. We selected and clinically characterized a cohort of 38 previously healthy individuals under 50 years of age who were treated in intensive care units due to critical COVID-19. Blood samples were collected after convalescence. Two patients had IFN-α autoantibodies. Genome sequencing revealed very rare variants in the type I IFN pathway in 31.6% of the patients, which was similar to controls. Analyses of cryopreserved leukocytes did not indicate any defect in plasmacytoid dendritic cell sensing of TLR7 and TLR9 agonists in patients carrying variants in these pathways. However, lymphocyte STAT phosphorylation and protein upregulation upon IFN-α stimulation revealed three possible cases of impaired type I IFN signaling in carriers of rare variants. Together, our results suggest a strategy of functional screening followed by genome analyses and biochemical validation to uncover undiagnosed causes of critical COVID-19.
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Affiliation(s)
- L E Covill
- Center for Hematology and Regenerative Medicine, Department of Medicine, Karolinska Institute, Stockholm, Sweden
| | - A Sendel
- Division of Clinical Immunology and Transfusion Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - T M Campbell
- Center for Hematology and Regenerative Medicine, Department of Medicine, Karolinska Institute, Stockholm, Sweden
| | - I Piiroinen
- Center for Hematology and Regenerative Medicine, Department of Medicine, Karolinska Institute, Stockholm, Sweden
| | - S Lind Enoksson
- Division of Clinical Immunology and Transfusion Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - E Wahren Borgström
- Division of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - S Hansen
- Division of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - K Ma
- Center for Hematology and Regenerative Medicine, Department of Medicine, Karolinska Institute, Stockholm, Sweden
| | - P Marits
- Division of Clinical Immunology and Transfusion Medicine, Karolinska University Hospital, Stockholm, Sweden
| | - A C Norlin
- Division of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
| | - C I E Smith
- Division of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
- Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden
| | - J Kåhlin
- Division of Perioperative Medicine and Intensive Care, Karolinska University Hospital, Stockholm, Sweden
| | - L I Eriksson
- Division of Perioperative Medicine and Intensive Care, Karolinska University Hospital, Stockholm, Sweden
| | - P Bergman
- Division of Clinical Immunology and Transfusion Medicine, Karolinska University Hospital, Stockholm, Sweden
- Division of Infectious Diseases, Karolinska University Hospital, Stockholm, Sweden
- Department of Laboratory Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Y T Bryceson
- Center for Hematology and Regenerative Medicine, Department of Medicine, Karolinska Institute, Stockholm, Sweden.
- Division of Clinical Immunology and Transfusion Medicine, Karolinska University Hospital, Stockholm, Sweden.
- Broegelmann Laboratory, Department of Clinical Sciences, University of Bergen, Bergen, Norway.
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6
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Schubach M, Maass T, Nazaretyan L, Röner S, Kircher M. CADD v1.7: using protein language models, regulatory CNNs and other nucleotide-level scores to improve genome-wide variant predictions. Nucleic Acids Res 2024; 52:D1143-D1154. [PMID: 38183205 PMCID: PMC10767851 DOI: 10.1093/nar/gkad989] [Citation(s) in RCA: 1] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Revised: 10/14/2023] [Accepted: 10/17/2023] [Indexed: 01/07/2024] Open
Abstract
Machine Learning-based scoring and classification of genetic variants aids the assessment of clinical findings and is employed to prioritize variants in diverse genetic studies and analyses. Combined Annotation-Dependent Depletion (CADD) is one of the first methods for the genome-wide prioritization of variants across different molecular functions and has been continuously developed and improved since its original publication. Here, we present our most recent release, CADD v1.7. We explored and integrated new annotation features, among them state-of-the-art protein language model scores (Meta ESM-1v), regulatory variant effect predictions (from sequence-based convolutional neural networks) and sequence conservation scores (Zoonomia). We evaluated the new version on data sets derived from ClinVar, ExAC/gnomAD and 1000 Genomes variants. For coding effects, we tested CADD on 31 Deep Mutational Scanning (DMS) data sets from ProteinGym and, for regulatory effect prediction, we used saturation mutagenesis reporter assay data of promoter and enhancer sequences. The inclusion of new features further improved the overall performance of CADD. As with previous releases, all data sets, genome-wide CADD v1.7 scores, scripts for on-site scoring and an easy-to-use webserver are readily provided via https://cadd.bihealth.org/ or https://cadd.gs.washington.edu/ to the community.
