Exploring the role of circulating proteins in multiple myeloma risk: a Mendelian randomization study

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Lee, Kate L. Burley, Emma L. Hazelwood, Sally Moore, and 2 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4800219/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 30 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted 14 You are reading this latest preprint version Abstract Background Multiple myeloma (MM) is an incurable blood cancer with unclear aetiology. Proteomics, the high-throughput measurement of circulating proteins, is a valuable tool in exploring mechanisms of disease. We investigated the causal relationship between circulating proteins and MM risk, using two of the largest cohorts with proteomics data to-date. Methods We performed bidirectional two-sample Mendelian randomization (MR; forward MR = causal effect estimation of proteins and MM risk; reverse MR = causal effect estimation of MM risk and proteins). Summary statistics for plasma proteins were obtained from genome-wide association studies performed using SomaLogic (N = 35,559; deCODE) and Olink (N = 34,557; UK Biobank; UKB) proteomic platforms and for MM risk from a meta-analysis of UKB and FinnGen (case = 1,649; control = 727,247) or FinnGen only (case = 1,085; control = 271,463). Cis- SNPs associated with protein levels were used to instrument circulating proteins. We evaluated proteins for the consistency of directions of effect across MR analyses (with 95% confidence intervals not overlapping the null) and corroborating evidence from genetic colocalization. Results In the forward MR, 994 (SomaLogic) and 1,570 (Olink) proteins were instrumentable. 440 proteins were analysed in both deCODE and UKB; 302 (69%) of these showed consistent directions of effect in the forward MR. Seven proteins had 95% confidence intervals (CIs) that did not overlap the null in both forward MR analyses and did not have evidence for an effect in the reverse direction. MR evidence was strongest for the effect of dermatopontin on MM risk (deCODE) OR: 1.49 per SD higher protein levels, 95% CI 1.06–2.09; (UKB) OR: 1.47; 95% CI 1.14–1.90). Evidence from genetic colocalization did not meet our threshold for a shared causal signal between this protein and MM risk (h4 < 0.8). Conclusions Our results highlight seven circulating proteins which may be involved in MM risk. Although evidence from genetic colocalization suggests these associations may not be robust to horizontal pleiotropy, these proteins may be useful markers of MM risk. Future work should explore the utility of these proteins in disease prediction or prevention using proteomic data from patients with MM or precursor conditions. Biological sciences/Cancer/Haematological cancer/Myeloma Biological sciences/Genetics Proteomics multiple myeloma Mendelian randomization genetic colocalization Figures Figure 1 Figure 2 Figure 3 Figure 4 Introduction Multiple myeloma (MM) is the second most common haematological malignancy in the UK, with ~ 5,951 new cases each year [ 1 ]. It is characterised by neoplastic proliferation of plasma cells in the bone marrow, resulting in overproduction of monoclonal immunoglobulins commonly referred to as paraprotein or M-protein. Nearly all cases of MM are preceded by the benign asymptomatic precursor condition, monoclonal gammopathy of unknown significance (MGUS) [ 2 , 3 ]. The diagnosis of active MM is based on the presence of M-protein in serum or urine, along with evidence of end-organ damage including hypercalcemia, renal insufficiency, anaemia, and bone lesions [ 4 ]. Patients with MM are at an increased risk of developing blood clots and are susceptible to recurrent and severe infections [ 5 – 7 ]. Periods of disease remission can be induced using chemotherapy regimens including proteosome inhibitors (e.g. bortezomib), immunomodulatory drugs (e.g. lenalidomide), and monoclonal antibodies (e.g. daratumumab), plus autologous stem cell transplant for a subset of eligible patients [ 8 ] However, MM is currently incurable with a median overall survival of 6 years [ 8 ]. The pathogenesis of MM is not fully understood, potentially limiting the identification of curative therapeutic strategies. Observational studies have reported multiple risk factors associated with the development of MM, including age, sex, ancestry, family history, and adiposity [ 9 – 14 ]. However, observational studies are often limited by reverse causation and confounding [ 15 ]. Mendelian randomization (MR) is an approach which uses genetic variants (alleles randomly assigned during gametogenesis; typically, single nucleotide polymorphisms (SNPs)) to estimate the causal effect of an exposure on an outcome [ 16 – 18 ]. When core assumptions are satisfied, MR is robust to biases observed in conventional observational analyses (e.g., reverse causation and confounding)[ 15 , 19 ]. Previous studies have used MR to investigate evidence for a causal role of known MM risk factors (such as obesity) in disease development and identified novel factors such as increased telomere length to be implicated in MM risk [ 20 ]. Alterations in the abundance of specific circulating proteins has been used previously in cancer diagnosis and risk stratification [ 21 ] and can also highlight mechanistic pathways [ 22 , 23 ]. There is evidence that the inclusion of proteomics in clinical prediction tools improves MM risk prediction in comparison to using clinical risk factors alone [ 24 ]. Furthermore, as proteins are the main target for small molecule drugs and biologics [ 25 ], exploring the role of circulating proteins in MM risk using MR may identify targets for intervention. A recent MR study identified 13 potentially causal proteins [ 26 ] but was limited in scale, may suffer bias due to horizontal pleiotropy through the inclusion of trans genetic instruments (variants not in or near the gene coding region), and did not use an independent sample to replicate results [ 26 ]. We set out to systematically explore the relationship between circulating proteins and MM risk using two-sample MR with robust instrument selection [ 15 , 19 ] and corroborate our findings using genetic colocalization. Methods Overview Using data for circulating proteins from two genome-wide association studies (GWAS), we performed bidirectional two sample MR analyses to explore the causal role of circulating proteins in MM risk (Fig. 1 ). We performed a forward MR to estimate the effect of circulating proteins on MM risk and a reverse MR to estimate the effect of MM risk and circulating proteins. The latter was performed to evaluate evidence for reverse causality, where MM may influence protein levels. We performed genetic colocalization to investigate shared causal signals between circulating proteins and MM risk, which may strengthen evidence for causal effects identified in the MR analyses or highlight non-causal markers of MM risk that warrant further investigation [ 27 ]. Data for circulating proteins were obtained from GWAS performed in two independent studies of European ancestry: deCODE (SomaLogic) and UK Biobank (Olink). Data for MM risk were obtained from GWAS performed in two independent studies (UK Biobank and FinnGen) which we meta-analysed. Given overlap of UK Biobank data (which is a source of bias in MR [ 28 ]), we used deCODE proteins with the meta-analysis of MM risk and UK Biobank proteins with the FinnGen GWAS [ 29 , 30 ]. Circulating protein GWAS GWAS data for up-to 4,907 aptamers (4,719 unique proteins) were obtained from Ferkingstad et al. [ 31 ]. Protein concentrations were measured from ethylenediaminetetraacetic acid (EDTA) plasma samples from 35,559 Icelandic individuals (deCODE) using