Polymorphisms within autophagy-related genes as susceptibility biomarkers for multiple myeloma: a meta-analysis of three large cohorts and functional characterization

Citation: Clavero, E.; Sanchez-Maldonado, J.M.; Macauda, A.; Ter Horst, R.; Sampaio-Marques, B.; Jurczyszyn, A.; Clay-Gilmour, A.; Stein, A.; Hildebrandt, M.A.T.; Weinhold, N.; et al. Polymorphisms within Autophagy-Related Genes as Susceptibility Biomarkers for Multiple Myeloma: A Meta-Analysis of Three Large Cohorts and Functional Characterization. Int. J. Mol. Sci. 2023,24, 8500. https: //doi.org/10.3390/ijms24108500 Academic Editors: Despina Bazou and Paul Dowling Received: 4 March 2023 Revised: 10 April 2023 Accepted: 21 April 2023 Published: 9 May 2023 Copyright: © 2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). International Journal of Molecular Sciences Article Polymorphisms within Autophagy-Related Genes as Susceptibility Biomarkers for Multiple Myeloma: A Meta-Analysis of Three Large Cohorts and Functional Characterization Esther Clavero 1,†, JoséManuel Sanchez-Maldonado 2,3,† , Angelica Macauda 4, Rob Ter Horst 5,6, Belém Sampaio-Marques 7, Artur Jurczyszyn 8, Alyssa Clay-Gilmour 9,10, Angelika Stein 4, Michelle A. T. Hildebrandt 11, Niels Weinhold 12,13, Gabriele Buda 14, Ramón García-Sanz 15, Waldemar Tomczak 16 , Ulla Vogel 17 , Andrés Jerez 18 , Daria Zawirska 19, Marzena W ˛atek 20,21, Jonathan N. Hofmann 22, Stefano Landi 23 , John J. Spinelli 24,25, Aleksandra Butrym 26,27 , Abhishek Kumar 28,29, Joaquín Martínez-López 30, Sara Galimberti 14 , María Eugenia Sarasquete 15, Edyta Subocz 31 , Elzbieta Iskierka-Ja˙ zd˙ zewska 32, Graham G. Giles 33,34,35 , Malwina Rybicka-Ramos 36, Marcin Kruszewski 37, Niels Abildgaard 38, Francisco García Verdejo 39, Pedro Sánchez Rovira 39, Miguel Inacio da Silva Filho 40, Katalin Kadar 41 , Małgorzata Razny 42, Wendy Cozen 43, Matteo Pelosini 44, Manuel Jurado 1,3,45, Parveen Bhatti 46,47, Marek Dudzinski 48, Agnieszka Druzd-Sitek 49, Enrico Orciuolo 14, Yang Li 5,50 , Aaron D. Norman 10,51, Jan Maciej Zaucha 52 , Rui Manuel Reis 53,54 , Miroslaw Markiewicz 48 , Juan JoséRodríguez Sevilla 55 , Vibeke Andersen 56, Krzysztof Jamroziak 57, Kari Hemminki 58,59 , Sonja I. Berndt 22, Vicent Rajkumar 60, Grzegorz Mazur 61 , Shaji K. Kumar 60, Paula Ludovico 7, Arnon Nagler 62, Stephen J. Chanock 22, Charles Dumontet 63, Mitchell J. Machiela 22, Judit Varkonyi 64, Nicola J. Camp 65, Elad Ziv 66, Annette Juul Vangsted 67, Elizabeth E. Brown 68, Daniele Campa 23, Celine M. Vachon 10, Mihai G. Netea 5,69, Federico Canzian 4, Asta Försti 70,71 and Juan Sainz 2,3,72,* 1Hematology Department, Virgen de las Nieves University Hospital, 18012 Granada, Spain; [email protected] (E.C.); [email protected] (M.J.) 2Genomic Oncology Area, GENYO, Centre for Genomics and Oncological Research, Pfizer/University of Granada/Andalusian Regional Government, PTS, 18016 Granada, Spain; [email protected] 3Instituto de Investigación Biosanataria IBs, Granada, 18014 Granada, Spain 4Genomic Epidemiology Group, German Cancer Research Center (DKFZ), 69120 Heidelberg, Germany; [email protected] (A.M.); [email protected] (A.S.); [email protected] (F.C.) 5Department of Internal Medicine and Radboud Center for Infectious Diseases, Radboud University Medical Center, 6525 GA Nijmegen, The Netherlands; rob.ter[email protected] (R.T.H.); [email protected] (Y.L.); [email protected] (M.G.N.) 6CeMM Research Center for Molecular Medicine of the Austrian Academy of Sciences, 1090 Vienna, Austria 7Life and Health Sciences Research Institute (ICVS), School of Medicine, University of Minho, 4710-057 Braga, Portugal; [email protected] (B.S.-M.); [email protected] (P.L.) 8Plasma Cell Dyscrasias Center, Department of Hematology, Jagiellonian University Medical College, 31-066 Kraków, Poland; [email protected] 9 Department of Biostatistics and Epidemiology, Arnold School of Public Health, University of South Carolina, Greenville, SC 29208, USA; [email protected] 10 Division of Epidemiology, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55902, USA; [email protected] (A.D.N.); [email protected] (C.M.V.) 11 Department of Lymphoma–Myeloma, Division of Cancer Medicine, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA; [email protected] 12 Myeloma Institute, University of Arkansas for Medical Sciences, Little Rock, AR 72205, USA; [email protected] 13 Department of Internal Medicine V, University of Heidelberg, 69120 Heidelberg, Germany 14 Haematology Unit, Department of Clinical and Experimental Medicine, University of Pisa/AOUP, 56126 Pisa, Italy; [email protected] (G.B.); [email protected] (S.G.); [email protected] (E.O.) 15 Diagnostic Laboratory Unit in Hematology, University Hospital of Salamanca, IBSAL, CIBERONC, Centro de Investigación del Cáncer-IBMCC (USAL-CSIC), 37007 Salamanca, Spain; r[email protected] (R.G.-S.); [email protected] (M.E.S.) 16 Department of Hematooncology and Bone Marrow Transplantation, Medical University of Lublin, 20-059 Lublin, Poland; waldemar[email protected] 17 National Research Centre for the Working Environment, DK-2100 Copenhagen, Denmark; [email protected] 18 Department of Hematology, Experimental Hematology Unit, Vall d’Hebron Institute of Oncology (VHIO), University Hospital Vall d’Hebron, 08035 Barcelona, Spain; [email protected] Int. J. Mol. Sci. 2023,24, 8500. https://doi.org/10.3390/ijms24108500 https://www.mdpi.com/journal/ijms Int. J. Mol. Sci. 2023,24, 8500 2 of 20 19 Department of Hematology, University Hospital, 30-688 Kraków, Poland; [email protected] 20 Holycross Medical Oncology Center, 25-735 Kielce, Poland; [email protected] 21 Institute of Hematology and Transfusion Medicine, 00-791 Warsaw, Poland 22 Division of Cancer Epidemiology and Genetics, National Cancer Institute, National Institutes of Health, Bethesda, MD 20892, USA; [email protected] (J.N.H.); [email protected] (S.I.B.); [email protected] (S.J.C.); [email protected] (M.J.M.) 23 Department of Biology, University of Pisa, 56126 Pisa, Italy; [email protected] (S.L.); [email protected] (D.C.) 24 Division of Population Oncology, BC Cancer, Vancouver, BC V5Z 4E6, Canada; [email protected] 25 School of Population and Public Health, University of British Columbia, Vancouver, BC V6T 1Z4, Canada 26 Department of Cancer Prevention and Therapy, Wroclaw Medical University, 50-367 Wroclaw, Poland; [email protected] 27 Alfred Sokolowski Specialist Hospital in Walbrzych Oncology Support Centre for Clinical Trials, 58-309 Walbrzych, Poland 28 Institute of Bioinformatics, International Technology Park, Bangalore 560066, India; [email protected] 29 Manipal Academy of Higher Education (MAHE), Manipal 576104, India 30 Hospital 12 de Octubre, Complutense University, CNIO, CIBERONC, 28041 Madrid, Spain; [email protected] 31 Department of Hematology, Military Institute of Medicine, 04-141 Warsaw, Poland; [email protected] 32 Department of Hematology, Medical University of Lodz, 90-419 Lodz, Poland; [email protected] 33 Cancer Epidemiology Division, Cancer Council Victoria, Melbourne, VIC 3004, Australia; [email protected] 34 Centre for Epidemiology and Biostatistics, School of Population and Global