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Don J, Schork AJ, Glusman G, Rappaport N, Cummings SR, Duggan D, Raju A, Hellberg KLG, Gunn S, Monti S, Perls T, Lapidus J, Goetz LH, Sebastiani P, Schork NJ. The relationship between 11 different polygenic longevity scores, parental lifespan, and disease diagnosis in the UK Biobank. GeroScience 2024; 46:3911-3927. [PMID: 38451433 PMCID: PMC11226417 DOI: 10.1007/s11357-024-01107-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 10/30/2023] [Accepted: 02/21/2024] [Indexed: 03/08/2024] Open
Abstract
Large-scale genome-wide association studies (GWAS) strongly suggest that most traits and diseases have a polygenic component. This observation has motivated the development of disease-specific "polygenic scores (PGS)" that are weighted sums of the effects of disease-associated variants identified from GWAS that correlate with an individual's likelihood of expressing a specific phenotype. Although most GWAS have been pursued on disease traits, leading to the creation of refined "Polygenic Risk Scores" (PRS) that quantify risk to diseases, many GWAS have also been pursued on extreme human longevity, general fitness, health span, and other health-positive traits. These GWAS have discovered many genetic variants seemingly protective from disease and are often different from disease-associated variants (i.e., they are not just alternative alleles at disease-associated loci) and suggest that many health-positive traits also have a polygenic basis. This observation has led to an interest in "polygenic longevity scores (PLS)" that quantify the "risk" or genetic predisposition of an individual towards health. We derived 11 different PLS from 4 different available GWAS on lifespan and then investigated the properties of these PLS using data from the UK Biobank (UKB). Tests of association between the PLS and population structure, parental lifespan, and several cancerous and non-cancerous diseases, including death from COVID-19, were performed. Based on the results of our analyses, we argue that PLS are made up of variants not only robustly associated with parental lifespan, but that also contribute to the genetic architecture of disease susceptibility, morbidity, and mortality.
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Affiliation(s)
- Janith Don
- Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
| | - Andrew J Schork
- The Institute of Biological Psychiatry, Copenhagen University Hospital, Copenhagen, Denmark
- GLOBE Institute, Copenhagen University, Copenhagen, Denmark
| | | | | | - Steve R Cummings
- San Francisco Coordinating Center, California Pacific Medical Center Research Institute, San Francisco, CA, USA
| | - David Duggan
- Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
| | - Anish Raju
- Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
| | - Kajsa-Lotta Georgii Hellberg
- The Institute of Biological Psychiatry, Copenhagen University Hospital, Copenhagen, Denmark
- GLOBE Institute, Copenhagen University, Copenhagen, Denmark
| | - Sophia Gunn
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Stefano Monti
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
| | - Thomas Perls
- Department of Medicine, Section of Geriatrics, Boston University, Boston, MA, USA
| | - Jodi Lapidus
- Department of Biostatistics, Oregon Health & Science University, Portland, OR, USA
| | - Laura H Goetz
- Translational Genomics Research Institute (TGen), Phoenix, AZ, USA
- Veterans Affairs Loma Linda Health Care, Loma Linda, CA, USA
| | - Paola Sebastiani
- Department of Biostatistics, Boston University School of Public Health, Boston, MA, USA
- Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA
- Tufts University School of Medicine and Data Intensive Study Center, Boston, MA, USA
| | - Nicholas J Schork
- Translational Genomics Research Institute (TGen), Phoenix, AZ, USA.
- The City of Hope National Medical Center, Duarte, CA, USA.
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Heyne HO, Pajuste FD, Wanner J, Daniel Onwuchekwa JI, Mägi R, Palotie A, Kälviainen R, Daly MJ. Polygenic risk scores as a marker for epilepsy risk across lifetime and after unspecified seizure events. Nat Commun 2024; 15:6277. [PMID: 39054313 PMCID: PMC11272783 DOI: 10.1038/s41467-024-50295-z] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/14/2023] [Accepted: 07/04/2024] [Indexed: 07/27/2024] Open
Abstract
A diagnosis of epilepsy has significant consequences for an individual but is often challenging in clinical practice. Novel biomarkers are thus greatly needed. Here, we investigated how common genetic factors (epilepsy polygenic risk scores, [PRSs]) influence epilepsy risk in detailed longitudinal electronic health records (EHRs) of > 700k Finns and Estonians. We found that a high genetic generalized epilepsy PRS (PRSGGE) increased risk for genetic generalized epilepsy (GGE) (hazard ratio [HR] 1.73 per PRSGGE standard deviation [SD]) across lifetime and within 10 years after an unspecified seizure event. The effect of PRSGGE was significantly larger on idiopathic generalized epilepsies, in females and for earlier epilepsy onset. Analogously, we found significant but more modest focal epilepsy PRS burden associated with non-acquired focal epilepsy (NAFE). Here, we outline the potential of epilepsy specific PRSs to serve as biomarkers after a first seizure event.
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Affiliation(s)
- Henrike O Heyne
- Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany.
- Hasso Plattner Institute, Mount Sinai School of Medicine, New York, NY, US.
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland.
- Program for Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA.
| | - Fanny-Dhelia Pajuste
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Julian Wanner
- Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
| | - Jennifer I Daniel Onwuchekwa
- Hasso Plattner Institute for Digital Engineering, University of Potsdam, Potsdam, Germany
- Faculty of Life Sciences, University of Siegen, Siegen, Germany
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Program for Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Reetta Kälviainen
- Kuopio Epilepsy Center, Neurocenter, Kuopio University Hospital, Member of ERN EpiCARE, Kuopio, Finland
- Institute of Clinical Medicine, School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Mark J Daly
- Institute for Molecular Medicine Finland (FIMM), University of Helsinki, Helsinki, Finland
- Program for Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
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3
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Siermann M, Vermeesch JR, Raivio T, Tšuiko O, Borry P. Polygenic embryo screening: quo vadis? J Assist Reprod Genet 2024; 41:1719-1726. [PMID: 38879662 PMCID: PMC11263429 DOI: 10.1007/s10815-024-03169-8] [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: 04/05/2024] [Accepted: 06/06/2024] [Indexed: 07/23/2024] Open
Abstract
Recently, the use of polygenic risk scores in embryo screening (PGT-P) has been introduced on the premise of reducing polygenic disease risk through embryo selection. However, it has been met with extensive critique: considered "technology-driven" rather than "evidence-based", concerns exist about its validity, utility, ethics, and societal effects. Its scientific foundations and criticisms thus need to be carefully considered. However, seeing as PGT-P is already offered in some settings, further questions need to be addressed, in order to give due diligence to various aspects of PGT-P. By examining the complexities of clinical introduction of PGT-P, we discuss whether PGT-P could be responsibly implemented in the first place, what elements need to be addressed if PGT-P is clinically implemented, and subsequently how counselling and decision-making of its users could be envisaged. By dissecting these elements, we provide an overview of important practical questions of PGT-P and emphasize elements of PGT-P that we think have yet to be given sufficient attention. These questions and elements are for example related to the potential target group, scope, and decision-making possibilities of PGT-P. The aspects we raise are crucial to consider by the scientific community and policy makers for the development of guidelines and/or an ethical framework for PGT-P.
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Affiliation(s)
- Maria Siermann
- Centre for Biomedical Ethics and Law, Department of Public Health and Primary Care, KU Leuven, Kapucijnenvoer 7, Box 7001, 3000, Leuven, Belgium.
- Faculty of Medicine, University of Helsinki, Haartmaninkatu 8, P.O. Box 63, 00014, Helsinki, Finland.
| | | | - Taneli Raivio
- Faculty of Medicine, University of Helsinki, Haartmaninkatu 8, P.O. Box 63, 00014, Helsinki, Finland
| | - Olga Tšuiko
- Center for Human Genetics, UZ Leuven, Herestraat 49, 3000, Leuven, Belgium
| | - Pascal Borry
- Centre for Biomedical Ethics and Law, Department of Public Health and Primary Care, KU Leuven, Kapucijnenvoer 7, Box 7001, 3000, Leuven, Belgium
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Xu W, Huang Z, Xiao Y, Li W, Xu M, Zhao Q, Yi P. HNRNPC promotes estrogen receptor-positive breast cancer cell cycle by stabilizing WDR77 mRNA in an m6A-dependent manner. Mol Carcinog 2024; 63:859-873. [PMID: 38353359 DOI: 10.1002/mc.23693] [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: 10/30/2023] [Revised: 01/13/2024] [Accepted: 01/17/2024] [Indexed: 04/13/2024]
Abstract
Breast cancer has become the most commonly diagnosed cancer. Heterogeneous nuclear ribonucleoprotein C (HNRNPC), a reader of N6-methyladenosine (m6A), has been observed to be upregulated in various types of cancer. Nevertheless, the role of HNRNPC in breast cancer and whether it is regulated by m6A modification deserve further investigation. The expression of HNRNPC in breast cancer was examined by quantitative real-time polymerase chain reaction and western blot analysis. RNA immunoprecipitation was performed to validate the binding relationships between HNRNPC and WD repeat domain 77 (WDR77). The effects of HNRNPC and m6A regulators on WDR77 were investigated by actinomycin D assay. The experiments in vivo were conducted in xenograft models. In this research, we found that HNRNPC was highly expressed in breast cancer, and played a crucial role in cell growth, especially in the luminal subtype. HNRNPC could combine and stabilize WDR77 mRNA. WDR77 successively drove the G1/S phase transition in the cell cycle and promoted cell proliferation. Notably, this regulation axis was closely tied to the m6A modification status of WDR77 mRNA. Overall, a critical regulatory mechanism was identified, as well as promising targets for potential treatment strategies for luminal breast cancer.
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Affiliation(s)
- Wenjie Xu
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ziwei Huang
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Yunxiao Xiao
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Wenhui Li
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Ming Xu
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Qiuyang Zhao
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
| | - Pengfei Yi
- Department of Breast and Thyroid Surgery, Union Hospital, Tongji Medical College, Huazhong University of Science and Technology, Wuhan, China
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Mars N, Kerminen S, Tamlander M, Pirinen M, Jakkula E, Aaltonen K, Meretoja T, Heinävaara S, Widén E, Ripatti S. Comprehensive Inherited Risk Estimation for Risk-Based Breast Cancer Screening in Women. J Clin Oncol 2024; 42:1477-1487. [PMID: 38422475 PMCID: PMC11095905 DOI: 10.1200/jco.23.00295] [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: 02/07/2023] [Revised: 11/24/2023] [Accepted: 12/20/2023] [Indexed: 03/02/2024] Open
Abstract
PURPOSE Family history (FH) and pathogenic variants (PVs) are used for guiding risk surveillance in selected high-risk women but little is known about their impact for breast cancer screening on population level. In addition, polygenic risk scores (PRSs) have been shown to efficiently stratify breast cancer risk through combining information about common genetic factors into one measure. METHODS In longitudinal real-life data, we evaluate PRS, FH, and PVs for stratified screening. Using FinnGen (N = 117,252), linked to the Mass Screening Registry for breast cancer (1992-2019; nationwide organized biennial screening for age 50-69 years), we assessed the screening performance of a breast cancer PRS and compared its performance with FH of breast cancer and PVs in moderate- (CHEK2)- to high-risk (PALB2) susceptibility genes. RESULTS Effect sizes for FH, PVs, and high PRS (>90th percentile) were comparable in screening-aged women, with similar implications for shifting age at screening onset. A high PRS identified women more likely to be diagnosed with breast cancer after a positive screening finding (positive predictive value [PPV], 39.5% [95% CI, 37.6 to 41.5]). Combinations of risk factors increased the PPVs up to 45% to 50%. A high PRS conferred an elevated risk of interval breast cancer (hazard ratio [HR], 2.78 [95% CI, 2.00 to 3.86] at age 50 years; HR, 2.48 [95% CI, 1.67 to 3.70] at age 60 years), and women with a low PRS (<10th percentile) had a low risk for both interval- and screen-detected breast cancers. CONCLUSION Using real-life screening data, this study demonstrates the effectiveness of a breast cancer PRS for risk stratification, alone and combined with FH and PVs. Further research is required to evaluate their impact in a prospective risk-stratified screening program, including cost-effectiveness.
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Affiliation(s)
- Nina Mars
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA
| | - Sini Kerminen
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Max Tamlander
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Matti Pirinen
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Helsinki Institute for Information Technology HIIT and Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
- Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Eveliina Jakkula
- Department of Clinical Genetics, HUSLAB, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Medical Genetics, University of Helsinki, Helsinki, Finland
| | - Kirsimari Aaltonen
- Department of Clinical Genetics, HUSLAB, HUS Diagnostic Center, University of Helsinki and Helsinki University Hospital, Helsinki, Finland
- Department of Medical Genetics, University of Helsinki, Helsinki, Finland
| | - Tuomo Meretoja
- Breast Surgery Unit, Comprehensive Cancer Center, Helsinki University Hospital, Helsinki, Finland
- University of Helsinki, Helsinki, Finland
| | - Sirpa Heinävaara
- Department of Public Health, University of Helsinki, Helsinki, Finland
- Finnish Cancer Registry, Cancer Society of Finland, Helsinki, Finland
| | - Elisabeth Widén
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
- Broad Institute of MIT and Harvard, Cambridge, MA
- Department of Public Health, University of Helsinki, Helsinki, Finland
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Andaluz S, Zhao B, Sinha S, Lagniton PNP, Costa DA, Ding X, Brito M, Wang SM. Using Portuguese BRCA pathogenic variation as a model to study the impact of human admixture on human health. BMC Genomics 2024; 25:416. [PMID: 38671360 PMCID: PMC11055274 DOI: 10.1186/s12864-024-10311-4] [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: 08/13/2023] [Accepted: 04/15/2024] [Indexed: 04/28/2024] Open
Abstract
BACKGROUND Admixture occurs between different ethnic human populations. The global colonization in recent centuries by Europeans led to the most significant admixture in human history. While admixture may enhance genetic diversity for better fitness, it may also impact on human health by transmitting genetic variants for disease susceptibility in the admixture population. The admixture by Portuguese global exploration initiated in the 15th century has reached over 20 million of Portuguese-heritage population worldwide. It provides a valuable model to study the impact of admixture on human health. BRCA1 and BRCA2 (BRCA) are two of the important tumor suppressor genes. The pathogenic variation (PV) in BRCA is well determined to cause high risk of hereditary breast and ovarian cancer. Tracing the distribution of Portuguese BRCA PV in Portuguese-heritage population will help to understand the impact of admixture on cancer susceptibility in modern humans. In this study, we analyzed the distribution of the Portuguese-originated BRCA variation in Brazilian population, which has high degree Portuguese-heritage. METHODS By comprehensive data mining, standardization and annotation, we generated a Portuguese-derived BRCA variation dataset and a Brazilian-derived BRCA variation dataset. We compared the two BRCA variation datasets to identify the BRCA variants shared between the two populations. RESULTS The Portuguese-derived BRCA variation dataset consists of 220 BRCA variants including 78 PVs from 11,482 Portuguese cancer patients, 93 (42.2%) in BRCA1 and 127 (57.7%) in BRCA2. Of the 556 Portuguese BRCA PV carriers carrying the 78 PVs, 331 (59.5%) carried the three Portuguese-BRCA founder PVs of BRCA1 c.2037delinsCC, BRCA1 c.3331_3334del and BRCA2 c.156_157insAlu. The Brazilian-derived BRCA variation dataset consists of 255 BRCA PVs from 7,711 cancer patients, 136 (53.3%) in BRCA1 and 119 (46.6%) in BRCA2. We developed an open database named dbBRCA-Portuguese ( https://genemutation.fhs.um.edu.mo/dbbrca-portuguese/ ) and an open database named dbBRCA-Brazilian ( https://genemutation.fhs.um.edu.mo/dbbrca-brazilian ) to host the BRCA variation data from Portuguese and Brazilian populations. We compared the BRCA PV datasets between Portuguese and Brazilian populations, and identified 29 Portuguese-specific BRCA PVs shared between Portuguese and Brazilian populations, 14 in BRCA1 including the Portuguese founder BRCA1 c.3331_3334del and BRCA1 c.2037delinsCC, and 15 in BRCA2 including the Portuguese founder BRCA2 c.156_157insAlu. Searching the 78 Portuguese BRCA PVs in over 5,000 ancient human genomes identified evolution origin for only 8 PVs in Europeans dated between 37,470 and 3,818 years before present, confirming the Portuguese-specificity of Portuguese BRCA PVs; comparing the 78 Portuguese BRCA PVs Portuguese, 255 Brazilian BRCA PVs, and 134 African BRCA PVs showed little overlapping, ruling out the possibility that the BRCA PVs shared between Portuguese and Brazilian may also be contributed by African. CONCLUSION Our study provides evidence that the admixture in recent human history contributed to cancer susceptibility in modern humans.
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Affiliation(s)
- Stephanie Andaluz
- Ministry of Education Frontiers Science Center for Precision Oncology, Cancer Center and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SRA, China
| | - Bojin Zhao
- Ministry of Education Frontiers Science Center for Precision Oncology, Cancer Center and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SRA, China
| | - Siddharth Sinha
- Ministry of Education Frontiers Science Center for Precision Oncology, Cancer Center and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SRA, China
| | - Philip Naderev Panuringan Lagniton
- Ministry of Education Frontiers Science Center for Precision Oncology, Cancer Center and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SRA, China
| | - Diogo Alpuim Costa
- Medical Oncology Department, Hospital de Cascais, Cascais; Haematology and Oncology Department, CUF Oncologia, Lisbon; NOVA Medical School, Faculdade de Ciências Médicas, Universidade NOVA de Lisboa, Lisbon, Portugal
| | - Xiaofan Ding
- Ministry of Education Frontiers Science Center for Precision Oncology, Cancer Center and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SRA, China
| | - Miguel Brito
- Escola Superior de Tecnologia da Saúde de Lisboa, Instituto Politécnico de Lisboa, Lisbon, Portugal.
| | - San Ming Wang
- Ministry of Education Frontiers Science Center for Precision Oncology, Cancer Center and Institute of Translational Medicine, Faculty of Health Sciences, University of Macau, Macao SRA, China.