SomaScan® (SomaLogic). Briefly, a large proportion of the Icelandic population enrolled in a nationwide programme administered by deCODE genetics; 49,708 enrolled individuals underwent whole-genome sequencing while 166,281 additional individuals were genotyped with imputation based on the whole-genome sequencing data [ 31 ]. The SomaScan platform uses Slow Off-rate Modified Aptamers which make direct contact with proteins, enabling their detection and quantification in relative fluorescence units (RFUs) using a DNA microarray. Multiple aptamers can bind to a single protein (e.g., because of splice-isoforms). UK Biobank is a population-based cohort of ~ 500,000 individuals aged 40–69 recruited between 2006–2010 in the United Kingdom. Genotyping and imputation have been described previously [ 32 ]. Briefly, the genotyping and imputation involved a two-step imputation process performed first using the Haplotype Reference Consortium and then performed using a merged UK10K and 1000 Genomes Phase 3 reference panel, these two imputations were combined and the HRC imputed variant was kept in instances of duplication. Prior to genome-wide analysis, protein values were inverse rank normal transformed and adjusted for age, sex and sample age. These residuals were standardised again using an inverse rank normal transformation and a linear mixed-model (LMM) GWAS was performed using BOLT-LMM [ 31 , 33 – 35 ]. Assuming the distribution of protein concentration was normal prior to inverse rank normal transformation, we interpret these units to be approximately equivalent to a normalized standard deviation (SD). Genome-wide summary level data were also obtained for up to 2,923 proteins from Sun et al. [ 36 ]. Protein concentrations were measured from EDTA plasma samples from 34,557 participants of European ancestry from UK Biobank using the Olink Explore 3072 panel. Briefly, Olink Explore 3072 uses a proximity extension assay (PEA) which uses matched pairs of antibodies with DNA tags that, once bound to their target protein, hybridise and can be amplified and quantified using polymerase chain reaction. Proteins are measured in normalised protein expression (NPX) units which are on a log2 scale [ 37 ]. Prior to genome-wide analysis, protein values were inverse rank normal transformed and a whole-genome regression model using a leave one chromosome out scheme was performed with REGENIE (version 2.2.1) [ 36 ] adjusting for age, age 2 , sex, age x sex, age 2 x sex, batch, centre, genetic array, time between blood sampling measurement and the first 20 principal components. Assuming the distribution of protein levels was normal prior to inverse rank normal transformation, we interpret these units to be approximately equivalent to a normalized SD. Genetic instruments for circulating protein levels In MR analyses of circulating proteins and MM risk we used cis -SNPs to instrument proteins. Cis -SNPs were obtained from the supplementary data of the original study. Briefly, a 1 mega base (1,000,000 bases; Mb) region was defined around each SNP reaching the genome-wide significance threshold specified in each GWAS (p-value < 1.8 x 10 − 9 in deCODE; p-value < 1.7x10 − 11 in UKB) which were ≤ 1 Mb from the transcription start site of the protein coding gene (discovery) or ≤ 1 Mb from the gene encoding the measured protein (replication). Starting with the SNP with the lowest p-value, any overlapping regions were merged until no overlapping regions remained (major histocompatibility complex was treated as a single region). Linkage disequilibrium (LD) based clumping was used to identify whether regions were associated with multiple proteins; regional SNPs with high LD (r 2 ≥ 0.8) were merged into a single region and the SNP with the lowest p-value was considered the sentinel SNP. In total, 1,192 of 4,907 aptamers (deCODE) and 1,860 of 2,923 proteins (UKB) had available cis -SNPs. Multiple myeloma genome-wide association studies For our MR analyses using deCODE SomaLogic protein GWAS as the exposure, genome-wide summary level data for MM risk were obtained from a meta-analysis of 1,649 cases and 727,247 controls from two GWAS conducted in UK Biobank and FinnGen. In UK Biobank [ 38 ] (cases = 564; controls = 455,784), MM was recorded according to the International Classification of Diseases 10th revision (ICD 10) [ 39 ] following mapping to Phecode v.1.2 [ 40 ], controls were defined as any individual who had not been diagnosed with MM (those who did not have the MM ICD 10 code), and models were adjusted for age, age 2 , sex, age × sex, age 2 × sex and the top 20 PCs provided by UK Biobank. In FinnGen [ 41 ] (cases = 1,085; controls = 271,463), MM was recorded according to the International Classification of Diseases (ICD-O-3) following linkage with the Finnish Cancer Registers, controls were defined as any individual without any cancer diagnosis, and models were adjusted for sex, age, top 10 PCs, and genotyping batch. Meta-analysis was performed using METAL (version 2011-03-25) and results were filtered to remove SNPs with a heterogeneity P-value ≤ 0.05 between the two GWAS [ 42 ]. The METAL software was used to combine test statistics and standard errors and control for population stratification as recommended in the METAL documentation [ 43 ]. Estimates for each SNP indicates the difference in disease risk for each copy of the effect allele. Instruments for multiple myeloma risk We used all SNPs which met the following requirement to instrument MM risk: a genome-wide significance threshold of p < 5 x 10 − 8 and an LD R 2 threshold of 0.001 within a 10 kilo-base (kb) window to identify robust and independently associated SNPs. In the meta-analysis of MM GWAS, 1 SNP met the genome-wide significance threshold and was used to instrument MM risk. In the FinnGen MM GWAS no SNPs met the genome-wide significance threshold; instead, a lower threshold of p < 5 x 10 − 7 was used and 3 SNPs were identified. Of these, 2 SNPs (rs555992394 and rs8141529) were available in the UKB proteomic GWAS data. In relaxing the p-value threshold for the MM GWAS in FinnGen, we may invalidate a core assumption of MR that the instrument is robustly associated with the exposure. As such, we caution that these analyses were employed to evaluate possible conflicting evidence to the forward MR and should not be interpreted as causal estimates of MM liability on protein levels. We also explored possible pleiotropic effects that may arise from this relaxation via searching the SNP rsIDs in the IEU Open GWAS Project [ 44 ], essentially performing a phenome-wide association study (PheWAS). Statistical analysis Mendelian randomization analysis MR relies upon three core assumptions (Fig. 1 ): (1) the genetic variant is associated with the exposure, (2) there are no confounders of the genetic variant and outcome association (such as population structure), and (3) the genetic variant is associated with the outcome only via its association with the exposure (i.e., not via alternate pathways) [ 16 ]. For all exposures, the following summary-level data were obtained from the original GWAS: rsID, effect allele, other allele, effect allele frequency (EAF), effect estimate, standard error of the effect estimate, p-value of the effect estimate, and where available sample size for each SNP. Where individual SNP sample size was not available the overall sample size was used. Genetic variants were extracted from each outcome GWAS and, where these were not available, proxy SNPs were included if LD was ≥ 0.8. For all SNPs, the inclusion of SNPs where the reference strand was ambiguous was allowed and the reference strand was inferred using minor allele frequency (where minor allele frequency was not ≥ 0.3, in which case the