Health, The University of Melbourne, Melbourne, VIC 3010, Australia 35 Precision Medicine, School of Clinical Sciences at Monash Health, Monash University, Clayton, VIC 3168, Australia 36 Department of Hematology, Specialist Hospital No. 1 in Bytom, Academy of Silesia, Faculty of Medicine, 40-055 Katowice, Poland; [email protected] 37 Department of Hematology, University Hospital No. 2, 85-168 Bydgoszcz, Poland; marcin.kr[email protected] 38 Department of Hematology, Odense University Hospital, DK-5000 Odense, Denmark; [email protected] 39 Department of Medical Oncology, Complejo Hospitalario de Jaén, 23007 Jaén, Spain; francisco.garcia.ver[email protected] (F.G.V.); [email protected] (P.S.R.) 40 Division of Molecular Genetic Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 580, D-69120 Heidelberg, Germany; [email protected] 41 St Johns Hospital, 62769 Budapest, Hungary; [email protected] 42 Department of Hematology, Rydygier Hospital, 31-826 Cracow, Poland; [email protected] 43 Division of Hematology/Oncology, Department of Medicine, School of Medicine, Department of Pathology, School of Medicine, Susan and Henry Samueli College of Health Sciences, Chao Family Comprehensive Cancer Center, University of California at Irvine, Irvine, CA 92697, USA; [email protected] 44 U.O. Dipartimento di Ematologia, Azienda USL Toscana Nord Ovest, 57124 Livorno, Italy; [email protected] 45 Department of Medicine, University of Granada, 18012 Granada, Spain 46 Cancer Control Research, BC Cancer, Vancouver, BC V5Z 4E6, Canada; [email protected] 47 Program in Epidemiology, Public Health Sciences Division, Fred Hutchinson Cancer Research Center, Seattle, WA 98109, USA 48 Department of Hematology, Institute of Medical Sciences, College of Medical Sciences, University of Rzeszow, 35-310 Rzeszow, Poland; mar[email protected] (M.D.); mir[email protected] (M.M.) 49 Department of Lymphoproliferative Diseases, Maria Skłodowska Curie National Research Institute of Oncology, 02-781 Warsaw, Poland; adr[email protected].pl 50 Centre for Individualised Infection Medicine (CiiM) & TWINCORE, Joint Ventures between the Helmholtz-Centre for Infection Research (HZI) and the Hannover Medical School (MHH), 30625 Hannover, Germany 51 Genetic Epidemiology and Risk Assessment Program, Mayo Clinic Comprehensive Cancer Center, Division of Biomedical Statistics and Informatics, Department of Health Sciences Research, Mayo Clinic, Rochester, MN 55902, USA 52 Department of Hematology and Transplantology, Medical University of Gdansk, 80-210 Gdansk, Poland; [email protected] 53 Life and Health Sciences Research Institute (ICVS), School of Health Sciences, University of Minho, 4710-057 Braga, Portugal and ICVS/3B’s-PT Government Associate Laboratory, 4710-057 Braga/Guimarães, Portugal; [email protected] Int. J. Mol. Sci. 2023,24, 8500 3 of 20 54 Molecular Oncology Research Center, Barretos Cancer Hospital, Barretos 14784-400, Brazil 55 Department of Hematology, Hospital del Mar, 08003 Barcelona, Spain; jr[email protected] 56 Molecular Diagnostics and Clinical Research Unit, Institute of Regional Health Research, University Hospital of Southern Denmark, DK-6200 Aabenraa, Denmark; [email protected] 57 Department of Hematology, Transplantology and Internal Medicine, Medical University of Warsaw, 02-097 Warsaw, Poland; [email protected] 58 Division of Cancer Epidemiology, German Cancer Research Center (DKFZ), Im Neuenheimer Feld 280, 69120 Heidelberg, Germany; [email protected] 59 Faculty of Medicine and Biomedical Center in Pilsen, Charles University in Prague, 30605 Pilsen, Czech Republic 60 Division of Hematology, Department of Internal Medicine, Mayo Clinic, Rochester, MN 55902, USA; rajkumar[email protected] (V.R.); kumar[email protected] (S.K.K.) 61 Department of Internal Diseases, Occupational Medicine, Hypertension and Clinical Oncology, Wroclaw Medical University, 50-368 Wroclaw, Poland; [email protected] 62 Hematology Division, Chaim Sheba Medical Center, Tel Hashomer 52621, Israel; [email protected].il 63 UMR INSERM 1052/CNRS 5286, University of Lyon, Hospices Civils de Lyon, 69008 Lyon, France; [email protected] 64 Semmelweis University, 1083 Budapest, Hungary; [email protected].hu 65 Division of Hematology, Huntsman Cancer Institute, University of Utah, Salt Lake City, UT 84112, USA; [email protected] 66 Department of Medicine, University of California San Francisco Helen Diller Family Comprehensive Cancer Center, San Francisco, CA 94143, USA; [email protected] 67 Department of Hematology, Rigshospitalet, Copenhagen University, DK-2100 Copenhagen, Denmark; [email protected] 68 Department of Pathology, Heersink School of Medicine, The University of Alabama at Birmingham, Birmingham, AL 35294, USA; [email protected] 69 Department for Immunology & Metabolism, Life and Medical Sciences Institute (LIMES), University of Bonn, 53115 Bonn, Germany 70 Division of Pediatric Neurooncology, German Cancer Research Center (DKFZ), German Cancer Consortium (DKTK), 69120 Heidelberg, Germany; [email protected] 71 Hopp Children’s Cancer Center (KiTZ), 69120 Heidelberg, Germany 72 Department of Biochemistry and Molecular Biology I, University of Granada, 18071 Granada, Spain *Correspondence: [email protected]; Tel.: +34-958715500 (ext. 126 or 127); Fax: +34-958-637071 † These authors contributed equally to this work. Simple Summary: We investigated the influence of autophagy-related variants in modulating Multiple Myeloma (MM) risk through a meta-analysis of germline genetic data on 234 autophagy-related genes from three independent study populations including 13,387 subjects of European ancestry (6863 MM patients and 6524 controls) and examined the functional mechanisms behind the observed associations. We identified SNPs within the six CD46,IKBKE,PARK2,ULK4,ATG5, and CDKN2A loci associated with MM risk and observed that their effect on disease risk was mediated by specific subsets of immune cells, as well as vitamin D3-, MCP-2-, and IL20-dependent mechanisms. Abstract: Multiple myeloma (MM) arises following malignant proliferation of plasma cells in the bone marrow, that secrete high amounts of specific monoclonal immunoglobulins or light chains, resulting in the massive production of unfolded or misfolded proteins. Autophagy can have a dual role in tumorigenesis, by eliminating these abnormal proteins to avoid cancer development, but also ensuring MM cell survival and promoting resistance to treatments. To date no studies have determined the impact of genetic variation in autophagy-related genes on MM risk. We performed meta-analysis of germline genetic data on 234 autophagy-related genes from three independent study populations including 13,387 subjects of European ancestry (6863 MM patients and 6524 controls) and examined correlations of statistically significant single nucleotide polymorphisms (SNPs; p< 1 × 10 −9 ) with immune responses in whole blood, peripheral blood mononuclear cells (PBMCs), and monocyte-derived macrophages (MDM) from a large population of healthy donors from the Human Functional Genomic Project (HFGP). We identified SNPs in six loci, CD46, IKBKE,PARK2,ULK4,ATG5, and CDKN2A associated with