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Weon B, Jang Y, Jo J, Jin W, Ha S, Ko A, Oh YK, Lim CS, Lee JP, Won S, Lee J. Association between dyslipidemia and the risk of incident chronic kidney disease affected by genetic susceptibility: Polygenic risk score analysis. PLoS One 2024; 19:e0299605. [PMID: 38626061 PMCID: PMC11020804 DOI: 10.1371/journal.pone.0299605] [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: 08/13/2023] [Accepted: 02/13/2024] [Indexed: 04/18/2024] Open
Abstract
BACKGROUND The effect of dyslipidemia on kidney disease outcomes has been inconclusive, and it requires further clarification. Therefore, we aimed to investigate the effects of genetic factors on the association between dyslipidemia and the risk of chronic kidney disease (CKD) using polygenic risk score (PRS). METHODS We analyzed data from 373,523 participants from the UK Biobank aged 40-69 years with no history of CKD. Baseline data included plasma levels of total cholesterol, low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglyceride, as well as genome-wide genotype data for PRS. Our primary outcome, incident CKD, was defined as a composite of estimated glomerular filtration rate < 60 ml/min/1.73 m2 and CKD diagnosis according to International Classification of Disease-10 codes. The effects of the association between lipid levels and PRS on incident CKD were assessed using the Cox proportional hazards model. To investigate the effect of this association, we introduced multiplicative interaction terms into a multivariate analysis model and performed subgroup analysis stratified by PRS tertiles. RESULTS In total, 4,424 participants developed CKD. In the multivariable analysis, PRS was significantly predictive of the risk of incident CKD as both a continuous variable and a categorized variable. In addition, lower total cholesterol, LDL-C, HDL-C, and higher triglyceride levels were significantly associated with the risk of incident CKD. There were interactions between triglycerides and intermediate and high PRS, and the interactions were inversely associated with the risk of incident CKD. CONCLUSIONS This study showed that PRS presented significant predictive power for incident CKD and individuals in the low-PRS group had a higher risk of triglyceride-related incident CKD.
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Affiliation(s)
- Boram Weon
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
| | | | - Jinyeon Jo
- Department of Public Health Sciences, Institute of Health & Environment, School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Wencheng Jin
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Seounguk Ha
- Korea Medical Institute, Seoul, Republic of Korea
| | - Ara Ko
- Department of Internal Medicine, Seoul National University Hospital, Seoul, Republic of Korea
| | - Yun Kyu Oh
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Chun Soo Lim
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Jung Pyo Lee
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
| | - Sungho Won
- Rexsoft Corporation, Seoul, Republic of Korea
- Department of Public Health Sciences, Institute of Health & Environment, School of Public Health, Seoul National University, Seoul, Republic of Korea
| | - Jeonghwan Lee
- Department of Internal Medicine, Seoul National University Boramae Medical Center, Seoul, Republic of Korea
- Department of Internal Medicine, Seoul National University College of Medicine, Seoul, Republic of Korea
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Youssef O, Loukola A, Zidi-Mouaffak YHS, Tamlander M, Ruotsalainen S, Kilpeläinen E, Mars N, Ripatti S, Palotie A, Donner K, Carpén O. High-Resolution Genotyping of Formalin-Fixed Tissue Accurately Estimates Polygenic Risk Scores in Human Diseases. J Transl Med 2024; 104:100325. [PMID: 38220043 DOI: 10.1016/j.labinv.2024.100325] [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: 08/10/2023] [Revised: 12/11/2023] [Accepted: 01/05/2024] [Indexed: 01/16/2024] Open
Abstract
Formalin-fixed paraffin-embedded (FFPE) tissues stored in biobanks and pathology archives are a vast but underutilized source for molecular studies on different diseases. Beyond being the "gold standard" for preservation of diagnostic human tissues, FFPE samples retain similar genetic information as matching blood samples, which could make FFPE samples an ideal resource for genomic analysis. However, research on this resource has been hindered by the perception that DNA extracted from FFPE samples is of poor quality. Here, we show that germline disease-predisposing variants and polygenic risk scores (PRS) can be identified from FFPE normal tissue (FFPE-NT) DNA with high accuracy. We optimized the performance of FFPE-NT DNA on a genome-wide array containing 657,675 variants. Via a series of testing and validation phases, we established a protocol for FFPE-NT genotyping with results comparable with blood genotyping. The median call rate of FFPE-NT samples in the validation phase was 99.85% (range 98.26%-99.94%) and median concordance with matching blood samples was 99.79% (range 98.85%-99.9%). We also demonstrated that a rare pathogenic PALB2 genetic variant predisposing to cancer can be correctly identified in FFPE-NT samples. We further imputed the FFPE-NT genotype data and calculated the FFPE-NT genome-wide PRS in 3 diseases and 4 disease risk variables. In all cases, FFPE-NT and matching blood PRS were highly concordant (all Pearson's r > 0.95). The ability to precisely genotype FFPE-NT on a genome-wide array enables translational genomics applications of archived FFPE-NT samples with the possibility to link to corresponding phenotypes and longitudinal health data.
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Affiliation(s)
- Omar Youssef
- Department of Pathology, University of Helsinki, Helsinki, Finland; Clinical and Chemical Pathology Department, National Cancer Institute, Cairo University, Cairo, Egypt; Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland.
| | - Anu Loukola
- Helsinki Biobank, Helsinki University Hospital (HUS), Helsinki, Finland
| | - Yossra H S Zidi-Mouaffak
- Department of Pathology, University of Helsinki, Helsinki, Finland; Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Helsinki Biobank, Helsinki University Hospital (HUS), Helsinki, Finland
| | - Max Tamlander
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Sanni Ruotsalainen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Elina Kilpeläinen
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Nina Mars
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland; Broad Institute of MIT and Harvard, Cambridge, Massachusetts
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland; Broad Institute of MIT and Harvard, Cambridge, Massachusetts; Clinicum, Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland; Broad Institute of MIT and Harvard, Cambridge, Massachusetts; Psychiatric and Neurodevelopmental Genetics Unit, Department of Psychiatry, Analytic and Translational Genetics Unit, Department of Medicine, and the Department of Neurology, Massachusetts General Hospital, Boston, Massachusetts
| | - Kati Donner
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Olli Carpén
- Department of Pathology, University of Helsinki, Helsinki, Finland; Research Program in Systems Oncology, Faculty of Medicine, University of Helsinki, Helsinki, Finland; Helsinki Biobank, Helsinki University Hospital (HUS), Helsinki, Finland
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9
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Zheng T, Roda G, Zabana Y, Escudero-Hernández C, Liu X, Chen Y, Camargo Tavares L, Bonfiglio F, Mellander MR, Janczewska I, Vigren L, Sjöberg K, Ohlsson B, Almer S, Halfvarson J, Miehlke S, Madisch A, Lieb W, Kupčinskas J, Weersma RK, Bujanda L, Julià A, Marsal S, Esteve M, Guagnozzi D, Fernández-Bañares F, Ferrer C, Peter I, Ludvigsson JF, Pardi D, Verhaegh B, Jonkers D, Pierik M, Münch A, Franke A, Bresso F, Khalili H, Colombel JF, D'Amato M. Human Leukocyte Antigen Signatures as Pathophysiological Discriminants of Microscopic Colitis Subtypes. J Crohns Colitis 2024; 18:349-359. [PMID: 37768647 DOI: 10.1093/ecco-jcc/jjad165] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 05/17/2023] [Revised: 07/29/2023] [Accepted: 09/26/2023] [Indexed: 09/29/2023]
Abstract
BACKGROUND AND AIMS Microscopic colitis [MC] is currently regarded as an inflammatory bowel disease that manifests as two subtypes: collagenous colitis [CC] and lymphocytic colitis [LC]. Whether these represent a clinical continuum or distinct entities is, however, an open question. Genetic investigations may contribute important insight into their respective pathophysiologies. METHODS We conducted a genome-wide association study [GWAS] meta-analysis in 1498 CC, 373 LC patients, and 13 487 controls from Europe and the USA, combined with publicly available MC GWAS data from UK Biobank and FinnGen [2599 MC cases and 552 343 controls in total]. Human leukocyte antigen [HLA] alleles and polymorphic residues were imputed and tested for association, including conditional analyses for the identification of key causative variants and residues. Genetic correlations with other traits and diagnoses were also studied. RESULTS We detected strong HLA association with CC, and conditional analyses highlighted the DRB1*03:01 allele and its residues Y26, N77, and R74 as key to this association (best p = 1.4 × 10-23, odds ratio [OR] = 1.96). Nominally significant genetic correlations were detected between CC and pneumonia [rg = 0.77; p = 0.048] and oesophageal diseases [rg = 0.45, p = 0.023]. An additional locus was identified in MC GWAS analyses near the CLEC16A and RMI2 genes on chromosome 16 [rs35099084, p = 2.0 × 10-8, OR = 1.31]. No significant association was detected for LC. CONCLUSION Our results suggest CC and LC have distinct pathophysiological underpinnings, characterised by an HLA predisposing role only in CC. This challenges existing classifications, eventually calling for a re-evaluation of the utility of MC umbrella definitions.
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Affiliation(s)
- Tenghao Zheng
- School of Biological Sciences, Monash University, Clayton, VIC, Australia
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Giulia Roda
- Biostructures and Biosystems National Institute, Rome, Italy
| | - Yamile Zabana
- Gastroenterology Department, Hospital Universitari Mútua de Terrassa, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas, Barcelona, Spain
| | - Celia Escudero-Hernández
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Xingrong Liu
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Center for Molecular Medicine, Karolinska Institutet, Stockholm, Sweden
| | - Ye Chen
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA
| | | | - Ferdinando Bonfiglio
- School of Biological Sciences, Monash University, Clayton, VIC, Australia
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
| | | | | | - Lina Vigren
- Department of Clinical Sciences, Lund University, Lund, Sweden
| | - Klas Sjöberg
- Department of Clinical Sciences, Lund University, Skane University Hospital, Malmo, Sweden
| | - Bodil Ohlsson
- Department of Clinical Sciences, Lund University, Skane University Hospital, Malmo, Sweden
| | - Sven Almer
- Division of Gastroenterology, Department of Gastroenterology, Dermatology and Rheumatology, Karolinska University Hospital, Stockholm, Sweden
| | - Jonas Halfvarson
- Department of Gastroenterology, Faculty of Medicine and Health, Orebro University Hospital, Örebro, Sweden
| | - Stephan Miehlke
- Centre for Digestive Diseases, Internal Medicine Centre Eppendorf, and Centre for Oesophageal Disorders, University Hospital Eppendorf, Hamburg, Germany
| | - Ahmed Madisch
- Department of Gastroenterology, CRH Clinic Siloah, Hannover, Germany
| | - Wolfgang Lieb
- Institute of Epidemiology, Christian-Albrechts-University of Kiel, Kiel, Germany
| | - Juozas Kupčinskas
- Department of Gastroenterology, Lithuanian University of Health Sciences, Kaunas, Lithuania
| | - Rinse K Weersma
- Department of Gastroenterology and Hepatology, University of Groningen and University Medical Center Groningen, Groningen, The Netherlands
| | - Luis Bujanda
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas, Barcelona, Spain
- Department of Liver and Gastrointestinal Diseases, Biodonostia Health Research Institute, Donostia University Hospital, Universidad del País Vasco, San Sebastian, Spain
| | - Antonio Julià
- Rheumatology Research Group, Vall d' Hebron Research Institute, Barcelona, Spain
| | - Sara Marsal
- Rheumatology Research Group, Vall d' Hebron Research Institute, Barcelona, Spain
| | - Maria Esteve
- Gastroenterology Department, Hospital Universitari Mútua de Terrassa, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas, Barcelona, Spain
| | - Danila Guagnozzi
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas, Barcelona, Spain
- Department of Gastroenterology, Vall d'Hebron University Hospital, Neuro-Immuno-Gastroenterology Group, Digestive System Research Unit, Vall d'Hebron Institut de Recerca, Barcelona, Spain
| | - Fernando Fernández-Bañares
- Gastroenterology Department, Hospital Universitari Mútua de Terrassa, Barcelona, Spain
- Centro de Investigación Biomédica en Red de Enfermedades Hepáticas y Digestivas, Barcelona, Spain
| | - Carmen Ferrer
- Pathology Department, Hospital Universitari Mútua de Terrassa, Barcelona, Spain
| | - Inga Peter
- Department of Genetics and Genomic Sciences, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Jonas F Ludvigsson
- Department of Medical Epidemiology and Biostatistics, Karolinska Institutet, Stockholm, Sweden
| | - Darrell Pardi
- Division of Gastroenterology and Hepatology, Mayo Clinic, Rochester, MN, USA
| | - Bas Verhaegh
- Division Gastroenterology-Hepatology, Department of Internal Medicine, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Daisy Jonkers
- Division Gastroenterology-Hepatology, Department of Internal Medicine, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Marieke Pierik
- Division Gastroenterology-Hepatology, Department of Internal Medicine, NUTRIM School of Nutrition and Translational Research in Metabolism, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Andreas Münch
- Department of Biomedical and Clinical Sciences, Linköping University, Linköping, Sweden
- Department of Gastroenterology and Hepatology, Linköping University, Linköping, Sweden
- Department of Health, Medicine, and Caring Sciences, Linköping University, Linköping, Sweden
| | - Andre Franke
- Institute of Clinical Molecular Biology, Kiel University and University Medical Center Schleswig-Holstein, Kiel, Germany
| | - Francesca Bresso
- Division of Gastroenterology, Department of Gastroenterology, Dermatology and Rheumatology, Karolinska University Hospital, Stockholm, Sweden
| | - Hamed Khalili
- Division of Gastroenterology, Massachusetts General Hospital, Boston, MA, USA
- Clinical and Translational Epidemiology Unit, Massachusetts General Hospital, Boston, MA, USA
- Broad Institute of Massachusetts Institute of Technology and Harvard, Cambridge, MA, USA
| | - Jean-Frederic Colombel
- Dr Henry D. Janowitz Division of Gastroenterology, Icahn School of Medicine at Mount Sinai, New York, NY, USA
| | - Mauro D'Amato
- Clinical Epidemiology Division, Department of Medicine Solna, Karolinska Institutet, Stockholm, Sweden
- Gastrointestinal Genetics Lab, CIC bioGUNE - BRTA, Derio, Spain
- Ikerbasque, Basque Foundation for Science, Bilbao, Spain
- Department of Medicine and Surgery, LUM University, Casamassima, Italy
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10
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Liao K, Zöllner S. A Stacking Framework for Polygenic Risk Prediction in Admixed Individuals. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2024:2024.01.31.24302103. [PMID: 38434717 PMCID: PMC10907988 DOI: 10.1101/2024.01.31.24302103] [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: 03/05/2024]
Abstract
Polygenic risk scores (PRS) are summaries of an individual's personalized genetic risk for a trait or disease. However, PRS often perform poorly for phenotype prediction when the ancestry of the target population does not match the population in which GWAS effect sizes were estimated. For many populations this can be addressed by performing GWAS in the target population. However, admixed individuals (whose genomes can be traced to multiple ancestral populations) lie on an ancestry continuum and are not easily represented as a discrete population. Here, we propose slaPRS (stacking local ancestry PRS), which incorporates multiple ancestry GWAS to alleviate the ancestry dependence of PRS in admixed samples. slaPRS uses ensemble learning (stacking) to combine local population specific PRS in regions across the genome. We compare slaPRS to single population PRS and a method that combines single population PRS globally. In simulations, slaPRS outperformed existing approaches and reduced the ancestry dependence of PRS in African Americans. In lipid traits from African British individuals (UK Biobank), slaPRS again improved on single population PRS while performing comparably to the globally combined PRS. slaPRS provides a data-driven and flexible framework to incorporate multiple population-specific GWAS and local ancestry in samples of admixed ancestry.
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Affiliation(s)
- Kevin Liao
- University of Michigan, Department of Biostatistics, Ann Arbor, MI, 48109, USA
| | - Sebastian Zöllner
- University of Michigan, Department of Biostatistics, Ann Arbor, MI, 48109, USA
- University of Michigan, Department of Psychiatry, Ann Arbor, MI, 48109, USA
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11
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Schreurs MAC, Ramón Y Cajal T, Adank MA, Collée JM, Hollestelle A, van Rooij J, Schmidt MK, Hooning MJ. The benefit of adding polygenic risk scores, lifestyle factors, and breast density to family history and genetic status for breast cancer risk and surveillance classification of unaffected women from germline CHEK2 c.1100delC families. Breast 2024; 73:103611. [PMID: 38039887 PMCID: PMC10730863 DOI: 10.1016/j.breast.2023.103611] [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: 09/25/2023] [Revised: 11/13/2023] [Accepted: 11/18/2023] [Indexed: 12/03/2023] Open
Abstract
To determine the changes in surveillance category by adding a polygenic risk score based on 311 breast cancer (BC)-associated variants (PRS311), questionnaire-based risk factors and breast density on personalized BC risk in unaffected women from Dutch CHEK2 c.1100delC families. In total, 117 unaffected women (58 heterozygotes and 59 non-carriers) from CHEK2 families were included. Blood-derived DNA samples were genotyped with the GSAMDv3-array to determine PRS311. Lifetime BC risk was calculated in CanRisk, which uses data from the Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm (BOADICEA). Women, were categorized into three surveillance groups. The surveillance advice was reclassified in 37.9 % of heterozygotes and 32.2 % of non-carriers after adding PRS311. Including questionnaire-based risk factors resulted in an additional change in 20.0 % of heterozygotes and 13.2 % of non-carriers; and a subanalysis showed that adding breast density on top shifted another 17.9 % of heterozygotes and 33.3 % of non-carriers. Overall, the majority of heterozygotes were reclassified to a less intensive surveillance, while non-carriers would require intensified surveillance. The addition of PRS311, questionnaire-based risk factors and breast density to family history resulted in a more personalized BC surveillance advice in CHEK2-families, which may lead to more efficient use of surveillance.
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Affiliation(s)
- Maartje A C Schreurs
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
| | - Teresa Ramón Y Cajal
- Familial Cancer Clinic, Medical Oncology Service, Hospital Sant Pau, Barcelona, Spain
| | - Muriel A Adank
- Department of Clinical Genetics, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | - J Margriet Collée
- Department of Clinical Genetics, Erasmus MC Cancer Institute, Rotterdam, the Netherlands
| | | | - Jeroen van Rooij
- Department of Internal Medicine, Erasmus MC, Rotterdam, the Netherlands
| | - Marjanka K Schmidt
- Division of Molecular Pathology, Netherlands Cancer Institute - Antoni van Leeuwenhoek Hospital, Amsterdam, the Netherlands
| | - Maartje J Hooning
- Department of Medical Oncology, Erasmus MC Cancer Institute, Rotterdam, the Netherlands.