proxy SNP was excluded). Data were harmonized such that the exposure effect allele was on the increasing scale. As such, MR estimates for the effect of circulating proteins on MM risk are given as the per effect allele normalised SD unit increase in protein concentration whereas estimates for the effect of MM risk on circulating protein levels are given as the normalised SD unit difference in protein per effect allele increase in disease risk. We used F-statistics to assess instrument strength, with an F-statistic > 10 indicating a strong instrument [ 45 ]. F-statistics were calculated as: F = R 2 x (N-1-k)/((1-R 2 ) x k), where k is the number of SNPs in the instrument and N is sample size of the SNP-exposure GWAS. R 2 was calculated as: R 2 = (2(b2) x EAFx (1-EAF))/((2(2) x EAFx (1-EAF)) + ((SE2) x (2 x N) x EAFx (1-EAF))), where b is the SNP-exposure association, EAF is the effect allele frequency of the SNP, SE is the standard error of the SNP-exposure association, and N is sample size of the SNP-exposure GWAS. All exposure data are given in Supplementary Table 1 and Supplementary Table 2 for proteins and Supplementary Table 3 for MM. For the forward MR analysis for circulating proteins and MM risk, two protein GWAS were used. Where proteins were measured by SomaLogic in deCODE, the MM GWAS used was the meta-analysis of FinnGen and UKB. Where proteins were measured by Olink in UKB, the MM GWAS was in FinnGen alone (to avoid sample overlap). To maximise power for MM risk, and given instrument strength for proteins was high [ 46 ], we re-ran analyses for the UKB Olink proteins using the meta-analysis of the MM GWAS. Each protein was instrumented with a single cis -SNP. As such, the Wald ratio [ 47 ], which is the ratio of the SNP-outcome association divided by the SNP-exposure association, was used to estimate the effect of the protein on MM risk [ 47 ]. For the reverse MR, where there was a single SNP the Wald ratio was implemented, and where there were 2 or more (when instrumenting MM) an inverse variance weighted multiplicative random effects (IVW-MRE) model, which combines Wald ratios together in a meta-analysis, adjusting for heterogeneity [ 48 ], was used as the primary model. The IVW-MRE model assumes that the strength of association of genetic instruments with the exposure does not correlate with the size of the pleiotropic effects and that the pleiotropic effects have an average of zero. We performed Steiger directionality tests to assess whether the direction of effect being tested (either protein-MM risk or MM risk-protein) was supported [ 49 , 50 ]. The Steiger test calculates the variance explained in the exposure and the variance explained in the outcome by the exposure-related instruments. If more variance is explained in the outcome than the exposure, this may indicate a violation of MR assumption 3, that the genetic instrument is only associated with the outcome via the exposure [ 49 ]. Proportion of variance liability was calculated for the UK Biobank MM GWAS using prevalence data for the United Kingdom and for the FinnGen MM GWAS using prevalence data for Finland. Prevalence data were obtained from the World Health Organization [ 51 ]. The 5-year prevalence of MM in the United Kingdom in 2022 was 1.4 per 100,000, and for Finland the prevalence was 1.1 per 100,000. These prevalence statistics were used to calculate a weighted prevalence for the meta-analysis of UK Biobank and FinnGen [ 52 ]. Colocalization We performed genetic colocalization analyses of all circulating proteins performed in the MR and MM risk. We extracted 125kb, 250kb, 500kb and 1Mb windows around the cis -SNP used in the MR analysis. We extracted all SNPs in these windows from the MM GWAS. We used the 1Mb window as our main analysis and used the other windows to examine sensitivities to the number of SNPs included in the colocalization analysis. Signals present in multiple windows are unlikely to be driven by window size. Colocalization was implemented using the single causal variant assumption of Giambartolomei et al. (2014) [ 53 ]. The European population of the 1000 genomes reference panel (phase 3) was used to generate LD matrices. Priors were set based on 5,000 SNPs[ 54 ]: p 1 = 10 − 6 , p 2 = 10 − 6 , and p 12 = 10 − 7 ; where: p1 is the prior probability that a random SNP in the region is associated with the protein and not MM risk, p2 is the prior probability that a random SNP in the region is associated with MM risk and not the protein, and p12 is the prior probability that a random SNP in the region is associated with the protein and MM risk. Identifying causal effects Potential causal relationships were identified if the effect estimate was consistent in direction across both forward MR analyses, the 95% CI did not overlap the null in both forward MR analyses, a true direction of effect indicated by the Steiger directionality test and there was no evidence for an effect in the reverse MR analysis. We consider evidence for a causal effect to be strongest where there is also evidence from colocalization (h4 > 0.8). Results Causal effects of circulating proteins on MM risk In our MR analyses using two independent protein GWAS to instrument protein levels, a total of 2,565 proteins had suitable genetic instruments – 994 proteins measured by SomaLogic and 1,570 measured by Olink. 440 unique proteins (based on gene name) were measured and had instruments in both protein GWAS and 36% of these were instrumented by the same SNP. MR estimates for the forward MR are presented as odds ratios for MM risk, which are calculated for a per unit increase in protein. Results for all MR analyses are presented in Supplementary Table 4 and Supplementary Table 5 . In the MR analysis using data from deCODE (SomaLogic) proteins as the exposure and the FinnGen/UKB meta-analysis of MM risk as the outcome, 53 circulating proteins had 95% CIs that did not cross the null ( Additional File 1; Supplementary Table 4; Fig. 2 ). There was evidence that higher levels of proteins such as dermatopontin (DPT) and Beta-crystallin 1 (CRYBB1) had an increasing effect on MM risk (ORs per normalised SD unit of protein 1.44 (95% CI 1.18–1.77) and 1.95 (95% CI 1.30–2.92), respectively). For all 53 proteins, Steiger directionality tests suggested the tested direction was the true causal direction ( Additional File 1; Supplementary Table 6 ). In the reverse MR, there was evidence that MM risk may impact levels of 1 of the 53 proteins: matrix metalloproteinase-9 (MMP-9; Supplementary Table 7 ). In the MR analysis using data from UK Biobank (Olink) proteins as the exposure and FinnGen myeloma GWAS as the outcome, 78 circulating proteins had MR estimates with 95% CIs that did not cross the null ( Additional File 1; Supplementary Table 5 , Fig. 3 ) and results were concordant when using the MM meta-analysis ( Additional File 1; Supplementary Table 8 ). For example, higher levels of proteins such as granulocyte-macrophage colony stimulating factor (CSF2, OR 0.53, 95% CI 0.37 to 0.78), R-spondin-3 (RSPO3, OR 0.41, 95% CI 0.24 to 0.70) and tumour necrosis factor ligand superfamily member 10 (TNFSF10, OR 0.56, 95% CI 0.39 to 0.81) decreased MM risk and higher levels of dermatopontin (DPT) increased MM risk (OR 1.46, 95% CI 1.18 to 1.80). For all 78 proteins, Steiger directionality tests suggested the tested direction was the true causal direction ( Additional File 1; Supplementary Table 9 ). In the reverse MR, 1 of the 78 proteins (Odorant-binding protein 2b, OBP2B) had evidence for an effect of MM risk on protein levels ( Additional File 1; Supplementary Table 10 ). Both SNPs used to instrument MM risk were also associated with amyloidosis (rs555992394) and had evidence for having an effect on