1
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Lewis ACF, Chisholm RL, Connolly JJ, Esplin ED, Glessner J, Gordon A, Green RC, Hakonarson H, Harr M, Holm IA, Jarvik GP, Karlson E, Kenny EE, Kottyan L, Lennon N, Linder JE, Luo Y, Martin LJ, Perez E, Puckelwartz MJ, Rasmussen-Torvik LJ, Sabatello M, Sharp RR, Smoller JW, Sterling R, Terek S, Wei WQ, Fullerton SM. Managing differential performance of polygenic risk scores across groups: Real-world experience of the eMERGE Network. Am J Hum Genet 2024; 111:999-1005. [PMID: 38688278 PMCID: PMC11179244 DOI: 10.1016/j.ajhg.2024.04.005] [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: 02/27/2024] [Revised: 04/10/2024] [Accepted: 04/11/2024] [Indexed: 05/02/2024] Open
Abstract
The differential performance of polygenic risk scores (PRSs) by group is one of the major ethical barriers to their clinical use. It is also one of the main practical challenges for any implementation effort. The social repercussions of how people are grouped in PRS research must be considered in communications with research participants, including return of results. Here, we outline the decisions faced and choices made by a large multi-site clinical implementation study returning PRSs to diverse participants in handling this issue of differential performance. Our approach to managing the complexities associated with the differential performance of PRSs serves as a case study that can help future implementers of PRSs to plot an anticipatory course in response to this issue.
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Affiliation(s)
- Anna C F Lewis
- Edmond and Lily Safra Center for Ethics, Harvard University, Cambridge, MA, USA; Department of Genetics, Brigham and Women's Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Harvard Medical School, Boston, MA, USA.
| | - Rex L Chisholm
- Center for Genetic Medicine, Northwestern University, Evanston, IL, USA
| | - John J Connolly
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | | | - Joe Glessner
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Adam Gordon
- Center for Genetic Medicine, Northwestern University, Evanston, IL, USA; Department of Pharmacology, Northwestern University, Evanston, IL, USA
| | - Robert C Green
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Broad Institute of MIT and Harvard, Cambridge, MA, USA; Ariadne Labs, Boston, MA, USA; Harvard Medical School, Boston, MA, USA
| | - Hakon Hakonarson
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Department of Pediatrics, The Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA; Division of Human Genetics, Children's Hospital of Philadelphia, Philadelphia, PA, USA; Division of Pulmonary Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Margaret Harr
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ingrid A Holm
- Division of Genetics and Genomics, Boston Children's Hospital, Boston, MA, USA; Department of Pediatrics, Harvard Medical School, Boston, MA, USA
| | - Gail P Jarvik
- Division of Medical Genetics, Department of Medicine and Department of Genome Science, University of Washington Medical Center, Seattle, WA, USA
| | - Elizabeth Karlson
- Department of Medicine, Brigham and Women's Hospital, Boston, MA, USA; Mass General Brigham Personalized Medicine, Boston, MA, USA
| | - Eimear E Kenny
- Institute for Genomic Health, Icahn School of Medicine, New York City, NY, USA; Center for Clinical Translational Genomics, Icahn School of Medicine, New York City, NY, USA; Division of Genomic Medicine, Department of Medicine, Icahn School of Medicine, New York City, NY, USA; Department of Genetics and Genomic Sciences, Icahn School of Medicine, New York City, NY, USA
| | - Leah Kottyan
- Center for Autoimmune Genomics and Etiology, Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA
| | - Niall Lennon
- Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jodell E Linder
- Vanderbilt Institute for Clinical and Translational Research, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yuan Luo
- Department of Preventive Medicine, Northwestern University, Evanston, IL, USA
| | - Lisa J Martin
- Cincinnati Children's Hospital Medical Center, Cincinnati, OH, USA; University of Cincinnati College of Medicine, Cincinnati, OH, USA
| | - Emma Perez
- Mass General Brigham Personalized Medicine, Boston, MA, USA
| | - Megan J Puckelwartz
- Center for Genetic Medicine, Northwestern University, Evanston, IL, USA; Department of Pharmacology, Northwestern University, Evanston, IL, USA
| | - Laura J Rasmussen-Torvik
- Center for Genetic Medicine, Northwestern University, Evanston, IL, USA; Department of Preventive Medicine, Northwestern University, Evanston, IL, USA
| | - Maya Sabatello
- Center for Precision Medicine and Genomics, Department of Medicine, Columbia University Irving Medical Center, New York City, NY, USA; Division of Ethics, Department of Medical Humanities and Ethics, Columbia University Irving Medical Center, New York City, NY, USA
| | | | - Jordan W Smoller
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA; Psychiatric & Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA; Stanley Center for Psychiatric Research, Broad Institute, Cambridge, MA, USA
| | - Rene Sterling
- Division of Genomics and Society, National Human Genome Research Institute, National Institutes of Health, Bethesda, MD, USA
| | - Shannon Terek
- Center for Applied Genomics, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Wei-Qi Wei
- Department of Biomedical Informatics, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Stephanie M Fullerton
- Department of Bioethics & Humanities, University of Washington School of Medicine, Seattle, WA, USA
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2
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Grunin M, Triffon D, Beykin G, Rahmani E, Schweiger R, Tiosano L, Khateb S, Hagbi-Levi S, Rinsky B, Munitz R, Winkler TW, Heid IM, Halperin E, Carmi S, Chowers I. Genome wide association study and genomic risk prediction of age related macular degeneration in Israel. Sci Rep 2024; 14:13034. [PMID: 38844476 PMCID: PMC11156861 DOI: 10.1038/s41598-024-63065-0] [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: 12/22/2023] [Accepted: 05/24/2024] [Indexed: 06/09/2024] Open
Abstract
The risk of developing age-related macular degeneration (AMD) is influenced by genetic background. In 2016, the International AMD Genomics Consortium (IAMDGC) identified 52 risk variants in 34 loci, and a polygenic risk score (PRS) from these variants was associated with AMD. The Israeli population has a unique genetic composition: Ashkenazi Jewish (AJ), Jewish non-Ashkenazi, and Arab sub-populations. We aimed to perform a genome-wide association study (GWAS) for AMD in Israel, and to evaluate PRSs for AMD. Our discovery set recruited 403 AMD patients and 256 controls at Hadassah Medical Center. We genotyped individuals via custom exome chip. We imputed non-typed variants using cosmopolitan and AJ reference panels. We recruited additional 155 cases and 69 controls for validation. To evaluate predictive power of PRSs for AMD, we used IAMDGC summary-statistics excluding our study and developed PRSs via clumping/thresholding or LDpred2. In our discovery set, 31/34 loci reported by IAMDGC were AMD-associated (P < 0.05). Of those, all effects were directionally consistent with IAMDGC and 11 loci had a P-value under Bonferroni-corrected threshold (0.05/34 = 0.0015). At a 5 × 10-5 threshold, we discovered four suggestive associations in FAM189A1, IGDCC4, C7orf50, and CNTNAP4. Only the FAM189A1 variant was AMD-associated in the replication cohort after Bonferroni-correction. A prediction model including LDpred2-based PRS + covariates had an AUC of 0.82 (95% CI 0.79-0.85) and performed better than covariates-only model (P = 5.1 × 10-9). Therefore, previously reported AMD-associated loci were nominally associated with AMD in Israel. A PRS developed based on a large international study is predictive in Israeli populations.
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Affiliation(s)
- Michelle Grunin
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, POB 12271, 9112102, Jerusalem, Israel
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Daria Triffon
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, POB 12271, 9112102, Jerusalem, Israel
| | - Gala Beykin
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Elior Rahmani
- Department of Computational Medicine, University of California, Los Angeles, Los Angeles, CA, USA
| | - Regev Schweiger
- Molecular Microbiology and Biotechnology, Tel Aviv University, Tel Aviv, Israel
- Department of Genetics, University of Cambridge, CB21TN, Cambridge, UK
| | - Liran Tiosano
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Samer Khateb
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Shira Hagbi-Levi
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Batya Rinsky
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Refael Munitz
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel
| | - Thomas W Winkler
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Iris M Heid
- Department of Genetic Epidemiology, University of Regensburg, Regensburg, Germany
| | - Eran Halperin
- Molecular Microbiology and Biotechnology, Tel Aviv University, Tel Aviv, Israel
- Department of Anesthesiology, University of California, Los Angeles, Los Angeles, CA, USA
- Department of Human Genetics, University of California, Los Angeles, Los Angeles, CA, USA
| | - Shai Carmi
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, POB 12271, 9112102, Jerusalem, Israel.
| | - Itay Chowers
- Department of Ophthalmology, Hadassah-Hebrew University Medical Center, POB 12000, 91120, Jerusalem, Israel.
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3
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Gao Y, Cui Y. Optimizing clinico-genomic disease prediction across ancestries: a machine learning strategy with Pareto improvement. Genome Med 2024; 16:76. [PMID: 38835075 DOI: 10.1186/s13073-024-01345-0] [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: 01/08/2024] [Accepted: 05/17/2024] [Indexed: 06/06/2024] Open
Abstract
BACKGROUND Accurate prediction of an individual's predisposition to diseases is vital for preventive medicine and early intervention. Various statistical and machine learning models have been developed for disease prediction using clinico-genomic data. However, the accuracy of clinico-genomic prediction of diseases may vary significantly across ancestry groups due to their unequal representation in clinical genomic datasets. METHODS We introduced a deep transfer learning approach to improve the performance of clinico-genomic prediction models for data-disadvantaged ancestry groups. We conducted machine learning experiments on multi-ancestral genomic datasets of lung cancer, prostate cancer, and Alzheimer's disease, as well as on synthetic datasets with built-in data inequality and distribution shifts across ancestry groups. RESULTS Deep transfer learning significantly improved disease prediction accuracy for data-disadvantaged populations in our multi-ancestral machine learning experiments. In contrast, transfer learning based on linear frameworks did not achieve comparable improvements for these data-disadvantaged populations. CONCLUSIONS This study shows that deep transfer learning can enhance fairness in multi-ancestral machine learning by improving prediction accuracy for data-disadvantaged populations without compromising prediction accuracy for other populations, thus providing a Pareto improvement towards equitable clinico-genomic prediction of diseases.
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Affiliation(s)
- Yan Gao
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA
- Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA
| | - Yan Cui
- Department of Genetics, Genomics and Informatics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
- Center for Integrative and Translational Genomics, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
- Center for Cancer Research, University of Tennessee Health Science Center, Memphis, TN, 38163, USA.
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4
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Du M. Prospective of Mitochondrial DNA Variations in Cancer on Genomic Medicine. Cancer Epidemiol Biomarkers Prev 2024; 33:766-768. [PMID: 38826080 DOI: 10.1158/1055-9965.epi-24-0233] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/13/2024] [Revised: 03/15/2024] [Accepted: 03/29/2024] [Indexed: 06/04/2024] Open
Abstract
Mitochondrial DNA (mtDNA) has emerged as a pivotal component in understanding the etiology and susceptibility of cancer. A recent study by Chen and colleagues delineated the germline genetic effect of mtDNA single-nucleotide polymorphisms (SNP) and haplogroups across pan-cancer risk. They identified a subset of mtSNPs and the corresponding risk score, as well as haplogroups A and M7 alongside their genetic interactions, conferring a protective effect against various cancers. These findings underscored the value of mtDNA variations as biomarkers for cancer etiology and as tools for cancer risk stratification. Future investigations are encouraged to integrate comprehensive omics data of genomics, transcriptomics, proteomics, and metabolomics, etc., from nuclear DNA with mtDNA variations, alongside single-cell and spatial technologies, to unravel the tumor mechanism and identify the drug targets. Moreover, the incorporation of polygenic risk score, that included mtDNA variations with both rare and common frequencies, and liquid biopsy-based biomarkers would enhance the predictive performance of cancer risk assessment and refine the risk stratification of population-based cancer screening. This commentary advocates for the validation across diverse populations to harness the full potential of mitochondrial genomics, and ultimately paves the prospective way for advancements in personalized cancer therapeutics and prevention strategies. See related article by Chen and colleagues, Cancer Epidemiol Biomarkers Prev 2024;33:381-8.