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Affiliation(s)
- Max Schubach
- Exploratory Diagnostic Sciences, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Thorben Maass
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck, Lübeck, Germany
| | - Lusiné Nazaretyan
- Exploratory Diagnostic Sciences, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Sebastian Röner
- Exploratory Diagnostic Sciences, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
| | - Martin Kircher
- Exploratory Diagnostic Sciences, Berlin Institute of Health at Charité – Universitätsmedizin Berlin, Berlin, Germany
- Institute of Human Genetics, University Hospital Schleswig-Holstein, University of Lübeck, Lübeck, Germany
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7
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Li T, Ferraro N, Strober BJ, Aguet F, Kasela S, Arvanitis M, Ni B, Wiel L, Hershberg E, Ardlie K, Arking DE, Beer RL, Brody J, Blackwell TW, Clish C, Gabriel S, Gerszten R, Guo X, Gupta N, Johnson WC, Lappalainen T, Lin HJ, Liu Y, Nickerson DA, Papanicolaou G, Pritchard JK, Qasba P, Shojaie A, Smith J, Sotoodehnia N, Taylor KD, Tracy RP, Van Den Berg D, Wheeler MT, Rich SS, Rotter JI, Battle A, Montgomery SB. The functional impact of rare variation across the regulatory cascade. CELL GENOMICS 2023; 3:100401. [PMID: 37868038 PMCID: PMC10589633 DOI: 10.1016/j.xgen.2023.100401] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Received: 10/07/2022] [Revised: 03/08/2023] [Accepted: 08/10/2023] [Indexed: 10/24/2023]
Abstract
Each human genome has tens of thousands of rare genetic variants; however, identifying impactful rare variants remains a major challenge. We demonstrate how use of personal multi-omics can enable identification of impactful rare variants by using the Multi-Ethnic Study of Atherosclerosis, which included several hundred individuals, with whole-genome sequencing, transcriptomes, methylomes, and proteomes collected across two time points, 10 years apart. We evaluated each multi-omics phenotype's ability to separately and jointly inform functional rare variation. By combining expression and protein data, we observed rare stop variants 62 times and rare frameshift variants 216 times as frequently as controls, compared to 13-27 times as frequently for expression or protein effects alone. We extended a Bayesian hierarchical model, "Watershed," to prioritize specific rare variants underlying multi-omics signals across the regulatory cascade. With this approach, we identified rare variants that exhibited large effect sizes on multiple complex traits including height, schizophrenia, and Alzheimer's disease.
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Affiliation(s)
- Taibo Li
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Nicole Ferraro
- Biomedical Informatics Training Program, Stanford University, Stanford, CA, USA
| | - Benjamin J. Strober
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Harvard School of Public Health, Epidemiology Department, Boston, MA, USA
| | | | - Silva Kasela
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Marios Arvanitis
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Medicine, Division of Cardiology, Johns Hopkins School of Medicine, Baltimore, MD, USA
| | - Bohan Ni
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
| | - Laurens Wiel
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | | | | | - Dan E. Arking
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
| | - Rebecca L. Beer
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Jennifer Brody
- Cardiovascular Health Research Unit, Departments of Medicine and Epidemiology, University of Washington, Seattle, WA, USA
| | - Thomas W. Blackwell
- Department of Biostatistics, School of Public Health, University of Michigan, Ann Arbor, MI, USA
| | - Clary Clish
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Robert Gerszten
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Cardiovascular Institute, Beth Israel Deaconess Medical Center, Harvard Medical School, Boston, MA, USA
| | - Xiuqing Guo
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Namrata Gupta
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - W. Craig Johnson
- Collaborative Health Studies Coordinating Center, University of Washington, Seattle, WA, USA
| | - Tuuli Lappalainen
- New York Genome Center, New York, NY, USA
- Department of Systems Biology, Columbia University, New York, NY, USA