blood cell counts including lymphocyte count and red cell distribution width (rs8141529) in the pheWAS analysis. These effects may be part of the shared causal pathway rather than pleiotropic given that amyloidosis and MM both share the precursor condition, MGUS [ 55 ], and that MM is known to have an effect on blood cell counts through myelosuppression [ 56 ]. A total of 440 aptamers/proteins were shared across both MR analyses exploring the effect of circulating proteins on MM risk, therefore resulting in two MR estimates. For 157 of these aptamers/proteins, the cis-SNP identified for each from the Olink GWAS was also the cis-SNP identified from the SomaLogic GWAS. Where proteins were included in MR analyses using instruments from both protein GWAS, effect estimates from both forward MR analyses are available in Supplementary Table 11 . Of these shared proteins, a total of 302 had consistent directions of effect (both negative or both positive beta coefficients) across MR analyses, seven of which had 95% CIs which did not overlap the null in both analyses. In the reverse MR, MM risk had little evidence for an effect on all seven of these circulating proteins (Fig. 4 ). Of these seven circulating proteins, an increase in abundance of four proteins (dermatopontin (DPT), beta-crystallin B1 (CRYBB1), interleukin-18-binding protein (IL18BP) and vascular endothelial growth factor receptor 2 (KDR)) was associated with an increase in MM risk, while an increase in the abundance of 3 proteins (odorant-binding protein 2b (OBP2B), glutamate-cysteine ligase regulatory subunit (GCLM) and gamma-crystallin D (CRYGD)) was associated with decreased MM risk. Colocalization In colocalization analyses for the seven proteins with MR evidence for a causal effect on MM risk (Fig. 4 ), evidence to support colocalization was limited across all windows (h4 < 0.8 Supplementary Table 12, Supplementary Table 13 ). There was evidence for a shared causal variant between one protein, uncharacterized family 31 glucosidase KIAA1161, and multiple myeloma (h4 = 0.84 across all colocalization windows). This protein was detected and quantified in deCODE (SomaLogic), where there was MR evidence to support a causal relationship, but it was not measured in UKB (Olink). Discussion In this study, bidirectional MR and genetic colocalization analyses were performed to identify whether circulating proteins causally impact MM risk. There was evidence using two protein GWAS that higher levels of four circulating proteins may increase MM risk and higher levels of three circulating proteins may decrease MM risk, however none of these results were supported by genetic colocalization, possibly indicative of low power or that estimates may not be robust to horizontal pleiotropy. A single protein, KIAA1161, measured only by SomaLogic, with evidence of an increasing effect on MM risk was supported by evidence from genetic colocalization. Two previous MR studies have explored the effect of circulating proteins on MM risk. The first focused on inflammatory proteins alone [ 26 ], whereas the second used GWAS data from protein levels measured by the SomaScan in a smaller sample (3,301 participants) from the INTERVAL study [ 57 ]. Four of the 13 proteins with evidence for a causal relationship with MM risk by Wang et al. were also instrumented in our analysis. Our MR evidence (also using SomaScan) only supported a causal relationship for one of these proteins and MM risk (follistatin-related protein 1, FSTL1). We did not find evidence for a causal effect for the other three proteins, this may be due to the use of trans SNPs by Wang et al. which likely included pleiotropic pathways [ 57 ]. All seven proteins with consistent evidence across our two protein datasets have limited evidence in the literature of having previously been implicated in the pathogenesis or progression to MM [ 58 ]. The strongest evidence for an effect was with dermatopontin on MM risk, where higher levels were associated with an increase in MM risk. DPT is an extracellular matrix protein and has been shown to promote adherence of whole bone marrow to ECM proteins in mice [ 59 ]. As this protein may have a role in the bone marrow microenvironment, it is possible that dysregulation of this protein could contribute to the MM pathology. The involvement of DPT in MM pathology needs to be further characterised, such as through mouse models of MM and by exploring whether DPT is dysregulated in the bone marrow in patients with MM. In the current study, MR evidence suggested that higher levels of KDR (VEGFR2) increased risk of MM, and there was no evidence for an effect in the reverse direction (MM risk on levels of VEGFR2). VEGFR2 is involved in endothelial migration and proliferation and is implicated in liver, renal and thyroid cancers, where it is now exploited as a drug target [ 60 ]. The role of VEGFR2 in the progression from healthy, through the precursors of MM (MGUS and smouldering myeloma), and to MM, should be further characterised, for instance, by generating proteomic data on patient samples. MR evidence suggested that higher levels of GCLM may result in a decrease in MM risk. GCLM is a subunit of an enzyme involved in the cellular glutathione (GSH) biosynthetic pathway, which is critical to cell survival. Treating MM cells with a proteasome inhibitor, bortezomib (an approved MM treatment), has been shown to lead to higher levels of GCLM. This is directionally consistent with the MR results, where higher levels of GCLM had a lowering effect on MM risk [ 61 ]. In addition, there was one protein with evidence of genetic colocalization: uncharacterized family 31 glucosidase KIAA1161, it is unclear how this protein might be involved in MM risk; MR analysis should be replicated using a large independent protein GWAS using SomaScan to investigate this further. Our results point towards putative causal relationships between circulating proteins and MM risk. However, there are limitations to these analyses that need to be considered and results should be interpreted with caution. Firstly, we did not adjust for multiple testing in each individual MR analyses. As we attempted to perform a discovery and replication approach, we believe that adjusting for multiple testing would be too conservative, especially given that the MM GWAS used are not highly powered. Suitable genetic instruments were also not available for all proteins, therefore some potentially important protein-MM or MM-protein effects will inevitably be missed. Additionally, perturbations in one protein do not occur in isolation. Effects of a single protein may be because of its role in one or more pathways, and therefore it is likely that there is a much more complex interaction between circulating proteins and risk of MM, rather than one or a few proteins being solely responsible for the change in risk. Currently, exploring the contribution of proteins together (as opposed to performing univariable analyses) to MM risk remains a challenge. These analyses were performed in participants only of European ancestry living in the UK, Iceland and Finland, therefore findings may not be generalisable to participants of other ancestries or in other contexts. More highly powered GWAS are required in non-European ancestries in order to evaluate the role of circulating proteins in MM risk more broadly. Another possible limitation is that there may be some misclassification, where participants who were deemed as controls could include those with undetected MGUS or smouldering myeloma, and this may lead to estimates being biased (towards or away from the null). We identified seven proteins which have consistent MR evidence across two proteomic datasets for a role in MM risk. Some