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Affiliation(s)
- Mulong Du
- Department of Biostatistics, Center for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
- Department of Environmental Genomics, Jiangsu Key Laboratory of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Center for Cancer Personalized Medicine, Nanjing Medical University, Nanjing, China
- Department of Genetic Toxicology, The Key Laboratory of Modern Toxicology of Ministry of Education, School of Public Health, Nanjing Medical University, Nanjing, China
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5
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Yang X, Sullivan PF, Li B, Fan Z, Ding D, Shu J, Guo Y, Paschou P, Bao J, Shen L, Ritchie MD, Nave G, Platt ML, Li T, Zhu H, Zhao B. Multi-organ imaging-derived polygenic indexes for brain and body health. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.04.18.23288769. [PMID: 38883759 PMCID: PMC11177904 DOI: 10.1101/2023.04.18.23288769] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/18/2024]
Abstract
The UK Biobank (UKB) imaging project is a crucial resource for biomedical research, but is limited to 100,000 participants due to cost and accessibility barriers. Here we used genetic data to predict heritable imaging-derived phenotypes (IDPs) for a larger cohort. We developed and evaluated 4,375 IDP genetic scores (IGS) derived from UKB brain and body images. When applied to UKB participants who were not imaged, IGS revealed links to numerous phenotypes and stratified participants at increased risk for both brain and somatic diseases. For example, IGS identified individuals at higher risk for Alzheimer's disease and multiple sclerosis, offering additional insights beyond traditional polygenic risk scores of these diseases. When applied to independent external cohorts, IGS also stratified those at high disease risk in the All of Us Research Program and the Alzheimer's Disease Neuroimaging Initiative study. Our results demonstrate that, while the UKB imaging cohort is largely healthy and may not be the most enriched for disease risk management, it holds immense potential for stratifying the risk of various brain and body diseases in broader external genetic cohorts.
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6
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Eastwood SV, Hemani G, Watkins SH, Scally A, Davey Smith G, Chaturvedi N. Ancestry, ethnicity, and race: explaining inequalities in cardiometabolic disease. Trends Mol Med 2024; 30:541-551. [PMID: 38677980 DOI: 10.1016/j.molmed.2024.04.002] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/04/2024] [Revised: 03/30/2024] [Accepted: 04/03/2024] [Indexed: 04/29/2024]
Abstract
Population differences in cardiometabolic disease remain unexplained. Misleading assumptions over genetic explanations are partly due to terminology used to distinguish populations, specifically ancestry, race, and ethnicity. These terms differentially implicate environmental and biological causal pathways, which should inform their use. Genetic variation alone accounts for a limited fraction of population differences in cardiometabolic disease. Research effort should focus on societally driven, lifelong environmental determinants of population differences in disease. Rather than pursuing population stratifiers to personalize medicine, we advocate removing socioeconomic barriers to receipt of and adherence to healthcare interventions, which will have markedly greater impact on improving cardiometabolic outcomes. This requires multidisciplinary collaboration and public and policymaker engagement to address inequalities driven by society rather than biology per se.
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Affiliation(s)
- Sophie V Eastwood
- MRC Unit for Lifelong Health and Ageing at UCL Population Sciences and Experimental Medicine, Institute of Cardiovascular Sciences Faculty of Population Health Sciences, University College London, London, UK
| | - Gibran Hemani
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Sarah H Watkins
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Aylwyn Scally
- Department of Genetics, University of Cambridge, Downing Street, Cambridge, UK
| | - George Davey Smith
- Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK; MRC Integrative Epidemiology Unit, Population Health Sciences, Bristol Medical School, University of Bristol, Bristol, UK
| | - Nishi Chaturvedi
- MRC Unit for Lifelong Health and Ageing at UCL Population Sciences and Experimental Medicine, Institute of Cardiovascular Sciences Faculty of Population Health Sciences, University College London, London, UK.
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7
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Irvin MR, Ge T, Patki A, Srinivasasainagendra V, Armstrong ND, Davis B, Jones AC, Perez E, Stalbow L, Lebo M, Kenny E, Loos RJ, Ng MC, Smoller JW, Meigs JB, Lange LA, Karlson EW, Limdi NA, Tiwari HK. Polygenic Risk for Type 2 Diabetes in African Americans. Diabetes 2024; 73:993-1001. [PMID: 38470993 PMCID: PMC11109789 DOI: 10.2337/db23-0232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/25/2023] [Accepted: 03/06/2024] [Indexed: 03/14/2024]
Abstract
African Americans (AAs) have been underrepresented in polygenic risk score (PRS) studies. Here, we integrated genome-wide data from multiple observational studies on type 2 diabetes (T2D), encompassing a total of 101,987 AAs, to train and optimize an AA-focused T2D PRS (PRSAA), using a Bayesian polygenic modeling method. We further tested the score in three independent studies with a total of 7,275 AAs and compared the PRSAA with other published scores. Results show that a 1-SD increase in the PRSAA was associated with 40-60% increase in the odds of T2D (odds ratio [OR] 1.60, 95% CI 1.37-1.88; OR 1.40, 95% CI 1.16-1.70; and OR 1.45, 95% CI 1.30-1.62) across three testing cohorts. These models captured 1.0-2.6% of the variance (R2) in T2D on the liability scale. The positive predictive values for three calculated score thresholds (the top 2%, 5%, and 10%) ranged from 14 to 35%. The PRSAA, in general, performed similarly to existing T2D PRS. The need remains for larger data sets to continue to evaluate the utility of within-ancestry scores in the AA population. ARTICLE HIGHLIGHTS
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Affiliation(s)
- Marguerite R. Irvin
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
| | - Amit Patki
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL
| | | | - Nicole D. Armstrong
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL
| | - Brittney Davis
- Department of Neurology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL
| | - Alana C. Jones
- Department of Epidemiology, School of Public Health, University of Alabama at Birmingham, Birmingham, AL
| | - Emma Perez
- Department of Medicine, Brigham and Women's Hospital, Boston, MA
- Mass General Brigham Personalized Medicine, Boston, MA
| | - Lauren Stalbow
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Matthew Lebo
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
- Mass General Brigham Personalized Medicine, Boston, MA
- Department of Pathology, Brigham and Women's Hospital, Boston, MA
| | - Eimear Kenny
- Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY
- Division of Genomic Medicine, Department of Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
| | - Ruth J.F. Loos
- The Charles Bronfman Institute for Personalized Medicine, Icahn School of Medicine at Mount Sinai, New York, NY
- The Novo Nordisk Foundation Center for Basic Metabolic Research, Faculty of Health and Medicine, University of Copenhagen, Copenhagen, Denmark
| | - Maggie C.Y. Ng
- Vanderbilt Genetics Institute, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN
| | - Jordan W. Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA
- Center for Precision Psychiatry, Massachusetts General Hospital, Boston, MA
- Department of Psychiatry, Massachusetts General Hospital, Boston, MA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
| | - James B. Meigs
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Medicine, Massachusetts General Hospital, Boston, MA
- Division of General Internal Medicine, Massachusetts General Hospital, Boston, MA
| | - Leslie A. Lange
- Department of Epidemiology, University of Colorado School of Public Health, Aurora, CO
| | - Elizabeth W. Karlson
- Department of Medicine, Brigham and Women's Hospital, Boston, MA
- Mass General Brigham Personalized Medicine, Boston, MA
| | - Nita A. Limdi
- Department of Neurology, School of Medicine, University of Alabama at Birmingham, Birmingham, AL
| | - Hemant K. Tiwari
- Department of Biostatistics, School of Public Health, University of Alabama at Birmingham, Birmingham, AL
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8
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Capalbo A, de Wert G, Mertes H, Klausner L, Coonen E, Spinella F, Van de Velde H, Viville S, Sermon K, Vermeulen N, Lencz T, Carmi S. Screening embryos for polygenic disease risk: a review of epidemiological, clinical, and ethical considerations. Hum Reprod Update 2024:dmae012. [PMID: 38805697 DOI: 10.1093/humupd/dmae012] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/10/2024] [Revised: 03/25/2024] [Indexed: 05/30/2024] Open
Abstract
BACKGROUND The genetic composition of embryos generated by in vitro fertilization (IVF) can be examined with preimplantation genetic testing (PGT). Until recently, PGT was limited to detecting single-gene, high-risk pathogenic variants, large structural variants, and aneuploidy. Recent advances have made genome-wide genotyping of IVF embryos feasible and affordable, raising the possibility of screening embryos for their risk of polygenic diseases such as breast cancer, hypertension, diabetes, or schizophrenia. Despite a heated debate around this new technology, called polygenic embryo screening (PES; also PGT-P), it is already available to IVF patients in some countries. Several articles have studied epidemiological, clinical, and ethical perspectives on PES; however, a comprehensive, principled review of this emerging field is missing. OBJECTIVE AND RATIONALE This review has four main goals. First, given the interdisciplinary nature of PES studies, we aim to provide a self-contained educational background about PES to reproductive specialists interested in the subject. Second, we provide a comprehensive and critical review of arguments for and against the introduction of PES, crystallizing and prioritizing the key issues. We also cover the attitudes of IVF patients, clinicians, and the public towards PES. Third, we distinguish between possible future groups of PES patients, highlighting the benefits and harms pertaining to each group. Finally, our review, which is supported by ESHRE, is intended to aid healthcare professionals and policymakers in decision-making regarding whether to introduce PES in the clinic, and if so, how, and to whom. SEARCH METHODS We searched for PubMed-indexed articles published between 1/1/2003 and 1/3/2024 using the terms 'polygenic embryo screening', 'polygenic preimplantation', and 'PGT-P'. We limited the review to primary research papers in English whose main focus was PES for medical conditions. We also included papers that did not appear in the search but were deemed relevant. OUTCOMES The main theoretical benefit of PES is a reduction in lifetime polygenic disease risk for children born after screening. The magnitude of the risk reduction has been predicted based on statistical modelling, simulations, and sibling pair analyses. Results based on all methods suggest that under the best-case scenario, large relative risk reductions are possible for one or more diseases. However, as these models abstract several practical limitations, the realized benefits may be smaller, particularly due to a limited number of embryos and unclear future accuracy of the risk estimates. PES may negatively impact patients and their future children, as well as society. The main personal harms are an unindicated IVF treatment, a possible reduction in IVF success rates, and patient confusion, incomplete counselling, and choice overload. The main possible societal harms include discarded embryos, an increasing demand for 'designer babies', overemphasis of the genetic determinants of disease, unequal access, and lower utility in people of non-European ancestries. Benefits and harms will vary across the main potential patient groups, comprising patients already requiring IVF, fertile people with a history of a severe polygenic disease, and fertile healthy people. In the United States, the attitudes of IVF patients and the public towards PES seem positive, while healthcare professionals are cautious, sceptical about clinical utility, and concerned about patient counselling. WIDER IMPLICATIONS The theoretical potential of PES to reduce risk across multiple polygenic diseases requires further research into its benefits and harms. Given the large number of practical limitations and possible harms, particularly unnecessary IVF treatments and discarded viable embryos, PES should be offered only within a research context before further clarity is achieved regarding its balance of benefits and harms. The gap in attitudes between healthcare professionals and the public needs to be narrowed by expanding public and patient education and providing resources for informative and unbiased genetic counselling.