MM risk (p= 4.47 × 10 −4− 5.79 × 10 −14 ). Mechanistically, we found that the ULK4 rs6599175 SNP correlated with circulating concentrations of vitamin D3 (p= 4.0 × 10 −4 ), whereas the IKBKE rs17433804 SNP correlated with the number of transitional CD24 + CD38 + B cells (p= 4.8 × 10 −4 ) and circulating serum concentrations of Monocyte Int. J. Mol. Sci. 2023,24, 8500 4 of 20 Chemoattractant Protein (MCP)-2 (p= 3.6 × 10 −4 ). We also found that the CD46 rs1142469 SNP correlated with numbers of CD19 + B cells, CD19 + CD3 − B cells, CD5 + IgD − cells, IgM − cells, IgD − IgM − cells, and CD4 − CD8 − PBMCs (p= 4.9 × 10 −4− 8.6 × 10 −4 ) and circulating concentrations of interleukin (IL)-20 (p= 0.00082). Finally, we observed that the CDKN2A rs2811710 SNP correlated with levels of CD4 + EMCD45RO + CD27 − cells (p= 9.3 × 10 −4 ). These results suggest that genetic variants within these six loci influence MM risk through the modulation of specific subsets of immune cells, as well as vitamin D3−, MCP-2−, and IL20-dependent pathways. Keywords: multiple myeloma; autophagy; genetic variants; genetic susceptibility 1. Introduction Multiple myeloma (MM) is a relatively common and incurable hematological malignancy arising from post–germinal mature B cells and it is characterized by the presence of proliferating plasma cells in the bone marrow that secrete specific monoclonal immunoglobulin (also called M-protein) [ 1 ]. Given the exacerbated production of immunoglobulin, MM patients often suffer from a concomitant decrease in normal immunoglobulins [ 2 ] that causes immune dysfunction, increases susceptibility to opportunistic infections, and impacts on disease severity and prognosis [3,4]. Although the high production of monoclonal immunoglobulins occurring in MM invariably results in the presence of a high amount of unfolded or misfolded proteins that might be toxic for MM cells in the bone marrow, recent studies demonstrated that plasma cells are able to exploit molecular pathways to protect themselves from damage caused by toxic proteins [ 5 ]. These molecular pathways include the activation of unfolded protein response (UPR) [ 6 – 8 ], heat protein chaperones [ 9 ], aggresome formation [ 10 ], and the induction of cellular autophagy [ 5 ]. Autophagy is a lysosome-dependent catabolic degradation process by which cells remove toxic aggregated cytosolic proteins and malfunctioning organelles. It has been well-documented that autophagy not only is an autonomous mechanism that modulates cell homeostasis at basal level, but it is also involved in B-cell development and proliferation [ 11 ], cell survival [ 12 , 13 ], apoptosis [ 14 , 15 ], tumorigenesis, anti-tumoral immune responses [ 16 ], and resistance to chemotherapeutic agents. In this regard, a growing number of studies have suggested that autophagy shapes anti-tumoral immune responses by acting at multiple levels [ 17 ]. This includes the promotion of signals to activate phagocytosis of tumor cells [ 18 , 19 ], as well as signals to induce myeloid cell recruitment [ 20 ], MHC-class-I and -II presentation [ 21 ], Band T-cell activation, development, maintenance [ 11 , 22 ], and self-tolerance [ 23 ]. Strikingly, experimental studies have demonstrated that the inhibition of autophagy enhances the sensitivity of MM cells to a number of anticancer agents and induces MM cell death [ 24 , 25 ]. Based on these findings, it has been suggested that autophagy could be targeted to treat MM [ 5 , 24 , 26 ] and recent studies and clinical trials have indeed confirmed the therapeutic implications of new and less toxic autophagy inhibitors in MM, used in combination with current anti-MM drugs [27–29]. In support of a possible role of autophagy in MM, it was reported that aberrant expression of autophagy-related genes is associated with cancer development [ 30 ] and that multiple activators of autophagy or specific autophagy-related genes are commonly found in cancer-associated regions [ 31 ]. Interestingly, it was also described that expression of autophagy-related genes influences the response to conventional treatments in MM [ 32 – 34 ] and, thereby, disease progression [ 35 , 36 ], arguing that they may be useful to predict disease risk [ 37 ]. However, despite these findings suggesting a key role of autophagy in the etiology of MM and the existence of a genetic component controlling this catalytic process in MM, so far only ULK4,ATG5, and CDKN2A polymorphisms have been suggested to have an impact on the risk of MM [ 38 , 39 ]. Therefore, it is vital to perform a systematic analysis of autophagy-related markers to identify new susceptibility variants for MM and validate those associations already reported. Int. J. Mol. Sci. 2023,24, 8500 5 of 20 In this context, the aim of this study was to comprehensively evaluate the impact of common genetic variation of 234 autophagy-related genes in determining the risk of developing MM. We also assessed the influence of the most promising markers on modulating immune responses in whole blood, peripheral blood mononuclear cells (PBMCs), and monocyte-derived macrophages (MDM) from a large population of healthy donors from the Human Functional Genomic Project (HFGP). Additionally, we measured the autophagy flux in an independent population. 2. Results 2.1. Association of Autophagy-Related Polymorphisms with the Risk of Developing MM This study included 8719 individuals (3916 MM cases and 4803 controls) from the German GWAS consisting of 1512 MM patients and 2107 controls and the InterLymph MM GWAS that included 2404 MM cases and 2696 healthy controls. Selected polymorphisms showed no deviation from HWE (p< 0.001), either in the German GWAS or in the InterLymph MM GWAS. The association analysis of the German cohort showed that 440 independent SNPs (r 2 < 0.1) were significantly associated with MM risk at p ≤ 0.05. The association of these SNPs with MM risk was then validated through meta-analysis with data from the InterLymph MM GWAS. The meta-analysis of these large independent studies confirmed the association of 12 genetic variants within the ATG5,CD46,CDKN2A, CTSD,HSPB8,IKBKE,PARK2,RPTOR,ULK4, and USP10 loci with MM risk (Table 1), with loci in ULK4 and ATG5 reaching the highest level of statistical significance. Table 1. Association analysis of autophagy-related SNPs in the discovery cohorts. German GWAS (n= 3619) InterLymph GWAS (n= 5100) Meta-Analysis (n= 8719) SNP Gene A1 OR (95%CI) pOR (95%CI) pOR (95%CI) p PHet rs2299864 ATG5 T 1.18 (1.03–1.33) 0.015 1.24 (1.12–1.36) 1.96 × 10 −51.22 (1.13–1.32) 1.48 × 10 −60.545 rs1142469 CD46 A 1.14 (1.02–1.25) 0.020 1.11 (1.02–1.21) 0.012 1.12 (1.05–1.20) 9.68 × 10 −40.694 rs2811710 CDKN2A C 1.15 (1.03–1.29) 0.011 1.11 (1.02–1.20) 0.0176 1.12 (1.05–1.20) 5.28 × 10 −40.623 rs143309009 CTSD G 2.22 (1.46–3.38) 2.1 ×10−41.24 (0.89–1.72) 0.2114 1.55 (1.20–2.01) 9.14 × 10 −40.032 rs11064698 HSPB8 T 1.53 (1.05–2.24) 0.027 1.16 (1.06–1.27) 0.0012 1.18 (1.08–1.28) 2.46 × 10 −40.163 rs12739461 IKBKE T 1.18 (1.05–1.32) 0.0053 1.10 (1.01–1.20) 0.028 1.13 (1.05–1.21) 6.36 × 10 −40.337 rs2297546 IKBKE G 1.22 (1.09–1.37) 7.2 ×10−41.09 (1.01–1.18) 0.037 1.13 (1.06–1.21) 