of these proteins have previously been implicated in the other cancer types (VEGFR2 and GCLM), however relatively little is known about these seven proteins in relation to MM risk. Generating proteomic data from patients with myeloma (or its precursor conditions) and characterising these proteins further represent important steps in further understanding MM development. Abbreviations CRYBB1 Beta-crystallin 1 CRYGD Gamma-crystallin D CSF2 Granulocyte-macrophage colony stimulating factor 2 DPT Dermatopontin FSTL1 Follistatin-related protein 1 GCLM Glutamate-cysteine ligase regulatory subunit GWAS Genome-wide association study IL18BP Interleukin 18 binding protein IVW MRE-Inverse variance weighted multiplicative random effects Kb Kilobase KDR Vascular endothelial growth factor 2 LD Linkage disequilibrium Mb Megabase MGUS Monoclonal gammopathy of unknown significance MM Multiple myeloma MMP 9-Matrix metalloproteinase-9 MR Mendelian randomization NPX Normalised protein expression OBP2b Odorant-binding protein 2b PheWAS Phenome-wide association study RFU Relative fluorescence unit EDTA Ethylenediaminetetraacetic acid RSPO3 R-spondin-3 SNP Single nucleotide polymorphism TNFSF10 Tumour necrosis factor ligand superfamily member 10 UKB UK Biobank Declarations Where authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer/World Health Organization. Funding LJG is supported by a Cancer Research UK 25 (C18281/A29019) programme grant (the Integrative Cancer Epidemiology Programme). KB, NIHR Academic Clinical Lecturer, is funded by Health Education England (HEE)/NIHR. EH is supported by a Cancer Research UK Population Research Committee Studentship (C18281/A30905), is supported by the CRUK Integrative Cancer Epidemiology Programme (C18281/A29019) and is part of the Medical Research Council Integrative Epidemiology Unit at the University of Bristol which is supported by the Medical Research Council (MC_UU_00032/03) and the University of Bristol. Author Contribution MAL: analysis, interpretation of results, writing, study management. KLB: interpretation of results, writing. ELH: interpretation of results, writing, analysis. SM: interpretation of results, writing. SJL: interpretation of results, writing. LJG: study conceptualization, analysis, interpretation of results, writing, study management. Data Availability All scripts are archived on Zenodo (https://zenodo.org/records/12784512) and available on GitHub (https://github.com/mattlee821/protein_myeloma). Meta-analysis was performed following the METAL online documentation (https://genome.sph.umich.edu/wiki/METAL_Documentation). All analyses were performed using R version 4.1.2. MR analyses were performed using TwoSampleMR (version 0.4.22). Colocalisation was performed using coloc (version 5.2.0). Weighted prevalence was calculated using the metaprop() function from the meta package (version 6.5-0). Conflicts of interest All authors declare no conflicts of interest. References CRUK. Cancer research UK: Myeloma statistics . 2023; Available from: https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/myeloma . Atkin, C., A. Richter, and E. Sapey, What is the significance of monoclonal gammopathy of undetermined significance? Clin Med (Lond), 2018. 18(5): p. 391–396. UK, C.R., Myeloma statistics . 2023. Nakaya, A., et al., Impact of CRAB Symptoms in Survival of Patients with Symptomatic Myeloma in Novel Agent Era . Hematol Rep, 2017. 9(1): p. 6887. De Stefano, V., et al., Thrombosis in multiple myeloma: risk stratification, antithrombotic prophylaxis, and management of acute events. A consensus-based position paper from an . Haematologica, 2022. 107(11): p. 2536–2547. Blimark, C., et al., Multiple myeloma and infections: a population-based study on 9253 multiple myeloma patients . Haematologica, 2015. 100(1): p. 107–13. Terpos, E., et al., Management of patients with multiple myeloma in the era of COVID-19 pandemic: a consensus paper from the European Myeloma Network (EMN) . Leukemia, 2020. 34(8): p. 2000–2011. Rajkumar, S.V., Multiple myeloma: 2022 update on diagnosis, risk stratification, and management . Am J Hematol, 2022. 97(8): p. 1086–1107. Dores, G.M., et al., Plasmacytoma of bone, extramedullary plasmacytoma, and multiple myeloma: incidence and survival in the United States, 1992–2004 . Br J Haematol, 2009. 144(1): p. 86–94. Landgren, O., et al., Risk of plasma cell and lymphoproliferative disorders among 14621 first-degree relatives of 4458 patients with monoclonal gammopathy of undetermined significance in Sweden . Blood, 2009. 114(4): p. 791–5. Kristinsson, S.Y., et al., Patterns of hematologic malignancies and solid tumors among 37,838 first-degree relatives of 13,896 patients with multiple myeloma in Sweden . Int J Cancer, 2009. 125(9): p. 2147–50. Landgren, O. and B.M. Weiss, Patterns of monoclonal gammopathy of undetermined significance and multiple myeloma in various ethnic/racial groups: support for genetic factors in pathogenesis . Leukemia, 2009. 23(10): p. 1691–7. Blair, C.K., et al., Anthropometric characteristics and risk of multiple myeloma . Epidemiology, 2005. 16(5): p. 691–4. Renehan, A.G., et al., Body-mass index and incidence of cancer: a systematic review and meta-analysis of prospective observational studies . Lancet, 2008. 371(9612): p. 569–78. Davey Smith, G. and G. Hemani, Mendelian randomization: genetic anchors for causal inference in epidemiological studies . Hum Mol Genet, 2014. 23(R1): p. R89-98. Davies, N.M., M.V. Holmes, and G. Davey Smith, Reading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians . BMJ, 2018. 362: p. k601. Smith, G.D. and S. Ebrahim, 'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease? Int J Epidemiol, 2003. 32(1): p. 1–22. Bowden, J. and M.V. Holmes, Meta-analysis and Mendelian randomization: A review . Res Synth Methods, 2019. 10(4): p. 486–496. Lawlor, D.A., Commentary: Two-sample Mendelian randomization: opportunities and challenges . Int J Epidemiol, 2016. 45(3): p. 908–15. Went, M., et al., Search for multiple myeloma risk factors using Mendelian randomization . Blood Adv, 2020. 4(10): p. 2172–2179. Landegren, U. and M. Hammond, Cancer diagnostics based on plasma protein biomarkers: hard times but great expectations . Mol Oncol, 2021. 15(6): p. 1715–1726. SomaLogic SomaScan® v4 Data Standardization and File Specification Technical Note [White paper] . 2022. Wik, L., et al., Proximity Extension Assay in Combination with Next-Generation Sequencing for High-throughput Proteome-wide Analysis . Mol Cell Proteomics, 2021. 20: p. 100168. Carrasco-Zanini, J. et al., Proteomic prediction of common and rare diseases . 2023: MedRXiv. Imming, P., C. Sinning, and A. Meyer, Drugs, their targets and the nature and number of drug targets . Nat Rev Drug Discov, 2006. 5(10): p. 821–34. Wang, Q., et al., Causal relationships between inflammatory factors and multiple myeloma: A bidirectional Mendelian randomization study . Int J Cancer, 2022. 151(10): p. 1750–1759. Zuber, V., et al., Combining evidence from Mendelian randomization and colocalization: Review and comparison of approaches . Am J Hum Genet, 2022. 109(5): p. 767–782. Burgess, S., N.M. Davies, and S.G. Thompson, Bias due to participant overlap in two-sample Mendelian randomization . Genet Epidemiol, 2016. 40(7): p. 597–608. Hemani, G., et al., MR-Base: a platform for systematic causal inference across the phenome using billions of genetic associations . bioRxiv, 2016. Wallace, C., Eliciting priors and relaxing the single causal variant assumption in colocalisation analyses . PLoS Genet, 2020. 16(4): p. e1008720. Ferkingstad, E., et al., Large-scale integration of the plasma proteome with genetics and disease . Nat Genet, 2021. 