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Affiliation(s)
- Antonio Capalbo
- Juno Genetics, Department of Reproductive Genetics, Rome, Italy
- Center for Advanced Studies and Technology (CAST), Department of Medical Genetics, "G. d'Annunzio" University of Chieti-Pescara, Chieti, Italy
| | - Guido de Wert
- Department of Health, Ethics & Society, CAPHRI-School for Public Health and Primary Care and GROW School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
| | - Heidi Mertes
- Department of Philosophy and Moral Sciences, Ghent University, Ghent, Belgium
- Department of Public Health and Primary Care, Ghent University, Ghent, Belgium
| | - Liraz Klausner
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
| | - Edith Coonen
- Departments of Clinical Genetics and Reproductive Medicine, Maastricht University Medical Centre, Maastricht, The Netherlands
- School for Oncology and Developmental Biology, GROW, Maastricht University, Maastricht, The Netherlands
| | - Francesca Spinella
- Eurofins GENOMA Group Srl, Molecular Genetics Laboratories, Department of Scientific Communication, Rome, Italy
| | - Hilde Van de Velde
- Research Group Genetics Reproduction and Development (GRAD), Vrije Universiteit Brussel, Brussel, Belgium
- Brussels IVF, UZ Brussel, Brussel, Belgium
| | - Stephane Viville
- Laboratoire de Génétique Médicale LGM, Institut de Génétique Médicale d'Alsace IGMA, INSERM UMR 1112, Université de Strasbourg, France
- Laboratoire de Diagnostic Génétique, Unité de Génétique de l'infertilité (UF3472), Hôpitaux Universitaires de Strasbourg, Strasbourg, France
| | - Karen Sermon
- Research Group Genetics Reproduction and Development (GRAD), Vrije Universiteit Brussel, Brussel, Belgium
| | | | - Todd Lencz
- Institute of Behavioral Science, Feinstein Institutes for Medical Research, Manhasset, NY, USA
- Departments of Psychiatry and Molecular Medicine, Zucker School of Medicine at Hofstra/Northwell, Hempstead, NY 11549, USA
| | - Shai Carmi
- Braun School of Public Health and Community Medicine, The Hebrew University of Jerusalem, Jerusalem, Israel
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9
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Step K, Ndong Sima CAA, Mata I, Bardien S. Exploring the role of underrepresented populations in polygenic risk scores for neurodegenerative disease risk prediction. Front Neurosci 2024; 18:1380860. [PMID: 38859922 PMCID: PMC11163124 DOI: 10.3389/fnins.2024.1380860] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2024] [Accepted: 05/14/2024] [Indexed: 06/12/2024] Open
Affiliation(s)
- Kathryn Step
- Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Carene Anne Alene Ndong Sima
- Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
| | - Ignacio Mata
- Genomic Medicine, Lerner Research Institute, Cleveland Clinic Foundation, Cleveland, OH, United States
| | - Soraya Bardien
- Division of Molecular Biology and Human Genetics, Faculty of Medicine and Health Sciences, Stellenbosch University, Cape Town, South Africa
- South African Medical Research Council/Stellenbosch University Genomics of Brain Disorders Research Unit, Stellenbosch University, Cape Town, South Africa
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10
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Staerk C, Klinkhammer H, Wistuba T, Maj C, Mayr A. Generalizability of polygenic prediction models: how is the R 2 defined on test data? BMC Med Genomics 2024; 17:132. [PMID: 38755654 PMCID: PMC11100126 DOI: 10.1186/s12920-024-01905-8] [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: 09/01/2023] [Accepted: 05/08/2024] [Indexed: 05/18/2024] Open
Abstract
BACKGROUND Polygenic risk scores (PRS) quantify an individual's genetic predisposition for different traits and are expected to play an increasingly important role in personalized medicine. A crucial challenge in clinical practice is the generalizability and transferability of PRS models to populations with different ancestries. When assessing the generalizability of PRS models for continuous traits, the R 2 is a commonly used measure to evaluate prediction accuracy. While the R 2 is a well-defined goodness-of-fit measure for statistical linear models, there exist different definitions for its application on test data, which complicates interpretation and comparison of results. METHODS Based on large-scale genotype data from the UK Biobank, we compare three definitions of the R 2 on test data for evaluating the generalizability of PRS models to different populations. Polygenic models for several phenotypes, including height, BMI and lipoprotein A, are derived based on training data with European ancestry using state-of-the-art regression methods and are evaluated on various test populations with different ancestries. RESULTS Our analysis shows that the choice of the R 2 definition can lead to considerably different results on test data, making the comparison of R 2 values from the literature problematic. While the definition as the squared correlation between predicted and observed phenotypes solely addresses the discriminative performance and always yields values between 0 and 1, definitions of the R 2 based on the mean squared prediction error (MSPE) with reference to intercept-only models assess both discrimination and calibration. These MSPE-based definitions can yield negative values indicating miscalibrated predictions for out-of-target populations. We argue that the choice of the most appropriate definition depends on the aim of PRS analysis - whether it primarily serves for risk stratification or also for individual phenotype prediction. Moreover, both correlation-based and MSPE-based definitions of R 2 can provide valuable complementary information. CONCLUSIONS Awareness of the different definitions of the R 2 on test data is necessary to facilitate the reporting and interpretation of results on PRS generalizability. It is recommended to explicitly state which definition was used when reporting R 2 values on test data. Further research is warranted to develop and evaluate well-calibrated polygenic models for diverse populations.
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Affiliation(s)
- Christian Staerk
- Department of Medical Biometry, Informatics and Epidemiology, Medical Faculty, University of Bonn, Bonn, Germany.
- Institute of Statistics, RWTH Aachen University, Aachen, Germany.
| | - Hannah Klinkhammer
- Department of Medical Biometry, Informatics and Epidemiology, Medical Faculty, University of Bonn, Bonn, Germany
- Institute for Genomic Statistics and Bioinformatics, Medical Faculty, University of Bonn, Bonn, Germany
| | - Tobias Wistuba
- Department of Medical Biometry, Informatics and Epidemiology, Medical Faculty, University of Bonn, Bonn, Germany
| | - Carlo Maj
- Center for Human Genetics, University of Marburg, Marburg, Germany
| | - Andreas Mayr
- Department of Medical Biometry, Informatics and Epidemiology, Medical Faculty, University of Bonn, Bonn, Germany
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11
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Brandenburg JT, Chen WC, Boua PR, Govender MA, Agongo G, Micklesfield LK, Sorgho H, Tollman S, Asiki G, Mashinya F, Hazelhurst S, Morris AP, Fabian J, Ramsay M. Genetic association and transferability for urinary albumin-creatinine ratio as a marker of kidney disease in four Sub-Saharan African populations and non-continental individuals of African ancestry. Front Genet 2024; 15:1372042. [PMID: 38812969 PMCID: PMC11134365 DOI: 10.3389/fgene.2024.1372042] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/18/2024] [Accepted: 04/12/2024] [Indexed: 05/31/2024] Open
Abstract
Background Genome-wide association studies (GWAS) have predominantly focused on populations of European and Asian ancestry, limiting our understanding of genetic factors influencing kidney disease in Sub-Saharan African (SSA) populations. This study presents the largest GWAS for urinary albumin-to-creatinine ratio (UACR) in SSA individuals, including 8,970 participants living in different African regions and an additional 9,705 non-resident individuals of African ancestry from the UK Biobank and African American cohorts. Methods Urine biomarkers and genotype data were obtained from two SSA cohorts (AWI-Gen and ARK), and two non-resident African-ancestry studies (UK Biobank and CKD-Gen Consortium). Association testing and meta-analyses were conducted, with subsequent fine-mapping, conditional analyses, and replication studies. Polygenic scores (PGS) were assessed for transferability across populations. Results Two genome-wide significant (P < 5 × 10-8) UACR-associated loci were identified, one in the BMP6 region on chromosome 6, in the meta-analysis of resident African individuals, and another in the HBB region on chromosome 11 in the meta-analysis of non-resident SSA individuals, as well as the combined meta-analysis of all studies. Replication of previous significant results confirmed associations in known UACR-associated regions, including THB53, GATM, and ARL15. PGS estimated using previous studies from European ancestry, African ancestry, and multi-ancestry cohorts exhibited limited transferability of PGS across populations, with less than 1% of observed variance explained. Conclusion This study contributes novel insights into the genetic architecture of kidney disease in SSA populations, emphasizing the need for conducting genetic research in diverse cohorts. The identified loci provide a foundation for future investigations into the genetic susceptibility to chronic kidney disease in underrepresented African populations Additionally, there is a need to develop integrated scores using multi-omics data and risk factors specific to the African context to improve the accuracy of predicting disease outcomes.
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Affiliation(s)
- Jean-Tristan Brandenburg
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Strengthening Oncology Services Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Wenlong Carl Chen
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Strengthening Oncology Services Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- National Cancer Registry, National Health Laboratory Service, Johannesburg, South Africa
| | - Palwende Romuald Boua
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Clinical Research Unit of Nanoro, Institut de Recherche en Sciences de la Santé, Nanoro, Burkina Faso
| | | | - Godfred Agongo
- Navrongo Health Research Centre, Navrongo, Ghana
- Department of Biochemistry and Forensic Sciences, School of Chemical and Biochemical Sciences, C. K. Tedam University of Technology and Applied Sciences, Navrongo, Ghana
| | - Lisa K. Micklesfield
- SAMRC/Wits Developmental Pathways for Health Research Unit, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Hermann Sorgho
- Clinical Research Unit of Nanoro, Institut de Recherche en Sciences de la Santé, Nanoro, Burkina Faso
| | - Stephen Tollman
- Medical Research Council/Wits University Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Gershim Asiki
- African Population and Health Research Center, Nairobi, Kenya
- Department of Women’s and Children’s Health, Karolinska Institute, Stockholm, Sweden
| | - Felistas Mashinya
- Department of Pathology and Medical Sciences, School of Healthcare Sciences, Faculty of Health Sciences, University of Limpopo, Polokwane, South Africa
| | - Scott Hazelhurst
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- School of Electrical and Information Engineering, University of the Witwatersrand, Johannesburg, South Africa
| | - Andrew P. Morris
- Centre for Genetics and Genomics Versus Arthritis, Centre for Musculoskeletal Research, The University of Manchester, Manchester, United Kingdom
| | - June Fabian
- Medical Research Council/Wits University Rural Public Health and Health Transitions Research Unit (Agincourt), School of Public Health, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Wits Donald Gordon Medical Centre, School of Clinical Medicine, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
| | - Michèle Ramsay
- Sydney Brenner Institute for Molecular Bioscience, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
- Division of Human Genetics, National Health Laboratory Service and School of Pathology, Faculty of Health Sciences, University of the Witwatersrand, Johannesburg, South Africa
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12
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Koch L. Global genomic diversity for All of Us. Nat Rev Genet 2024; 25:303. [PMID: 38509161 DOI: 10.1038/s41576-024-00727-9] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 03/22/2024]
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13
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Wei A, Border R, Fu B, Cullina S, Brandes N, Jang SK, Sankararaman S, Kenny E, Udler MS, Ntranos V, Zaitlen N, Arboleda V. Investigating the sources of variable impact of pathogenic variants in monogenic metabolic conditions. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2023.09.14.23295564. [PMID: 37745486 PMCID: PMC10516069 DOI: 10.1101/2023.09.14.23295564] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/26/2023]
Abstract
Over three percent of people carry a dominant pathogenic variant, yet only a fraction of carriers develop disease. Disease phenotypes from carriers of variants in the same gene range from mild to severe. Here, we investigate underlying mechanisms for this heterogeneity: variable variant effect sizes, carrier polygenic backgrounds, and modulation of carrier effect by genetic background (marginal epistasis). We leveraged exomes and clinical phenotypes from the UK Biobank and the Mt. Sinai BioMe Biobank to identify carriers of pathogenic variants affecting cardiometabolic traits. We employed recently developed methods to study these cohorts, observing strong statistical support and clinical translational potential for all three mechanisms of variable carrier penetrance and disease severity. For example, scores from our recent model of variant pathogenicity were tightly correlated with phenotype amongst clinical variant carriers, they predicted effects of variants of unknown significance, and they distinguished gain- from loss-of-function variants. We also found that polygenic scores predicted phenotypes amongst pathogenic carriers and that epistatic effects can exceed main carrier effects by an order of magnitude.
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14
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Zhang J, Zhan J, Jin J, Ma C, Zhao R, O'Connell J, Jiang Y, Koelsch BL, Zhang H, Chatterjee N. An ensemble penalized regression method for multi-ancestry polygenic risk prediction. Nat Commun 2024; 15:3238. [PMID: 38622117 DOI: 10.1038/s41467-024-47357-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/21/2023] [Accepted: 03/28/2024] [Indexed: 04/17/2024] Open
Abstract
Great efforts are being made to develop advanced polygenic risk scores (PRS) to improve the prediction of complex traits and diseases. However, most existing PRS are primarily trained on European ancestry populations, limiting their transferability to non-European populations. In this article, we propose a novel method for generating multi-ancestry Polygenic Risk scOres based on enSemble of PEnalized Regression models (PROSPER). PROSPER integrates genome-wide association studies (GWAS) summary statistics from diverse populations to develop ancestry-specific PRS with improved predictive power for minority populations. The method uses a combination ofL 1 (lasso) andL 2 (ridge) penalty functions, a parsimonious specification of the penalty parameters across populations, and an ensemble step to combine PRS generated across different penalty parameters. We evaluate the performance of PROSPER and other existing methods on large-scale simulated and real datasets, including those from 23andMe Inc., the Global Lipids Genetics Consortium, and All of Us. Results show that PROSPER can substantially improve multi-ancestry polygenic prediction compared to alternative methods across a wide variety of genetic architectures. In real data analyses, for example, PROSPER increased out-of-sample prediction R2 for continuous traits by an average of 70% compared to a state-of-the-art Bayesian method (PRS-CSx) in the African ancestry population. Further, PROSPER is computationally highly scalable for the analysis of large SNP contents and many diverse populations.
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Affiliation(s)
- Jingning Zhang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
| | | | - Jin Jin
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, PA, USA
| | - Cheng Ma
- Department of Statistics, University of Michigan, Ann Arbor, MI, USA
| | - Ruzhang Zhao
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA
| | | | | | | | - Haoyu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
- Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD, USA.