2.25 × 10 −40.110 rs17433804 IKBKE C 1.24 (1.09–1.41) 0.0011 1.08 (1.00–1.18) 0.066 1.13 (1.05–1.21) 7.51 × 10 −40.086 rs1884158 PARK2 T 1.21 (1.08–1.35) 0.00081 1.09 (1.00–1.18) 0.049 1.14 (1.06–1.21) 3.07 × 10 −40.141 rs34048269 RPTOR A 1.30 (1.14–1.48) 1.3 ×10−41.06 (0.97–1.17) 0.199 1.14 (1.05–1.23) 0.0024 0.013 rs6599175 ULK4 C 1.33 (1.16–1.53) 5.7 ×10−51.25 (1.13–1.39) 3.6 ×10−51.28 (1.18–1.39) 4.00 × 10 −90.482 rs7202154 USP10 G 1.31 (1.06–1.62) 0.011 1.23 (1.05–1.44) 0.0126 1.26 (1.11–1.43) 3.83 × 10 −40.640 Abbreviations: SNP, single nucleotide polymorphism; A1, effect-allele. Although the lack of significant heterogeneity between both study populations suggested that the association found for these 12 SNPs might represent true associations, we decided to replicate these findings in a third independent population ascertained through the IMMEnSE consortium that included 2696 MM cases and 1701 controls. Results are reported in Table 2. After correction for multiple testing (p Bonferroni = 1.14 × 10 −4 ), we could confirm the previously reported association for ULK4,ATG5, and CDKN2A polymorphisms. Importantly, we also found that the association of the IKBKE rs17433804 SNP with the risk of MM was observed after multiple testing correction, which suggests that this gene could represent a new susceptibility locus for MM. In addition, although the associations were borderline significant after correction for multiple comparisons, we found of interest the associations of CD46 and PARK2 variants with the risk of developing MM. Although these associations need to be further validated, it was important to confirm that, with the exception of the CDKN2A, all the association signals included several SNPs (LD blocks), which reinforced the role of these loci in determining disease risk (Supplementary Figure S1A–F). Int. J. Mol. Sci. 2023,24, 8500 6 of 20 Table 2. Association estimates of the 12 autophagy SNPs representing susceptibility regions for MM. German GWAS (n= 3619) InterLymph GWAS (n= 5100) IMMEnSE (n= 3957) Meta-Analysis (n= 12676) Gene_SNP OR (95%CI) pOR (95%CI) pOR (95%CI) pOR (95%CI) PMeta PHet ATG5_rs2299864T 1.18 (1.03–1.33) 0.015 1.24 (1.12–1.36) 1.96 ×10−51.07 (0.92–1.23) 0.40 1.18 (1.11–1.27) 1.3 ×10−6 Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW 6 of 22 decided to replicate these findings in a third independent population ascertained through the IMMEnSE consortium that included 2696 MM cases and 1701 controls. Results are reported in Table 2. After correction for multiple testing (pBonferroni = 1.14·× 10−4), we could confirm the previously reported association for ULK4, ATG5, and CDKN2A polymorphisms. Importantly, we also found that the association of the IKBKErs17433804 SNP with the risk of MM was observed after multiple testing correction, which suggests that this gene could represent a new susceptibility locus for MM. In addition, although the associations were borderline significant after correction for multiple comparisons, we found of interest the associations of CD46 and PARK2 variants with the risk of developing MM. Although these associations need to be further validated, it was important to confirm that, with the exception of the CDKN2A, all the association signals included several SNPs (LD blocks), which reinforced the role of these loci in determining disease risk (Supplementary Figure S1A–F). Table 2. Association estimates of the 12 autophagy SNPs representing susceptibility regions for MM. German GWAS (n = 3619) InterLymph GWAS (n = 5100) IMMEnSE (n = 3957) Meta-Analysis (n = 12676) Gene_SNP OR (95%CI) p OR (95%CI) p OR (95%CI) p OR (95%CI) PMeta PHet ATG5_rs2299864T 1.18 (1.03–1.33) 0.015 1.24 (1.12–1.36) 1.96 × 10−5 1.07 (0.92–1.23) 0.40 1.18 (1.11–1.27) 1.3 × 10−6 Ϯ 0.254 CD46_rs1142469A 1.14 (1.02–1.25) 0.020 1.11 (1.02–1.21) 0.012 1.09 (0.96–1.23) 0.19 1.12 (1.05–1.18) 2.2 × 10−4 0.851 CDKN2A_rs2811710C 1.15 (1.03–1.29) 0.011 1.11 (1.02–1.20) 0.0176 1.18 (1.06–1.32) 0.003 1.14 (1.08–1.20) 7.0 × 10−6 Ϯ 0.666 CTSD_rs143309009G 2.22 (1.46–3.38) 2.1 × 10−4 1.24 (0.89–1.72) 0.2114 0.72 (0.47–1.10) 0.13 1.26 (1.01–1.57) 0.042 0.001 HSPB8_rs11064698T 1.53 (1.05–2.24) 0.027 1.16 (1.06–1.27) 0.0012 0.84 (0.72–0.99) 0.035 1.09 (1.01–1.18) 0.029 0.001 IKBKE_rs12739461T 1.18 (1.05–1.32) 0.0053 1.10 (1.01–1.20) 0.028 1.01 (0.90–1.14) 0.88 1.10 (1.03–1.16) 0.0022 0.179 IKBKE_rs2297546G 1.22 (1.09–1.37) 7.2 × 10−4 1.09 (1.01–1.18) 0.037 1.00 (0.90–1.12) 0.94 1.10 (1.04–1.16) 0.0013 0.047 IKBKE_rs17433804C 1.24 (1.09–1.41) 0.0011 1.08 (1.00–1.18) 0.066 1.16 (1.02–1.31) 0.024 1.14 (1.07–1.21) 4.6 × 10−5 Ϯ 0.213 PARK2_rs1884158T 1.21 (1.08–1.35) 0.00081 1.09 (1.00–1.18) 0.049 1.04 (0.91–1.19) 0.56 1.11 (1.05–1.18) 4.5 × 10−4 0.184 RPTOR_rs34048269A 1.30 (1.14–1.48) 1.3 × 10−4 1.06 (0.97–1.17) 0.199 1.04 (0.91–1.19) 0.56 1.11 (1.04–1.19) 0.0017 0.024 ULK4_rs6599175C 1.33 (1.16–1.53) 5.7 × 10−5 1.25 (1.13–1.39) 3.6 × 10−5 1.37 (1.21–1.56) 2.6 × 10−6 1.31 (1.22–1.40) 5.8 × 10−14 Ϯ 0.522 USP10_rs7202154G 1.31 (1.06–1.62) 0.011 1.23 (1.05–1.44) 0.0126 0.93 (0.76–1.15) 0.50 1.16 (1.04–1.29) 0.0075 0.048 Abbreviations: SNP, single nucleotide polymorphism; A1, effect allele; Ϯ Significant after correction for multiple testing. 2.2. Functional Relevance of Autophagy-Related SNPs Given the relatively strong association of the genetic variants within the CD46, IKBKE, PARK2, ULK4, ATG5, and CDKN2A loci on MM risk, we explored whether these variants could modulate host immune responses, serum steroid hormones, circulating immunological proteins, and blood-derived cell populations using data from the HFGP population. Although the genetic association of the USP10rs7202154 SNP with MM risk was modest, we decided to include it in the functional analysis due to the known role of USP proteins on the modulation of MM cell apoptosis. Interestingly, we found a significant association between the ULK4rs6599175 SNP and circulating concentrations of vitamin D3 (p = 4.0 × 10−4, Figure 1A), which suggests a possible involvement of this SNP in modulating MM risk through a vitamin D-dependent mechanism. We also observed that the presence of the IKBKErs17433804C allele was associated with increased numbers of transitional CD24+CD38+ B cells (p = 4.8·× 10−4, Figure 1B), whereas carriers of the IKBKErs17433804C/C genotype showed decreased circulating serum concentrations of MCP-2 (p = 3.6·× 10−4; Figure 1C), a chemotactic molecule involved in the activation of multiple immune cells and linked to MM cell migration. 