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12
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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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13
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Hsu YC, Chen HL, Cheng CF, Chattopadhyay A, Chen PS, Lin CC, Chiang HY, Liu TY, Huang CH, Kuo CC, Chuang EY, Lu TP, Tsai FJ. The largest genome-wide association study for breast cancer in Taiwanese Han population. Breast Cancer Res Treat 2024; 203:291-306. [PMID: 37851288 DOI: 10.1007/s10549-023-07133-5] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/22/2023] [Accepted: 09/23/2023] [Indexed: 10/19/2023]
Abstract
PURPOSE Breast cancer is a molecularly heterogeneous disease, and multiple genetic variants contribute to its development and prognosis. Most of previous genome-wide association studies (GWASs) and polygenic risk scores (PRSs) analyses focused on studying breast cancers of Caucasian populations, which may not be applicable to other population. Therefore, we conducted the largest breast cancer cohort of Taiwanese population to fill in the knowledge gap. METHODS A total of 152,534 Participants recruited by China Medical University Hospital between 2003 and 2019 were filtered by several patient selection criteria and GWAS quality control steps, resulting in the inclusion of 2496 cases and 9984 controls for this study. We then conducted GWAS for all breast cancers and PRS analyses for all breast cancers and the four breast cancer subtypes, including luminal A, luminal B, basal-like, and HER2-enriched. RESULTS The GWAS analyses identified 113 SNPs, 50 of which were novel. The PRS models for all breast cancers and the luminal A subtype showed positively correlated trends between the PRS and the risk of developing breast cancer. The odds ratios (95% confidence intervals) for the groups with the highest PRS in all breast cancers and the luminal A subtype were 5.33 (3.79-7.66) and 3.55 (2.13-6.14), respectively. CONCLUSION In summary, we explored the association of genetic variants with breast cancer in the largest Taiwanese cohort and developed two PRS models that can predict the risk of developing any breast cancer and the luminal A subtype in Taiwanese women.
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Affiliation(s)
- Yu-Ching Hsu
- Bioinformatics Program, Taiwan International Graduate Program, National Taiwan University, Taipei, Taiwan
- Bioinformatics Program, Institute of Statistical Science, Taiwan International Graduate Program, Academia Sinica, Taipei, Taiwan
- Institute of Health Data Analytics and Statistics, Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Hung-Lin Chen
- Big Data Center, China Medical University Hospital, Taichung, Taiwan
| | - Chi-Fung Cheng
- Big Data Center, China Medical University Hospital, Taichung, Taiwan
| | - Amrita Chattopadhyay
- Center for Translational Genomic Research, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Pei-Shan Chen
- Big Data Center, China Medical University Hospital, Taichung, Taiwan
| | - Che-Chen Lin
- Big Data Center, China Medical University Hospital, Taichung, Taiwan
| | - Hsiu-Yin Chiang
- Big Data Center, China Medical University Hospital, Taichung, Taiwan
| | - Ting-Yuan Liu
- Million-Person Precision Medicine Initiative, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Chi-Hao Huang
- Division of Breast Surgery, Department of Surgery, China Medical University Hospital, Taichung, Taiwan
| | - Chin-Chi Kuo
- Big Data Center, China Medical University Hospital, Taichung, Taiwan
- Division of Nephrology, Department of Internal Medicine, China Medical University Hospital, Taichung, Taiwan
- Department of Medical Research, China Medical University Hospital, Taichung, Taiwan
| | - Eric Y Chuang
- Bioinformatics and Biostatistics Core, Center of Genomic and Precision Medicine, National Taiwan University, Taipei, Taiwan
- Graduate Institute of Biomedical Electronics and Bioinformatics, National Taiwan University, Taipei, Taiwan
- Biomedical Technology and Device Research Laboratories, Industrial Technology Research Institute, Hsinchu, Taiwan
| | - Tzu-Pin Lu
- Institute of Health Data Analytics and Statistics, Department of Public Health, College of Public Health, National Taiwan University, Taipei, Taiwan.
| | - Fuu-Jen Tsai
- Genetic Center, Department of Medical Research, China Medical University Hospital, Taichung, Taiwan.
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14
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Heyne HO, Pajuste FD, Wanner J, Onwuchekwa JID, Mägi R, Palotie A, Kälviainen R, Daly MJ. Polygenic risk scores as a marker for epilepsy risk across lifetime and after unspecified seizure events. MEDRXIV : THE PREPRINT SERVER FOR HEALTH SCIENCES 2023:2023.11.27.23297542. [PMID: 38076931 PMCID: PMC10705659 DOI: 10.1101/2023.11.27.23297542] [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: 12/20/2023]
Abstract
A diagnosis of epilepsy has significant consequences for an individual but is often challenging in clinical practice. Novel biomarkers are thus greatly needed. Here, we investigated how common genetic factors (epilepsy polygenic risk scores, [PRSs]) influence epilepsy risk in detailed longitudinal electronic health records (EHRs) of > 360k Finns spanning up to 50 years of individuals' lifetimes. Individuals with a high genetic generalized epilepsy PRS (PRSGGE) in FinnGen had an increased risk for genetic generalized epilepsy (GGE) (hazard ratio [HR] 1.55 per PRSGGE standard deviation [SD]) across their lifetime and after unspecified seizure events. Effect sizes of epilepsy PRSs were comparable to effect sizes in clinically curated data supporting our EHR-derived epilepsy diagnoses. Within 10 years after an unspecified seizure, the GGE rate was 37% when PRSGGE > 2 SD compared to 5.6% when PRSGGE < -2 SD. The effect of PRSGGE was even larger on GGE subtypes of idiopathic generalized epilepsy (IGE) (HR 2.1 per SD PRSGGE). We further report significantly larger effects of PRSGGE on epilepsy in females and in younger age groups. Analogously, we found significant but more modest focal epilepsy PRS burden associated with non-acquired focal epilepsy (NAFE). We found PRSGGE specifically associated with GGE in comparison with >2000 independent diseases while PRSNAFE was also associated with other diseases than NAFE such as back pain. Here, we show that epilepsy specific PRSs have good discriminative ability after a first seizure event i.e. in circumstances where the prior probability of epilepsy is high outlining a potential to serve as biomarkers for an epilepsy diagnosis.
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Affiliation(s)
- Henrike O Heyne
- Hasso Plattner Institute for Digital Engineering, University of Potsdam, Germany
- Hasso Plattner Institute, Mount Sinai School of Medicine, NY, US
- Finnish Institute for Molecular Medicine (FIMM), University of Helsinki, Helsinki, Finland
- Program for Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Fanny-Dhelia Pajuste
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Julian Wanner
- Hasso Plattner Institute for Digital Engineering, University of Potsdam, Germany
- Finnish Institute for Molecular Medicine (FIMM), University of Helsinki, Helsinki, Finland
| | - Jennifer I Daniel Onwuchekwa
- Hasso Plattner Institute for Digital Engineering, University of Potsdam, Germany
- Faculty of Life Sciences, University of Siegen, Germany
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
- Institute of Molecular and Cell Biology, University of Tartu, Tartu, Estonia
| | - Aarno Palotie
- Finnish Institute for Molecular Medicine (FIMM), University of Helsinki, Helsinki, Finland
- Program for Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Reetta Kälviainen
- Kuopio Epilepsy Center, Neurocenter, Kuopio University Hospital, Member of ERN EpiCARE, Kuopio, Finland
- Institute of Clinical Medicine, School of Medicine, Faculty of Health Sciences, University of Eastern Finland, Kuopio, Finland
| | - Mark J Daly
- Finnish Institute for Molecular Medicine (FIMM), University of Helsinki, Helsinki, Finland
- Program for Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA
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15
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Kim DJ, Kang JH, Kim JW, Cheon MJ, Kim SB, Lee YK, Lee BC. Evaluation of optimal methods and ancestries for calculating polygenic risk scores in East Asian population. Sci Rep 2023; 13:19195. [PMID: 37932343 PMCID: PMC10628155 DOI: 10.1038/s41598-023-45859-w] [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: 01/18/2023] [Accepted: 10/25/2023] [Indexed: 11/08/2023] Open
Abstract
Polygenic risk scores (PRSs) have been studied for predicting human diseases, and various methods for PRS calculation have been developed. Most PRS studies to date have focused on European ancestry, and the performance of PRS has not been sufficiently assessed in East Asia. Herein, we evaluated the predictive performance of PRSs for East Asian populations under various conditions. Simulation studies using data from the Korean cohort, Health Examinees (HEXA), demonstrated that SBayesRC and PRS-CS outperformed other PRS methods (lassosum, LDpred-funct, and PRSice) in high fixed heritability (0.3 and 0.7). In addition, we generated PRSs using real-world data from HEXA for ten diseases: asthma, breast cancer, cataract, coronary artery disease, gastric cancer, glaucoma, hyperthyroidism, hypothyroidism, osteoporosis, and type 2 diabetes (T2D). We utilized the five previous PRS methods and genome-wide association study (GWAS) data from two biobank-scale datasets [European (UK Biobank) and East Asian (BioBank Japan) ancestry]. Additionally, we employed PRS-CSx, a PRS method that combines GWAS data from both ancestries, to generate a total of 110 PRS for ten diseases. Similar to the simulation results, SBayesRC showed better predictive performance for disease risk than the other methods. Furthermore, the East Asian GWAS data outperformed those from European ancestry for breast cancer, cataract, gastric cancer, and T2D, but neither of the two GWAS ancestries showed a significant advantage on PRS performance for the remaining six diseases. Based on simulation data and real data studies, it is expected that SBayesRC will offer superior performance for East Asian populations, and PRS generated using GWAS from non-East Asian may also yield good results.
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16
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Wang Y, Kanai M, Tan T, Kamariza M, Tsuo K, Yuan K, Zhou W, Okada Y, Huang H, Turley P, Atkinson EG, Martin AR. Polygenic prediction across populations is influenced by ancestry, genetic architecture, and methodology. CELL GENOMICS 2023; 3:100408. [PMID: 37868036 PMCID: PMC10589629 DOI: 10.1016/j.xgen.2023.100408] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/31/2023] [Revised: 06/21/2023] [Accepted: 08/22/2023] [Indexed: 10/24/2023]
Abstract
Polygenic risk scores (PRSs) developed from multi-ancestry genome-wide association studies (GWASs), PRSmulti, hold promise for improving PRS accuracy and generalizability across populations. To establish best practices for leveraging the increasing diversity of genomic studies, we investigated how various factors affect the performance of PRSmulti compared with PRSs constructed from single-ancestry GWASs (PRSsingle). Through extensive simulations and empirical analyses, we showed that PRSmulti overall outperformed PRSsingle in understudied populations, except when the understudied population represented a small proportion of the multi-ancestry GWAS. Furthermore, integrating PRSs based on local ancestry-informed GWASs and large-scale, European-based PRSs improved predictive performance in understudied African populations, especially for less polygenic traits with large-effect ancestry-enriched variants. Our work highlights the importance of diversifying genomic studies to achieve equitable PRS performance across ancestral populations and provides guidance for developing PRSs from multiple studies.
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Affiliation(s)
- Ying Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Taotao Tan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | | | - Kristin Tsuo
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Kai Yuan
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Wei Zhou
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Center for Infectious Disease Education and Research (CiDER), and Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita 565-0871, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan
| | - the BioBank Japan Project
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Biomedical Informatics, Harvard Medical School, Boston, MA, USA
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
- Society of Fellows, Harvard University, Cambridge, MA 02138, USA
- Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Yokohama, Japan
- Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Center for Infectious Disease Education and Research (CiDER), and Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita 565-0871, Japan
- Department of Genome Informatics, Graduate School of Medicine, the University of Tokyo, Tokyo 113-0033, Japan
- Department of Economics, and Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
| | - Hailiang Huang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Patrick Turley
- Department of Economics, and Center for Economic and Social Research, University of Southern California, Los Angeles, CA, USA
| | - Elizabeth G. Atkinson
- Department of Molecular and Human Genetics, Baylor College of Medicine, Houston, TX, USA
| | - Alicia R. Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA 02114, USA
- Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
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17
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Riddle L, Joseph G, Caruncho M, Koenig BA, James JE. The role of polygenic risk scores in breast cancer risk perception and decision-making. J Community Genet 2023; 14:489-501. [PMID: 37311883 PMCID: PMC10576692 DOI: 10.1007/s12687-023-00655-x] [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: 12/01/2022] [Accepted: 06/01/2023] [Indexed: 06/15/2023] Open
Abstract
Polygenic risk scores (PRS) have the potential to improve the accuracy of clinical risk assessments, yet questions about their clinical validity and readiness for clinical implementation persist. Understanding how individuals integrate and act on the information provided by PRS is critical for their effective integration into routine clinical care, yet few studies have examined how individuals respond to the receipt of polygenic risk information. We conducted an embedded Ethical, Legal, and Social Implications (ELSI) study to examine if and how unaffected participants in a US population breast cancer screening trial understood and utilized PRS, as part of a multifactorial risk score combining traditional risk factors with a genetic risk assessment, to make screening and risk-reduction decisions. Semi-structured qualitative interviews were conducted with 24 trial participants who were designated at elevated risk for breast cancer due to their combined risk score. Interviews were analyzed using a grounded theory approach. Participants understood PRS conceptually and accepted it as one of many risk factors to consider, yet the value and meaning they ascribed to this risk estimate varied. Most participants reported financial and insurance barriers to enhanced screening with MRI and were not interested in taking risk-reducing medications. These findings contribute to our understanding of how PRS may be best translated from research to clinical care. Furthermore, they illuminate ethical concerns about identifying risk and making recommendations based on polygenic risk in a population screening context where many may have trouble accessing appropriate care.
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Affiliation(s)
- Leslie Riddle
- Department of Humanities and Social Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Galen Joseph
- Department of Humanities and Social Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Mikaella Caruncho
- Department of Humanities and Social Sciences, University of California, San Francisco, San Francisco, CA, USA
| | - Barbara Ann Koenig
- Department of Humanities and Social Sciences, University of California, San Francisco, San Francisco, CA, USA
- Institute for Health and Aging, University of California, San Francisco, San Francisco, CA, USA
| | - Jennifer Elyse James
- Institute for Health and Aging, University of California, San Francisco, San Francisco, CA, USA.
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18
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Sarkis-Tannous D, Sukol RB, Sullivan E. Toward more personalized breast cancer risk assessment: The polygenic risk score. JAAPA 2023; 36:37-40. [PMID: 37751256 DOI: 10.1097/01.jaa.0000977692.63075.f3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 09/27/2023]
Abstract
ABSTRACT Healthcare providers often are uncertain about how best to assess and manage breast cancer risk. Women at average risk wonder when to start mammography and how often to go. Women at increased risk might inquire about genetic testing, MRI screening, and preventive measures. Patients who carry gene mutations face higher stakes and more complex risk management choices, but only some are aware of their status. This article helps clinicians stratify breast cancer risk and discusses a newer genomic test, the polygenic risk score, that may enable more personalized risk management and decision-making.
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Affiliation(s)
- Daad Sarkis-Tannous
- At the time this article was written, Daad Sarkis-Tannous, Roxanne B. Sukol, and Erika Sullivan were medical breast specialists at the Cleveland (Ohio) Clinic. Dr. Sukol is now retired. The authors have disclosed no potential conflicts of interest, financial or otherwise
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19
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Hanson H, Astiazaran-Symonds E, Amendola LM, Balmaña J, Foulkes WD, James P, Klugman S, Ngeow J, Schmutzler R, Voian N, Wick MJ, Pal T, Tischkowitz M, Stewart DR. Management of individuals with germline pathogenic/likely pathogenic variants in CHEK2: A clinical practice resource of the American College of Medical Genetics and Genomics (ACMG). Genet Med 2023; 25:100870. [PMID: 37490054 PMCID: PMC10623578 DOI: 10.1016/j.gim.2023.100870] [Citation(s) in RCA: 4] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/14/2023] [Revised: 04/21/2023] [Accepted: 04/24/2023] [Indexed: 07/26/2023] Open
Abstract
PURPOSE Although the role of CHEK2 germline pathogenic variants in cancer predisposition is well known, resources for managing CHEK2 heterozygotes in clinical practice are limited. METHODS An international workgroup developed guidance on clinical management of CHEK2 heterozygotes informed by peer-reviewed publications from PubMed. RESULTS Although CHEK2 is considered a moderate penetrance gene, cancer risks may be considered as a continuous variable, which are influenced by family history and other modifiers. Consequently, early cancer detection and prevention for CHEK2 heterozygotes should be guided by personalized risk estimates. Such estimates may result in both downgrading lifetime breast cancer risks to those similar to the general population or upgrading lifetime risk to a level at which CHEK2 heterozygotes are offered high-risk breast surveillance according to country-specific guidelines. Risk-reducing mastectomy should be guided by personalized risk estimates and shared decision making. Colorectal and prostate cancer surveillance should be considered based on assessment of family history. For CHEK2 heterozygotes who develop cancer, no specific targeted medical treatment is recommended at this time. CONCLUSION Systematic prospective data collection is needed to establish the spectrum of CHEK2-associated cancer risks and to determine yet-unanswered questions, such as the outcomes of surveillance, response to cancer treatment, and survival after cancer diagnosis.