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15
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Brandenburg JT, Chen WC, Boua PR, Govender MA, Agongo G, Micklesfield LK, Sorgho H, Tollman S, Asiki G, Mashinya F, Hazelhurst S, Morris AP, Fabian J, Ramsay M. Genetic Association and Transferability for Urinary Albumin-Creatinine Ratio as a Marker of Kidney Disease in four Sub-Saharan African Populations and non-continental Individuals of African Ancestry. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.17.24301398. [PMID: 38293229 PMCID: PMC10827237 DOI: 10.1101/2024.01.17.24301398] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/01/2024]
Abstract
BACKGROUND Genome-wide association studies (GWAS) have predominantly focused on populations of European and Asian ancestry, limiting our understanding of genetic factors influencing kidney disease in Sub-Saharan African (SSA) populations. This study presents the largest GWAS for urinary albumin-to-creatinine ratio (UACR) in SSA individuals, including 8,970 participants living in different African regions and an additional 9,705 non-resident individuals of African ancestry from the UK Biobank and African American cohorts. METHODS Urine biomarkers and genotype data were obtained from two SSA cohorts (AWI-Gen and ARK), and two non-resident African-ancestry studies (UK Biobank and CKD-Gen Consortium). Association testing and meta-analyses were conducted, with subsequent fine-mapping, conditional analyses, and replication studies. Polygenic scores (PGS) were assessed for transferability across populations. RESULTS Two genome-wide significant (P<5x10-8) UACR-associated loci were identified, one in the BMP6 region on chromosome 6, in the meta-analysis of resident African individuals, and another in the HBB region on chromosome 11 in the meta-analysis of non-resident SSA individuals, as well as the combined meta-analysis of all studies. Replication of previous significant results confirmed associations in known UACR-associated regions, including THB53, GATM, and ARL15. PGS estimated using previous studies from European ancestry, African ancestry, and multi-ancestry cohorts exhibited limited transferability of PGS across populations, with less than 1% of observed variance explained. CONCLUSION This study contributes novel insights into the genetic architecture of kidney disease in SSA populations, emphasizing the need for conducting genetic research in diverse cohorts. The identified loci provide a foundation for future investigations into the genetic susceptibility to chronic kidney disease in underrepresented African populations Additionally, there is a need to develop integrated scores using multi-omics data and risk factors specific to the African context to improve the accuracy of predicting disease outcomes. METHODS Urine biomarkers and genotype data were obtained from two SSA cohorts (AWI-Gen and ARK), and two non-resident African-ancestry studies (UK Biobank and CKD-Gen Consortium). Association testing and meta-analyses were conducted, with subsequent fine-mapping, conditional analyses, and replication studies. Polygenic scores (PGS) were assessed for transferability across populations. RESULTS Two genome-wide significant (P<5x10-8) UACR-associated loci were identified, one in the BMP6 region on chromosome 6, in the meta-analysis of resident African individuals, and another in the HBB region on chromosome 11 in the meta-analysis of non-resident SSA individuals, as well as the combined meta-analysis of all studies. Replication of previous significant results confirmed associations in known UACR-associated regions, including THB53, GATM, and ARL15. PGS estimated using previous studies from European ancestry, African ancestry, and multi-ancestry cohorts exhibited limited transferability of PGS across populations, with less than 1% of observed variance explained. CONCLUSION This study contributes novel insights into the genetic architecture of kidney function in SSA populations, emphasizing the need for conducting genetic research in diverse cohorts. The identified loci provide a foundation for future investigations into the genetic susceptibility to chronic kidney disease in underrepresented African populations.
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16
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Zhang T, Zhou G, Klei L, Liu P, Chouldechova A, Zhao H, Roeder K, G'Sell M, Devlin B. Evaluating and improving health equity and fairness of polygenic scores. HGG ADVANCES 2024; 5:100280. [PMID: 38402414 PMCID: PMC10937319 DOI: 10.1016/j.xhgg.2024.100280] [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: 09/28/2023] [Revised: 02/14/2024] [Accepted: 02/14/2024] [Indexed: 02/26/2024] Open
Abstract
Polygenic scores (PGSs) are quantitative metrics for predicting phenotypic values, such as human height or disease status. Some PGS methods require only summary statistics of a relevant genome-wide association study (GWAS) for their score. One such method is Lassosum, which inherits the model selection advantages of Lasso to select a meaningful subset of the GWAS single-nucleotide polymorphisms as predictors from their association statistics. However, even efficient scores like Lassosum, when derived from European-based GWASs, are poor predictors of phenotype for subjects of non-European ancestry; that is, they have limited portability to other ancestries. To increase the portability of Lassosum, when GWAS information and estimates of linkage disequilibrium are available for both ancestries, we propose Joint-Lassosum (JLS). In the simulation settings we explore, JLS provides more accurate PGSs compared to other methods, especially when measured in terms of fairness. In analyses of UK Biobank data, JLS was computationally more efficient but slightly less accurate than a Bayesian comparator, SDPRX. Like all PGS methods, JLS requires selection of predictors, which are determined by data-driven tuning parameters. We describe a new approach to selecting tuning parameters and note its relevance for model selection for any PGS. We also draw connections to the literature on algorithmic fairness and discuss how JLS can help mitigate fairness-related harms that might result from the use of PGSs in clinical settings. While no PGS method is likely to be universally portable, due to the diversity of human populations and unequal information content of GWASs for different ancestries, JLS is an effective approach for enhancing portability and reducing predictive bias.
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Affiliation(s)
- Tianyu Zhang
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA.
| | - Geyu Zhou
- Department of Biostatistics, Yale University, New Haven, CT 06511, USA
| | - Lambertus Klei
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA
| | - Peng Liu
- Merck Research Laboratories, Merck & Co., Inc., Rahway, NJ 07065, USA
| | - Alexandra Chouldechova
- Microsoft Research NYC, New York, NY 10012, USA; Heinz College of Information Systems and Public Policy, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale University, New Haven, CT 06511, USA
| | - Kathryn Roeder
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA; Computational Biology Department, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Max G'Sell
- Department of Statistics and Data Science, Carnegie Mellon University, Pittsburgh, PA 15213, USA
| | - Bernie Devlin
- Department of Psychiatry, University of Pittsburgh, Pittsburgh, PA 15213, USA.
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Jin J, Zhan J, Zhang J, Zhao R, O'Connell J, Jiang Y, Buyske S, Gignoux C, Haiman C, Kenny EE, Kooperberg C, North K, Koelsch BL, Wojcik G, Zhang H, Chatterjee N. MUSSEL: Enhanced Bayesian polygenic risk prediction leveraging information across multiple ancestry groups. CELL GENOMICS 2024; 4:100539. [PMID: 38604127 PMCID: PMC11019365 DOI: 10.1016/j.xgen.2024.100539] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 03/23/2023] [Revised: 09/07/2023] [Accepted: 03/14/2024] [Indexed: 04/13/2024]
Abstract
Polygenic risk scores (PRSs) are now showing promising predictive performance on a wide variety of complex traits and diseases, but there exists a substantial performance gap across populations. We propose MUSSEL, a method for ancestry-specific polygenic prediction that borrows information in summary statistics from genome-wide association studies (GWASs) across multiple ancestry groups via Bayesian hierarchical modeling and ensemble learning. In our simulation studies and data analyses across four distinct studies, totaling 5.7 million participants with a substantial ancestral diversity, MUSSEL shows promising performance compared to alternatives. For example, MUSSEL has an average gain in prediction R2 across 11 continuous traits of 40.2% and 49.3% compared to PRS-CSx and CT-SLEB, respectively, in the African ancestry population. The best-performing method, however, varies by GWAS sample size, target ancestry, trait architecture, and linkage disequilibrium reference samples; thus, ultimately a combination of methods may be needed to generate the most robust PRSs across diverse populations.
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Affiliation(s)
- Jin Jin
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA; Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania, Philadelphia, PA 19103, USA.
| | | | - Jingning Zhang
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Ruzhang Zhao
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | | | | | - Steven Buyske
- Department of Statistics, Rutgers University, New Brunswick, NJ 08854, USA
| | - Christopher Gignoux
- Colorado Center for Personalized Medicine, University of Colorado Anschutz Medical Campus, Aurora, CO 80045, USA
| | - Christopher Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA 90032, USA
| | - Eimear E Kenny
- Icahn Institute for Genomic Health, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Charles Kooperberg
- Division of Public Health Sciences, Fred Hutchinson Cancer Center, Seattle, WA 98109, USA
| | - Kari North
- Department of Epidemiology, University of North Carolina at Chapel Hill, Chapel Hill, NC 27516, USA
| | | | - Genevieve Wojcik
- Department of Epidemiology, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA
| | - Haoyu Zhang
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA; Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD 20892, USA
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD 21205, USA; Department of Oncology, School of Medicine, Johns Hopkins University, Baltimore, MD 21205, USA.
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18
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Hou K, Gogarten S, Kim J, Hua X, Dias JA, Sun Q, Wang Y, Tan T, Atkinson EG, Martin A, Shortt J, Hirbo J, Li Y, Pasaniuc B, Zhang H. Admix-kit: an integrated toolkit and pipeline for genetic analyses of admixed populations. Bioinformatics 2024; 40:btae148. [PMID: 38490256 PMCID: PMC10980565 DOI: 10.1093/bioinformatics/btae148] [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: 09/30/2023] [Revised: 02/08/2024] [Accepted: 03/13/2024] [Indexed: 03/17/2024] Open
Abstract
SUMMARY Admixed populations, with their unique and diverse genetic backgrounds, are often underrepresented in genetic studies. This oversight not only limits our understanding but also exacerbates existing health disparities. One major barrier has been the lack of efficient tools tailored for the special challenges of genetic studies of admixed populations. Here, we present admix-kit, an integrated toolkit and pipeline for genetic analyses of admixed populations. Admix-kit implements a suite of methods to facilitate genotype and phenotype simulation, association testing, genetic architecture inference, and polygenic scoring in admixed populations. AVAILABILITY AND IMPLEMENTATION Admix-kit package is open-source and available at https://github.com/KangchengHou/admix-kit. Additionally, users can use the pipeline designed for admixed genotype simulation available at https://github.com/UW-GAC/admix-kit_workflow.
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Affiliation(s)
- Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095, United States
| | - Stephanie Gogarten
- Department of Biostatistics, University of Washington, Seattle, WA, 98195, United States
| | - Joohyun Kim
- Vanderbilt Genetics Institute and Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, United States
| | - Xing Hua
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, 20892, United States
| | - Julie-Alexia Dias
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, 02120, United States
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, United States
| | - Ying Wang
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, United States
| | - Taotao Tan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, United States
| | - Elizabeth G Atkinson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, 77030, United States
| | - Alicia Martin
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, 02142, United States
| | - Jonathan Shortt
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, 80045, United States
| | - Jibril Hirbo
- Division of Genetic Medicine, Department of Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, United States
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, 27599, United States
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095, United States
| | - Haoyu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, 20892, United States
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Chen T, Pham G, Fox L, Zhang J, Byun J, Han Y, Saunders GRB, Liu D, Bray MJ, Ramsey AT, McKay J, Bierut L, Amos CI, Hung RJ, Lin X, Zhang H, Chen LS. Genomic Insights for Personalized Care: Motivating At-Risk Individuals Toward Evidence-Based Health Practices. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.03.19.24304556. [PMID: 38562690 PMCID: PMC10984046 DOI: 10.1101/2024.03.19.24304556] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 04/04/2024]
Abstract
Lung cancer and tobacco use pose significant global health challenges and require a comprehensive translational roadmap for improved prevention strategies. We propose the GREAT care paradigm ( G enomic Informed Care for Motivating High R isk Individuals E ligible for Evidence-b a sed Prevention), which employs polygenic risk scores (PRSs) to stratify disease risk and personalize interventions, such as lung cancer screening and tobacco treatment. We developed PRSs using large-scale multi-ancestry genome-wide association studies and adjusted for genetic ancestry for standardized risk stratification across diverse populations. We applied our PRSs to over 340,000 individuals of diverse ethnic background and found significant odds ratios for lung cancer and difficulty quitting smoking. These findings enable the evaluation of PRS-based interventions in ongoing trials aimed at motivating health behavior changes in high-risk patients. This pioneering approach enhances primary care with genomic insights, promising improved outcomes in cancer prevention and tobacco treatment, and is currently under assessment in clinical trials.