0.254 CD46_rs1142469A 1.14 (1.02–1.25) 0.020 1.11 (1.02–1.21) 0.012 1.09 (0.96–1.23) 0.19 1.12 (1.05–1.18) 2.2 ×10−40.851 CDKN2A_rs2811710C 1.15 (1.03–1.29) 0.011 1.11 (1.02–1.20) 0.0176 1.18 (1.06–1.32) 0.003 1.14 (1.08–1.20) 7.0 ×10−6 Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW 6 of 22 decided to replicate these findings in a third independent population ascertained through the IMMEnSE consortium that included 2696 MM cases and 1701 controls. Results are reported in Table 2. After correction for multiple testing (pBonferroni = 1.14·× 10−4), we could confirm the previously reported association for ULK4, ATG5, and CDKN2A polymorphisms. Importantly, we also found that the association of the IKBKErs17433804 SNP with the risk of MM was observed after multiple testing correction, which suggests that this gene could represent a new susceptibility locus for MM. In addition, although the associations were borderline significant after correction for multiple comparisons, we found of interest the associations of CD46 and PARK2 variants with the risk of developing MM. Although these associations need to be further validated, it was important to confirm that, with the exception of the CDKN2A, all the association signals included several SNPs (LD blocks), which reinforced the role of these loci in determining disease risk (Supplementary Figure S1A–F). Table 2. Association estimates of the 12 autophagy SNPs representing susceptibility regions for MM. German GWAS (n = 3619) InterLymph GWAS (n = 5100) IMMEnSE (n = 3957) Meta-Analysis (n = 12676) Gene_SNP OR (95%CI) p OR (95%CI) p OR (95%CI) p OR (95%CI) PMeta PHet ATG5_rs2299864T 1.18 (1.03–1.33) 0.015 1.24 (1.12–1.36) 1.96 × 10−5 1.07 (0.92–1.23) 0.40 1.18 (1.11–1.27) 1.3 × 10−6 Ϯ 0.254 CD46_rs1142469A 1.14 (1.02–1.25) 0.020 1.11 (1.02–1.21) 0.012 1.09 (0.96–1.23) 0.19 1.12 (1.05–1.18) 2.2 × 10−4 0.851 CDKN2A_rs2811710C 1.15 (1.03–1.29) 0.011 1.11 (1.02–1.20) 0.0176 1.18 (1.06–1.32) 0.003 1.14 (1.08–1.20) 7.0 × 10−6 Ϯ 0.666 CTSD_rs143309009G 2.22 (1.46–3.38) 2.1 × 10−4 1.24 (0.89–1.72) 0.2114 0.72 (0.47–1.10) 0.13 1.26 (1.01–1.57) 0.042 0.001 HSPB8_rs11064698T 1.53 (1.05–2.24) 0.027 1.16 (1.06–1.27) 0.0012 0.84 (0.72–0.99) 0.035 1.09 (1.01–1.18) 0.029 0.001 IKBKE_rs12739461T 1.18 (1.05–1.32) 0.0053 1.10 (1.01–1.20) 0.028 1.01 (0.90–1.14) 0.88 1.10 (1.03–1.16) 0.0022 0.179 IKBKE_rs2297546G 1.22 (1.09–1.37) 7.2 × 10−4 1.09 (1.01–1.18) 0.037 1.00 (0.90–1.12) 0.94 1.10 (1.04–1.16) 0.0013 0.047 IKBKE_rs17433804C 1.24 (1.09–1.41) 0.0011 1.08 (1.00–1.18) 0.066 1.16 (1.02–1.31) 0.024 1.14 (1.07–1.21) 4.6 × 10−5 Ϯ 0.213 PARK2_rs1884158T 1.21 (1.08–1.35) 0.00081 1.09 (1.00–1.18) 0.049 1.04 (0.91–1.19) 0.56 1.11 (1.05–1.18) 4.5 × 10−4 0.184 RPTOR_rs34048269A 1.30 (1.14–1.48) 1.3 × 10−4 1.06 (0.97–1.17) 0.199 1.04 (0.91–1.19) 0.56 1.11 (1.04–1.19) 0.0017 0.024 ULK4_rs6599175C 1.33 (1.16–1.53) 5.7 × 10−5 1.25 (1.13–1.39) 3.6 × 10−5 1.37 (1.21–1.56) 2.6 × 10−6 1.31 (1.22–1.40) 5.8 × 10−14 Ϯ 0.522 USP10_rs7202154G 1.31 (1.06–1.62) 0.011 1.23 (1.05–1.44) 0.0126 0.93 (0.76–1.15) 0.50 1.16 (1.04–1.29) 0.0075 0.048 Abbreviations: SNP, single nucleotide polymorphism; A1, effect allele; Ϯ Significant after correction for multiple testing. 2.2. Functional Relevance of Autophagy-Related SNPs Given the relatively strong association of the genetic variants within the CD46, IKBKE, PARK2, ULK4, ATG5, and CDKN2A loci on MM risk, we explored whether these variants could modulate host immune responses, serum steroid hormones, circulating immunological proteins, and blood-derived cell populations using data from the HFGP population. Although the genetic association of the USP10rs7202154 SNP with MM risk was modest, we decided to include it in the functional analysis due to the known role of USP proteins on the modulation of MM cell apoptosis. Interestingly, we found a significant association between the ULK4rs6599175 SNP and circulating concentrations of vitamin D3 (p = 4.0 × 10−4, Figure 1A), which suggests a possible involvement of this SNP in modulating MM risk through a vitamin D-dependent mechanism. We also observed that the presence of the IKBKErs17433804C allele was associated with increased numbers of transitional CD24+CD38+ B cells (p = 4.8·× 10−4, Figure 1B), whereas carriers of the IKBKErs17433804C/C genotype showed decreased circulating serum concentrations of MCP-2 (p = 3.6·× 10−4; Figure 1C), a chemotactic molecule involved in the activation of multiple immune cells and linked to MM cell migration. 0.666 CTSD_rs143309009G 2.22 (1.46–3.38) 2.1 ×10−41.24 (0.89–1.72) 0.2114 0.72 (0.47–1.10) 0.13 1.26 (1.01–1.57) 0.042 0.001 HSPB8_rs11064698T 1.53 (1.05–2.24) 0.027 1.16 (1.06–1.27) 0.0012 0.84 (0.72–0.99) 0.035 1.09 (1.01–1.18) 0.029 0.001 IKBKE_rs12739461T 1.18 (1.05–1.32) 0.0053 1.10 (1.01–1.20) 0.028 1.01 (0.90–1.14) 0.88 1.10 (1.03–1.16) 0.0022 0.179 IKBKE_rs2297546G 1.22 (1.09–1.37) 7.2 ×10−41.09 (1.01–1.18) 0.037 1.00 (0.90–1.12) 0.94 1.10 (1.04–1.16) 0.0013 0.047 IKBKE_rs17433804C 1.24 (1.09–1.41) 0.0011 1.08 (1.00–1.18) 0.066 1.16 (1.02–1.31) 0.024 1.14 (1.07–1.21) 4.6 ×10−5 Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW 6 of 22 decided to replicate these findings in a third independent population ascertained through the IMMEnSE consortium that included 2696 MM cases and 1701 controls. Results are reported in Table 2. After correction for multiple testing (pBonferroni = 1.14·× 10−4), we could confirm the previously reported association for ULK4, ATG5, and CDKN2A polymorphisms. Importantly, we also found that the association of the IKBKErs17433804 SNP with the risk of MM was observed after multiple testing correction, which suggests that this gene could represent a new susceptibility locus for MM. In addition, although the associations were borderline significant after correction for multiple comparisons, we found of interest the associations of CD46 and PARK2 variants with the risk of developing MM. Although these associations need to be further validated, it was important to confirm that, with the exception of the CDKN2A, all the association signals included several SNPs (LD blocks), which reinforced the role of these loci in determining disease risk (Supplementary Figure S1A–F). Table 2. Association estimates of the 12 autophagy SNPs representing susceptibility regions for MM. German GWAS (n = 3619) InterLymph GWAS (n = 5100) IMMEnSE (n = 3957) Meta-Analysis (n = 12676) Gene_SNP OR (95%CI) p OR (95%CI) p OR (95%CI) p OR (95%CI) PMeta PHet ATG5_rs2299864T 1.18 (1.03–1.33) 0.015 1.24 (1.12–1.36) 1.96 × 10−5 1.07 (0.92–1.23) 0.40 1.18 (1.11–1.27) 1.3 × 10−6 Ϯ 0.254 CD46_rs1142469A 1.14 (1.02–1.25) 0.020 1.11 (1.02–1.21) 0.012 1.09 (0.96–1.23) 0.19 1.12 (1.05–1.18) 2.2 × 10−4 0.851 CDKN2A_rs2811710C 1.15 (1.03–1.29) 0.011 1.11 (1.02–1.20) 0.0176 1.18 (1.06–1.32) 0.003 1.14 (1.08–1.20) 7.0 × 10−6 Ϯ 0.666 CTSD_rs143309009G 2.22 (1.46–3.38) 2.1 × 10−4 1.24 (0.89–1.72) 0.2114 0.72 (0.47–1.10) 0.13 1.26 (1.01–1.57) 0.042 0.001 HSPB8_rs11064698T 1.53 (1.05–2.24) 0.027 1.16 (1.06–1.27) 0.0012 0.84 (0.72–0.99) 0.035 1.09 (1.01–1.18) 0.029 0.001 IKBKE_rs12739461T 1.18 (1.05–1.32) 0.0053 1.10 (1.01–1.20) 0.028 1.01 (0.90–1.14) 0.88 1.10 (1.03–1.16) 0.0022 0.179 IKBKE_rs2297546G 1.22 (1.09–1.37) 7.2 × 10−4 1.09 (1.01–1.18) 0.037 1.00 (0.90–1.12) 0.94 1.10 (1.04–1.16) 0.0013 0.047 IKBKE_rs17433804C 1.24 (1.09–1.41) 0.0011 1.08 (1.00–1.18) 0.066 1.16 (1.02–1.31) 0.024 1.14 (1.07–1.21) 4.6 × 10−5 Ϯ 0.213 PARK2_rs1884158T 1.21 (1.08–1.35) 0.00081 1.09 (1.00–1.18) 0.049 1.04 (0.91–1.19) 0.56 1.11 (1.05–1.18) 4.5 × 10−4 0.184 RPTOR_rs34048269A 1.30 (1.14–1.48) 1.3 × 10−4 1.06 (0.97–1.17) 0.199 1.04 (0.91–1.19) 0.56 1.11 (1.04–1.19) 0.0017 0.024 ULK4_rs6599175C 1.33 (1.16–1.53) 5.7 × 10−5 1.25 (1.13–1.39) 3.6 × 10−5 1.37 (1.21–1.56) 2.6 × 10−6 1.31 (1.22–1.40) 5.8 × 10−14 Ϯ 0.522 USP10_rs7202154G 1.31 (1.06–1.62) 0.011 1.23 (1.05–1.44) 0.0126 0.93 (0.76–1.15) 0.50 1.16 (1.04–1.29) 0.0075 0.048 Abbreviations: SNP, single nucleotide polymorphism; A1, effect allele; Ϯ Significant after correction for multiple testing. 