A polygenic risk score for multiple myeloma risk prediction

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作者
Federico Canzian
Chiara Piredda
Angelica Macauda
Daria Zawirska
Niels Frost Andersen
Arnon Nagler
Jan Maciej Zaucha
Grzegorz Mazur
Charles Dumontet
Marzena Wątek
Krzysztof Jamroziak
Juan Sainz
Judit Várkonyi
Aleksandra Butrym
Katia Beider
Niels Abildgaard
Fabienne Lesueur
Marek Dudziński
Annette Juul Vangsted
Matteo Pelosini
Edyta Subocz
Mario Petrini
Gabriele Buda
Małgorzata Raźny
Federica Gemignani
Herlander Marques
Enrico Orciuolo
Katalin Kadar
Artur Jurczyszyn
Agnieszka Druzd-Sitek
Ulla Vogel
Vibeke Andersen
Rui Manuel Reis
Anna Suska
Hervé Avet-Loiseau
Marcin Kruszewski
Waldemar Tomczak
Marcin Rymko
Stephane Minvielle
Daniele Campa
机构
[1] German Cancer Research Center (DKFZ),Genomic Epidemiology Group
[2] University of Pisa,Department of Biology
[3] University Hospital of Cracow,Department of Hematology
[4] Aarhus University Hospital,Department of Hematology
[5] Chaim Sheba Medical Center,Hematology Division
[6] Sea Hospital,Department of Hematology
[7] Hypertension and Clinical Oncology,Department of Internal and Occupational Diseases
[8] Medical University Wroclaw,Department of Hematology
[9] Cancer Research Center of Lyon/Hospices Civils de Lyon,Genomic Oncology Area, GENYO. Centre for Genomics and Oncological Research: Pfizer
[10] Hematology Clinic,Hematology department
[11] Holycross Cancer Center,Third Department of Internal Medicine
[12] Institute of Hematology and Transfusion Medicine,Department of Internal and Occupational Diseases
[13] University of Granada/Andalusian Regional Government,Department of Hematology
[14] Virgen de las Nieves University Hospital,Hematology Department
[15] Semmelweis University,Department of Hematology
[16] Medical University Wroclaw,Clinical and Experimental Medicine, Section of Hematology
[17] Odense University Hospital,Department of Haematology
[18] Institut Curie,Department of Hematology
[19] PSL Research University,Life and Health Sciences Research Institute (ICVS), School of Health Sciences/Molecular Oncology Research Center
[20] Mines ParisTech Inserm,Department of Hematology
[21] Teaching Hospital No 1,Institute of Molecular Medicine
[22] Rigshospitalet,Unité de Génomique du Myélome
[23] Copenhagen University,Department of Hematology
[24] University of Pisa,Department of Hematology
[25] Military Institute of Medicine,CRCINA, INSERM, CNRS
[26] Rydygier Specialistic Hospital,undefined
[27] University of Minho,undefined
[28] Jagiellonian University Medical College,undefined
[29] Maria Sklodowska-Curie National Research Institute of Oncology,undefined
[30] National Research Centre for the Working Environment,undefined
[31] University of Southern Denmark,undefined
[32] ICVS/3B’s - PT Government Associate Laboratory,undefined
[33] Molecular Oncology Research Center,undefined
[34] Barretos Cancer Hospital,undefined
[35] Institut Universitaire du Cancer Toulouse – Oncopole,undefined
[36] University Hospital Bydgoszcz,undefined
[37] Medical University of Lublin,undefined
[38] N. Copernicus Town Hospital,undefined
[39] Université d’Angers,undefined
[40] Université de Nantes,undefined
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摘要
There is overwhelming epidemiologic evidence that the risk of multiple myeloma (MM) has a solid genetic background. Genome-wide association studies (GWAS) have identified 23 risk loci that contribute to the genetic susceptibility of MM, but have low individual penetrance. Combining the SNPs in a polygenic risk score (PRS) is a possible approach to improve their usefulness. Using 2361 MM cases and 1415 controls from the International Multiple Myeloma rESEarch (IMMEnSE) consortium, we computed a weighted and an unweighted PRS. We observed associations with MM risk with OR = 3.44, 95% CI 2.53–4.69, p = 3.55 × 10−15 for the highest vs. lowest quintile of the weighted score, and OR = 3.18, 95% CI 2.1 = 34–4.33, p = 1.62 × 10−13 for the highest vs. lowest quintile of the unweighted score. We found a convincing association of a PRS generated with 23 SNPs and risk of MM. Our work provides additional validation of previously discovered MM risk variants and of their combination into a PRS, which is a first step towards the use of genetics for risk stratification in the general population.
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页码:474 / 479
页数:5
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