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Yiangou K, Mavaddat N, Dennis J, Zanti M, Wang Q, Bolla MK, Abubakar M, Ahearn TU, Andrulis IL, Anton-Culver H, Antonenkova NN, Arndt V, Aronson KJ, Augustinsson A, Baten A, Behrens S, Bermisheva M, de Gonzalez AB, Białkowska K, Boddicker N, Bodelon C, Bogdanova NV, Bojesen SE, Brantley KD, Brauch H, Brenner H, Camp NJ, Canzian F, Castelao JE, Cessna MH, Chang-Claude J, Chenevix-Trench G, Chung WK, Colonna SV, Couch FJ, Cox A, Cross SS, Czene K, Daly MB, Devilee P, Dörk T, Dunning AM, Eccles DM, Eliassen AH, Engel C, Eriksson M, Evans DG, Fasching PA, Fletcher O, Flyger H, Fritschi L, Gago-Dominguez M, Gentry-Maharaj A, González-Neira A, Guénel P, Hahnen E, Haiman CA, Hamann U, Hartikainen JM, Ho V, Hodge J, Hollestelle A, Honisch E, Hooning MJ, Hoppe R, Hopper JL, Howell S, Howell A, Jakovchevska S, Jakubowska A, Jernström H, Johnson N, Kaaks R, Khusnutdinova EK, Kitahara CM, Koutros S, Kristensen VN, Lacey JV, Lambrechts D, Lejbkowicz F, Lindblom A, Lush M, Mannermaa A, Mavroudis D, Menon U, Murphy RA, Nevanlinna H, Obi N, Offit K, Park-Simon TW, Patel AV, Peng C, Peterlongo P, Pita G, Plaseska-Karanfilska D, Pylkäs K, Radice P, Rashid MU, Rennert G, Roberts E, Rodriguez J, Romero A, Rosenberg EH, Saloustros E, Sandler DP, Sawyer EJ, Schmutzler RK, Scott CG, Shu XO, Southey MC, Stone J, Taylor JA, Teras LR, van de Beek I, Willett W, Winqvist R, Zheng W, Vachon CM, Schmidt MK, Hall P, MacInnis RJ, Milne RL, Pharoah PD, Simard J, Antoniou AC, Easton DF, Michailidou K. Differences in polygenic score distributions in European ancestry populations: implications for breast cancer risk prediction. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.02.12.24302043. [PMID: 38410445 PMCID: PMC10896416 DOI: 10.1101/2024.02.12.24302043] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 02/28/2024]
Abstract
The 313-variant polygenic risk score (PRS313) provides a promising tool for breast cancer risk prediction. However, evaluation of the PRS313 across different European populations which could influence risk estimation has not been performed. Here, we explored the distribution of PRS313 across European populations using genotype data from 94,072 females without breast cancer, of European-ancestry from 21 countries participating in the Breast Cancer Association Consortium (BCAC) and 225,105 female participants from the UK Biobank. The mean PRS313 differed markedly across European countries, being highest in south-eastern Europe and lowest in north-western Europe. Using the overall European PRS313 distribution to categorise individuals leads to overestimation and underestimation of risk in some individuals from south-eastern and north-western countries, respectively. Adjustment for principal components explained most of the observed heterogeneity in mean PRS. Country-specific PRS distributions may be used to calibrate risk categories in individuals from different countries.
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Affiliation(s)
- Kristia Yiangou
- Biostatistics Unit, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus, 2371
| | - Nasim Mavaddat
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, CB1 8RN
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, CB1 8RN
| | - Maria Zanti
- Biostatistics Unit, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus, 2371
| | - Qin Wang
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, CB1 8RN
| | - Manjeet K. Bolla
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, CB1 8RN
| | - Mustapha Abubakar
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA, 20850
| | - Thomas U. Ahearn
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA, 20850
| | - Irene L. Andrulis
- Fred A, Litwin Center for Cancer Genetics, Lunenfeld-Tanenbaum Research Institute of Mount Sinai Hospital, Toronto, Ontario, Canada, M5G 1X5
- Department of Molecular Genetics, University of Toronto, Toronto, Ontario, Canada, M5S 1A8
| | - Hoda Anton-Culver
- Department of Medicine, Genetic Epidemiology Research Institute, University of California Irvine, Irvine, CA, USA, 92617
| | - Natalia N. Antonenkova
- NN Alexandrov Research Institute of Oncology and Medical Radiology, Minsk, Belarus, 223040
| | - Volker Arndt
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
| | - Kristan J. Aronson
- Department of Public Health Sciences, and Cancer Research Institute, Queen’s University, Kingston, ON, Canada, K7L 3N6
| | | | - Adinda Baten
- Leuven Multidisciplinary Breast Center, Department of Oncology, Leuven Cancer Institute, University Hospitals Leuven, Leuven, Belgium, 3000
| | - Sabine Behrens
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
| | - Marina Bermisheva
- Institute of Biochemistry and Genetics of the Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, Russia, 450054
- St Petersburg State University, St, Petersburg, Russia, 199034
| | | | - Katarzyna Białkowska
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland, 71-252
| | - Nicholas Boddicker
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA, 55905
| | - Clara Bodelon
- Department of Population Science, American Cancer Society, Atlanta, GA, USA, 30303
| | - Natalia V. Bogdanova
- NN Alexandrov Research Institute of Oncology and Medical Radiology, Minsk, Belarus, 223040
- Department of Radiation Oncology, Hannover Medical School, Hannover, Germany, 30625
- Gynaecology Research Unit, Hannover Medical School, Hannover, Germany, 30625
| | - Stig E. Bojesen
- Copenhagen General Population Study, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark, 2730
- Department of Clinical Biochemistry, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark, 2730
- Faculty of Health and Medical Sciences, University of Copenhagen, Copenhagen, Denmark, 2200
| | - Kristen D. Brantley
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA, 02115
| | - Hiltrud Brauch
- Dr Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany, 70376
- iFIT-Cluster of Excellence, University of Tübingen, Tübingen, Germany, 72074
- German Cancer Consortium (DKTK) and German Cancer Research Center (DKFZ), Partner Site Tübingen, Tübingen, Germany, 72074
| | - Hermann Brenner
- Division of Clinical Epidemiology and Aging Research, German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
- Division of Preventive Oncology, German Cancer Research Center (DKFZ) and National Center for Tumor Diseases (NCT), Heidelberg, Germany, 69120
- German Cancer Consortium (DKTK), German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
| | - Nicola J. Camp
- Department of Internal Medicine and Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA, 84112
| | - Federico Canzian
- Genomic Epidemiology Group, German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
| | - Jose E. Castelao
- Oncology and Genetics Unit, Instituto de Investigación Sanitaria de Santiago de Compostela (IDIS) Foundation, Complejo Hospitalario Universitario de Santiago, SERGAS, Vigo, Spain, 36312
| | - Melissa H. Cessna
- Department of Pathology, Intermountain Healthcare, Salt Lake City, UT, USA, 84143
- Intermountain Biorepository, Intermountain Healthcare, Salt Lake City, UT, USA, 84143
| | - Jenny Chang-Claude
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
- Cancer Epidemiology Group, University Cancer Center Hamburg (UCCH), University Medical Center Hamburg-Eppendorf, Hamburg, Germany, 20246
| | - Georgia Chenevix-Trench
- Cancer Research Program, QIMR Berghofer Medical Research Institute, Brisbane, Queensland, Australia, 4006
| | - Wendy K. Chung
- Departments of Pediatrics and Medicine, Columbia University, New York, NY, USA, 10032
| | - NBCS Collaborators
- Department of Cancer Genetics, Institute for Cancer Research, Oslo University Hospital-Radiumhospitalet, Oslo, Norway, 0379
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway, 0450
- Department of Research, Vestre Viken Hospital, Drammen, Norway, 3019
- Section for Breast- and Endocrine Surgery, Department of Cancer, Division of Surgery, Cancer and Transplantation Medicine, Oslo University Hospital-Ullevål, Oslo, Norway, 0450
- Department of Radiology and Nuclear Medicine, Oslo University Hospital, Oslo, Norway, 0379
- Department of Pathology, Akershus University Hospital, Lørenskog, Norway, 1478
- Department of Tumor Biology, Institute for Cancer Research, Oslo University Hospital, Oslo, Norway, 0379
- Department of Oncology, Division of Surgery, Cancer and Transplantation Medicine, Oslo University Hospital-Radiumhospitalet, Oslo, Norway, 0379
- National Advisory Unit on Late Effects after Cancer Treatment, Oslo University Hospital, Oslo, Norway, 0379
- Department of Oncology, Akershus University Hospital, Lørenskog, Norway, 1478
- Oslo Breast Cancer Research Consortium, Oslo University Hospital, Oslo, Norway, 0379
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway, 0379
| | - Sarah V. Colonna
- Department of Internal Medicine and Huntsman Cancer Institute, University of Utah, Salt Lake City, UT, USA, 84112
| | - Fergus J. Couch
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA, 55905
| | - Angela Cox
- Division of Clinical Medicine, School of Medicine and Population Health, University of Sheffield, Sheffield, UK, S10 2TN
| | - Simon S. Cross
- Division of Neuroscience, School of Medicine and Population Health, University of Sheffield, Sheffield, UK, S10 2TN
| | - Kamila Czene
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, 171 65
| | - Mary B. Daly
- Department of Clinical Genetics, Fox Chase Cancer Center, Philadelphia, PA, USA, 19111
| | - Peter Devilee
- Department of Pathology, Leiden University Medical Center, Leiden, the Netherlands, 2333 ZA
- Department of Human Genetics, Leiden University Medical Center, Leiden, the Netherlands, 2333 ZA
| | - Thilo Dörk
- Gynaecology Research Unit, Hannover Medical School, Hannover, Germany, 30625
| | - Alison M. Dunning
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK, CB1 8RN
| | - Diana M. Eccles
- Faculty of Medicine, University of Southampton, Southampton, UK, SO17 1BJ
| | - A. Heather Eliassen
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA, 02115
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA, 02115
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA, 02115
| | - Christoph Engel
- Institute for Medical Informatics, Statistics and Epidemiology, University of Leipzig, Leipzig, Germany, 04107
- LIFE - Leipzig Research Centre for Civilization Diseases, University of Leipzig, Leipzig, Germany, 04103
| | - Mikael Eriksson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, 171 65
| | - D. Gareth Evans
- Division of Evolution and Genomic Sciences, School of Biological Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK, M13 9WL
- North West Genomics Laboratory Hub, Manchester Centre for Genomic Medicine, St Mary’s Hospital, Manchester University NHS Foundation Trust, Manchester Academic Health Science Centre, Manchester, UK, M13 9WL
| | - Peter A. Fasching
- Department of Gynecology and Obstetrics, Comprehensive Cancer Center Erlangen-EMN, Friedrich-Alexander University Erlangen-Nuremberg, University Hospital Erlangen, Erlangen, Germany, 91054
| | - Olivia Fletcher
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK, SW7 3RP
| | - Henrik Flyger
- Department of Breast Surgery, Herlev and Gentofte Hospital, Copenhagen University Hospital, Herlev, Denmark, 2730
| | - Lin Fritschi
- School of Population Health, Curtin University, Perth, Western Australia, Australia, 6102
| | - Manuela Gago-Dominguez
- Cancer Genetics and Epidemiology Group, Genomic Medicine Group, Fundación Instituto de Investigación Sanitaria de Santiago de Compostela (FIDIS), Complejo Hospitalario Universitario de Santiago, SERGAS, Santiago de Compostela, Spain, 15706
| | - Aleksandra Gentry-Maharaj
- MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London, London, UK, WC1V 6LJ
- Department of Women’s Cancer, Elizabeth Garrett Anderson Institute for Women’s Health, University College London, London, UK
| | - Anna González-Neira
- Human Genotyping Unit-CeGen, Spanish National Cancer Research Centre (CNIO), Madrid, Spain, 28029
- Spanish Network on Rare Diseases (CIBERER)
| | - Pascal Guénel
- Team ‘Exposome and Heredity’, CESP, Gustave Roussy, INSERM, University Paris-Saclay, UVSQ, Villejuif, France, 94805
| | - Eric Hahnen
- Center for Familial Breast and Ovarian Cancer, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany, 50937
- Center for Integrated Oncology (CIO), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany, 50937
| | - Christopher A. Haiman
- Department of Preventive Medicine, Keck School of Medicine, University of Southern California, Los Angeles, CA, USA, 90033
| | - Ute Hamann
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
| | - Jaana M. Hartikainen
- Cancer RC, University of Eastern Finland, Kuopio, Finland, 70210
- Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland, 70210
| | - Vikki Ho
- Health Innovation and Evaluation Hub, Université de Montréal Hospital Research Centre (CRCHUM), Montréal, Québec, Canada
| | - James Hodge
- Department of Population Science, American Cancer Society, Atlanta, GA, USA, 30303
| | - Antoinette Hollestelle
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands, 3015 GD
| | - Ellen Honisch
- Department of Gynecology and Obstetrics, University Hospital Düsseldorf, Heinrich-Heine University Düsseldorf, Düsseldorf, Germany, 40225
| | - Maartje J. Hooning
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands, 3015 GD
| | - Reiner Hoppe
- Dr Margarete Fischer-Bosch-Institute of Clinical Pharmacology, Stuttgart, Germany, 70376
- University of Tübingen, Tübingen, Germany, 72074
| | - John L. Hopper
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia, 3010
| | - Sacha Howell
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
- Nightingale/Prevent Breast Cancer Centre, Wythenshawe Hospital, Manchester University NHS Foundation Trust, Manchester, UK