2.2. Functional Relevance of Autophagy-Related SNPs Given the relatively strong association of the genetic variants within the CD46, IKBKE, PARK2, ULK4, ATG5, and CDKN2A loci on MM risk, we explored whether these variants could modulate host immune responses, serum steroid hormones, circulating immunological proteins, and blood-derived cell populations using data from the HFGP population. Although the genetic association of the USP10rs7202154 SNP with MM risk was modest, we decided to include it in the functional analysis due to the known role of USP proteins on the modulation of MM cell apoptosis. Interestingly, we found a significant association between the ULK4rs6599175 SNP and circulating concentrations of vitamin D3 (p = 4.0 × 10−4, Figure 1A), which suggests a possible involvement of this SNP in modulating MM risk through a vitamin D-dependent mechanism. We also observed that the presence of the IKBKErs17433804C allele was associated with increased numbers of transitional CD24+CD38+ B cells (p = 4.8·× 10−4, Figure 1B), whereas carriers of the IKBKErs17433804C/C genotype showed decreased circulating serum concentrations of MCP-2 (p = 3.6·× 10−4; Figure 1C), a chemotactic molecule involved in the activation of multiple immune cells and linked to MM cell migration. 0.213 PARK2_rs1884158T 1.21 (1.08–1.35) 0.00081 1.09 (1.00–1.18) 0.049 1.04 (0.91–1.19) 0.56 1.11 (1.05–1.18) 4.5 ×10−40.184 RPTOR_rs34048269A 1.30 (1.14–1.48) 1.3 ×10−41.06 (0.97–1.17) 0.199 1.04 (0.91–1.19) 0.56 1.11 (1.04–1.19) 0.0017 0.024 ULK4_rs6599175C 1.33 (1.16–1.53) 5.7 ×10−51.25 (1.13–1.39) 3.6 ×10−51.37 (1.21–1.56) 2.6 ×10−61.31 (1.22–1.40) 5.8 ×10−14 Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW 6 of 22 decided to replicate these findings in a third independent population ascertained through the IMMEnSE consortium that included 2696 MM cases and 1701 controls. Results are reported in Table 2. After correction for multiple testing (pBonferroni = 1.14·× 10−4), we could confirm the previously reported association for ULK4, ATG5, and CDKN2A polymorphisms. Importantly, we also found that the association of the IKBKErs17433804 SNP with the risk of MM was observed after multiple testing correction, which suggests that this gene could represent a new susceptibility locus for MM. In addition, although the associations were borderline significant after correction for multiple comparisons, we found of interest the associations of CD46 and PARK2 variants with the risk of developing MM. Although these associations need to be further validated, it was important to confirm that, with the exception of the CDKN2A, all the association signals included several SNPs (LD blocks), which reinforced the role of these loci in determining disease risk (Supplementary Figure S1A–F). Table 2. Association estimates of the 12 autophagy SNPs representing susceptibility regions for MM. German GWAS (n = 3619) InterLymph GWAS (n = 5100) IMMEnSE (n = 3957) Meta-Analysis (n = 12676) Gene_SNP OR (95%CI) p OR (95%CI) p OR (95%CI) p OR (95%CI) PMeta PHet ATG5_rs2299864T 1.18 (1.03–1.33) 0.015 1.24 (1.12–1.36) 1.96 × 10−5 1.07 (0.92–1.23) 0.40 1.18 (1.11–1.27) 1.3 × 10−6 Ϯ 0.254 CD46_rs1142469A 1.14 (1.02–1.25) 0.020 1.11 (1.02–1.21) 0.012 1.09 (0.96–1.23) 0.19 1.12 (1.05–1.18) 2.2 × 10−4 0.851 CDKN2A_rs2811710C 1.15 (1.03–1.29) 0.011 1.11 (1.02–1.20) 0.0176 1.18 (1.06–1.32) 0.003 1.14 (1.08–1.20) 7.0 × 10−6 Ϯ 0.666 CTSD_rs143309009G 2.22 (1.46–3.38) 2.1 × 10−4 1.24 (0.89–1.72) 0.2114 0.72 (0.47–1.10) 0.13 1.26 (1.01–1.57) 0.042 0.001 HSPB8_rs11064698T 1.53 (1.05–2.24) 0.027 1.16 (1.06–1.27) 0.0012 0.84 (0.72–0.99) 0.035 1.09 (1.01–1.18) 0.029 0.001 IKBKE_rs12739461T 1.18 (1.05–1.32) 0.0053 1.10 (1.01–1.20) 0.028 1.01 (0.90–1.14) 0.88 1.10 (1.03–1.16) 0.0022 0.179 IKBKE_rs2297546G 1.22 (1.09–1.37) 7.2 × 10−4 1.09 (1.01–1.18) 0.037 1.00 (0.90–1.12) 0.94 1.10 (1.04–1.16) 0.0013 0.047 IKBKE_rs17433804C 1.24 (1.09–1.41) 0.0011 1.08 (1.00–1.18) 0.066 1.16 (1.02–1.31) 0.024 1.14 (1.07–1.21) 4.6 × 10−5 Ϯ 0.213 PARK2_rs1884158T 1.21 (1.08–1.35) 0.00081 1.09 (1.00–1.18) 0.049 1.04 (0.91–1.19) 0.56 1.11 (1.05–1.18) 4.5 × 10−4 0.184 RPTOR_rs34048269A 1.30 (1.14–1.48) 1.3 × 10−4 1.06 (0.97–1.17) 0.199 1.04 (0.91–1.19) 0.56 1.11 (1.04–1.19) 0.0017 0.024 ULK4_rs6599175C 1.33 (1.16–1.53) 5.7 × 10−5 1.25 (1.13–1.39) 3.6 × 10−5 1.37 (1.21–1.56) 2.6 × 10−6 1.31 (1.22–1.40) 5.8 × 10−14 Ϯ 0.522 USP10_rs7202154G 1.31 (1.06–1.62) 0.011 1.23 (1.05–1.44) 0.0126 0.93 (0.76–1.15) 0.50 1.16 (1.04–1.29) 0.0075 0.048 Abbreviations: SNP, single nucleotide polymorphism; A1, effect allele; Ϯ Significant after correction for multiple testing. 2.2. Functional Relevance of Autophagy-Related SNPs Given the relatively strong association of the genetic variants within the CD46, IKBKE, PARK2, ULK4, ATG5, and CDKN2A loci on MM risk, we explored whether these variants could modulate host immune responses, serum steroid hormones, circulating immunological proteins, and blood-derived cell populations using data from the HFGP population. Although the genetic association of the USP10rs7202154 SNP with MM risk was modest, we decided to include it in the functional analysis due to the known role of USP proteins on the modulation of MM cell apoptosis. Interestingly, we found a significant association between the ULK4rs6599175 SNP and circulating concentrations of vitamin D3 (p = 4.0 × 10−4, Figure 1A), which suggests a possible involvement of this SNP in modulating MM risk through a vitamin D-dependent mechanism. We also observed that the presence of the IKBKErs17433804C allele was associated with increased numbers of transitional CD24+CD38+ B cells (p = 4.8·× 10−4, Figure 1B), whereas carriers of the IKBKErs17433804C/C genotype showed decreased circulating serum concentrations of MCP-2 (p = 3.6·× 10−4; Figure 1C), a chemotactic molecule involved in the activation of multiple immune cells and linked to MM cell migration. 