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Affiliation(s)
- Helen Hanson
- Southwest Thames Regional Genetics Service, St George's University Hospitals NHS Foundation Trust, London, United Kingdom
| | - Esteban Astiazaran-Symonds
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD; Department of Medicine, College of Medicine-Tucson, University of Arizona, Tucson, AZ
| | | | - Judith Balmaña
- Hereditary Cancer Genetics Group, Vall d'Hebron Institute of Oncology (VHIO), Barcelona, Spain; Medical Oncology Department, Hospital Universitari Vall d'Hebron, Vall d'Hebron Hospital Campus, Barcelona, Spain
| | - William D Foulkes
- Departments of Human Genetics, Oncology and Medicine, McGill University, Montréal, QC, Canada
| | - Paul James
- Sir Peter MacCallum Department of Oncology, The University of Melbourne, Melbourne, VIC, Australia; Parkville Familial Cancer Centre, Peter MacCallum Cancer Centre, Melbourne, VIC, Australia
| | - Susan Klugman
- Division of Reproductive & Medical Genetics, Department of Obstetrics & Gynecology and Women's Health, Montefiore Medical Center/Albert Einstein College of Medicine, Bronx, NY
| | - Joanne Ngeow
- Genomic Medicine, Lee Kong Chian School of Medicine, Nanyang Technological University, Singapore; Cancer Genetics Service, Division of Medical Oncology, National Cancer Centre Singapore, Singapore
| | - Rita Schmutzler
- Center of Integrated Oncology (CIO), University of Cologne, Cologne, Germany; Center for Hereditary Breast and Ovarian Cancer, University Hospital of Cologne, Cologne, Germany
| | - Nicoleta Voian
- Providence Genetic Risk Clinic, Providence Cancer Institute, Portland, OR
| | - Myra J Wick
- Departments of Obstetrics and Gynecology and Clinical Genomics, Mayo Clinic, Rochester, MN
| | - Tuya Pal
- Department of Medicine, Vanderbilt University Medical Center/Vanderbilt-Ingram Cancer Center, Nashville, TN
| | - Marc Tischkowitz
- Department of Medical Genetics, National Institute for Health Research Cambridge Biomedical Research Centre, University of Cambridge, Cambridge, United Kingdom
| | - Douglas R Stewart
- Clinical Genetics Branch, Division of Cancer Epidemiology and Genetics, National Cancer Institute, Rockville, MD
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20
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Ho PJ, Lim EH, Hartman M, Wong FY, Li J. Breast cancer risk stratification using genetic and non-genetic risk assessment tools for 246,142 women in the UK Biobank. Genet Med 2023; 25:100917. [PMID: 37334786 DOI: 10.1016/j.gim.2023.100917] [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: 01/31/2023] [Revised: 06/08/2023] [Accepted: 06/09/2023] [Indexed: 06/20/2023] Open
Abstract
PURPOSE The benefit of using individual risk prediction tools to identify high-risk individuals for breast cancer (BC) screening is uncertain, despite the personalized approach of risk-based screening. METHODS We studied the overlap of predicted high-risk individuals among 246,142 women enrolled in the UK Biobank. Risk predictors assessed include the Gail model (Gail), BC family history (FH, binary), BC polygenic risk score (PRS), and presence of loss-of-function (LoF) variants in BC predisposition genes. Youden J-index was used to select optimal thresholds for defining high-risk. RESULTS In total, 147,399 were considered at high risk for developing BC within the next 2 years by at least 1 of the 4 risk prediction tools examined (Gail2-year > 0.5%: 47%, PRS2-yea r > 0.7%: 30%, FH: 6%, and LoF: 1%); 92,851 (38%) were flagged by only 1 risk predictor. The overlap between individuals flagged as high-risk because of genetic (PRS) and Gail model risk factors was 30%. The best-performing combinatorial model comprises a union of high-risk women identified by PRS, FH, and, LoF (AUC2-year [95% CI]: 62.2 [60.8 to 63.6]). Assigning individual weights to each risk prediction tool increased discriminatory ability. CONCLUSION Risk-based BC screening may require a multipronged approach that includes PRS, predisposition genes, FH, and other recognized risk factors.
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Affiliation(s)
- Peh Joo Ho
- Laboratory of Women's Health and Genetics, Genome Institute of Singapore, A∗STAR Research Entities, Singapore; Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Saw Swee Hock School of Public Health, National University of Singapore, Singapore
| | - Elaine H Lim
- Division of Medical Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Mikael Hartman
- Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore; Saw Swee Hock School of Public Health, National University of Singapore, Singapore; Department of Surgery, University Surgical Cluster, National University Hospital, Singapore, Singapore
| | - Fuh Yong Wong
- Division of Radiation Oncology, National Cancer Centre Singapore, Singapore, Singapore
| | - Jingmei Li
- Laboratory of Women's Health and Genetics, Genome Institute of Singapore, A∗STAR Research Entities, Singapore; Department of Surgery, Yong Loo Lin School of Medicine, National University of Singapore, Singapore.
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21
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Cheng X, Du F, Long X, Huang J. Genetic Inheritance Models of Non-Syndromic Cleft Lip with or without Palate: From Monogenic to Polygenic. Genes (Basel) 2023; 14:1859. [PMID: 37895208 PMCID: PMC10606748 DOI: 10.3390/genes14101859] [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: 08/14/2023] [Revised: 09/18/2023] [Accepted: 09/21/2023] [Indexed: 10/29/2023] Open
Abstract
Non-syndromic cleft lip with or without palate (NSCL/P) is a prevalent birth defect that affects 1/500-1/1400 live births globally. The genetic basis of NSCL/P is intricate and involves both genetic and environmental factors. In the past few years, various genetic inheritance models have been proposed to elucidate the underlying mechanisms of NSCL/P. These models range from simple monogenic inheritance to more complex polygenic inheritance. Here, we present a comprehensive overview of the genetic inheritance model of NSCL/P exemplified by representative genes and regions from both monogenic and polygenic perspectives. We also summarize existing association studies and corresponding loci of NSCL/P within the Chinese population and highlight the potential of utilizing polygenic risk scores for risk stratification of NSCL/P. The potential application of polygenic models offers promising avenues for improved risk assessment and personalized approaches in the prevention and management of NSCL/P individuals.
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Affiliation(s)
- Xi Cheng
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China; (X.C.); (F.D.); (X.L.)
| | - Fengzhou Du
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China; (X.C.); (F.D.); (X.L.)
- Department of Plastic Surgery, Peking Union Medical College Hospital, Beijing 100730, China
| | - Xiao Long
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China; (X.C.); (F.D.); (X.L.)
- Department of Plastic Surgery, Peking Union Medical College Hospital, Beijing 100730, China
| | - Jiuzuo Huang
- Peking Union Medical College Hospital, Chinese Academy of Medical Sciences & Peking Union Medical College, Beijing 100730, China; (X.C.); (F.D.); (X.L.)
- Department of Plastic Surgery, Peking Union Medical College Hospital, Beijing 100730, China
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22
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Al-Jumaan M, Chu H, Alsulaiman A, Camp SY, Han S, Gillani R, Al Marzooq Y, Almulhim F, Vatte C, Al Nemer A, Almuhanna A, Van Allen EM, Al-Ali A, AlDubayan SH. Interplay of Mendelian and polygenic risk factors in Arab breast cancer patients. Genome Med 2023; 15:65. [PMID: 37658461 PMCID: PMC10474689 DOI: 10.1186/s13073-023-01220-4] [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: 05/04/2023] [Accepted: 08/09/2023] [Indexed: 09/03/2023] Open
Abstract
BACKGROUND Breast cancer patients from the indigenous Arab population present much earlier than patients from Western countries and have traditionally been underrepresented in cancer genomics studies. The contribution of polygenic and Mendelian risk toward the earlier onset of breast cancer in the population remains elusive. METHODS We performed low-pass whole genome sequencing (lpWGS) and whole-exome sequencing (WES) from 220 female breast cancer patients unselected for positive family history from the indigenous Arab population. Using publicly available resources, we imputed population-specific variants and calculated breast cancer burden-sensitive polygenic risk scores (PRS). Variant pathogenicity was also evaluated on exome variants with high coverage. RESULTS Variants imputed from lpWGS showed high concordance with paired exome (median dosage correlation: 0.9459, Interquartile range: 0.9410-0.9490). After adjusting the PRS to the Arab population, we found significant associations between PRS performance in risk prediction and first-degree relative breast cancer history prediction (Spearman rho=0.43, p = 0.03), where breast cancer patients in the top PRS decile are 5.53 (95% CI 1.76-17.97, p = 0.003) times more likely also to have a first-degree relative diagnosed with breast cancer compared to those in the middle deciles. In addition, we found evidence for the genetic liability threshold model of breast cancer where among patients with a family history of breast cancer, pathogenic rare variant carriers had significantly lower PRS than non-carriers (p = 0.0205, Mann-Whitney U test) while for non-carriers every standard deviation increase in PRS corresponded to 4.52 years (95% CI 8.88-0.17, p = 0.042) earlier age of presentation. CONCLUSIONS Overall, our study provides a framework to assess polygenic risk in an understudied population using lpWGS and identifies common variant risk as a factor independent of pathogenic variant carrier status for earlier age of onset of breast cancer among indigenous Arab breast cancer patients.
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Affiliation(s)
- Mohammed Al-Jumaan
- College of Medicine, Imam Abdulrahman bin Faisal University, Dammam, Saudi Arabia
| | - Hoyin Chu
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Abdullah Alsulaiman
- College of Medicine, Imam Abdulrahman bin Faisal University, Dammam, Saudi Arabia
| | - Sabrina Y Camp
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Seunghun Han
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Harvard Medical School, Boston, MA, USA
| | - Riaz Gillani
- Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Department of Pediatric Oncology, Dana-Farber Cancer Institute, Boston, MA, USA
- Department of Pediatrics, Harvard Medical School, Boston, MA, USA
- Boston Children's Hospital, Boston, MA, USA
| | - Yousef Al Marzooq
- College of Medicine, Imam Abdulrahman bin Faisal University, Dammam, Saudi Arabia
| | - Fatmah Almulhim
- College of Medicine, Imam Abdulrahman bin Faisal University, Dammam, Saudi Arabia
| | - Chittibabu Vatte
- College of Medicine, Imam Abdulrahman bin Faisal University, Dammam, Saudi Arabia
| | - Areej Al Nemer
- College of Medicine, Imam Abdulrahman bin Faisal University, Dammam, Saudi Arabia
| | - Afnan Almuhanna
- College of Medicine, Imam Abdulrahman bin Faisal University, Dammam, Saudi Arabia
| | - Eliezer M Van Allen
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA
- Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
- Center for Cancer Genomics, Dana-Farber Cancer Institute, Boston, MA, 02115, USA
| | - Amein Al-Ali
- College of Medicine, Imam Abdulrahman bin Faisal University, Dammam, Saudi Arabia
| | - Saud H AlDubayan
- Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA, USA.
- Cancer Program, The Broad Institute of MIT and Harvard, Cambridge, MA, USA.
- Division of Genetics, Brigham and Women's Hospital, Boston, MA, USA.
- College of Medicine, King Saud bin Abdulaziz University for Health Sciences, Riyadh, Saudi Arabia.
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23
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Tang Z, Shen M, Man X. Association between atopic dermatitis and hidradenitis suppurativa: A two-sample Mendelian randomization study. J Dermatol 2023; 50:e287-e288. [PMID: 36987689 DOI: 10.1111/1346-8138.16796] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/02/2023] [Revised: 03/05/2023] [Accepted: 03/14/2023] [Indexed: 03/30/2023]
Affiliation(s)
- Zhenwei Tang
- Department of Dermatology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
| | - Minxue Shen
- Department of Dermatology, Xiangya Hospital, Central South University, Changsha, China
- Hunan Engineering Research Center of Skin Health and Disease, Hunan Key Laboratory of Skin Cancer and Psoriasis (Xiangya Hospital), Changsha, China
- Department of Social Medicine and Health Management, Xiangya School of Public Health, Central South University, Changsha, China
| | - Xiaoyong Man
- Department of Dermatology, Second Affiliated Hospital, Zhejiang University School of Medicine, Hangzhou, China
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24
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Julkunen V, Schwarz C, Kalapudas J, Hallikainen M, Piironen AK, Mannermaa A, Kujala H, Laitinen T, Kosma VM, Paajanen TI, Kälviäinen R, Hiltunen M, Herukka SK, Kärkkäinen S, Kokkola T, Urjansson M, Perola M, Palotie A, Vuoksimaa E, Runz H. A FinnGen pilot clinical recall study for Alzheimer's disease. Sci Rep 2023; 13:12641. [PMID: 37537264 PMCID: PMC10400697 DOI: 10.1038/s41598-023-39835-7] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/27/2023] [Accepted: 07/31/2023] [Indexed: 08/05/2023] Open
Abstract
Successful development of novel therapies requires that clinical trials are conducted in patient cohorts with the highest benefit-to-risk ratio. Population-based biobanks with comprehensive health and genetic data from large numbers of individuals hold promise to facilitate identification of trial participants, particularly when interventions need to start while symptoms are still mild, such as for Alzheimer's disease (AD). This study describes a process for clinical recall studies from FinnGen. We demonstrate the feasibility to systematically ascertain customized clinical data from FinnGen participants with ICD10 diagnosis of AD or mild cognitive disorder (MCD) in a single-center cross-sectional study testing blood-based biomarkers and cognitive functioning in-person, computer-based and remote. As a result, 19% (27/140) of a pre-specified FinnGen subcohort were successfully recalled and completed the study. Hospital records largely validated registry entries. For 8/12 MCD patients, other reasons than AD were identified as underlying diagnosis. Cognitive measures correlated across platforms, with highest consistencies for dementia screening (r = 0.818) and semantic fluency (r = 0.764), respectively, for in-person versus telephone-administered tests. Glial fibrillary acidic protein (GFAP) (p < 0.002) and phosphorylated-tau 181 (pTau-181) (p < 0.020) most reliably differentiated AD from MCD participants. We conclude that informative, customized clinical recall studies from FinnGen are feasible.
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Affiliation(s)
- Valtteri Julkunen
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland.
- Department of Neurology, Neurocenter, Kuopio University Hospital, Kuopio, Finland.
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
| | - Claudia Schwarz
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Department of Neurology, University Medicine Greifswald, Greifswald, Germany
| | - Juho Kalapudas
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
| | - Merja Hallikainen
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
| | | | | | | | | | | | - Teemu I Paajanen
- Work Ability and Working Careers, Finnish Institute of Occupational Health, Helsinki, Finland
| | - Reetta Kälviäinen
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
| | - Mikko Hiltunen
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
| | - Sanna-Kaisa Herukka
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
| | - Sari Kärkkäinen
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
| | - Tarja Kokkola
- Institute of Clinical Medicine/Neurology, University of Eastern Finland, Kuopio, Finland
| | - Mia Urjansson
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
| | - Markus Perola
- Finnish Institute for Health and Welfare (THL), Helsinki, Finland
| | - Aarno Palotie
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland
- Analytic and Translational Genetics Unit, Department of Medicine, Department of Neurology and Department of Psychiatry, Massachusetts General Hospital, Boston, MA, USA
- The Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, The Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Eero Vuoksimaa
- Institute for Molecular Medicine Finland (FIMM), HiLIFE, University of Helsinki, Helsinki, Finland.
| | - Heiko Runz
- Translational Sciences, Biogen, Cambridge, MA, USA.
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25
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Zhu S, Yang M, Wang T, Ding Z. Causal relationships between telomere length and liver disease: a Mendelian randomization study. Front Genet 2023; 14:1164024. [PMID: 37588048 PMCID: PMC10426290 DOI: 10.3389/fgene.2023.1164024] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/11/2023] [Accepted: 07/13/2023] [Indexed: 08/18/2023] Open
Abstract
Background: Leukocyte telomere length and hepatic disorders have been linked in various research studies, although their causative association has not been clarified. This study investigated the causal relationship between the length of telomeres on peripheral blood leukocytes and certain liver disorders. Methods: Mendelian randomization (MR) analysis was used to examine the relationship between leukocyte telomere length and risk of liver disease using the publicly accessible worldwide gene-wide association study (GWAS) database. The weighted mode, weighted median, and inverse variance weighted (IVW) methods were employed as supplements to the IVW approach, which is the main analytical method. Results: Leukocytes with longer telomeres may have a lower risk of developing cirrhosis [OR = 0.645 (0.524, 0.795), p = 3.977E-05] and a higher chance of developing benign liver tumors [OR = 3.087 (1.721, 5.539), p = 1.567E-04]. There was no direct link between telomere length and fatty liver, hepatic fibrosis, or liver cancer. Our findings in the replication analysis agreed with those of the previous studies. Conclusion: Further research is needed to examine the mechanisms underlying the probable causal association between the length of leukocyte telomeres and cirrhosis and benign liver cancer.
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Affiliation(s)
| | | | | | - Zhen Ding
- Department of Hepatobiliary Surgery, Chaohu Hospital of Anhui Medical University, Hefei, China
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26
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Berga-Švītiņa E, Maksimenko J, Miklaševičs E, Fischer K, Vilne B, Mägi R. Polygenic Risk Score Predicts Modified Risk in BRCA1 Pathogenic Variant c.4035del and c.5266dup Carriers in Breast Cancer Patients. Cancers (Basel) 2023; 15:cancers15112957. [PMID: 37296919 DOI: 10.3390/cancers15112957] [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: 04/28/2023] [Revised: 05/22/2023] [Accepted: 05/23/2023] [Indexed: 06/12/2023] Open
Abstract
The aim of this study was to assess the power of the polygenic risk score (PRS) in estimating the overall genetic risk of women carrying germline BRCA1 pathogenic variants (PVs) c.4035del or c.5266dup to develop breast (BC) or ovarian cancer (OC) due to additional genetic variations. In this study, PRSs previously developed from two joint models using summary statistics of age-at-onset (BayesW model) and case-control data (BayesRR-RC model) from a genome-wide association analysis (GWAS) were applied to 406 germline BRCA1 PV (c.4035del or c.5266dup) carriers affected by BC or OC, compared with unaffected individuals. A binomial logistic regression model was used to assess the association of PRS with BC or OC development risk. We observed that the best-fitting BayesW PRS model effectively predicted the individual's BC risk (OR = 1.37; 95% CI = 1.03-1.81, p = 0.02905 with AUC = 0.759). However, none of the applied PRS models was a good predictor of OC risk. The best-fitted PRS model (BayesW) contributed to assessing the risk of developing BC for germline BRCA1 PV (c.4035del or c.5266dup) carriers and may facilitate more precise and timely patient stratification and decision-making to improve the current BC treatment or even prevention strategies.