- Manchester Breast Centre, Manchester Cancer Research Centre, The Christie Hospital, Manchester, UK
| | - Anthony Howell
- Division of Cancer Sciences, University of Manchester, Manchester, UK, M13 9PL
| | - ABCTB Investigators
- Australian Breast Cancer Tissue Bank, Westmead Institute for Medical Research, University of Sydney, Sydney, New South Wales, Australia, 2145
| | - kConFab Investigators
- Research Department, Peter MacCallum Cancer Center, Melbourne, Victoria, Australia, 3000
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Parkville, Victoria, Australia, 3000
| | - Simona Jakovchevska
- Research Centre for Genetic Engineering and Biotechnology ‘Georgi D, Efremov’, MASA, Skopje, Republic of North Macedonia, 1000
| | - Anna Jakubowska
- Department of Genetics and Pathology, Pomeranian Medical University, Szczecin, Poland, 71-252
- Independent Laboratory of Molecular Biology and Genetic Diagnostics, Pomeranian Medical University, Szczecin, Poland, 171-252
| | - Helena Jernström
- Oncology, Clinical Sciences in Lund, Lund University, Lund, Sweden, 221 85
| | - Nichola Johnson
- The Breast Cancer Now Toby Robins Research Centre, The Institute of Cancer Research, London, UK, SW7 3RP
| | - Rudolf Kaaks
- Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
| | - Elza K. Khusnutdinova
- Institute of Biochemistry and Genetics of the Ufa Federal Research Centre of the Russian Academy of Sciences, Ufa, Russia, 450054
- Department of Genetics and Fundamental Medicine, Ufa University of Science and Technology, Ufa, Russia, 450076
| | - Cari M. Kitahara
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, NIH, Bethesda, MD, USA, 20892
| | - Stella Koutros
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Department of Health and Human Services, Bethesda, MD, USA, 20850
| | - Vessela N. Kristensen
- Institute of Clinical Medicine, Faculty of Medicine, University of Oslo, Oslo, Norway, 0450
- Department of Medical Genetics, Oslo University Hospital and University of Oslo, Oslo, Norway, 0379
| | - James V. Lacey
- Department of Computational and Quantitative Medicine, City of Hope, Duarte, CA, USA, 91010
- City of Hope Comprehensive Cancer Center, City of Hope, Duarte, CA, USA, 91010
| | - Diether Lambrechts
- Laboratory for Translational Genetics, Department of Human Genetics, KU Leuven, Leuven, Belgium, 3000
- VIB Center for Cancer Biology, VIB, Leuven, Belgium, 3001
| | | | - Annika Lindblom
- Department of Molecular Medicine and Surgery, Karolinska Institutet, Stockholm, Sweden, 171 76
- Department of Clinical Genetics, Karolinska University Hospital, Stockholm, Sweden, 171 76
| | - Michael Lush
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, CB1 8RN
| | - Arto Mannermaa
- Institute of Clinical Medicine, Pathology and Forensic Medicine, University of Eastern Finland, Kuopio, Finland, 70210
- Translational Cancer Research Area, University of Eastern Finland, Kuopio, Finland, 70210
- Biobank of Eastern Finland, Kuopio University Hospital, Kuopio, Finland
| | - Dimitrios Mavroudis
- Department of Medical Oncology, University Hospital of Heraklion, Heraklion, Greece, 711 10
| | - Usha Menon
- MRC Clinical Trials Unit, Institute of Clinical Trials and Methodology, University College London, London, UK, WC1V 6LJ
| | - Rachel A. Murphy
- School of Population and Public Health, University of British Columbia, Vancouver, BC, Canada, V6T 1Z4
- Cancer Control Research, BC Cancer Agency, Vancouver, BC, Canada, V5Z 1L3
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, Helsinki University Hospital, University of Helsinki, Helsinki, Finland, 00290
| | - Nadia Obi
- Institute for Occupational and Maritime Medicine, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, 20246
- Institute for Medical Biometry and Epidemiology, University Medical Center Hamburg-Eppendorf, Hamburg, Germany, 20246
| | - Kenneth Offit
- Clinical Genetics Research Lab, Department of Cancer Biology and Genetics, Memorial Sloan Kettering Cancer Center, New York, NY, USA, 10065
- Clinical Genetics Service, Department of Medicine, Memorial Sloan Kettering Cancer Center, New York, NY, USA, 10065
| | | | - Alpa V. Patel
- Department of Population Science, American Cancer Society, Atlanta, GA, USA, 30303
| | - Cheng Peng
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA, 02115
| | - Paolo Peterlongo
- Genome Diagnostics Program, IFOM ETS - the AIRC Institute of Molecular Oncology, Milan, Italy, 20139
| | - Guillermo Pita
- Human Genotyping Unit-CeGen, Spanish National Cancer Research Centre (CNIO), Madrid, Spain, 28029
| | - Dijana Plaseska-Karanfilska
- Research Centre for Genetic Engineering and Biotechnology ‘Georgi D, Efremov’, MASA, Skopje, Republic of North Macedonia, 1000
| | - Katri Pylkäs
- Laboratory of Cancer Genetics and Tumor Biology, Translational Medicine Research Unit, Biocenter Oulu, University of Oulu, Oulu, Finland, 90220
- Laboratory of Cancer Genetics and Tumor Biology, Northern Finland Laboratory Centre Oulu, Oulu, Finland, 90220
| | - Paolo Radice
- Unit of Predictice Medicine, Molecular Bases of Genetic Risk, Department of Research, Fondazione IRCCS Istituto Nazionale dei Tumori (INT), Milan, Italy, 20133
| | - Muhammad U. Rashid
- Molecular Genetics of Breast Cancer, German Cancer Research Center (DKFZ), Heidelberg, Germany, 69120
- Department of Basic Sciences, Shaukat Khanum Memorial Cancer Hospital and Research Centre (SKMCH & RC), Lahore, Pakistan, 54000
| | - Gad Rennert
- Technion, Faculty of Medicine and Association for Promotion of Research in Precision Medicine, Haifa, Israel
| | - Eleanor Roberts
- Division of Cancer Sciences, Faculty of Biology, Medicine and Health, University of Manchester, Manchester Academic Health Science Centre, Manchester, UK
| | - Juan Rodriguez
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, 171 65
| | - Atocha Romero
- Medical Oncology Department, Hospital Universitario Puerta de Hierro, Madrid, Spain, 28222
| | - Efraim H. Rosenberg
- Department of Pathology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital, Amsterdam, the Netherlands, 1066 CX
| | | | - Dale P. Sandler
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA, 27709
| | - Elinor J. Sawyer
- School of Cancer & Pharmaceutical Sciences, Comprehensive Cancer Centre, Guy’s Campus, King’s College London, London, UK
| | - Rita K. Schmutzler
- Center for Familial Breast and Ovarian Cancer, Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany, 50937
- Center for Integrated Oncology (CIO), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany, 50937
- Center for Molecular Medicine Cologne (CMMC), Faculty of Medicine and University Hospital Cologne, University of Cologne, Cologne, Germany, 50931
| | - Christopher G. Scott
- Department of Quantitative Health Sciences, Mayo Clinic, Rochester, MN, USA, 55905
| | - Xiao-Ou Shu
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37232
| | - Melissa C. Southey
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia, 3168
- Department of Clinical Pathology, The University of Melbourne, Melbourne, Victoria, Australia, 3010
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia, 3004
| | - Jennifer Stone
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia, 3010
- Genetic Epidemiology Group, School of Population and Global Health, University of Western Australia, Perth, Western Australia, Australia, 6000
| | - Jack A. Taylor
- Epidemiology Branch, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA, 27709
- Epigenetic and Stem Cell Biology Laboratory, National Institute of Environmental Health Sciences, NIH, Research Triangle Park, NC, USA, 27709
| | - Lauren R. Teras
- Department of Population Science, American Cancer Society, Atlanta, GA, USA, 30303
| | - Irma van de Beek
- Department of Clinical Genetics, The Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital, Amsterdam, the Netherlands, 1066 CX
| | - Walter Willett
- Department of Epidemiology, Harvard TH Chan School of Public Health, Boston, MA, USA, 02115
- Channing Division of Network Medicine, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA, USA, 02115
- Department of Nutrition, Harvard TH Chan School of Public Health, Boston, MA, USA, 02115
| | - Robert Winqvist
- Laboratory of Cancer Genetics and Tumor Biology, Translational Medicine Research Unit, Biocenter Oulu, University of Oulu, Oulu, Finland, 90220
- Laboratory of Cancer Genetics and Tumor Biology, Northern Finland Laboratory Centre Oulu, Oulu, Finland, 90220
| | - Wei Zheng
- Division of Epidemiology, Department of Medicine, Vanderbilt Epidemiology Center, Vanderbilt-Ingram Cancer Center, Vanderbilt University School of Medicine, Nashville, TN, USA, 37232
| | - Celine M. Vachon
- Department of Quantitative Health Sciences, Division of Epidemiology, Mayo Clinic, Rochester, MN, USA, 55905
| | - Marjanka K. Schmidt
- Division of Molecular Pathology, The Netherlands Cancer Institute, Amsterdam, the Netherlands, 1066 CX
- Division of Psychosocial Research and Epidemiology, The Netherlands Cancer Institute - Antoni van Leeuwenhoek hospital, Amsterdam, the Netherlands, 1066 CX
- Department of Clinical Genetics, Leiden University Medical Center, Leiden, the Netherlands, 2333 ZA
| | - Per Hall
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden, 171 65
- Department of Oncology, Södersjukhuset, Stockholm, Sweden, 118 83
| | - Robert J. MacInnis
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia, 3010
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia, 3004
| | - Roger L. Milne
- Centre for Epidemiology and Biostatistics, Melbourne School of Population and Global Health, The University of Melbourne, Melbourne, Victoria, Australia, 3010
- Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, Victoria, Australia, 3168
- Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, Victoria, Australia, 3004
| | - Paul D.P. Pharoah
- Department of Computational Biomedicine, Cedars-Sinai Medical Center, West Hollywood, CA, USA, 90069
| | - Jacques Simard
- Genomics Center, Centre Hospitalier Universitaire de Québec – Université Laval Research Center, Québec City, Québec, Canada, G1V 4G2
| | - Antonis C. Antoniou
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, CB1 8RN
| | - Douglas F. Easton
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, CB1 8RN
- Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UK, CB1 8RN
| | - Kyriaki Michailidou
- Biostatistics Unit, The Cyprus Institute of Neurology and Genetics, Nicosia, Cyprus, 2371
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK, CB1 8RN
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Fatumo S. Including diverse populations enhances the discovery of type 2 diabetes loci. Nat Rev Genet 2024; 25:82. [PMID: 37993608 DOI: 10.1038/s41576-023-00678-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/24/2023]
Affiliation(s)
- Segun Fatumo
- MRC/UVRI and LSHTM Uganda Research Unit, Entebbe, Uganda.
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22
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Bolze A, Cirulli ET, Hajek C, Schnell Blitstein JM, Grzymski JJ. The Potential of Genetics in Identifying Women at Lower Risk of Breast Cancer. JAMA Oncol 2024; 10:236-239. [PMID: 38153744 PMCID: PMC10870185 DOI: 10.1001/jamaoncol.2023.5468] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/31/2023] [Accepted: 09/13/2023] [Indexed: 12/29/2023]
Abstract
Importance Genetic information is not being used to identify women at lower risk of breast cancer or other diseases in clinical practice. With the new US Preventive Services Task Force guidelines lowering the age for mammogram screening for all, there is a potential benefit in identifying women at lower risk of disease who may defer the start of mammographic screening. This genetic risk-based approach would help mitigate overscreening, associated costs, and anxiety. Objective To assess breast cancer incidence and age of onset among women at low genetic risk compared with women at average risk and evaluate the potential to delay mammography on the basis of genetic risk stratification. Design, Setting, and Participants This retrospective case-control study included 25 591 women from the Healthy Nevada Project sequenced by Helix between 2018 and 2022. Data extracted from electronic health records at the end of 2022 (mean length of electronic health record available was 12 years) were used for the analysis in 2023. Main Outcomes and Measures Breast cancer diagnosis was identified from electronic health records. Classification to the low-risk genetic group required (1) the absence of pathogenic variants or a variant of uncertain significance in BRCA1, BRCA2, PALB2, ATM, or CHEK2, and (2) a low polygenic risk score (bottom 10%) using a 313-single-nucleotide variant model. Results Of 25 591 women in the study (mean [SD] age was 53.8 [16.9] years), 2338 women (9.1%) were classified as having low risk for breast cancer; 410 women (1.6%) were classified as high risk; and 22 843 women (89.3%) as average risk. There was a significant reduction in breast cancer diagnosis among the low-risk group (hazard ratio, 0.39; 95% CI, 0.29-0.52; P < .001). By 45 years of age, 0.69% of women in the average-risk group were diagnosed with breast cancer, whereas women in the low-risk group reached this rate at 51 years. By 50 years of age, 1.41% of those in the average-risk group were diagnosed with breast cancer, whereas those in the low-risk group reached this rate at age 58 years. These findings suggest that deferring mammogram screening by 5 to 10 years for women at low risk of breast cancer aligns with new draft recommendations. Conclusions and Relevance The findings of this retrospective case-control study underscore the value of genetics in individualizing the onset of breast cancer screening. Improving breast cancer risk stratification by implementing both high-risk and low-risk strategies in screening can refine preventive measures and optimize health care resource allocation.