53(12): p. 1712–1721. Bycroft, C., et al., The UK Biobank resource with deep phenotyping and genomic data . Nature, 2018. 562(7726): p. 203–209. Loh, P.R., et al., Efficient Bayesian mixed-model analysis increases association power in large cohorts . Nat Genet, 2015. 47(3): p. 284–90. Gudbjartsson, D.F., et al., Large-scale whole-genome sequencing of the Icelandic population . Nat Genet, 2015. 47(5): p. 435–44. Bulik-Sullivan, B.K., et al., LD Score regression distinguishes confounding from polygenicity in genome-wide association studies . Nat Genet, 2015. 47(3): p. 291–5. Sun, B.B., et al., Plasma proteomic associations with genetics and health in the UK Biobank . Nature, 2023. 622(7982): p. 329–338. Lundberg, M., et al., Homogeneous antibody-based proximity extension assays provide sensitive and specific detection of low-abundant proteins in human blood . Nucleic Acids Res, 2011. 39(15): p. e102. Jiang, L., et al., A generalized linear mixed model association tool for biobank-scale data . Nat Genet, 2021. 53(11): p. 1616–1621. WHO, International Statistical Classification of Diseases and Related Health Problems 10th revision (ICD-10) . 2016. Wu, P., et al., Mapping ICD-10 and ICD-10-CM Codes to Phecodes: Workflow Development and Initial Evaluation . JMIR Med Inform, 2019. 7(4): p. e14325. Kurki, M.I., et al., FinnGen provides genetic insights from a well-phenotyped isolated population . Nature, 2023. 613(7944): p. 508–518. Willer, C.J., Y. Li, and G.R. Abecasis, METAL: fast and efficient meta-analysis of genomewide association scans . Bioinformatics, 2010. 26(17): p. 2190–1. Center for Statistical Genetics METAL Documentation . 2017 [cited 2024 14th June]; Available from: https://genome.sph.umich.edu/wiki/METAL_Documentation . Hemani, G., et al., The MR-Base platform supports systematic causal inference across the human phenome . Elife, 2018. 7. Haycock, P.C., et al., Best (but oft-forgotten) practices: the design, analysis, and interpretation of Mendelian randomization studies . Am J Clin Nutr, 2016. 103(4): p. 965–78. Sadreev, I.I., et al., Navigating sample overlap, winner’s curse and weak instrument bias in Mendelian randomization studies using the UK Biobank. medRxiv, 2021: p. 2021.06.28.21259622. Burgess, S., D.S. Small, and S.G. Thompson, A review of instrumental variable estimators for Mendelian randomization . Stat Methods Med Res, 2017. 26(5): p. 2333–2355. Bowden, J., et al., A framework for the investigation of pleiotropy in two-sample summary data Mendelian randomization . Stat Med, 2017. 36(11): p. 1783–1802. Hemani, G., K. Tilling, and G. Davey Smith, Orienting the causal relationship between imprecisely measured traits using GWAS summary data . PLoS Genet, 2017. 13(11): p. e1007081. Hemani, G., et al., Automating Mendelian randomization through machine learning to construct a putative causal map of the human phenome. bioRxiv, 2017: p. 173682. World Health Organization Population Factsheets . [cited 2023 December 1st]; Available from: https://gco.iarc.fr/today/en/fact-sheets-populations#countries . Balduzzi, S., G. Rücker, and G. Schwarzer, How to perform a meta-analysis with R: a practical tutorial . Evid Based Ment Health, 2019. 22(4): p. 153–160. Giambartolomei, C., et al., Bayesian test for colocalisation between pairs of genetic association studies using summary statistics . PLoS Genet, 2014. 10(5): p. e1004383. Wallace, C. Prior Explorer For Coloc . 2023 [cited 2023 30th August]; Available from: https://chr1swallace.shinyapps.io/coloc-priors/ Saunders, C.N., et al., Search for AL amyloidosis risk factors using Mendelian randomization . Blood Adv, 2021. 5(13): p. 2725–2731. Bogun, L., et al., Stromal alterations in patients with monoclonal gammopathy of undetermined significance, smoldering myeloma, and multiple myeloma . Blood Adv, 2024. 8(10): p. 2575–2588. Wang, Q., et al., Integrating plasma proteomes with genome-wide association data for causal protein identification in multiple myeloma . BMC Med, 2023. 21(1): p. 377. Falchetti, M., et al., Omics-based identification of an NRF2-related auranofin resistance signature in cancer: Insights into drug repurposing . Comput Biol Med, 2023. 152: p. 106347. Kramer, A.C., et al., Dermatopontin in Bone Marrow Extracellular Matrix Regulates Adherence but Is Dispensable for Murine Hematopoietic Cell Maintenance . Stem Cell Reports, 2017. 9(3): p. 770–778. Wishart, D.S., et al., DrugBank: a comprehensive resource for in silico drug discovery and exploration . Nucleic Acids Res, 2006. 34(Database issue): p. D668-72. Nerini-Molteni, S., et al., Redox homeostasis modulates the sensitivity of myeloma cells to bortezomib . Br J Haematol, 2008. 141(4): p. 494–503. Lee, M.A. Exploring the role of circulating proteins in multiple myeloma risk: a Mendelian randomization study - Zenodo archived scripts 2024 [cited 2024 22nd July]; Available from: https://zenodo.org/records/12784512 . Lee, M.A. Exploring the role of circulating proteins in multiple myeloma risk: a Mendelian randomization study - scripts on GitHub . 2024 [cited 2024 22nd July]; Available from: https://github.com/mattlee821/protein_myeloma . Additional Declarations No competing interests reported. Supplementary Files SupplementaryMaterial.xlsx Cite Share Download PDF Status: Published Journal Publication published 30 Jan, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Revision requested 10 Dec, 2024 Reviews received at journal 23 Nov, 2024 Reviewers agreed at journal 15 Nov, 2024 Reviews received at journal 27 Oct, 2024 Reviewers agreed at journal 24 Oct, 2024 Reviews received at journal 03 Oct, 2024 Reviewers agreed at journal 27 Sep, 2024 Reviewers agreed at journal 25 Sep, 2024 Reviewers agreed at journal 25 Sep, 2024 Reviewers invited by journal 25 Sep, 2024 Editor assigned by journal 29 Aug, 2024 Editor invited by journal 06 Aug, 2024 Submission checks completed at journal 01 Aug, 2024 First submitted to journal 25 Jul, 2024 You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-4800219","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":335009168,"identity":"20ec9ce7-13ae-4e76-a233-f15b7d5930bb","order_by":0,"name":"Matthew A. 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Goudswaard","email":"data:image/png;base64,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","orcid":"","institution":"University of Bristol","correspondingAuthor":true,"prefix":"","firstName":"Lucy","middleName":"J.","lastName":"Goudswaard","suffix":""}],"badges":[],"createdAt":"2024-07-25 08:26:55","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-4800219/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-4800219/v1","draftVersion":[],"editorialEvents":[{"content":"https://doi.org/10.1038/s41598-025-86222-5","type":"published","date":"2025-01-30T15:56:53+00:00"}],"editorialNote":"","failedWorkflow":false,"files":[{"id":61666393,"identity":"d74b8333-14b7-42b7-b2c8-d4d77f654145","added_by":"auto","created_at":"2024-08-02 16:13:48","extension":"jpeg","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":282442,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eDirected acyclic graph of bidirectional Mendelian randomisation analyses. \u003c/strong\u003e(A) The effect of circulating proteins on multiple myeloma risk. (B) The effect of multiple myeloma risk on circulating proteins. The Mendelian randomization assumptions are given as 1-3: (1) the genetic variant is robustly associated with the exposure; (2) there are no confounders of the genetic variant and outcome association; (3) the genetic variant is associated with the outcome only via its association with the exposure. SNPs: single nucleotide polymorphism; pQTL: protein quantitative trait loci (SNPs associated with the abundance of a protein).\u003c/p\u003e","description":"","filename":"floatimage1.