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Affiliation(s)
- Egija Berga-Švītiņa
- Bioinformatics Lab, Rīga Stradiņš University, Dzirciema Street 16, LV-1007 Riga, Latvia
- Institute of Oncology, Rīga Stradiņš University, Pilsoņu Street 13, Block 13, LV-1002 Riga, Latvia
| | - Jeļena Maksimenko
- Institute of Oncology, Rīga Stradiņš University, Pilsoņu Street 13, Block 13, LV-1002 Riga, Latvia
- Pauls Stradiņš Clinical University Hospital, Pilsoņu Street 13, LV-1002 Riga, Latvia
| | - Edvīns Miklaševičs
- Institute of Oncology, Rīga Stradiņš University, Pilsoņu Street 13, Block 13, LV-1002 Riga, Latvia
- Department of Biology and Microbiology, Rīga Stradiņš University, LV-1007 Riga, Latvia
| | - Krista Fischer
- Institute of Genomics, University of Tartu, Riia 23b, 51010 Tartu, Estonia
- Institute of Mathematics and Statistics, University of Tartu, Narva mnt 18, 51009 Tartu, Estonia
| | - Baiba Vilne
- Bioinformatics Lab, Rīga Stradiņš University, Dzirciema Street 16, LV-1007 Riga, Latvia
| | - Reedik Mägi
- Institute of Genomics, University of Tartu, Riia 23b, 51010 Tartu, Estonia
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Kerr SM, Cowan E, Klaric L, Bell C, O'Sullivan D, Buchanan D, Grzymski JJ, van Hout CV, Tzoneva G, Shuldiner AR, Wilson JF, Miedzybrodzka Z. Clinical case study meets population cohort: identification of a BRCA1 pathogenic founder variant in Orcadians. Eur J Hum Genet 2023; 31:588-595. [PMID: 36927983 PMCID: PMC10172333 DOI: 10.1038/s41431-023-01297-w] [Citation(s) in RCA: 2] [Impact Index Per Article: 2.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/02/2022] [Revised: 12/20/2022] [Accepted: 01/18/2023] [Indexed: 03/18/2023] Open
Abstract
We multiply ascertained the BRCA1 pathogenic missense variant c.5207T > C; p.Val1736Ala (V1736A) in clinical investigation of breast and ovarian cancer families from Orkney in the Northern Isles of Scotland, UK. We sought to investigate the frequency and clinical relevance of this variant in those of Orcadian ancestry as an exemplar of the value of population cohorts in clinical care, especially in isolated populations. Oral history and birth, marriage and death registrations indicated genealogical linkage of the clinical cases to ancestors from the Isle of Westray, Orkney. Further clinical cases were identified through targeted testing for V1736A in women of Orcadian ancestry attending National Health Service (NHS) genetic clinics for breast and ovarian cancer family risk assessments. The variant segregates with female breast and ovarian cancer in clinically ascertained cases. Separately, exome sequence data from 2088 volunteer participants with three or more Orcadian grandparents, in the ORCADES research cohort, was interrogated to estimate the population prevalence of V1736A in Orcadians. The effects of the variant were assessed using Electronic Health Record (EHR) linkage. Twenty out of 2088 ORCADES research volunteers (~1%) carry V1736A, with a common haplotype around the variant. This allele frequency is ~480-fold higher than in UK Biobank participants. Cost-effectiveness of population screening for BRCA1 founder pathogenic variants has been demonstrated at a carrier frequency below the ~1% observed here. Thus we suggest that Orcadian women should be offered testing for the BRCA1 V1736A founder pathogenic variant, starting with those with known Westray ancestry.
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Affiliation(s)
- Shona M Kerr
- MRC Human Genetics Unit, University of Edinburgh, Institute of Genetics and Cancer, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU, UK
| | - Emma Cowan
- Department of Medical Genetics, Ashgrove House, NHS Grampian, Aberdeen, AB25 2ZA, UK
| | - Lucija Klaric
- MRC Human Genetics Unit, University of Edinburgh, Institute of Genetics and Cancer, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU, UK
| | - Christine Bell
- Department of Medical Genetics, Ashgrove House, NHS Grampian, Aberdeen, AB25 2ZA, UK
| | - Dawn O'Sullivan
- Department of Medical Genetics, Ashgrove House, NHS Grampian, Aberdeen, AB25 2ZA, UK
| | - David Buchanan
- MRC Human Genetics Unit, University of Edinburgh, Institute of Genetics and Cancer, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU, UK
| | - Joseph J Grzymski
- Center for Genomic Medicine, Desert Research Institute, Reno, NV, USA
- Renown Health, Reno, NV, USA
| | - Cristopher V van Hout
- Regeneron Genetics Center, Tarrytown, NY, USA
- Laboratorio Internacional de Investigatión sobre el Genoma Humano, Campus Juriquilla de la Universidad Nacional Autónoma de México, Querétaro, Querétaro, 76230, México
| | | | | | - James F Wilson
- MRC Human Genetics Unit, University of Edinburgh, Institute of Genetics and Cancer, Western General Hospital, Crewe Road, Edinburgh, EH4 2XU, UK
- Centre for Global Health Research, Usher Institute, University of Edinburgh, Teviot Place, Edinburgh, EH8 9AG, UK
| | - Zosia Miedzybrodzka
- Department of Medical Genetics, Ashgrove House, NHS Grampian, Aberdeen, AB25 2ZA, UK.
- Medical Genetics Group, School of Medicine, Medical Sciences, Nutrition and Dentistry, University of Aberdeen, Polwarth Building, Aberdeen, AB25 2ZD, UK.
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28
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Scarpa JR, Elemento O. Multi-omic molecular profiling and network biology for precision anaesthesiology: a narrative review. Br J Anaesth 2023:S0007-0912(23)00125-3. [PMID: 37055274 DOI: 10.1016/j.bja.2023.03.006] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/11/2022] [Revised: 02/21/2023] [Accepted: 03/04/2023] [Indexed: 04/15/2023] Open
Abstract
Technological advancement, data democratisation, and decreasing costs have led to a revolution in molecular biology in which the entire set of DNA, RNA, proteins, and various other molecules - the 'multi-omic' profile - can be measured in humans. Sequencing 1 million bases of human DNA now costs US$0.01, and emerging technologies soon promise to reduce the cost of sequencing the whole genome to US$100. These trends have made it feasible to sample the multi-omic profile of millions of people, much of which is publicly available for medical research. Can anaesthesiologists use these data to improve patient care? This narrative review brings together a rapidly growing literature in multi-omic profiling across numerous fields that points to the future of precision anaesthesiology. Here, we discuss how DNA, RNA, proteins, and other molecules interact in molecular networks that can be used for preoperative risk stratification, intraoperative optimisation, and postoperative monitoring. This literature provides evidence for four fundamental insights: (1) Clinically similar patients have different molecular profiles and, as a consequence, different outcomes. (2) Vast, publicly available, and rapidly growing molecular datasets have been generated in chronic disease patients and can be repurposed to estimate perioperative risk. (3) Multi-omic networks are altered in the perioperative period and influence postoperative outcomes. (4) Multi-omic networks can serve as empirical, molecular measurements of a successful postoperative course. With this burgeoning universe of molecular data, the anaesthesiologist-of-the-future will tailor their clinical management to an individual's multi-omic profile to optimise postoperative outcomes and long-term health.
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Affiliation(s)
- Joseph R Scarpa
- Department of Anesthesiology, Weill Cornell Medicine, New York, NY, USA.
| | - Olivier Elemento
- Caryl and Israel Englander Institute for Precision Medicine, Weill Cornell Medicine, Cornell University, New York, NY, USA
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29
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Lakeman IMM, Rodríguez-Girondo MDM, Lee A, Celosse N, Braspenning ME, van Engelen K, van de Beek I, van der Hout AH, Gómez García EB, Mensenkamp AR, Ausems MGEM, Hooning MJ, Adank MA, Hollestelle A, Schmidt MK, van Asperen CJ, Devilee P. Clinical applicability of the Polygenic Risk Score for breast cancer risk prediction in familial cases. J Med Genet 2023; 60:327-336. [PMID: 36137616 DOI: 10.1136/jmg-2022-108502] [Citation(s) in RCA: 7] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/10/2022] [Accepted: 07/19/2022] [Indexed: 11/04/2022]
Abstract
BACKGROUND Common low-risk variants are presently not used to guide clinical management of familial breast cancer (BC). We explored the additive impact of a 313-variant-based Polygenic Risk Score (PRS313) relative to standard gene testing in non-BRCA1/2 Dutch BC families. METHODS We included 3918 BC cases from 3492 Dutch non-BRCA1/2 BC families and 3474 Dutch population controls. The association of the standardised PRS313 with BC was estimated using a logistic regression model, adjusted for pedigree-based family history. Family history of the controls was imputed for this analysis. SEs were corrected to account for relatedness of individuals. Using the BOADICEA (Breast and Ovarian Analysis of Disease Incidence and Carrier Estimation Algorithm) V.5 model, lifetime risks were retrospectively calculated with and without individual PRS313. For 2586 cases and 2584 controls, the carrier status of pathogenic variants (PVs) in ATM, CHEK2 and PALB2 was known. RESULTS The family history-adjusted PRS313 was significantly associated with BC (per SD OR=1.97, 95% CI 1.84 to 2.11). Including the PRS313 in BOADICEA family-based risk prediction would have changed screening recommendations in up to 27%, 36% and 34% of cases according to BC screening guidelines from the USA, UK and the Netherlands (National Comprehensive Cancer Network, National Institute for Health and Care Excellence, and Netherlands Comprehensive Cancer Organisation), respectively. For the population controls, without information on family history, this was up to 39%, 44% and 58%, respectively. Among carriers of PVs in known moderate BC susceptibility genes, the PRS313 had the largest impact for CHEK2 and ATM. CONCLUSIONS Our results support the application of the PRS313 in risk prediction for genetically uninformative BC families and families with a PV in moderate BC risk genes.
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Affiliation(s)
- Inge M M Lakeman
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Mar D M Rodríguez-Girondo
- Department of Medical Statistics and Bioinformatics, Leiden University Medical Center, Leiden, The Netherlands
| | - Andrew Lee
- Public Health and Primary Care, University of Cambridge Centre for Cancer Genetic Epidemiology, Cambridge, UK
| | - Nandi Celosse
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Merel E Braspenning
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Klaartje van Engelen
- Department of Human Genetics, Amsterdam UMC Locatie VUmc, Amsterdam, The Netherlands
| | - Irma van de Beek
- Department of Human Genetics, Amsterdam UMC Locatie VUmc, Amsterdam, The Netherlands
| | - Annemiek H van der Hout
- Department of Clinical Genetics, University Medical Centre Groningen, Groningen, The Netherlands
| | - Encarna B Gómez García
- Department of Clinical Genetics, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Arjen R Mensenkamp
- Department of Human Genetics, University Medical Center Nijmegen, Nijmegen, The Netherlands
| | - Margreet G E M Ausems
- Department of Medical Genetics, University Medical Centre Utrecht, Utrecht, The Netherlands
| | - Maartje J Hooning
- Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Muriel A Adank
- Family Cancer Clinic, Antoni van Leeuwenhoek Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Antoinette Hollestelle
- Department of Medical Oncology, Erasmus MC Cancer Institute, Erasmus Medical Center, Rotterdam, The Netherlands
| | - Marjanka K Schmidt
- Department of Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Division of Psychosocial Research and Epidemiology, Antoni van Leeuwenhoek Netherlands Cancer Institute, Amsterdam, The Netherlands
| | - Christi J van Asperen
- Department of Clinical Genetics, Leiden University Medical Center, Leiden, The Netherlands
| | - Peter Devilee
- Department of Human Genetics, Leiden University Medical Center, Leiden, The Netherlands
- Department of Pathology, Leiden University Medical Center, Leiden, The Netherlands
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30
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Hassanin E, Spier I, Bobbili DR, Aldisi R, Klinkhammer H, David F, Dueñas N, Hüneburg R, Perne C, Brunet J, Capella G, Nöthen MM, Forstner AJ, Mayr A, Krawitz P, May P, Aretz S, Maj C. Clinically relevant combined effect of polygenic background, rare pathogenic germline variants, and family history on colorectal cancer incidence. BMC Med Genomics 2023; 16:42. [PMID: 36872334 PMCID: PMC9987090 DOI: 10.1186/s12920-023-01469-z] [Citation(s) in RCA: 3] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/12/2022] [Accepted: 02/21/2023] [Indexed: 03/07/2023] Open
Abstract
BACKGROUND AND AIMS Summarised in polygenic risk scores (PRS), the effect of common, low penetrant genetic variants associated with colorectal cancer (CRC), can be used for risk stratification. METHODS To assess the combined impact of the PRS and other main factors on CRC risk, 163,516 individuals from the UK Biobank were stratified as follows: 1. carriers status for germline pathogenic variants (PV) in CRC susceptibility genes (APC, MLH1, MSH2, MSH6, PMS2), 2. low (< 20%), intermediate (20-80%), or high PRS (> 80%), and 3. family history (FH) of CRC. Multivariable logistic regression and Cox proportional hazards models were applied to compare odds ratios and to compute the lifetime incidence, respectively. RESULTS Depending on the PRS, the CRC lifetime incidence for non-carriers ranges between 6 and 22%, compared to 40% and 74% for carriers. A suspicious FH is associated with a further increase of the cumulative incidence reaching 26% for non-carriers and 98% for carriers. In non-carriers without FH, but high PRS, the CRC risk is doubled, whereas a low PRS even in the context of a FH results in a decreased risk. The full model including PRS, carrier status, and FH improved the area under the curve in risk prediction (0.704). CONCLUSION The findings demonstrate that CRC risks are strongly influenced by the PRS for both a sporadic and monogenic background. FH, PV, and common variants complementary contribute to CRC risk. The implementation of PRS in routine care will likely improve personalized risk stratification, which will in turn guide tailored preventive surveillance strategies in high, intermediate, and low risk groups.
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Affiliation(s)
- Emadeldin Hassanin
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Bonn, Germany.,Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Sur-Alzette, Luxembourg
| | - Isabel Spier
- Institute of Human Genetics, Medical Faculty, University of Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.,National Center for Hereditary Tumor Syndromes, University Hospital Bonn, Bonn, Germany.,European Reference Network on Genetic Tumour Rsik Syndromes (ERNGENTURIS) - Project ID No 739547, Nijmegen, The Netherlands
| | - Dheeraj R Bobbili
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Sur-Alzette, Luxembourg
| | - Rana Aldisi
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Bonn, Germany
| | - Hannah Klinkhammer
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Bonn, Germany.,Medical Faculty, Institute for Medical Biometry, Informatics and Epidemiology, University Bonn, Bonn, Germany
| | - Friederike David
- Institute of Human Genetics, Medical Faculty, University of Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Nuria Dueñas
- Hereditary Cancer Program, Catalan Institute of Oncology-IDIBELL, ONCOBELL, Hospitalet de Llobregat, Barcelona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Instituto Salud Carlos III, Madrid, Spain
| | - Robert Hüneburg
- National Center for Hereditary Tumor Syndromes, University Hospital Bonn, Bonn, Germany.,Department of Internal Medicine I, University Hospital Bonn, Bonn, Germany
| | - Claudia Perne
- Institute of Human Genetics, Medical Faculty, University of Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.,National Center for Hereditary Tumor Syndromes, University Hospital Bonn, Bonn, Germany
| | - Joan Brunet
- European Reference Network on Genetic Tumour Rsik Syndromes (ERNGENTURIS) - Project ID No 739547, Nijmegen, The Netherlands.,Hereditary Cancer Program, Catalan Institute of Oncology-IDIBELL, ONCOBELL, Hospitalet de Llobregat, Barcelona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Instituto Salud Carlos III, Madrid, Spain.,Hereditary Cancer Program, Catalan Institute of Oncology-IDBIGI, 17007, Girona, Spain
| | - Gabriel Capella
- European Reference Network on Genetic Tumour Rsik Syndromes (ERNGENTURIS) - Project ID No 739547, Nijmegen, The Netherlands.,Hereditary Cancer Program, Catalan Institute of Oncology-IDIBELL, ONCOBELL, Hospitalet de Llobregat, Barcelona, Spain.,Centro de Investigación Biomédica en Red de Cáncer (CIBERONC), Instituto Salud Carlos III, Madrid, Spain
| | - Markus M Nöthen
- Institute of Human Genetics, Medical Faculty, University of Bonn, Venusberg-Campus 1, 53127, Bonn, Germany
| | - Andreas J Forstner
- Institute of Human Genetics, Medical Faculty, University of Bonn, Venusberg-Campus 1, 53127, Bonn, Germany.,Centre for Human Genetics, University of Marburg, Marburg, Germany.,Institute of Neuroscience and Medicine (INM-1), Research Center Jülich, Jülich, Germany
| | - Andreas Mayr
- Medical Faculty, Institute for Medical Biometry, Informatics and Epidemiology, University Bonn, Bonn, Germany
| | - Peter Krawitz
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Bonn, Germany
| | - Patrick May
- Luxembourg Centre for Systems Biomedicine, University of Luxembourg, Esch-Sur-Alzette, Luxembourg
| | - Stefan Aretz
- Institute of Human Genetics, Medical Faculty, University of Bonn, Venusberg-Campus 1, 53127, Bonn, Germany. .,National Center for Hereditary Tumor Syndromes, University Hospital Bonn, Bonn, Germany. .,European Reference Network on Genetic Tumour Rsik Syndromes (ERNGENTURIS) - Project ID No 739547, Nijmegen, The Netherlands.
| | - Carlo Maj
- Institute for Genomic Statistics and Bioinformatics, University Hospital Bonn, Bonn, Germany
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31
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Mendelian inheritance revisited: dominance and recessiveness in medical genetics. Nat Rev Genet 2023:10.1038/s41576-023-00574-0. [PMID: 36806206 DOI: 10.1038/s41576-023-00574-0] [Citation(s) in RCA: 18] [Impact Index Per Article: 18.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 12/14/2022] [Indexed: 02/22/2023]
Abstract
Understanding the consequences of genotype for phenotype (which ranges from molecule-level effects to whole-organism traits) is at the core of genetic diagnostics in medicine. Many measures of the deleteriousness of individual alleles exist, but these have limitations for predicting the clinical consequences. Various mechanisms can protect the organism from the adverse effects of functional variants, especially when the variant is paired with a wild type allele. Understanding why some alleles are harmful in the heterozygous state - representing dominant inheritance - but others only with the biallelic presence of pathogenic variants - representing recessive inheritance - is particularly important when faced with the deluge of rare genetic alterations identified by high throughput DNA sequencing. Both awareness of the specific quantitative and/or qualitative effects of individual variants and the elucidation of allelic and non-allelic interactions are essential to optimize genetic diagnosis and counselling.