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Aw AJ, McRae J, Rahmani E, Song YS. Highly parameterized polygenic scores tend to overfit to population stratification via random effects. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2024:2024.01.27.577589. [PMID: 38352303 PMCID: PMC10862757 DOI: 10.1101/2024.01.27.577589] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Indexed: 02/22/2024]
Abstract
Polygenic scores (PGSs), increasingly used in clinical settings, frequently include many genetic variants, with performance typically peaking at thousands of variants. Such highly parameterized PGSs often include variants that do not pass a genome-wide significance threshold. We propose a mathematical perspective that renders the effects of many of these non-significant variants random rather than causal, with the randomness capturing population structure. We devise methods to assess variant effect randomness and population stratification bias. Applying these methods to 141 traits from the UK Biobank, we find that, for many PGSs, the effects of non-significant variants are considerably random, with the extent of randomness associated with the degree of overfitting to population structure of the discovery cohort. Our findings explain why highly parameterized PGSs simultaneously have superior cohort-specific performance and limited generalizability, suggesting the critical need for variant randomness tests in PGS evaluation. Supporting code and a dashboard are available at https://github.com/songlab-cal/StratPGS.
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Affiliation(s)
- Alan J. Aw
- Department of Statistics, University of California, Berkeley
- Center for Computational Biology, University of California, Berkeley
- Artificial Intelligence Laboratory, Illumina Inc
| | - Jeremy McRae
- Artificial Intelligence Laboratory, Illumina Inc
| | - Elior Rahmani
- Department of Computational Medicine, University of California, Los Angeles
| | - Yun S. Song
- Department of Statistics, University of California, Berkeley
- Center for Computational Biology, University of California, Berkeley
- Computer Science Division, University of California, Berkeley
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24
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Bhalala OG, Watson R, Yassi N. Multi-Omic Blood Biomarkers as Dynamic Risk Predictors in Late-Onset Alzheimer's Disease. Int J Mol Sci 2024; 25:1231. [PMID: 38279230 PMCID: PMC10816901 DOI: 10.3390/ijms25021231] [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: 12/07/2023] [Revised: 01/17/2024] [Accepted: 01/18/2024] [Indexed: 01/28/2024] Open
Abstract
Late-onset Alzheimer's disease is the leading cause of dementia worldwide, accounting for a growing burden of morbidity and mortality. Diagnosing Alzheimer's disease before symptoms are established is clinically challenging, but would provide therapeutic windows for disease-modifying interventions. Blood biomarkers, including genetics, proteins and metabolites, are emerging as powerful predictors of Alzheimer's disease at various timepoints within the disease course, including at the preclinical stage. In this review, we discuss recent advances in such blood biomarkers for determining disease risk. We highlight how leveraging polygenic risk scores, based on genome-wide association studies, can help stratify individuals along their risk profile. We summarize studies analyzing protein biomarkers, as well as report on recent proteomic- and metabolomic-based prediction models. Finally, we discuss how a combination of multi-omic blood biomarkers can potentially be used in memory clinics for diagnosis and to assess the dynamic risk an individual has for developing Alzheimer's disease dementia.
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Affiliation(s)
- Oneil G. Bhalala
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia; (R.W.); (N.Y.)
- Department of Neurology, Melbourne Brain Centre at The Royal Melbourne Hospital, University of Melbourne, Parkville 3050, Australia
| | - Rosie Watson
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia; (R.W.); (N.Y.)
- Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Parkville 3050, Australia
| | - Nawaf Yassi
- Population Health and Immunity Division, The Walter and Eliza Hall Institute of Medical Research, Parkville 3052, Australia; (R.W.); (N.Y.)
- Department of Neurology, Melbourne Brain Centre at The Royal Melbourne Hospital, University of Melbourne, Parkville 3050, Australia
- Department of Medicine, The Royal Melbourne Hospital, University of Melbourne, Parkville 3050, Australia
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25
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Nakase T, Guerra G, Ostrom QT, Ge T, Melin B, Wrensch M, Wiencke JK, Jenkins RB, Eckel-Passow JE, Bondy ML, Francis SS, Kachuri L. Genome-wide Polygenic Risk Scores Predict Risk of Glioma and Molecular Subtypes. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.10.24301112. [PMID: 38260701 PMCID: PMC10802631 DOI: 10.1101/2024.01.10.24301112] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/24/2024]
Abstract
Background Polygenic risk scores (PRS) aggregate the contribution of many risk variants to provide a personalized genetic susceptibility profile. Since sample sizes of glioma genome-wide association studies (GWAS) remain modest, there is a need to find efficient ways of capturing genetic risk factors using available germline data. Methods We developed a novel PRS (PRS-CS) that uses continuous shrinkage priors to model the joint effects of over 1 million polymorphisms on disease risk and compared it to an approach (PRS-CT) that selects a limited set of independent variants that reach genome-wide significance (P<5×10-8). PRS models were trained using GWAS results stratified by histological (10,346 cases, 14,687 controls) and molecular subtype (2,632 cases, 2,445 controls), and validated in two independent cohorts. Results PRS-CS was consistently more predictive than PRS-CT across glioma subtypes with an average increase in explained variance (R2) of 21%. Improvements were particularly pronounced for glioblastoma tumors, with PRS-CS yielding larger effect sizes (odds ratio (OR)=1.93, P=2.0×10-54 vs. OR=1.83, P=9.4×10-50) and higher explained variance (R2=2.82% vs. R2=2.56%). Individuals in the 95th percentile of the PRS-CS distribution had a 3-fold higher lifetime absolute risk of IDH mutant (0.63%) and IDH wildtype (0.76%) glioma relative to individuals with average PRS. PRS-CS also showed high classification accuracy for IDH mutation status among cases (AUC=0.895). Conclusions Our novel genome-wide PRS may improve the identification of high-risk individuals and help distinguish between prognostic glioma subtypes, increasing the potential clinical utility of germline genetics in glioma patient management.
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Affiliation(s)
- Taishi Nakase
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, USA
| | - Geno Guerra
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Quinn T. Ostrom
- Department of Neurosurgery, Duke University School of Medicine, Durham, NC, USA
| | - Tian Ge
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA
- Center for Precision Psychiatry, Department of Psychiatry, Massachusetts General Hospital, Harvard Medical School, Boston, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Beatrice Melin
- Department of Radiation Sciences, Oncology Umeå University, Umeå, Sweden
| | - Margaret Wrensch
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - John K. Wiencke
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
| | - Robert B. Jenkins
- Department of Laboratory Medicine and Pathology, Mayo Clinic, Rochester, MN, USA
| | | | | | - Melissa L. Bondy
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
| | - Stephen S. Francis
- Department of Neurological Surgery, University of California San Francisco, San Francisco, CA, USA
- Department of Epidemiology and Biostatistics, University of California San Francisco, San Francisco, CA, USA
- Weill Institute for Neurosciences, University of California San Francisco, San Francisco, CA, USA
| | - Linda Kachuri
- Department of Epidemiology & Population Health, Stanford University School of Medicine, Stanford, CA, USA
- Stanford Cancer Institute, Stanford University School of Medicine, Stanford, CA, USA
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26
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Hodonsky CJ, Turner AW, Khan MD, Barrientos NB, Methorst R, Ma L, Lopez NG, Mosquera JV, Auguste G, Farber E, Ma WF, Wong D, Onengut-Gumuscu S, Kavousi M, Peyser PA, van der Laan SW, Leeper NJ, Kovacic JC, Björkegren JLM, Miller CL. Multi-ancestry genetic analysis of gene regulation in coronary arteries prioritizes disease risk loci. CELL GENOMICS 2024; 4:100465. [PMID: 38190101 PMCID: PMC10794848 DOI: 10.1016/j.xgen.2023.100465] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Received: 04/17/2023] [Revised: 09/07/2023] [Accepted: 11/19/2023] [Indexed: 01/09/2024]
Abstract
Genome-wide association studies (GWASs) have identified hundreds of risk loci for coronary artery disease (CAD). However, non-European populations are underrepresented in GWASs, and the causal gene-regulatory mechanisms of these risk loci during atherosclerosis remain unclear. We incorporated local ancestry and haplotypes to identify quantitative trait loci for expression (eQTLs) and splicing (sQTLs) in coronary arteries from 138 ancestrally diverse Americans. Of 2,132 eQTL-associated genes (eGenes), 47% were previously unreported in coronary artery; 19% exhibited cell-type-specific expression. Colocalization revealed subgroups of eGenes unique to CAD and blood pressure GWAS. Fine-mapping highlighted additional eGenes, including TBX20 and IL5. We also identified sQTLs for 1,690 genes, among which TOR1AIP1 and ULK3 sQTLs demonstrated the importance of evaluating splicing to accurately identify disease-relevant isoform expression. Our work provides a patient-derived coronary artery eQTL resource and exemplifies the need for diverse study populations and multifaceted approaches to characterize gene regulation in disease processes.
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Affiliation(s)
- Chani J Hodonsky
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Adam W Turner
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Mohammad Daud Khan
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Nelson B Barrientos
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; Department of Genetic Medicine, Johns Hopkins University, Baltimore, MD 21205, USA
| | - Ruben Methorst
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, the Netherlands
| | - Lijiang Ma
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA
| | - Nicolas G Lopez
- Division of Vascular Surgery, Department of Surgery, Stanford University, Stanford, CA 94305, USA
| | - Jose Verdezoto Mosquera
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA
| | - Gaëlle Auguste
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Emily Farber
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Wei Feng Ma
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; Medical Scientist Training Program, Department of Pathology, University of Virginia, Charlottesville, VA 22908, USA
| | - Doris Wong
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; Department of Biochemistry and Molecular Genetics, University of Virginia, Charlottesville, VA 22908, USA
| | - Suna Onengut-Gumuscu
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA
| | - Maryam Kavousi
- Department of Epidemiology, Erasmus University Medical Center, 3000 CA Rotterdam, the Netherlands
| | - Patricia A Peyser
- Department of Epidemiology, University of Michigan, Ann Arbor, MI 48019, USA
| | - Sander W van der Laan
- Central Diagnostics Laboratory, Division Laboratories, Pharmacy, and Biomedical Genetics, University Medical Center Utrecht, Utrecht University, 3584 CX Utrecht, the Netherlands
| | - Nicholas J Leeper
- Division of Vascular Surgery, Department of Surgery, Stanford University, Stanford, CA 94305, USA
| | - Jason C Kovacic
- Cardiovascular Research Institute, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Victor Chang Cardiac Research Institute, Darlinghurst, NSW 2010, Australia; St. Vincent's Clinical School, University of New South Wales, Sydney, NSW 2052, Australia
| | - Johan L M Björkegren
- Department of Genetics and Genomic Sciences, Icahn Institute for Genomics and Multiscale Biology, Icahn School of Medicine at Mount Sinai, New York, NY 10029, USA; Department of Medicine, Huddinge, Karolinska Institutet, 141 52 Huddinge, Sweden
| | - Clint L Miller
- Center for Public Health Genomics, University of Virginia, Charlottesville, VA 22908, USA; Division of Vascular Surgery, Department of Surgery, Stanford University, Stanford, CA 94305, USA; Department of Public Health Sciences, University of Virginia, Charlottesville, VA 22908, USA.
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27
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Finkel D, Jaffee SR. Introduction to IDEA Special Issue. Behav Genet 2024; 54:1-3. [PMID: 38148347 DOI: 10.1007/s10519-023-10173-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/28/2023]
Affiliation(s)
- Deborah Finkel
- Center for Economic and Social Research, University of Southern California, Los Angeles, USA.
- Institute for Gerontology, Jönköping University, Jönköping, Sweden.
| | - Sara R Jaffee
- Department of Psychology, University of Pennsylvania, Pennsylvania, USA
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28
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Sigala RE, Lagou V, Shmeliov A, Atito S, Kouchaki S, Awais M, Prokopenko I, Mahdi A, Demirkan A. Machine Learning to Advance Human Genome-Wide Association Studies. Genes (Basel) 2023; 15:34. [PMID: 38254924 PMCID: PMC10815885 DOI: 10.3390/genes15010034] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/16/2023] [Revised: 12/19/2023] [Accepted: 12/22/2023] [Indexed: 01/24/2024] Open
Abstract
Machine learning, including deep learning, reinforcement learning, and generative artificial intelligence are revolutionising every area of our lives when data are made available. With the help of these methods, we can decipher information from larger datasets while addressing the complex nature of biological systems in a more efficient way. Although machine learning methods have been introduced to human genetic epidemiological research as early as 2004, those were never used to their full capacity. In this review, we outline some of the main applications of machine learning to assigning human genetic loci to health outcomes. We summarise widely used methods and discuss their advantages and challenges. We also identify several tools, such as Combi, GenNet, and GMSTool, specifically designed to integrate these methods for hypothesis-free analysis of genetic variation data. We elaborate on the additional value and limitations of these tools from a geneticist's perspective. Finally, we discuss the fast-moving field of foundation models and large multi-modal omics biobank initiatives.