0.522 USP10_rs7202154G 1.31 (1.06–1.62) 0.011 1.23 (1.05–1.44) 0.0126 0.93 (0.76–1.15) 0.50 1.16 (1.04–1.29) 0.0075 0.048 Abbreviations: SNP, single nucleotide polymorphism; A1, effect allele; Int. J. Mol. Sci. 2023, 24, x FOR PEER REVIEW 6 of 22 decided to replicate these findings in a third independent population ascertained through the IMMEnSE consortium that included 2696 MM cases and 1701 controls. Results are reported in Table 2. After correction for multiple testing (pBonferroni = 1.14·× 10−4), we could confirm the previously reported association for ULK4, ATG5, and CDKN2A polymorphisms. Importantly, we also found that the association of the IKBKErs17433804 SNP with the risk of MM was observed after multiple testing correction, which suggests that this gene could represent a new susceptibility locus for MM. In addition, although the associations were borderline significant after correction for multiple comparisons, we found of interest the associations of CD46 and PARK2 variants with the risk of developing MM. Although these associations need to be further validated, it was important to confirm that, with the exception of the CDKN2A, all the association signals included several SNPs (LD blocks), which reinforced the role of these loci in determining disease risk (Supplementary Figure S1A–F). Table 2. Association estimates of the 12 autophagy SNPs representing susceptibility regions for MM. German GWAS (n = 3619) InterLymph GWAS (n = 5100) IMMEnSE (n = 3957) Meta-Analysis (n = 12676) Gene_SNP OR (95%CI) p OR (95%CI) p OR (95%CI) p OR (95%CI) PMeta PHet ATG5_rs2299864T 1.18 (1.03–1.33) 0.015 1.24 (1.12–1.36) 1.96 × 10−5 1.07 (0.92–1.23) 0.40 1.18 (1.11–1.27) 1.3 × 10−6 Ϯ 0.254 CD46_rs1142469A 1.14 (1.02–1.25) 0.020 1.11 (1.02–1.21) 0.012 1.09 (0.96–1.23) 0.19 1.12 (1.05–1.18) 2.2 × 10−4 0.851 CDKN2A_rs2811710C 1.15 (1.03–1.29) 0.011 1.11 (1.02–1.20) 0.0176 1.18 (1.06–1.32) 0.003 1.14 (1.08–1.20) 7.0 × 10−6 Ϯ 0.666 CTSD_rs143309009G 2.22 (1.46–3.38) 2.1 × 10−4 1.24 (0.89–1.72) 0.2114 0.72 (0.47–1.10) 0.13 1.26 (1.01–1.57) 0.042 0.001 HSPB8_rs11064698T 1.53 (1.05–2.24) 0.027 1.16 (1.06–1.27) 0.0012 0.84 (0.72–0.99) 0.035 1.09 (1.01–1.18) 0.029 0.001 IKBKE_rs12739461T 1.18 (1.05–1.32) 0.0053 1.10 (1.01–1.20) 0.028 1.01 (0.90–1.14) 0.88 1.10 (1.03–1.16) 0.0022 0.179 IKBKE_rs2297546G 1.22 (1.09–1.37) 7.2 × 10−4 1.09 (1.01–1.18) 0.037 1.00 (0.90–1.12) 0.94 1.10 (1.04–1.16) 0.0013 0.047 IKBKE_rs17433804C 1.24 (1.09–1.41) 0.0011 1.08 (1.00–1.18) 0.066 1.16 (1.02–1.31) 0.024 1.14 (1.07–1.21) 4.6 × 10−5 Ϯ 0.213 PARK2_rs1884158T 1.21 (1.08–1.35) 0.00081 1.09 (1.00–1.18) 0.049 1.04 (0.91–1.19) 0.56 1.11 (1.05–1.18) 4.5 × 10−4 0.184 RPTOR_rs34048269A 1.30 (1.14–1.48) 1.3 × 10−4 1.06 (0.97–1.17) 0.199 1.04 (0.91–1.19) 0.56 1.11 (1.04–1.19) 0.0017 0.024 ULK4_rs6599175C 1.33 (1.16–1.53) 5.7 × 10−5 1.25 (1.13–1.39) 3.6 × 10−5 1.37 (1.21–1.56) 2.6 × 10−6 1.31 (1.22–1.40) 5.8 × 10−14 Ϯ 0.522 USP10_rs7202154G 1.31 (1.06–1.62) 0.011 1.23 (1.05–1.44) 0.0126 0.93 (0.76–1.15) 0.50 1.16 (1.04–1.29) 0.0075 0.048 Abbreviations: SNP, single nucleotide polymorphism; A1, effect allele; Ϯ Significant after correction for multiple testing. 2.2. Functional Relevance of Autophagy-Related SNPs Given the relatively strong association of the genetic variants within the CD46, IKBKE, PARK2, ULK4, ATG5, and CDKN2A loci on MM risk, we explored whether these variants could modulate host immune responses, serum steroid hormones, circulating immunological proteins, and blood-derived cell populations using data from the HFGP population. Although the genetic association of the USP10rs7202154 SNP with MM risk was modest, we decided to include it in the functional analysis due to the known role of USP proteins on the modulation of MM cell apoptosis. Interestingly, we found a significant association between the ULK4rs6599175 SNP and circulating concentrations of vitamin D3 (p = 4.0 × 10−4, Figure 1A), which suggests a possible involvement of this SNP in modulating MM risk through a vitamin D-dependent mechanism. We also observed that the presence of the IKBKErs17433804C allele was associated with increased numbers of transitional CD24+CD38+ B cells (p = 4.8·× 10−4, Figure 1B), whereas carriers of the IKBKErs17433804C/C genotype showed decreased circulating serum concentrations of MCP-2 (p = 3.6·× 10−4; Figure 1C), a chemotactic molecule involved in the activation of multiple immune cells and linked to MM cell migration. Significant after correction for multiple testing. 2.2. Functional Relevance of Autophagy-Related SNPs Given the relatively strong association of the genetic variants within the CD46,IKBKE, PARK2,ULK4,ATG5, and CDKN2A loci on MM risk, we explored whether these variants could modulate host immune responses, serum steroid hormones, circulating immunological proteins, and blood-derived cell populations using data from the HFGP population. Although the genetic association of the USP10 rs7202154 SNP with MM risk was modest, we decided to include it in the functional analysis due to the known role of USP proteins on the modulation of MM cell apoptosis. Interestingly, we found a significant association between the ULK4 rs6599175 SNP and circulating concentrations of vitamin D3 (p= 4.0 × 10 −4 , Figure 1A), which suggests a possible involvement of this SNP in modulating MM risk through a vitamin D-dependent mechanism. We also observed that the presence of the IKBKE rs17433804C allele was associated with increased numbers of transitional CD24 + CD38 + B cells (p= 4.8 × 10 −4 , Figure 1B), whereas carriers of the IKBKE rs17433804C/C genotype showed decreased circulating serum concentrations of MCP-2 (p= 3.6 × 10 −4 ; Figure 1C), a chemotactic molecule involved in the activation of multiple immune cells and linked to MM cell migration. We also found that homozygous carriers of the CD46 rs1142469A allele, which was associated with an increased risk of MM, had decreased numbers of CD19 + B cells, CD19 + CD3 − B cells, CD5 + IgD − cells, IgM − cells, IgD − IgM − cells, and CD4 − CD8 − PBMCs compared with those subjects carrying the G allele (p= 0.00025–0.00086; Figure 2A–F). Moreover, we found that homozygous carriers of the CD46 rs1142469A risk allele had increased circulating concentrations of IL20 compared with those subjects carrying the G allele (p= 0.00082; Figure 2G), which suggests a role of this angiogenic cytokine in modulating MM risk. In addition, we observed that carriers of the CDKN2A rs2811710C allele had decreased levels of CD4 + EMCD45RO + CD27 − cells (p= 0.00093; Figure 3A), which constitutes a subset of memory T cells that does not require co-stimulation for T-cell receptors to display a high antigen recall response. Finally, we found that carriers of the USP10 rs7202154G/G genotype had decreased synthesis of p62 and LC3-II compared to non-carriers (p= 0.005 and p= 0.047; Figure 3B,C). Given that p62 serves as a useful marker for the induction of autophagy, clearance of protein aggregates, and the inhibition of autophagy and LC3-II serves to track the binding of p62 and subsequent recruitment of autophagosomes, this result might suggest that this genetic variant could be involved in determining autophagy activation. Given that the association of the USP10 SNP with autophagy flux markers (LC3-II/Actin ratio) did not remain statistically significant after correction for multiple testing, additional studies are needed to confirm whether the weak association of this SNP with MM risk might be mediated by the regulation of the autophagy flux. No functional Int. J. Mol. Sci. 2023,24, 8500 7 of 20 effect on host immune responses or autophagy flux was detected for the remaining selected SNPs, which suggests that the effect of these variants on MM risk is not mediated by these biological processes. Figure 1. Functional impact of the ULK4 rs6599175 and IKBKE rs17433804 SNPs [A-C]. ( A ) Vitamin D3 levels (pg/mL) according to the ULK4 rs6599175 SNP; ( B ) Numbers of transitional CD24 + CD38 + B cells according to the IKBKE rs17433804 SNP; ( C ) Serum levels of MCP-2 according to the IKBKErs17433804 SNP. Int. J. Mol. Sci. 2023,24, 8500 8 of 20 Figure 2. Functional impact of the CD46 rs1142469 SNP ( A – G ). ( A ) Numbers of CD19 + B cells according to the CD46 rs1142469 SNP; ( B ) Numbers of CD19 + CD3 − B cells according to the CD46 rs1142469 SNP; ( C ) Numbers of IgD − CD5 + cells according to the CD46 rs1142469 SNP; ( D ) Numbers of IgM − cells according to the CD46 rs1142469 SNP; ( E ) Numbers of IgD + IgM − cells according to the CD46 rs1142469 SNP; ( F ) Numbers of CD4 + CD8 − PBMCs according to the CD46 rs1142469 SNP; and ( G ) Serum IL20 levels (ng/mL) according to the CD46rs1142469 SNP. Int. J. Mol. Sci. 2023,24, 8500 9 of 20 Figure 3. Functional impact of the CDKN2A rs2811710 and USP10 rs7202154 SNPs. ( A ) Numbers of CD4 + Effector Memory CD45RO − CD27 − cells according to the CDKN2A rs2811710 SNP; ( B ) p62 synthesis according to the USP10rs7202154 SNP; (C) LC3 synthesis according to the USP10rs7202154 SNP. Int. J. Mol. Sci. 2023,24, 8500 16 of 20 Signaling and Mouse anti-Actin antibody, Merck Millipore, Darmstadt, Germany). After washing with tris-buffered saline-tween (TBS-T), nitrocellulose membranes were incubated with the corresponding secondary antibodies (IgG anti-Rabbit for LC3A/B and IgG antiMouse for Actin). Protein levels were detected after incubation with SuperSignal West Femto Maximum Sensitivity Substrate (Thermofisher, Waltham, MA, USA) or Clarity Western ECL Substrate (Bio-Rad, Hercules, CA, USA). Digital images of the Western blots were obtained in a ChemiDoc XRS System (Bio-Rad) with Quantity One software V4.6.5 (BioRad). Autophagy flux was determined as the difference in the LC3-II/Actin ratio between cells treated or not with bafilomycin A1 and/or metformin. Linear regression analyses adjusted for age and sex were used to determine the correlation between autophagy-related SNPs and autophagy flux values. A significance threshold of p= 0.0028 was set according to the number of SNPs tested (n= 12) and the treatments administrated in vitro (n= 2). 4.7. In Silico Functional Analysis Haploreg (http://www.broadinstitute.org/mammals/haploreg/haploreg.php, accessed on 12 February 2020) and ENCODE annotation data (https://genome.ucsc.edu/ ENCODE) were also used to predict the functional role of the most interesting autophagy SNPs. We also analyzed whether selected SNPs could be expression quantitative loci (eQTL) for different cell types and tissues using data from public eQTL browsers such as GTex portal (https://gtexportal.org/home/, accessed on 12 February 2020), Blood eQTL browser (https://genenetwork.nl/bloodeqtlbrowser/, accessed on 12 February 2020), and Haploreg [43]. 5. Conclusions This study identifies novel associations for genetic polymorphisms within the CD46, IKBKE, and PARK2 loci and MM risk and validates previously reported associations for SNPs within the ULK4,ATG5, and CDKN2A genes. Although the biological effect of the CD46,IKBKE,ULK4, and CDKN2A SNPs seemed to be mediated by alterations of absolute numbers of certain Band T-cell subsets and MCP-2-, IL20-, and vitamin D3dependent mechanisms affecting cell differentiation and proliferation, angiogenesis, immune responses, and apoptosis, we could not identify the biological mechanisms underscoring the ATG5 association. Supplementary Materials: The following supporting information can be downloaded at: https: //www.mdpi.com/article/10.3390/ijms24108500/s1. Author Contributions: J.S. and A.F. designed and coordinated the study. E.C., J.M.S.-M., A.M. and J.S. were involved in the generation of genetic data from the CRuCIAL cohort and drafted the manuscript. J.S. obtained funding. J.M.S.-M., F.C., D.C. and J.S. performed data quality control and genetic association analyses. R.T.H., Y.L. and M.G.N. provided the functional raw data from the HFGP cohort and J.S. and R.T.H. performed the statistical analysis of functional data. A.J. (Artur Jurczyszyn), A.C.-G., A.S., M.A.T.H., N.W., G.B., R.G.-S., W.T., U.V., A.J. (Andrés Jerez), D.Z., M.W., J.N.H., S.L., J.J.S., A.B., A.K., J.M.-L., S.G., M.E.S., E.S., E.I.-J., G.G.G., M.R.-R., M.K., N.A., F.G.V., P.S.R., M.I.d.S.F., K.K., M.R., W.C., M.P., M.J., P.B., M.D., A.D.-S., E.O., A.D.N., J.M.Z., R.M.R., M.M., J.J.R.S., V.A., K.J., K.H., S.I.B., V.R., G.M., S.K.K., A.N., S.J.C., C.D., M.J.M., J.V., N.J.C., E.Z., A.J.V., E.E.B., C.M.V. and A.F. provided biological samples and clinical and demographic data of CLL patients and/or healthy controls from the InterLymph and/or CRuCIAL consortia. A.C.-G., S.I.B., V.R., E.E.B., C.M.V., K.H. and A.F. provided GWAS data from the GMMG or InterLymph cohorts. B.S.-M. and P.L. performed the autophagy flux experiments. All authors have read and agreed to the published version of the manuscript. Funding: This work was supported by the European Union’s Horizon 2020 research and innovation program, N ◦ 856620 and by grants from the Instituto de Salud Carlos III and FEDER (Madrid, Spain; PI17/02256 and PI20/01845), Consejería de Transformación Económica, Industria, Conocimiento y Universidades and FEDER (PY20/01282), from the CRIS foundation against cancer, from the Cancer Network of Excellence (RD12/10 Red de Cáncer), from the Dietmar Hopp Foundation and the German Ministry of Education and Science (BMBF: CLIOMMICS [01ZX1309]), and from National Cancer Int. J. Mol. Sci. 2023,24, 8500 17 of 20 Institute of the National Institutes of Health under award numbers: R01CA186646, U01CA249955 (EEB). This work was also funded d by Portuguese National funds, through the Foundation for Science and Technology (FCT)—project UIDB/50026/2020 and UIDP/50026/2020 and by the project NORTE-01-0145-FEDER-000055, supported by Norte Portugal Regional Operational Programme (NORTE 2020), under the PORTUGAL 2020 Partnership Agreement, through the European Regional Development Fund (ERDF). Institutional Review Board Statement: This study was conducted according to the guidelines of the Declaration of Helsinki and approved by the Institutional Review Boards of all institutions participating in patient recruitment. Informed Consent Statement: Informed consent was obtained from all subjects involved in this study. Data Availability Statement: The genotype data used in the present study are available by the corresponding authors upon reasonable request. Functional data used in this project have been meticulously catalogued and archived in the BBMRI-NL data infrastructure (https://hfgp.bbmri.nl/, accessed on 12 February 2020) using the MOLGENIS open-source platform for scientific data. This allows flexible data querying and download, including sufficiently rich metadata and interfaces for machine processing (R statistics, REST API) and using FAIR principles to optimize Findability, Accessibility, Interoperability, and Reusability. Acknowledgments: We kindly thank all individuals who agreed to participate in this study, as well as all cooperating physicians and students. Conflicts of Interest: The authors declare no conflict of interest. References 1. Palumbo, A.; Anderson, K. Multiple myeloma. N. Engl. J. Med. 2011,364, 1046–1060. [CrossRef] [PubMed] 2. Kyle, R.A.; Gertz, M.A.; Witzig, T.E.; Lust, J.A.; Lacy, M.Q.; Dispenzieri, A.; Fonseca, R.; Rajkumar, S.V.; Offord, J.R.; Larson, D.R.; et al. 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