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32
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Khunsriraksakul C, Li Q, Markus H, Patrick MT, Sauteraud R, McGuire D, Wang X, Wang C, Wang L, Chen S, Shenoy G, Li B, Zhong X, Olsen NJ, Carrel L, Tsoi LC, Jiang B, Liu DJ. Multi-ancestry and multi-trait genome-wide association meta-analyses inform clinical risk prediction for systemic lupus erythematosus. Nat Commun 2023; 14:668. [PMID: 36750564 PMCID: PMC9905560 DOI: 10.1038/s41467-023-36306-5] [Citation(s) in RCA: 13] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Accepted: 01/25/2023] [Indexed: 02/09/2023] Open
Abstract
Systemic lupus erythematosus is a heritable autoimmune disease that predominantly affects young women. To improve our understanding of genetic etiology, we conduct multi-ancestry and multi-trait meta-analysis of genome-wide association studies, encompassing 12 systemic lupus erythematosus cohorts from 3 different ancestries and 10 genetically correlated autoimmune diseases, and identify 16 novel loci. We also perform transcriptome-wide association studies, computational drug repurposing analysis, and cell type enrichment analysis. We discover putative drug classes, including a histone deacetylase inhibitor that could be repurposed to treat lupus. We also identify multiple cell types enriched with putative target genes, such as non-classical monocytes and B cells, which may be targeted for future therapeutics. Using this newly assembled result, we further construct polygenic risk score models and demonstrate that integrating polygenic risk score with clinical lab biomarkers improves the diagnostic accuracy of systemic lupus erythematosus using the Vanderbilt BioVU and Michigan Genomics Initiative biobanks.
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Affiliation(s)
- Chachrit Khunsriraksakul
- Program in Bioinformatics and Genomics, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA.,Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Qinmengge Li
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Havell Markus
- Program in Bioinformatics and Genomics, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA.,Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Matthew T Patrick
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Renan Sauteraud
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Daniel McGuire
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Xingyan Wang
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Chen Wang
- Program in Bioinformatics and Genomics, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Lida Wang
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Siyuan Chen
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Ganesh Shenoy
- Department of Neurosurgery, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Bingshan Li
- Department of Molecular Physiology & Biophysics, Vanderbilt University, Nashville, TN, 37235, USA
| | - Xue Zhong
- Department of Medicine, Division of Genetic Medicine, Vanderbilt University Medical Center, Nashville, TN, 37232, USA
| | - Nancy J Olsen
- Department of Medicine, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Laura Carrel
- Department of Biochemistry and Molecular Biology, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Lam C Tsoi
- Department of Dermatology, University of Michigan Medical School, Ann Arbor, MI, 48109, USA
| | - Bibo Jiang
- Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA
| | - Dajiang J Liu
- Program in Bioinformatics and Genomics, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA. .,Institute for Personalized Medicine, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA. .,Department of Public Health Sciences, Pennsylvania State University College of Medicine, Hershey, PA, 17033, USA.
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33
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Ohbe H, Hachiya T, Yamaji T, Nakano S, Miyamoto Y, Sutoh Y, Otsuka-Yamasaki Y, Shimizu A, Yasunaga H, Sawada N, Inoue M, Tsugane S, Iwasaki M. Development and validation of genome-wide polygenic risk scores for predicting breast cancer incidence in Japanese females: a population-based case-cohort study. Breast Cancer Res Treat 2023; 197:661-671. [PMID: 36538246 DOI: 10.1007/s10549-022-06843-6] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2022] [Accepted: 12/08/2022] [Indexed: 12/24/2022]
Abstract
PURPOSE This study aimed to develop an ancestry-specific polygenic risk scores (PRSs) for the prediction of breast cancer events in Japanese females and validate it in a longitudinal cohort study. METHODS Using publicly available summary statistics of female breast cancer genome-wide association study (GWAS) of Japanese and European ancestries, we, respectively, developed 31 candidate genome-wide PRSs using pruning and thresholding (P + T) and LDpred methods with varying parameters. Among the candidate PRS models, the best model was selected using a case-cohort dataset (63 breast cancer cases and 2213 sub-cohorts of Japanese females during a median follow-up of 11.9 years) according to the maximal predictive ability by Harrell's C-statistics. The best-performing PRS for each derivation GWAS was evaluated in another independent case-cohort dataset (260 breast cancer cases and 7845 sub-cohorts of Japanese females during a median follow-up of 16.9 years). RESULTS For the best PRS model involving 46,861 single nucleotide polymorphisms (SNPs; P + T method with PT = 0.05 and R2 = 0.2) derived from Japanese-ancestry GWAS, the Harrell's C-statistic was 0.598 ± 0.018 in the evaluation dataset. The age-adjusted hazard ratio for breast cancer in females with the highest PRS quintile compared with those in the lowest PRS quintile was 2.47 (95% confidence intervals, 1.64-3.70). The PRS constructed using Japanese-ancestry GWAS demonstrated better predictive performance for breast cancer in Japanese females than that using European-ancestry GWAS (Harrell's C-statistics 0.598 versus 0.586). CONCLUSION This study developed a breast cancer PRS for Japanese females and demonstrated the usefulness of the PRS for breast cancer risk stratification.
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Affiliation(s)
- Hiroyuki Ohbe
- Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Tsuyoshi Hachiya
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Iwate Medical University, 1-1-1 Idaidori, Yahaba, Shiwa, Iwate, 028-3694, Japan.
| | - Taiki Yamaji
- Division of Epidemiology, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.
| | - Shiori Nakano
- Division of Epidemiology, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Yoshihisa Miyamoto
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Yoichi Sutoh
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Iwate Medical University, 1-1-1 Idaidori, Yahaba, Shiwa, Iwate, 028-3694, Japan
| | - Yayoi Otsuka-Yamasaki
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Iwate Medical University, 1-1-1 Idaidori, Yahaba, Shiwa, Iwate, 028-3694, Japan
| | - Atsushi Shimizu
- Division of Biomedical Information Analysis, Iwate Tohoku Medical Megabank Organization, Iwate Medical University, 1-1-1 Idaidori, Yahaba, Shiwa, Iwate, 028-3694, Japan
| | - Hideo Yasunaga
- Department of Clinical Epidemiology and Health Economics, School of Public Health, The University of Tokyo, 7-3-1 Hongo, Bunkyo-ku, Tokyo, 113-0033, Japan
| | - Norie Sawada
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Manami Inoue
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.,Division of Prevention, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
| | - Shoichiro Tsugane
- Division of Cohort Research, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.,National Institute of Health and Nutrition, National Institutes of Biomedical Innovation, Health and Nutrition, Tokyo, 162-8636, Japan
| | - Motoki Iwasaki
- Division of Epidemiology, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan.,Division of Cohort Research, National Cancer Center Institute for Cancer Control, 5-1-1 Tsukiji, Chuo-ku, Tokyo, 104-0045, Japan
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Sokolova A, Johnstone KJ, McCart Reed AE, Simpson PT, Lakhani SR. Hereditary breast cancer: syndromes, tumour pathology and molecular testing. Histopathology 2023; 82:70-82. [PMID: 36468211 PMCID: PMC10953374 DOI: 10.1111/his.14808] [Citation(s) in RCA: 16] [Impact Index Per Article: 16.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/01/2022] [Revised: 09/12/2022] [Accepted: 09/18/2022] [Indexed: 12/09/2022]
Abstract
Hereditary factors account for a significant proportion of breast cancer risk. Approximately 20% of hereditary breast cancers are attributable to pathogenic variants in the highly penetrant BRCA1 and BRCA2 genes. A proportion of the genetic risk is also explained by pathogenic variants in other breast cancer susceptibility genes, including ATM, CHEK2, PALB2, RAD51C, RAD51D and BARD1, as well as genes associated with breast cancer predisposition syndromes - TP53 (Li-Fraumeni syndrome), PTEN (Cowden syndrome), CDH1 (hereditary diffuse gastric cancer), STK11 (Peutz-Jeghers syndrome) and NF1 (neurofibromatosis type 1). Polygenic risk, the cumulative risk from carrying multiple low-penetrance breast cancer susceptibility alleles, is also a well-recognised contributor to risk. This review provides an overview of the established breast cancer susceptibility genes as well as breast cancer predisposition syndromes, highlights distinct genotype-phenotype correlations associated with germline mutation status and discusses molecular testing and therapeutic implications in the context of hereditary breast cancer.
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Affiliation(s)
- A Sokolova
- Sullivan and Nicolaides PathologyBrisbane
- Centre for Clinical Research, Faculty of MedicineThe University of QueenslandBrisbane
| | - K J Johnstone
- Centre for Clinical Research, Faculty of MedicineThe University of QueenslandBrisbane
- Pathology Queensland, The Royal Brisbane and Women's HospitalBrisbaneQueenslandAustralia
| | - A E McCart Reed
- Centre for Clinical Research, Faculty of MedicineThe University of QueenslandBrisbane
| | - P T Simpson
- Centre for Clinical Research, Faculty of MedicineThe University of QueenslandBrisbane
| | - S R Lakhani
- Centre for Clinical Research, Faculty of MedicineThe University of QueenslandBrisbane
- Pathology Queensland, The Royal Brisbane and Women's HospitalBrisbaneQueenslandAustralia
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Nurmi AK, Suvanto M, Dennis J, Aittomäki K, Blomqvist C, Nevanlinna H. Pathogenic Variant Spectrum in Breast Cancer Risk Genes in Finnish Patients. Cancers (Basel) 2022; 14:cancers14246158. [PMID: 36551643 PMCID: PMC9776204 DOI: 10.3390/cancers14246158] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/08/2022] [Revised: 12/03/2022] [Accepted: 12/09/2022] [Indexed: 12/24/2022] Open
Abstract
Recurrent pathogenic variants have been detected in several breast and ovarian cancer (BC/OC) risk genes in the Finnish population. We conducted a gene-panel sequencing and copy number variant (CNV) analysis to define a more comprehensive spectrum of pathogenic variants in BRCA1, BRCA2, PALB2, CHEK2, ATM, BARD1, RAD51C, RAD51D, BRIP1, and FANCM genes in Finnish BC patients. The combined frequency of pathogenic variants in the BRCA1/2 genes was 1.8% in 1356 unselected patients, whereas variants in the other genes were detected altogether in 8.3% of 1356 unselected patients and in 12.9% of 699 familial patients. CNVs were detected in 0.3% of both 1137 unselected and 612 familial patients. A few variants covered most of the pathogenic burden in the studied genes. Of the BRCA1/2 carriers, 70.8% had 1 of 10 recurrent variants. In the other genes combined, 92.1% of the carrier patients had at least 1 of 11 recurrent variants. In particular, PALB2 c.1592delT and CHEK2 c.1100delC accounted for 88.9% and 82.9%, respectively, of the pathogenic variation in each gene. Our results highlight the importance of founder variants in the BC risk genes in the Finnish population and could be used in the designing of population screening for the risk variants.
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Affiliation(s)
- Anna K. Nurmi
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, 00029 Helsinki, Finland
| | - Maija Suvanto
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, 00029 Helsinki, Finland
| | - Joe Dennis
- Centre for Cancer Genetic Epidemiology, Department of Public Health and Primary Care, University of Cambridge, Cambridge CB1 8RN, UK
| | - Kristiina Aittomäki
- Department of Clinical Genetics, University of Helsinki and Helsinki University Hospital, 00029 Helsinki, Finland
| | - Carl Blomqvist
- Department of Oncology, University of Helsinki and Helsinki University Hospital, 00029 Helsinki, Finland
| | - Heli Nevanlinna
- Department of Obstetrics and Gynecology, University of Helsinki and Helsinki University Hospital, 00029 Helsinki, Finland
- Correspondence:
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Mars N, Lindbohm JV, Della Briotta Parolo P, Widén E, Kaprio J, Palotie A, Ripatti S. Systematic comparison of family history and polygenic risk across 24 common diseases. Am J Hum Genet 2022; 109:2152-2162. [PMID: 36347255 PMCID: PMC9748261 DOI: 10.1016/j.ajhg.2022.10.009] [Citation(s) in RCA: 49] [Impact Index Per Article: 24.5] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/06/2022] [Accepted: 10/17/2022] [Indexed: 11/09/2022] Open
Abstract
Family history is the standard indirect measure of inherited susceptibility in clinical care, whereas polygenic risk scores (PRSs) have more recently demonstrated potential for more directly capturing genetic risk in many diseases. Few studies have systematically compared how these overlap and complement each other across common diseases. Within FinnGen (N = 306,418), we leverage family relationships, up to 50 years of nationwide registries, and genome-wide genotyping to examine the interplay of family history and genome-wide PRSs. We explore the dynamic for three types of family history across 24 common diseases: first- and second-degree family history and parental causes of death. Covering a large proportion of the burden of non-communicable diseases in adults, we show that family history and PRS are independent and not interchangeable measures, but instead provide complementary information on inherited disease susceptibility. The PRSs explained on average 10% of the effect of first-degree family history, and first-degree family history 3% of PRSs, and PRS effects were independent of both early- and late-onset family history. The PRS stratified the risk similarly in individuals with and without family history. In most diseases, including coronary artery disease, glaucoma, and type 2 diabetes, a positive family history with a high PRS was associated with a considerably elevated risk, whereas a low PRS compensated completely for the risk implied by positive family history. This study provides a catalogue of risk estimates for both family history of disease and PRSs and highlights opportunities for a more comprehensive way of assessing inherited disease risk across common diseases.
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Affiliation(s)
- Nina Mars
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Joni V Lindbohm
- Clinicum, Department of Public Health, University of Helsinki, Helsinki, Finland; Department of Epidemiology and Public Health, University College London, London, UK; Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | | | - Elisabeth Widén
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland
| | - Jaakko Kaprio
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Clinicum, Department of Public Health, University of Helsinki, Helsinki, Finland
| | - Aarno Palotie
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA; Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Helsinki, Finland; Clinicum, Department of Public Health, University of Helsinki, Helsinki, Finland; Broad Institute of MIT and Harvard, Cambridge, MA, USA.
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Qin N, Wang C, Chen C, Yang L, Liu S, Xiang J, Xie Y, Liang S, Zhou J, Xu X, Zhao X, Zhu M, Jin G, Ma H, Dai J, Hu Z, Shen H. Association of the interaction between mosaic chromosomal alterations and polygenic risk score with the risk of lung cancer: an array-based case-control association and prospective cohort study. Lancet Oncol 2022; 23:1465-1474. [PMID: 36265503 DOI: 10.1016/s1470-2045(22)00600-3] [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: 07/25/2022] [Revised: 09/13/2022] [Accepted: 09/15/2022] [Indexed: 11/06/2022]
Abstract
BACKGROUND Mosaic chromosomal alterations (mCAs) detected from blood-derived DNA are large structural alterations of clonal haematopoietic origin and are associated with various diseases, such as haematological malignancies, infections, and solid cancers. We aimed to investigate whether mCAs contribute to the risk of lung cancer and modify the effect of polygenic risk score (PRS) on lung cancer risk prediction. METHODS The blood-derived DNA of patients with lung cancer and cancer-free controls with Chinese ancestry from the Nanjing Lung Cancer Cohort (NJLCC) study were genotyped with a Global Screening Array, and mCAs were detected with the Mosaic Chromosomal Alterations (MoChA) pipeline. mCA call sets of individuals with European ancestry were obtained from the prospective cohort UK Biobank (UKB) study, including documented incident lung cancer. All patients with lung cancer from the NJLCC study (aged 15 years or older at diagnosis) were histopathologically confirmed as new lung cancer cases by at least two pathologists and were free of chemotherapy or radiotherapy before diagnosis. Participants with incident lung cancer (aged 37-73 years at assessment) diagnosed after recruitment to the UKB were identified through linkage to national cancer registries. Logistic regression and Cox proportional hazard models were applied to evaluate associations between mCAs and risk of lung cancer in the NJLCC (logistic regression) and UKB (Cox proportional hazard model) studies. FINDINGS The NJLCC study included 10 248 individuals (6445 [62·89%] men and 3803 [37·11%] women; median age 60·0 years [IQR 53·0-66·0]) with lung cancer and 9298 individuals (5871 [63·14%] men and 3427 [36·86%] women; median age 60·0 years [52·0-65·0]) without lung cancer recruited from three sub-regions (north, central, and south) across China between April 15, 2003, and Aug 18, 2017. The UKB included 450 821 individuals recruited from 22 centres across the UK between March 13, 2006, and Nov 1, 2010, including 2088 individuals with lung cancer (1075 [51·48%] men and 1013 [48·52%] women; median age 63·0 years [IQR 59·0-66·0]), and 448 733 participants were free of lung cancer (204 713 [45·62%] men and 244 020 [54·38%] women; median age 58·0 years [IQR 50·0-63·0]). Compared with non-carriers of mosaic losses, carriers had a significantly increased risk of lung cancer in the NJLCC (odds ratio [OR] 1·81, 95% CI 1·43-2·28; p=6·69 × 10-7) and UKB (hazard ratio [HR] 1·40, 95% CI 1·00-1·95; p=0·048) studies. This increased risk was even higher in patients with expanded cell fractions of mCAs (ie, cell fractions ≥10% vs cell fractions <10%) in the NJLCC (OR 1·61 [95% CI 1·26-2·08] vs 1·03 [0·83-1·26]; p for heterogeneity test=6·41 × 10-3). A significant multiplicative interaction was observed between PRS and mosaic losses on the risk of lung cancer in both the NJLCC (interaction p value=0·030) and UKB (p=0·043). Compared with non-carriers of mosaic loss abnormalities with low genetic risk, participants with expanded mosaic losses (cell fractions ≥10%) and high genetic risk had around a six-times increased risk of lung cancer in the NJLCC study (OR 6·40 [95% CI 3·22-12·69]), and an almost four-times increased risk of lung cancer (HR 3·75 [95% CI 1·86-7·55]) in the UKB study. The additive interaction also contributed a 3·67 (95% CI 0·49-6·85) relative excess risk of developing lung cancer in the NJLCC study, and a 2·15 (0·12-4·19) relative excess risk in the UKB study. INTERPRETATION mCAs act as a new endogenous indicator for the risk of lung cancer and might be jointly used with PRS to optimise personalised risk stratification for lung cancer. FUNDING National Natural Science Foundation of China, Outstanding Youth Foundation of Jiangsu Province, Natural Science Foundation of Jiangsu Province, and Postdoctoral Science Foundation of China. TRANSLATION For the Chinese translation of the abstract see Supplementary Materials section.