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4800219/v1/f7ee510b7631a20ae1863520.jpeg"},{"id":61665859,"identity":"974e37d2-b263-4b85-b7f9-e77c0bb84b97","added_by":"auto","created_at":"2024-08-02 16:05:48","extension":"jpeg","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":507217,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEstimates of the effect of circulating proteins (SomaLogic) on risk of multiple myeloma. \u003c/strong\u003eMendelian randomization analysis performed with protein genome-wide association study (GWAS) data from deCODE (SomaLogic) and outcome multiple myeloma data from meta-analysis of GWAS from UK Biobank and FinnGen. Proteins (Y axis) are represented by gene names (\u003cstrong\u003eAdditional File 1; Supplementary Table 4\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"floatimage2.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4800219/v1/237e8edcee641707a8ee2d0c.jpeg"},{"id":61665849,"identity":"4921e292-704a-4eb2-8b9e-e930f2beff34","added_by":"auto","created_at":"2024-08-02 16:05:47","extension":"jpeg","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":536815,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEstimates of the effect of Olink circulating proteins on risk of multiple myeloma. \u003c/strong\u003eMendelian randomization analysis using exposure protein genome-wide association study (GWAS) from UK Biobank (Olink) and outcome multiple myeloma GWAS data from FinnGen. Proteins (Y axis) are represented by gene names (\u003cstrong\u003eAdditional File 1; Supplementary Table 5\u003c/strong\u003e).\u003c/p\u003e","description":"","filename":"floatimage3.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4800219/v1/c9595bfe6816199197525ccf.jpeg"},{"id":61665858,"identity":"b44beaa5-3264-43bc-92f4-23d2862797e1","added_by":"auto","created_at":"2024-08-02 16:05:48","extension":"jpeg","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":359759,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eEffect of circulating proteins on multiple myeloma risk: consistent effects in Mendelian randomization analyses. \u003c/strong\u003eResults are given for 7 proteins with consistent directions of effect, 95% confidence intervals (CIs) that do not cross the null, and no evidence of reverse effect across both MR analyses.\u003cstrong\u003e \u003c/strong\u003eProteins (Y axis) are represented by gene names.\u003c/p\u003e","description":"","filename":"floatimage4.jpeg","url":"https://assets-eu.researchsquare.com/files/rs-4800219/v1/25be3b63de59f541d9fa47a8.jpeg"},{"id":75351198,"identity":"c4fa1df1-9aff-4963-b17e-d0caf8f0015e","added_by":"auto","created_at":"2025-02-03 16:07:44","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":2683964,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-4800219/v1/4029df52-43c0-4d6a-9a26-6cb27c520c31.pdf"},{"id":61665851,"identity":"b770edec-b523-4bb7-b40f-bed014e100c1","added_by":"auto","created_at":"2024-08-02 16:05:47","extension":"xlsx","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":3882933,"visible":true,"origin":"","legend":"","description":"","filename":"SupplementaryMaterial.xlsx","url":"https://assets-eu.researchsquare.com/files/rs-4800219/v1/f3da9cc78249ae7e587f1785.xlsx"}],"financialInterests":"No competing interests reported.","formattedTitle":"Exploring the role of circulating proteins in multiple myeloma risk: a Mendelian randomization study","fulltext":[{"header":"Introduction","content":"\u003cp\u003eMultiple myeloma (MM) is the second most common haematological malignancy in the UK, with ~\u0026thinsp;5,951 new cases each year [\u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e1\u003c/span\u003e]. It is characterised by neoplastic proliferation of plasma cells in the bone marrow, resulting in overproduction of monoclonal immunoglobulins commonly referred to as paraprotein or M-protein. Nearly all cases of MM are preceded by the benign asymptomatic precursor condition, monoclonal gammopathy of unknown significance (MGUS) [\u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2\u003c/span\u003e, \u003cspan citationid=\"CR3\" class=\"CitationRef\"\u003e3\u003c/span\u003e]. The diagnosis of active MM is based on the presence of M-protein in serum or urine, along with evidence of end-organ damage including hypercalcemia, renal insufficiency, anaemia, and bone lesions [\u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e4\u003c/span\u003e]. Patients with MM are at an increased risk of developing blood clots and are susceptible to recurrent and severe infections [\u003cspan additionalcitationids=\"CR6\" citationid=\"CR5\" class=\"CitationRef\"\u003e5\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR7\" class=\"CitationRef\"\u003e7\u003c/span\u003e]. Periods of disease remission can be induced using chemotherapy regimens including proteosome inhibitors (e.g. bortezomib), immunomodulatory drugs (e.g. lenalidomide), and monoclonal antibodies (e.g. daratumumab), plus autologous stem cell transplant for a subset of eligible patients [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e] However, MM is currently incurable with a median overall survival of 6 years [\u003cspan citationid=\"CR8\" class=\"CitationRef\"\u003e8\u003c/span\u003e]. The pathogenesis of MM is not fully understood, potentially limiting the identification of curative therapeutic strategies.\u003c/p\u003e \u003cp\u003eObservational studies have reported multiple risk factors associated with the development of MM, including age, sex, ancestry, family history, and adiposity [\u003cspan additionalcitationids=\"CR10 CR11 CR12 CR13\" citationid=\"CR9\" class=\"CitationRef\"\u003e9\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e14\u003c/span\u003e]. However, observational studies are often limited by reverse causation and confounding [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e]. Mendelian randomization (MR) is an approach which uses genetic variants (alleles randomly assigned during gametogenesis; typically, single nucleotide polymorphisms (SNPs)) to estimate the causal effect of an exposure on an outcome [\u003cspan additionalcitationids=\"CR17\" citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR18\" class=\"CitationRef\"\u003e18\u003c/span\u003e]. When core assumptions are satisfied, MR is robust to biases observed in conventional observational analyses (e.g., reverse causation and confounding)[\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e]. Previous studies have used MR to investigate evidence for a causal role of known MM risk factors (such as obesity) in disease development and identified novel factors such as increased telomere length to be implicated in MM risk [\u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e20\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAlterations in the abundance of specific circulating proteins has been used previously in cancer diagnosis and risk stratification [\u003cspan citationid=\"CR21\" class=\"CitationRef\"\u003e21\u003c/span\u003e] and can also highlight mechanistic pathways [\u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e22\u003c/span\u003e, \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e23\u003c/span\u003e]. There is evidence that the inclusion of proteomics in clinical prediction tools improves MM risk prediction in comparison to using clinical risk factors alone [\u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e24\u003c/span\u003e]. Furthermore, as proteins are the main target for small molecule drugs and biologics [\u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e25\u003c/span\u003e], exploring the role of circulating proteins in MM risk using MR may identify targets for intervention. A recent MR study identified 13 potentially causal proteins [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e] but was limited in scale, may suffer bias due to horizontal pleiotropy through the inclusion of \u003cem\u003etrans\u003c/em\u003e genetic instruments (variants not in or near the gene coding region), and did not use an independent sample to replicate results [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e]. We set out to systematically explore the relationship between circulating proteins and MM risk using two-sample MR with robust instrument selection [\u003cspan citationid=\"CR15\" class=\"CitationRef\"\u003e15\u003c/span\u003e, \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e19\u003c/span\u003e] and corroborate our findings using genetic colocalization.