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Affiliation(s)
- Rafaella E. Sigala
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, Guildford GU2 7XH, Surrey, UK; (R.E.S.); (V.L.); (A.S.); (I.P.)
| | - Vasiliki Lagou
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, Guildford GU2 7XH, Surrey, UK; (R.E.S.); (V.L.); (A.S.); (I.P.)
| | - Aleksey Shmeliov
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, Guildford GU2 7XH, Surrey, UK; (R.E.S.); (V.L.); (A.S.); (I.P.)
| | - Sara Atito
- Surrey Institute for People-Centred Artificial Intelligence, University of Surrey, Guildford GU2 7XH, Surrey, UK; (S.A.); (S.K.); (M.A.)
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, Surrey, UK
| | - Samaneh Kouchaki
- Surrey Institute for People-Centred Artificial Intelligence, University of Surrey, Guildford GU2 7XH, Surrey, UK; (S.A.); (S.K.); (M.A.)
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, Surrey, UK
| | - Muhammad Awais
- Surrey Institute for People-Centred Artificial Intelligence, University of Surrey, Guildford GU2 7XH, Surrey, UK; (S.A.); (S.K.); (M.A.)
- Centre for Vision, Speech and Signal Processing, University of Surrey, Guildford GU2 7XH, Surrey, UK
| | - Inga Prokopenko
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, Guildford GU2 7XH, Surrey, UK; (R.E.S.); (V.L.); (A.S.); (I.P.)
- Surrey Institute for People-Centred Artificial Intelligence, University of Surrey, Guildford GU2 7XH, Surrey, UK; (S.A.); (S.K.); (M.A.)
| | - Adam Mahdi
- Oxford Internet Institute, University of Oxford, Oxford OX1 3JS, Oxfordshire, UK;
| | - Ayse Demirkan
- Section of Statistical Multi-Omics, Department of Clinical and Experimental Medicine, Guildford GU2 7XH, Surrey, UK; (R.E.S.); (V.L.); (A.S.); (I.P.)
- Surrey Institute for People-Centred Artificial Intelligence, University of Surrey, Guildford GU2 7XH, Surrey, UK; (S.A.); (S.K.); (M.A.)
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Xu L, Zhou G, Jiang W, Guan L, Zhao H. Leveraging genetic correlations and multiple populations to improve genetic risk prediction for non-European populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.10.29.564615. [PMID: 37961111 PMCID: PMC10634936 DOI: 10.1101/2023.10.29.564615] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 11/15/2023]
Abstract
The disparity in genetic risk prediction accuracy between European and non-European individuals highlights a critical challenge in health inequality. To bridge this gap, we introduce JointPRS, a novel method that models multiple populations jointly to improve genetic risk predictions for non-European individuals. JointPRS has three key features. First, it encompasses all diverse populations to improve prediction accuracy, rather than relying solely on the target population with a singular auxiliary European group. Second, it autonomously estimates and leverages chromosome-wise cross-population genetic correlations to infer the effect sizes of genetic variants. Lastly, it provides an auto version that has comparable performance to the tuning version to accommodate the situation with no validation dataset. Through extensive simulations and real data applications to 22 quantitative traits and four binary traits in East Asian populations, nine quantitative traits and one binary trait in African populations, and four quantitative traits in South Asian populations, we demonstrate that JointPRS outperforms state-of-art methods, improving the prediction accuracy for both quantitative and binary traits in non-European populations.
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Affiliation(s)
- Leqi Xu
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Geyu Zhou
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Wei Jiang
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
| | - Leying Guan
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
| | - Hongyu Zhao
- Department of Biostatistics, Yale School of Public Health, New Haven, CT, USA
- Program in Computational Biology and Bioinformatics, Yale University, New Haven, CT, USA
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30
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Vaskimo LM, Gomon G, Naamane N, Cordell HJ, Pratt A, Knevel R. The Application of Genetic Risk Scores in Rheumatic Diseases: A Perspective. Genes (Basel) 2023; 14:2167. [PMID: 38136989 PMCID: PMC10743278 DOI: 10.3390/genes14122167] [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: 11/01/2023] [Revised: 11/29/2023] [Accepted: 11/30/2023] [Indexed: 12/24/2023] Open
Abstract
Modest effect sizes have limited the clinical applicability of genetic associations with rheumatic diseases. Genetic risk scores (GRSs) have emerged as a promising solution to translate genetics into useful tools. In this review, we provide an overview of the recent literature on GRSs in rheumatic diseases. We describe six categories for which GRSs are used: (a) disease (outcome) prediction, (b) genetic commonalities between diseases, (c) disease differentiation, (d) interplay between genetics and environmental factors, (e) heritability and transferability, and (f) detecting causal relationships between traits. In our review of the literature, we identified current lacunas and opportunities for future work. First, the shortage of non-European genetic data restricts the application of many GRSs to European populations. Next, many GRSs are tested in settings enriched for cases that limit the transferability to real life. If intended for clinical application, GRSs are ideally tested in the relevant setting. Finally, there is much to elucidate regarding the co-occurrence of clinical traits to identify shared causal paths and elucidate relationships between the diseases. GRSs are useful instruments for this. Overall, the ever-continuing research on GRSs gives a hopeful outlook into the future of GRSs and indicates significant progress in their potential applications.
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Affiliation(s)
- Lotta M. Vaskimo
- Department of Rheumatology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Georgy Gomon
- Department of Rheumatology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
| | - Najib Naamane
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne NE2 4AX, UK
| | - Heather J. Cordell
- Population Health Sciences Institute, Newcastle University, Newcastle upon Tyne NE2 4AX, UK
| | - Arthur Pratt
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
- Department of Rheumatology, Newcastle upon Tyne Hospitals NHS Foundation Trust, Newcastle upon Tyne NE7 7DN, UK
| | - Rachel Knevel
- Department of Rheumatology, Leiden University Medical Center, 2333 ZA Leiden, The Netherlands
- Translational and Clinical Research Institute, Newcastle University, Newcastle upon Tyne NE2 4HH, UK
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31
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Hou K, Gogarten S, Kim J, Hua X, Dias JA, Sun Q, Wang Y, Tan T, Atkinson EG, Martin A, Shortt J, Hirbo J, Li Y, Pasaniuc B, Zhang H. Admix-kit: An Integrated Toolkit and Pipeline for Genetic Analyses of Admixed Populations. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.30.560263. [PMID: 37873338 PMCID: PMC10592849 DOI: 10.1101/2023.09.30.560263] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/25/2023]
Abstract
Admixed populations, with their unique and diverse genetic backgrounds, are often underrepresented in genetic studies. This oversight not only limits our understanding but also exacerbates existing health disparities. One major barrier has been the lack of efficient tools tailored for the special challenges of genetic study of admixed populations. Here, we present admix-kit, an integrated toolkit and pipeline for genetic analyses of admixed populations. Admix-kit implements a suite of methods to facilitate genotype and phenotype simulation, association testing, genetic architecture inference, and polygenic scoring in admixed populations.
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Affiliation(s)
- Kangcheng Hou
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA
| | | | - Joohyun Kim
- Vanderbilt Genetics Institute and Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Xing Hua
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
| | - Julie-Alexia Dias
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA
| | - Quan Sun
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Ying Wang
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Taotao Tan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | | | - Elizabeth G. Atkinson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Alicia Martin
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Jonathan Shortt
- Department of Biomedical Informatics, University of Colorado Anschutz Medical Campus, Aurora, CO, USA
| | - Jibril Hirbo
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, USA
| | - Yun Li
- Department of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA
| | - Bogdan Pasaniuc
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, USA
| | - Haoyu Zhang
- Division of Cancer Epidemiology and Genetics, National Cancer Institute, Bethesda, MD, USA
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Chen T, Zhang H, Mazumder R, Lin X. Ensembled best subset selection using summary statistics for polygenic risk prediction. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.25.559307. [PMID: 37886515 PMCID: PMC10602024 DOI: 10.1101/2023.09.25.559307] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/28/2023]
Abstract
Polygenic risk scores (PRS) enhance population risk stratification and advance personalized medicine, yet existing methods face a tradeoff between predictive power and computational efficiency. We introduce ALL-Sum, a fast and scalable PRS method that combines an efficient summary statistic-based L 0 L 2 penalized regression algorithm with an ensembling step that aggregates estimates from different tuning parameters for improved prediction performance. In extensive large-scale simulations across a wide range of polygenicity and genome-wide association studies (GWAS) sample sizes, ALL-Sum consistently outperforms popular alternative methods in terms of prediction accuracy, runtime, and memory usage. We analyze 27 published GWAS summary statistics for 11 complex traits from 9 reputable data sources, including the Global Lipids Genetics Consortium, Breast Cancer Association Consortium, and FinnGen, evaluated using individual-level UKBB data. ALL-Sum achieves the highest accuracy for most traits, particularly for GWAS with large sample sizes. We provide ALL-Sum as a user-friendly command-line software with pre-computed reference data for streamlined user-end analysis.
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Zhang T, Klei L, Liu P, Chouldechova A, Roeder K, G'Sell M, Devlin B. Evaluating and Improving Health Equity and Fairness of Polygenic Scores. BIORXIV : THE PREPRINT SERVER FOR BIOLOGY 2023:2023.09.22.559051. [PMID: 37790341 PMCID: PMC10542523 DOI: 10.1101/2023.09.22.559051] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 10/05/2023]
Abstract
Polygenic scores (PGS) are quantitative metrics for predicting phenotypic values, such as human height or disease status. Some PGS methods require only summary statistics of a relevant genome-wide association study (GWAS) for their score. One such method is Lassosum, which inherits the model selection advantages of Lasso to select a meaningful subset of the GWAS single nucleotide polymorphisms as predictors from their association statistics. However, even efficient scores like Lassosum, when derived from European-based GWAS, are poor predictors of phenotype for subjects of non-European ancestry; that is, they have limited portability to other ancestries. To increase the portability of Lassosum, when GWAS information and estimates of linkage disequilibrium are available for both ancestries, we propose Joint-Lassosum. In the simulation settings we explore, Joint-Lassosum provides more accurate PGS compared with other methods, especially when measured in terms of fairness. Like all PGS methods, Joint-Lassosum requires selection of predictors, which are determined by data-driven tuning parameters. We describe a new approach to selecting tuning parameters and note its relevance for model selection for any PGS. We also draw connections to the literature on algorithmic fairness and discuss how Joint-Lassosum can help mitigate fairness-related harms that might result from the use of PGS scores in clinical settings. While no PGS method is likely to be universally portable, due to the diversity of human populations and unequal information content of GWAS for different ancestries, Joint-Lassosum is an effective approach for enhancing portability and reducing predictive bias.
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Grunin M, Triffon D, Beykin G, Rahmani E, Schweiger R, Tiosano L, Khateb S, Hagbi-Levi S, Rinsky B, Munitz R, Winkler TW, Heid IM, Halperin E, Carmi S, Chowers I. Genome-wide association study and genomic risk prediction of age-related macular degeneration in Israel. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.09.06.23295126. [PMID: 37732190 PMCID: PMC10508791 DOI: 10.1101/2023.09.06.23295126] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 09/22/2023]
Abstract
Purpose The risk of developing age-related macular degeneration(AMD) is influenced by genetic background. In 2016, International AMD Genomics Consortium(IAMDGC) identified 52 risk variants in 34 loci, and a polygenic risk score(PRS) based on these variants was associated with AMD. The Israeli population has a unique genetic composition: Ashkenazi Jewish(AJ), Jewish non-Ashkenazi, and Arab sub-populations. We aimed to perform a genome-wide association study(GWAS) for AMD in Israel, and to evaluate PRSs for AMD. Methods For our discovery set, we recruited 403 AMD patients and 256 controls at Hadassah Medical Center. We genotyped all individuals via custom exome chip. We imputed non-typed variants using cosmopolitan and AJ reference panels. We recruited additional 155 cases and 69 controls for validation. To evaluate predictive power of PRSs for AMD, we used IAMDGC summary statistics excluding our study and developed PRSs via either clumping/thresholding or LDpred2. Results In our discovery set, 31/34 loci previously reported by the IAMDGC were AMD associated with P<0.05. Of those, all effects were directionally consistent with the IAMDGC and 11 loci had a p-value under Bonferroni-corrected threshold(0.05/34=0.0015). At a threshold of 5x10 -5 , we discovered four suggestive associations in FAM189A1 , IGDCC4 , C7orf50 , and CNTNAP4 . However, only the FAM189A1 variant was AMD associated in the replication cohort after Bonferroni-correction. A prediction model including LDpred2-based PRS and other covariates had an AUC of 0.82(95%CI:0.79-0.85) and performed better than a covariates-only model(P=5.1x10 -9 ). Conclusions Previously reported AMD-associated loci were nominally associated with AMD in Israel. A PRS developed based on a large international study is predictive in Israeli populations.
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