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Affiliation(s)
- Na Qin
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Department of Epidemiology, Centre for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Centre for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Cheng Wang
- Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Centre for Cancer Medicine, Nanjing Medical University, Nanjing, China; Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Congcong Chen
- Department of Epidemiology, Centre for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Liu Yang
- Department of Epidemiology, Centre for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Su Liu
- Department of Epidemiology, Centre for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Jun Xiang
- Department of Epidemiology, Centre for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Yuan Xie
- Department of Bioinformatics, School of Biomedical Engineering and Informatics, Nanjing Medical University, Nanjing, China
| | - Shuang Liang
- Department of Epidemiology, Centre for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Jun Zhou
- Department of Epidemiology, Centre for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Xianfeng Xu
- Department of Epidemiology, Centre for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Xiaoyu Zhao
- Department of Epidemiology, Centre for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China
| | - Meng Zhu
- Department of Epidemiology, Centre for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Centre for Cancer Medicine, Nanjing Medical University, Nanjing, China
| | - Guangfu Jin
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Department of Epidemiology, Centre for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Centre for Cancer Medicine, Nanjing Medical University, Nanjing, China; State Key Laboratory of Reproductive Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Nanjing, China
| | - Hongxia Ma
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Department of Epidemiology, Centre for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Centre for Cancer Medicine, Nanjing Medical University, Nanjing, China; State Key Laboratory of Reproductive Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Nanjing, China; Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China
| | - Juncheng Dai
- Department of Epidemiology, Centre for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Centre for Cancer Medicine, Nanjing Medical University, Nanjing, China; State Key Laboratory of Reproductive Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Nanjing, China
| | - Zhibin Hu
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Department of Epidemiology, Centre for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Centre for Cancer Medicine, Nanjing Medical University, Nanjing, China; State Key Laboratory of Reproductive Medicine, The Affiliated Suzhou Hospital of Nanjing Medical University, Suzhou Municipal Hospital, Gusu School, Nanjing Medical University, Nanjing, China.
| | - Hongbing Shen
- State Key Laboratory of Reproductive Medicine, School of Public Health, Nanjing Medical University, Nanjing, China; Department of Epidemiology, Centre for Global Health, School of Public Health, Nanjing Medical University, Nanjing, China; Jiangsu Key Lab of Cancer Biomarkers, Prevention and Treatment, Collaborative Innovation Centre for Cancer Medicine, Nanjing Medical University, Nanjing, China; Research Units of Cohort Study on Cardiovascular Diseases and Cancers, Chinese Academy of Medical Sciences, Beijing, China.
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Genetic risk factors have a substantial impact on healthy life years. Nat Med 2022; 28:1893-1901. [PMID: 36097220 PMCID: PMC9499866 DOI: 10.1038/s41591-022-01957-2] [Citation(s) in RCA: 24] [Impact Index Per Article: 12.0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/13/2022] [Accepted: 07/19/2022] [Indexed: 12/15/2022]
Abstract
The impact of genetic variation on overall disease burden has not been comprehensively evaluated. We introduce an approach to estimate the effect of genetic risk factors on disability-adjusted life years (DALYs; 'lost healthy life years'). We use genetic information from 735,748 individuals and consider 80 diseases. Rare variants had the highest effect on DALYs at the individual level. Among common variants, rs3798220 (LPA) had the strongest individual-level effect, with 1.18 DALYs from carrying 1 versus 0 copies. Being in the top 10% versus the bottom 90% of a polygenic score for multisite chronic pain had an effect of 3.63 DALYs. Some common variants had a population-level effect comparable to modifiable risk factors such as high sodium intake and low physical activity. Attributable DALYs vary between males and females for some genetic exposures. Genetic risk factors can explain a sizable number of healthy life years lost both at the individual and population level.
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Johnson R, Ding Y, Venkateswaran V, Bhattacharya A, Boulier K, Chiu A, Knyazev S, Schwarz T, Freund M, Zhan L, Burch KS, Caggiano C, Hill B, Rakocz N, Balliu B, Denny CT, Sul JH, Zaitlen N, Arboleda VA, Halperin E, Sankararaman S, Butte MJ, Lajonchere C, Geschwind DH, Pasaniuc B. Leveraging genomic diversity for discovery in an electronic health record linked biobank: the UCLA ATLAS Community Health Initiative. Genome Med 2022; 14:104. [PMID: 36085083 PMCID: PMC9461263 DOI: 10.1186/s13073-022-01106-x] [Citation(s) in RCA: 20] [Impact Index Per Article: 10.0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/28/2021] [Accepted: 08/03/2022] [Indexed: 01/13/2023] Open
Abstract
BACKGROUND Large medical centers in urban areas, like Los Angeles, care for a diverse patient population and offer the potential to study the interplay between genetic ancestry and social determinants of health. Here, we explore the implications of genetic ancestry within the University of California, Los Angeles (UCLA) ATLAS Community Health Initiative-an ancestrally diverse biobank of genomic data linked with de-identified electronic health records (EHRs) of UCLA Health patients (N=36,736). METHODS We quantify the extensive continental and subcontinental genetic diversity within the ATLAS data through principal component analysis, identity-by-descent, and genetic admixture. We assess the relationship between genetically inferred ancestry (GIA) and >1500 EHR-derived phenotypes (phecodes). Finally, we demonstrate the utility of genetic data linked with EHR to perform ancestry-specific and multi-ancestry genome and phenome-wide scans across a broad set of disease phenotypes. RESULTS We identify 5 continental-scale GIA clusters including European American (EA), African American (AA), Hispanic Latino American (HL), South Asian American (SAA) and East Asian American (EAA) individuals and 7 subcontinental GIA clusters within the EAA GIA corresponding to Chinese American, Vietnamese American, and Japanese American individuals. Although we broadly find that self-identified race/ethnicity (SIRE) is highly correlated with GIA, we still observe marked differences between the two, emphasizing that the populations defined by these two criteria are not analogous. We find a total of 259 significant associations between continental GIA and phecodes even after accounting for individuals' SIRE, demonstrating that for some phenotypes, GIA provides information not already captured by SIRE. GWAS identifies significant associations for liver disease in the 22q13.31 locus across the HL and EAA GIA groups (HL p-value=2.32×10-16, EAA p-value=6.73×10-11). A subsequent PheWAS at the top SNP reveals significant associations with neurologic and neoplastic phenotypes specifically within the HL GIA group. CONCLUSIONS Overall, our results explore the interplay between SIRE and GIA within a disease context and underscore the utility of studying the genomes of diverse individuals through biobank-scale genotyping linked with EHR-based phenotyping.
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Affiliation(s)
- Ruth Johnson
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
| | - Yi Ding
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Vidhya Venkateswaran
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Oral Biology, School of Dentistry, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Arjun Bhattacharya
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Institute for Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Kristin Boulier
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Medicine, Division of Cardiology, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Alec Chiu
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Sergey Knyazev
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Institute for Quantitative and Computational Biosciences, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Tommer Schwarz
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Malika Freund
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Genetics, Stanford School of Medicine, Stanford, CA, 94305, USA
| | - Lingyu Zhan
- Molecular Biology Institute, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Kathryn S Burch
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Christa Caggiano
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Brian Hill
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Nadav Rakocz
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Brunilda Balliu
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Christopher T Denny
- Division of Hematology/Oncology, Department of Pediatrics, Gwynne Hazen Cherry Memorial Laboratories, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Molecular Biology Institute, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Jonsson Comprehensive Cancer Center, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Jae Hoon Sul
- Department of Psychiatry and Biobehavioral Sciences, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Noah Zaitlen
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Valerie A Arboleda
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Eran Halperin
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Anesthesiology and Perioperative Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Sriram Sankararaman
- Department of Computer Science, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Manish J Butte
- Department of Pediatrics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Clara Lajonchere
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Institute of Precision Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Daniel H Geschwind
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Program in Neurogenetics, Department of Neurology, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA
- Institute of Precision Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA
| | - Bogdan Pasaniuc
- Department of Pathology and Laboratory Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- Bioinformatics Interdepartmental Program, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- Department of Human Genetics, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- Department of Computational Medicine, David Geffen School of Medicine, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
- Institute of Precision Health, University of California, Los Angeles, Los Angeles, CA, 90095, USA.
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40
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Kullo IJ, Lewis CM, Inouye M, Martin AR, Ripatti S, Chatterjee N. Polygenic scores in biomedical research. Nat Rev Genet 2022; 23:524-532. [PMID: 35354965 PMCID: PMC9391275 DOI: 10.1038/s41576-022-00470-z] [Citation(s) in RCA: 68] [Impact Index Per Article: 34.0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Accepted: 03/03/2022] [Indexed: 12/12/2022]
Abstract
Public health strategies aimed at disease prevention or early detection and intervention have the potential to advance human health worldwide. However, their success depends on the identification of risk factors that underlie disease burden in the general population. Genome-wide association studies (GWAS) have implicated thousands of single-nucleotide polymorphisms (SNPs) in common complex diseases or traits. By calculating a weighted sum of the number of trait-associated alleles harboured by an individual, a polygenic score (PGS), also called a polygenic risk score (PRS), can be constructed that reflects an individual’s estimated genetic predisposition for a given phenotype. Here, we ask six experts to give their opinions on the utility of these probabilistic tools, their strengths and limitations, and the remaining barriers that need to be overcome for their equitable use.
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Affiliation(s)
- Iftikhar J Kullo
- Department of Cardiovascular Medicine, Mayo Clinic, Rochester, MN, USA.
| | - Cathryn M Lewis
- Social, Genetic and Developmental Psychiatry Centre & Department of Medical & Molecular, King's College London, London, UK.
| | - Michael Inouye
- Department of Public Health and Primary Care, University of Cambridge, Cambridge, UK.
- Baker Heart and Diabetes Institute, Melbourne, Victoria, Australia.
| | - Alicia R Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
| | - Samuli Ripatti
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, MA, USA.
- Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, MA, USA.
- Institute for Molecular Medicine Finland (FIMM), Helsinki Institute of Life Science (HiLIFE), University of Helsinki, Helsinki, Finland.
- Department of Public Health, University of Helsinki, Helsinki, Finland.
| | - Nilanjan Chatterjee
- Department of Biostatistics, Johns Hopkins Bloomberg School of Public Health, Baltimore, MD, USA.
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Wang Y, Tsuo K, Kanai M, Neale BM, Martin AR. Challenges and Opportunities for Developing More Generalizable Polygenic Risk Scores. Annu Rev Biomed Data Sci 2022; 5:293-320. [PMID: 35576555 PMCID: PMC9828290 DOI: 10.1146/annurev-biodatasci-111721-074830] [Citation(s) in RCA: 53] [Impact Index Per Article: 26.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/11/2023]
Abstract
Polygenic risk scores (PRS) estimate an individual's genetic likelihood of complex traits and diseases by aggregating information across multiple genetic variants identified from genome-wide association studies. PRS can predict a broad spectrum of diseases and have therefore been widely used in research settings. Some work has investigated their potential applications as biomarkers in preventative medicine, but significant work is still needed to definitively establish and communicate absolute risk to patients for genetic and modifiable risk factors across demographic groups. However, the biggest limitation of PRS currently is that they show poor generalizability across diverse ancestries and cohorts. Major efforts are underway through methodological development and data generation initiatives to improve their generalizability. This review aims to comprehensively discuss current progress on the development of PRS, the factors that affect their generalizability, and promising areas for improving their accuracy, portability, and implementation.
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Affiliation(s)
- Ying Wang
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Kristin Tsuo
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA,Biological and Biomedical Sciences, Harvard Medical School, Boston, Massachusetts, USA
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA,Department of Biomedical Informatics, Harvard Medical School, Boston, Massachusetts, USA,Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan
| | - Benjamin M. Neale
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
| | - Alicia R. Martin
- Analytic and Translational Genetics Unit, Massachusetts General Hospital, Boston, Massachusetts, USA,Stanley Center for Psychiatric Research and Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts, USA
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Polygenic risk scores: improving the prediction of future disease or added complexity? Br J Gen Pract 2022; 72:396-398. [PMID: 35902257 PMCID: PMC9343049 DOI: 10.3399/bjgp22x720437] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 12/14/2022] Open
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Hujoel ML, Loh PR, Neale BM, Price AL. Incorporating family history of disease improves polygenic risk scores in diverse populations. CELL GENOMICS 2022; 2:100152. [PMID: 35935918 PMCID: PMC9351615 DOI: 10.1016/j.xgen.2022.100152] [Citation(s) in RCA: 14] [Impact Index Per Article: 7.0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 04/07/2021] [Revised: 05/22/2022] [Accepted: 06/09/2022] [Indexed: 01/04/2023]
Abstract
Polygenic risk scores (PRSs) derived from genotype data and family history (FH) of disease provide valuable information for predicting disease risk, but PRSs perform poorly when applied to diverse populations. Here, we explore methods for combining both types of information (PRS-FH) in UK Biobank data. PRSs were trained using all British individuals (n = 409,000), and target samples consisted of unrelated non-British Europeans (n = 42,000), South Asians (n = 7,000), or Africans (n = 7,000). We evaluated PRS, FH, and PRS-FH using liability-scale R 2, primarily focusing on 3 well-powered diseases (type 2 diabetes, hypertension, and depression). PRS attained average prediction R 2s of 5.8%, 4.0%, and 0.53% in non-British Europeans, South Asians, and Africans, confirming poor cross-population transferability. In contrast, PRS-FH attained average prediction R 2s of 13%, 12%, and 10%, respectively, representing a large improvement in Europeans and an extremely large improvement in Africans. In conclusion, including family history improves the accuracy of polygenic risk scores, particularly in diverse populations.
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Affiliation(s)
- Margaux L.A. Hujoel
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Po-Ru Loh
- Division of Genetics, Department of Medicine, Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Benjamin M. Neale
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
| | - Alkes L. Price
- Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
- Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA
- Department of Epidemiology, Harvard T.H. Chan School of Public Health, Boston, MA 02115, USA
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Hung RJ, Khodayari Moez E, Kim SJ, Budhathoki S, Brooks JD. Considerations of Biomarker Application for Cancer Continuum in the Era of Precision Medicine. CURR EPIDEMIOL REP 2022; 9:200-211. [DOI: 10.1007/s40471-022-00295-8] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 11/25/2022]
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Aslam J, Ardanza-Trevijano S, Xiong J, Arsuaga J, Sazdanovic R. TAaCGH Suite for Detecting Cancer-Specific Copy Number Changes Using Topological Signatures. ENTROPY 2022; 24:e24070896. [PMID: 35885119 PMCID: PMC9318413 DOI: 10.3390/e24070896] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 03/23/2022] [Revised: 06/13/2022] [Accepted: 06/23/2022] [Indexed: 11/25/2022]
Abstract
Copy number changes play an important role in the development of cancer and are commonly associated with changes in gene expression. Persistence curves, such as Betti curves, have been used to detect copy number changes; however, it is known these curves are unstable with respect to small perturbations in the data. We address the stability of lifespan and Betti curves by providing bounds on the distance between persistence curves of Vietoris–Rips filtrations built on data and slightly perturbed data in terms of the bottleneck distance. Next, we perform simulations to compare the predictive ability of Betti curves, lifespan curves (conditionally stable) and stable persistent landscapes to detect copy number aberrations. We use these methods to identify significant chromosome regions associated with the four major molecular subtypes of breast cancer: Luminal A, Luminal B, Basal and HER2 positive. Identified segments are then used as predictor variables to build machine learning models which classify patients as one of the four subtypes. We find that no single persistence curve outperforms the others and instead suggest a complementary approach using a suite of persistence curves. In this study, we identified new cytobands associated with three of the subtypes: 1q21.1-q25.2, 2p23.2-p16.3, 23q26.2-q28 with the Basal subtype, 8p22-p11.1 with Luminal B and 2q12.1-q21.1 and 5p14.3-p12 with Luminal A. These segments are validated by the TCGA BRCA cohort dataset except for those found for Luminal A.
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Affiliation(s)
- Jai Aslam
- Department of Mathematics, NC State University, Raleigh, NC 27695, USA;
| | - Sergio Ardanza-Trevijano
- Department of Physics and Applied Mathematics, University of Navarra, 31008 Pamplona, Spain;
- Institute for Data Science and Artificial Intelligence, University of Navarra, 31009 Pamplona, Spain
| | - Jingwei Xiong
- Graduate Group in Biostatistics University of California Davis, Davis, CA 95616, USA;
| | - Javier Arsuaga
- Department of Molecular and Cellular Biology, University of California Davis, Davis, CA 95616, USA
- Department of Mathematics, University of California Davis, Davis, CA 95616, USA
- Correspondence: (J.A.); (R.S.)
| | - Radmila Sazdanovic
- Department of Mathematics, NC State University, Raleigh, NC 27695, USA;
- Correspondence: (J.A.); (R.S.)
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Oliver KL, Ellis CA, Scheffer IE, Ganesan S, Leu C, Sadleir LG, Heinzen EL, Mefford HC, Bass AJ, Curtis SW, Harris RV, Whiteman DC, Helbig I, Ottman R, Epstein MP, Bahlo M, Berkovic SF. Common risk variants for epilepsy are enriched in families previously targeted for rare monogenic variant discovery. EBioMedicine 2022; 81:104079. [PMID: 35636315 PMCID: PMC9156876 DOI: 10.1016/j.ebiom.2022.104079] [Citation(s) in RCA: 8] [Impact Index Per Article: 4.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/27/2022] [Revised: 05/12/2022] [Accepted: 05/12/2022] [Indexed: 01/18/2023] Open
Abstract
BACKGROUND The epilepsies are highly heritable conditions that commonly follow complex inheritance. While monogenic causes have been identified in rare familial epilepsies, most familial epilepsies remain unsolved. We aimed to determine (1) whether common genetic variation contributes to familial epilepsy risk, and (2) whether that genetic risk is enriched in familial compared with non-familial (sporadic) epilepsies. METHODS Using common variants derived from the largest epilepsy genome-wide association study, we calculated polygenic risk scores (PRS) for patients with familial epilepsy (n = 1,818 from 1,181 families), their unaffected relatives (n = 771), sporadic patients (n = 1,182), and population controls (n = 15,929). We also calculated separate PRS for genetic generalised epilepsy (GGE) and focal epilepsy. Statistical analyses used mixed-effects regression models to account for familial relatedness, sex, and ancestry. FINDINGS Patients with familial epilepsies had higher epilepsy PRS compared to population controls (OR 1·20, padj = 5×10-9), sporadic patients (OR 1·11, padj = 0.008), and their own unaffected relatives (OR 1·12, padj = 0.01). The top 1% of the PRS distribution was enriched 3.8-fold for individuals with familial epilepsy when compared to the lowest decile (padj = 5×10-11). Familial PRS enrichment was consistent across epilepsy type; overall, polygenic risk was greatest for the GGE clinical group. There was no significant PRS difference in familial cases with established rare variant genetic etiologies compared to unsolved familial cases. INTERPRETATION The aggregate effects of common genetic variants, measured as polygenic risk scores, play an important role in explaining why some families develop epilepsy, why specific family members are affected while their relatives are not, and why families manifest specific epilepsy types. Polygenic risk contributes to the complex inheritance of the epilepsies, including in individuals with a known genetic etiology. FUNDING National Health and Medical Research Council of Australia, National Institutes of Health, American Academy of Neurology, Thomas B and Jeannette E Laws McCabe Fund, Mirowski Family Foundation.