| | - Henry J. Lin
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Yongmei Liu
- Department of Medicine, Duke University School of Medicine, Durham, NC, USA
| | | | - George Papanicolaou
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | | | - Pankaj Qasba
- National Heart, Lung, and Blood Institute, National Institutes of Health, Bethesda, MD, USA
| | - Ali Shojaie
- Department of Biostatistics, University of Washington School of Public Health, Seattle, WA, USA
| | - Josh Smith
- Department of Genome Sciences, University of Washington, Seattle, WA, USA
| | - Nona Sotoodehnia
- Cardiovascular Health Research Unit, Departments of Medicine and Epidemiology, University of Washington, Seattle, WA, USA
| | - Kent D. Taylor
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Russell P. Tracy
- Laboratory for Clinical Biochemistry Research, University of Vermont, Burlington, VT, USA
| | - David Van Den Berg
- Department of Preventive Medicine, University of Southern California, Los Angeles, CA, USA
| | - Matthew T. Wheeler
- Division of Cardiovascular Medicine, Department of Medicine, Stanford University School of Medicine, Stanford, CA, USA
| | - Stephen S. Rich
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA, USA
| | - Jerome I. Rotter
- The Institute for Translational Genomics and Population Sciences, Department of Pediatrics, The Lundquist Institute for Biomedical Innovation at Harbor-UCLA Medical Center, Torrance, CA, USA
| | - Alexis Battle
- Department of Biomedical Engineering, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Department of Computer Science, Johns Hopkins University, Baltimore, MD, USA
- McKusick-Nathans Institute, Department of Genetic Medicine, Johns Hopkins University School of Medicine, Baltimore, MD, USA
- Malone Center for Engineering of Healthcare, Johns Hopkins University, Baltimore, MD, USA
| | - Stephen B. Montgomery
- Department of Genetics, Stanford University, Stanford, CA, USA
- Department of Pathology, Stanford University, Stanford, CA, USA
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8
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Cross B, Turner R, Pirmohamed M. Polygenic risk scores: An overview from bench to bedside for personalised medicine. Front Genet 2022; 13:1000667. [PMID: 36437929 PMCID: PMC9692112 DOI: 10.3389/fgene.2022.1000667] [Citation(s) in RCA: 23] [Impact Index Per Article: 11.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/22/2022] [Accepted: 10/24/2022] [Indexed: 11/13/2022] Open
Abstract
Since the first polygenic risk score (PRS) in 2007, research in this area has progressed significantly. The increasing number of SNPs that have been identified by large scale GWAS analyses has fuelled the development of a myriad of PRSs for a wide variety of diseases and, more recently, to PRSs that potentially identify differential response to specific drugs. PRSs constitute a composite genomic biomarker and potential applications for PRSs in clinical practice encompass risk prediction and disease screening, early diagnosis, prognostication, and drug stratification to improve efficacy or reduce adverse drug reactions. Nevertheless, to our knowledge, no PRSs have yet been adopted into routine clinical practice. Beyond the technical considerations of PRS development, the major challenges that face PRSs include demonstrating clinical utility and circumnavigating the implementation of novel genomic technologies at scale into stretched healthcare systems. In this review, we discuss progress in developing disease susceptibility PRSs across multiple medical specialties, development of pharmacogenomic PRSs, and future directions for the field.
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Affiliation(s)
- Benjamin Cross
- The Wolfson Centre for Personalised Medicine, Institute of Systems, Molecular and Integrative Biology, Faculty of Health & Life Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Richard Turner
- The Wolfson Centre for Personalised Medicine, Institute of Systems, Molecular and Integrative Biology, Faculty of Health & Life Sciences, University of Liverpool, Liverpool, United Kingdom
| | - Munir Pirmohamed
- The Wolfson Centre for Personalised Medicine, Institute of Systems, Molecular and Integrative Biology, Faculty of Health & Life Sciences, University of Liverpool, Liverpool, United Kingdom
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