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Affiliation(s)
- Karen L. Oliver
- Department of Medicine, Epilepsy Research Centre, University of Melbourne, Austin Health, 245 Burgundy St, Heidelberg, VIC 3084, Australia,Population Health and Immunity Division, the Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia,Department of Medical Biology, the University of Melbourne, Melbourne, VIC 3010, Australia
| | - Colin A. Ellis
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA,Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA,Department of Neurology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, PA, USA
| | - Ingrid E. Scheffer
- Department of Medicine, Epilepsy Research Centre, University of Melbourne, Austin Health, 245 Burgundy St, Heidelberg, VIC 3084, Australia,Department of Paediatrics, Royal Children's Hospital, The University of Melbourne, Parkville, VIC, Australia,The Florey Institute and Murdoch Children's Research Institute, VIC, Australia
| | - Shiva Ganesan
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA,Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA,Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Costin Leu
- Genomic Medicine Institute, Lerner Research Institute, Cleveland Clinic, Cleveland, OH 44195, USA,Department of Clinical and Experimental Epilepsy, UCL Institute of Neurology, Queen Square, London WC1N 3BG, UK,Stanley Center for Psychiatric Research, Broad Institute of Harvard and M.I.T, Cambridge, MA 02142, USA
| | - Lynette G. Sadleir
- Department of Paediatrics and Child Health, University of Otago, Wellington, New Zealand
| | - Erin L. Heinzen
- Eshelman School of Pharmacy, University of North Carolina at Chapel Hill, Chapel Hill, NC, USA,Institute for Genomic Medicine, Columbia University Irving Medical Center, New York, NY, USA
| | - Heather C. Mefford
- Center for Pediatric Neurological Disease Research, St Jude Children's Research Hospital, Memphis, TN, USA
| | - Andrew J. Bass
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
| | - Sarah W. Curtis
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
| | - Rebekah V. Harris
- Department of Medicine, Epilepsy Research Centre, University of Melbourne, Austin Health, 245 Burgundy St, Heidelberg, VIC 3084, Australia
| | | | - David C. Whiteman
- Department of Population Health, QIMR Berghofer Medical Research Institute, Brisbane, Australia
| | - Ingo Helbig
- The Epilepsy NeuroGenetics Initiative (ENGIN), Children's Hospital of Philadelphia, Philadelphia, PA, USA,Department of Biomedical and Health Informatics (DBHi), Children's Hospital of Philadelphia, Philadelphia, PA, USA,Division of Neurology, Children's Hospital of Philadelphia, Philadelphia, PA, USA
| | - Ruth Ottman
- Departments of Epidemiology and Neurology, and the Sergievsky Center, Columbia University, New York, NY, USA,Division of Translational Epidemiology, New York State Psychiatric Institute, New York, NY, USA
| | - Michael P. Epstein
- Department of Human Genetics, Emory University School of Medicine, Atlanta, GA, USA
| | - Melanie Bahlo
- Population Health and Immunity Division, the Walter and Eliza Hall Institute of Medical Research, Parkville, VIC 3052, Australia,Department of Medical Biology, the University of Melbourne, Melbourne, VIC 3010, Australia
| | - Samuel F. Berkovic
- Department of Medicine, Epilepsy Research Centre, University of Melbourne, Austin Health, 245 Burgundy St, Heidelberg, VIC 3084, Australia,Corresponding author.
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Jin X, Wang D, Lei M, Guo Y, Cui Y, Chen F, Sun W, Chen X. TPI1 activates the PI3K/AKT/mTOR signaling pathway to induce breast cancer progression by stabilizing CDCA5. J Transl Med 2022; 20:191. [PMID: 35509067 PMCID: PMC9066866 DOI: 10.1186/s12967-022-03370-2] [Citation(s) in RCA: 19] [Impact Index Per Article: 9.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/09/2022] [Accepted: 03/26/2022] [Indexed: 12/24/2022] Open
Abstract
Background Triosephosphate isomerase 1 (TPI1), as a key glycolytic enzyme, is upregulated in multiple cancers. However, expression profile and regulatory mechanism of TPI1 in breast cancer (BRCA) remain mysterious. Methods Western blotting and immunohistochemistry (IHC) assays were used to investigate the expression of TPI1 in BRCA specimens and cell lines. TPI1 correlation with the clinicopathological characteristics and prognosis of 362 BRCA patients was analyzed using a tissue microarray. Overexpression and knockdown function experiments in cells and mice models were performed to elucidate the function and mechanisms of TPI1-induced BRCA progression. Related molecular mechanisms were clarified using co-IP, IF, mass spectrometric analysis, and ubiquitination assay. Results We have found TPI1 is highly expressed in BRCA tissue and cell lines, acting as an independent indicator for prognosis in BRCA patients. TPI1 promotes BRCA cell glycolysis, proliferation and metastasis in vitro and in vivo. Mechanistically, TPI1 activates phosphoinositide 3-kinase (PI3K)/AKT/mammalian target of rapamycin (mTOR) pathway to regulate epithelial–mesenchymal transformation (EMT) and aerobic glycolysis, which is positively mediated by cell division cycle associated 5 (CDCA5). Moreover, TPI1 interacts with sequestosome-1 (SQSTM1)/P62, and P62 decreases the protein expression of TPI1 by promoting its ubiquitination in MDA-MB-231 cells. Conclusions TPI1 promotes BRCA progression by stabilizing CDCA5, which then activates the PI3K/AKT/mTOR pathway. P62 promotes ubiquitin-dependent proteasome degradation of TPI1. Collectively, TPI1 promotes tumor development and progression, which may serve as a therapeutic target for BRCA. Supplementary Information The online version contains supplementary material available at 10.1186/s12967-022-03370-2.
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Affiliation(s)
- Xiaoying Jin
- The Fourth Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, 150040, China
| | - Dandan Wang
- The Fourth Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, 150040, China
| | - Mengxia Lei
- The Fourth Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, 150040, China
| | - Yan Guo
- The Fourth Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, 150040, China
| | - Yuqing Cui
- The Fourth Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, 150040, China
| | - Fengzhi Chen
- The Fourth Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, 150040, China
| | - Weiling Sun
- Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, 150040, China.
| | - Xuesong Chen
- The Fourth Department of Medical Oncology, Harbin Medical University Cancer Hospital, Harbin, 150040, China.
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Kumar R, Abreu C, Toi M, Saini S, Casimiro S, Arora A, Paul AM, Velaga R, Rameshwar P, Lipton A, Gupta S, Costa L. Oncobiology and treatment of breast cancer in young women. Cancer Metastasis Rev 2022; 41:749-770. [PMID: 35488982 DOI: 10.1007/s10555-022-10034-6] [Citation(s) in RCA: 7] [Impact Index Per Article: 3.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 02/24/2022] [Accepted: 04/14/2022] [Indexed: 12/20/2022]
Abstract
Female breast cancer emerged as the leading cancer type in terms of incidence globally in 2020. Although mortality due to breast cancer has improved during the past three decades in many countries, this trend has reversed in women less than 40 years since the past decade. From the biological standpoint, there is consensus among experts regarding the clinically relevant definition of breast cancer in young women (BCYW), with an age cut-off of 40 years. The idea that breast cancer is an aging disease has apparently broken in the case of BCYW due to the young onset and an overall poor outcome of BCYW patients. In general, younger patients exhibit a worse prognosis than older pre- and postmenopausal patients due to the aggressive nature of cancer subtypes, a high percentage of cases with advanced stages at diagnosis, and a high risk of relapse and death in younger patients. Because of clinically and biologically unique features of BCYW, it is suspected to represent a distinct biologic entity. It is unclear why BCYW is more aggressive and has an inferior prognosis with factors that contribute to increased incidence. However, unique developmental features, adiposity and immune components of the mammary gland, hormonal interplay and crosstalk with growth factors, and a host of intrinsic and extrinsic risk factors and cellular regulatory interactions are considered to be the major contributing factors. In the present article, we discuss the status of BCYW oncobiology, therapeutic interventions and considerations, current limitations in fully understanding the basis and underlying cause(s) of BCYW, understudied areas of BCYW research, and postulated advances in the coming years for the field.
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Affiliation(s)
- Rakesh Kumar
- Cancer Research Institute, Himalayan Institute of Medical Sciences, Swami Rama Himalayan University, Dehradun, India. .,Cancer Research Program, Rajiv Gandhi Centre for Biotechnology, Trivandrum, India. .,Department of Medicine, Division of Hematology and Oncology, Rutgers New Jersey Medical School, Newark, NJ, USA. .,Department of Human and Molecular Genetics, Virginia Commonwealth University, School of Medicine, Richmond, VA, USA.
| | - Catarina Abreu
- Department of Medical Oncology, Hospital de Santa Maria- Centro Hospitalar Universitário Lisboa Norte, Lisbon, Portugal
| | - Masakazu Toi
- Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Sunil Saini
- Cancer Research Institute, Himalayan Institute of Medical Sciences, Swami Rama Himalayan University, Dehradun, India
| | - Sandra Casimiro
- Instituto de Medicina Molecular-João Lobo Antunes, Faculdade de Medicina da Universidade de Lisboa, Lisbon, Portugal
| | - Anshika Arora
- Cancer Research Institute, Himalayan Institute of Medical Sciences, Swami Rama Himalayan University, Dehradun, India
| | - Aswathy Mary Paul
- Cancer Research Program, Rajiv Gandhi Centre for Biotechnology, Trivandrum, India
| | - Ravi Velaga
- Graduate School of Medicine, Kyoto University, Kyoto, Japan
| | - Pranela Rameshwar
- Department of Medicine, Division of Hematology and Oncology, Rutgers New Jersey Medical School, Newark, NJ, USA
| | - Allan Lipton
- Hematology-Oncology, Department of Medicine, Penn State University School of Medicine, Hershey, PA, USA
| | - Sudeep Gupta
- Department of Medical Oncology, Tata Memorial Centre and Homi Bhabha National Institute, Mumbai, India
| | - Luis Costa
- Department of Medical Oncology, Hospital de Santa Maria- Centro Hospitalar Universitário Lisboa Norte, Lisbon, Portugal.,Instituto de Medicina Molecular-João Lobo Antunes, Faculdade de Medicina da Universidade de Lisboa, Lisbon, Portugal
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Pallonen TAS, Lempiäinen SMM, Joutsiniemi TK, Aaltonen RI, Pohjola PE, Kankuri-Tammilehto MK. Genetic, clinic and histopathologic characterization of BRCA-associated hereditary breast and ovarian cancer in southwestern Finland. Sci Rep 2022; 12:6704. [PMID: 35469032 PMCID: PMC9038668 DOI: 10.1038/s41598-022-10519-y] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/11/2021] [Accepted: 04/07/2022] [Indexed: 11/09/2022] Open
Abstract
AbstractWe have analyzed the histopathological, clinical, and genetic characteristics in hereditary breast and ovarian cancer patients of counselled families from 1996 up to today in the southwestern Finland population. In this study we analyzed the incidence of different BRCA1 and BRCA2 pathogenic variants (PV). 1211 families were evaluated, and the families were classified as 38 BRCA1 families, 48 BRCA2 families, 689 non-BRCA families and 436 other counselled families (criteria for genetic testing was not met). In those families, the study consisted of 44 BRCA1 breast and/or ovarian cancer patients, 58 BRCA2 cancer patients, 602 non-BRCA patients and 328 other counselled patients. Breast cancer mean onset was 4.6 years earlier in BRCA1 carriers compared to BRCA2 (p = 0.07, a trend) and ovarian cancer onset almost 11 years earlier in BRCA1 families (p < 0.05). In BRCA families the onset of ovarian cancer was later than 40 years, and BRCA2-origin breast cancer was seen as late as 78 years. The BRCA PV (9%) increases the risk for same patient having both ovarian and breast cancer with a twofold risk when compared to non-BRCA group (4%) (95% CI p < 0.05). Triple-negativity in BRCA1 (42%) carriers is approximately 2.6 times vs more common than in BRCA2 carriers (16%) (p < 0.05). The risk ratio for bilateral breast cancer is approximately four times when compared BRCA2 (17%) and other counselled patients’ group (4%) (p < 0.05). 27% southwestern BRCA2-families have a unique PV, and correspondingly 39% of BRCA1-families. The results of this analysis allow improved prediction of cancer risk in high-risk hereditary breast and ovarian families in southwestern Finland and improve long term follow-up programs. According to the result it could be justified to have the discussion about prophylactic salpingo-oophorectomy by the age of 40 years. The possibility of late breast cancer onset in BRCA2 carriers supports the lifelong follow-up in BRCA carriers. Cancer onset is similar between BRCA2 carries and non-BRCA high-risk families. This study evaluated mutation profile of BRCA in southwestern Finland. In this study genotype–phenotype correlation was not found
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50
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Mars N, Kerminen S, Feng YCA, Kanai M, Läll K, Thomas LF, Skogholt AH, della Briotta Parolo P, Neale BM, Smoller JW, Gabrielsen ME, Hveem K, Mägi R, Matsuda K, Okada Y, Pirinen M, Palotie A, Ganna A, Martin AR, Ripatti S. Genome-wide risk prediction of common diseases across ancestries in one million people. CELL GENOMICS 2022; 2:None. [PMID: 35591975 PMCID: PMC9010308 DOI: 10.1016/j.xgen.2022.100118] [Citation(s) in RCA: 26] [Impact Index Per Article: 13.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Subscribe] [Scholar Register] [Received: 01/21/2021] [Revised: 08/24/2021] [Accepted: 03/18/2022] [Indexed: 12/14/2022]
Abstract
Polygenic risk scores (PRS) measure genetic disease susceptibility by combining risk effects across the genome. For coronary artery disease (CAD), type 2 diabetes (T2D), and breast and prostate cancer, we performed cross-ancestry evaluation of genome-wide PRSs in six biobanks in Europe, the United States, and Asia. We studied transferability of these highly polygenic, genome-wide PRSs across global ancestries, within European populations with different health-care systems, and local population substructures in a population isolate. All four PRSs had similar accuracy across European and Asian populations, with poorer transferability in the smaller group of individuals of African ancestry. The PRSs had highly similar effect sizes in different populations of European ancestry, and in early- and late-settlement regions with different recent population bottlenecks in Finland. Comparing genome-wide PRSs to PRSs containing a smaller number of variants, the highly polygenic, genome-wide PRSs generally displayed higher effect sizes and better transferability across global ancestries. Our findings indicate that in the populations investigated, the current genome-wide polygenic scores for common diseases have potential for clinical utility within different health-care settings for individuals of European ancestry, but that the utility in individuals of African ancestry is currently much lower.
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Affiliation(s)
- Nina Mars
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Biomedicum 2U, Tukholmankatu 8, 00290 Helsinki, Finland
| | - Sini Kerminen
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Biomedicum 2U, Tukholmankatu 8, 00290 Helsinki, Finland
| | - Yen-Chen A. Feng
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA,Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Institute of Epidemiology and Preventive Medicine, College of Public Health, National Taiwan University, Taipei, Taiwan
| | - Masahiro Kanai
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Kristi Läll
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Laurent F. Thomas
- Department of Clinical and Molecular Medicine, Norwegian University of Science and Technology, Trondheim, Norway,K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway,BioCore - Bioinformatics Core Facility, Norwegian University of Science and Technology, Trondheim, Norway
| | - Anne Heidi Skogholt
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway
| | - Pietro della Briotta Parolo
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Biomedicum 2U, Tukholmankatu 8, 00290 Helsinki, Finland
| | | | | | - Benjamin M. Neale
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Harvard Medical School, Boston, MA, USA
| | - Jordan W. Smoller
- Psychiatric and Neurodevelopmental Genetics Unit, Center for Genomic Medicine, Massachusetts General Hospital, Boston, MA, USA,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Harvard Medical School, Boston, MA, USA
| | - Maiken E. Gabrielsen
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway,HUNT Research Center, Department of Public Health and Nursing, Faculty of Medicine and Health Sciences, Norwegian University of Science and Technology, Trondheim, Norway
| | - Kristian Hveem
- K. G. Jebsen Center for Genetic Epidemiology, Department of Public Health and Nursing, Faculty of Medicine and Health, Norwegian University of Science and Technology, Trondheim, Norway
| | - Reedik Mägi
- Estonian Genome Centre, Institute of Genomics, University of Tartu, Tartu, Estonia
| | - Koichi Matsuda
- Department of Computational Biology and Medical Sciences, Graduate school of Frontier Sciences, the University of Tokyo, Tokyo, Japan
| | - Yukinori Okada
- Department of Statistical Genetics, Osaka University Graduate School of Medicine, Suita, Japan,Laboratory of Statistical Immunology, Immunology Frontier Research Center (WPI-IFReC), Osaka University, Suita, Japan,Integrated Frontier Research for Medical Science Division, Institute for Open and Transdisciplinary Research Initiatives, Osaka University, Suita, Japan,Laboratory for Systems Genetics, RIKEN Center for Integrative Medical Sciences, Kanagawa, Japan
| | - Matti Pirinen
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Biomedicum 2U, Tukholmankatu 8, 00290 Helsinki, Finland,Department of Public Health, University of Helsinki, Helsinki, Finland,Department of Mathematics and Statistics, University of Helsinki, Helsinki, Finland
| | - Aarno Palotie
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Biomedicum 2U, Tukholmankatu 8, 00290 Helsinki, Finland,Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Andrea Ganna
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Biomedicum 2U, Tukholmankatu 8, 00290 Helsinki, Finland,Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA,Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Alicia R. Martin
- Analytic and Translational Genetics Unit, Department of Medicine, Massachusetts General Hospital, Boston, MA, USA,Stanley Center for Psychiatric Research, Broad Institute of MIT and Harvard, Cambridge, MA, USA,Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, MA, USA
| | - Samuli Ripatti
- Institute for Molecular Medicine Finland, FIMM, HiLIFE, University of Helsinki, Biomedicum 2U, Tukholmankatu 8, 00290 Helsinki, Finland,Department of Public Health, University of Helsinki, Helsinki, Finland,Broad Institute of MIT and Harvard, Cambridge, MA, USA,Corresponding author
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