\u003c/p\u003e"},{"header":"Methods","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003eOverview\u003c/h2\u003e \u003cp\u003eUsing data for circulating proteins from two genome-wide association studies (GWAS), we performed bidirectional two sample MR analyses to explore the causal role of circulating proteins in MM risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e). We performed a forward MR to estimate the effect of circulating proteins on MM risk and a reverse MR to estimate the effect of MM risk and circulating proteins. The latter was performed to evaluate evidence for reverse causality, where MM may influence protein levels. We performed genetic colocalization to investigate shared causal signals between circulating proteins and MM risk, which may strengthen evidence for causal effects identified in the MR analyses or highlight non-causal markers of MM risk that warrant further investigation [\u003cspan citationid=\"CR27\" class=\"CitationRef\"\u003e27\u003c/span\u003e]. Data for circulating proteins were obtained from GWAS performed in two independent studies of European ancestry: deCODE (SomaLogic) and UK Biobank (Olink). Data for MM risk were obtained from GWAS performed in two independent studies (UK Biobank and FinnGen) which we meta-analysed. Given overlap of UK Biobank data (which is a source of bias in MR [\u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e28\u003c/span\u003e]), we used deCODE proteins with the meta-analysis of MM risk and UK Biobank proteins with the FinnGen GWAS [\u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e29\u003c/span\u003e, \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e30\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003eCirculating protein GWAS\u003c/h2\u003e \u003cp\u003eGWAS data for up-to 4,907 aptamers (4,719 unique proteins) were obtained from Ferkingstad et al. [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. Protein concentrations were measured from ethylenediaminetetraacetic acid (EDTA) plasma samples from 35,559 Icelandic individuals (deCODE) using SomaScan\u0026reg; (SomaLogic). Briefly, a large proportion of the Icelandic population enrolled in a nationwide programme administered by deCODE genetics; 49,708 enrolled individuals underwent whole-genome sequencing while 166,281 additional individuals were genotyped with imputation based on the whole-genome sequencing data [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e]. The SomaScan platform uses Slow Off-rate Modified Aptamers which make direct contact with proteins, enabling their detection and quantification in relative fluorescence units (RFUs) using a DNA microarray. Multiple aptamers can bind to a single protein (e.g., because of splice-isoforms). UK Biobank is a population-based cohort of ~\u0026thinsp;500,000 individuals aged 40\u0026ndash;69 recruited between 2006\u0026ndash;2010 in the United Kingdom. Genotyping and imputation have been described previously [\u003cspan citationid=\"CR32\" class=\"CitationRef\"\u003e32\u003c/span\u003e]. Briefly, the genotyping and imputation involved a two-step imputation process performed first using the Haplotype Reference Consortium and then performed using a merged UK10K and 1000 Genomes Phase 3 reference panel, these two imputations were combined and the HRC imputed variant was kept in instances of duplication. Prior to genome-wide analysis, protein values were inverse rank normal transformed and adjusted for age, sex and sample age. These residuals were standardised again using an inverse rank normal transformation and a linear mixed-model (LMM) GWAS was performed using BOLT-LMM [\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e31\u003c/span\u003e, \u003cspan additionalcitationids=\"CR34\" citationid=\"CR33\" class=\"CitationRef\"\u003e33\u003c/span\u003e\u0026ndash;\u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e35\u003c/span\u003e]. Assuming the distribution of protein concentration was normal prior to inverse rank normal transformation, we interpret these units to be approximately equivalent to a normalized standard deviation (SD).\u003c/p\u003e \u003cp\u003eGenome-wide summary level data were also obtained for up to 2,923 proteins from Sun et al. [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e]. Protein concentrations were measured from EDTA plasma samples from 34,557 participants of European ancestry from UK Biobank using the Olink Explore 3072 panel. Briefly, Olink Explore 3072 uses a proximity extension assay (PEA) which uses matched pairs of antibodies with DNA tags that, once bound to their target protein, hybridise and can be amplified and quantified using polymerase chain reaction. Proteins are measured in normalised protein expression (NPX) units which are on a log2 scale [\u003cspan citationid=\"CR37\" class=\"CitationRef\"\u003e37\u003c/span\u003e]. Prior to genome-wide analysis, protein values were inverse rank normal transformed and a whole-genome regression model using a leave one chromosome out scheme was performed with REGENIE (version 2.2.1) [\u003cspan citationid=\"CR36\" class=\"CitationRef\"\u003e36\u003c/span\u003e] adjusting for age, age\u003csup\u003e2\u003c/sup\u003e, sex, age x sex, age\u003csup\u003e2\u003c/sup\u003e x sex, batch, centre, genetic array, time between blood sampling measurement and the first 20 principal components. Assuming the distribution of protein levels was normal prior to inverse rank normal transformation, we interpret these units to be approximately equivalent to a normalized SD.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003eGenetic instruments for circulating protein levels\u003c/h2\u003e \u003cp\u003eIn MR analyses of circulating proteins and MM risk we used \u003cem\u003ecis\u003c/em\u003e-SNPs to instrument proteins. \u003cem\u003eCis\u003c/em\u003e-SNPs were obtained from the supplementary data of the original study. Briefly, a 1 mega base (1,000,000 bases; Mb) region was defined around each SNP reaching the genome-wide significance threshold specified in each GWAS (p-value\u0026thinsp;\u0026lt;\u0026thinsp;1.8 x 10\u003csup\u003e\u0026minus;\u0026thinsp;9\u003c/sup\u003e in deCODE; p-value\u0026thinsp;\u0026lt;\u0026thinsp;1.7x10\u003csup\u003e\u0026minus;\u0026thinsp;11\u003c/sup\u003e in UKB) which were \u0026le;\u0026thinsp;1 Mb from the transcription start site of the protein coding gene (discovery) or \u0026le;\u0026thinsp;1 Mb from the gene encoding the measured protein (replication). Starting with the SNP with the lowest p-value, any overlapping regions were merged until no overlapping regions remained (major histocompatibility complex was treated as a single region). Linkage disequilibrium (LD) based clumping was used to identify whether regions were associated with multiple proteins; regional SNPs with high LD (r\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;\u0026ge;\u0026thinsp;0.8) were merged into a single region and the SNP with the lowest p-value was considered the sentinel SNP. In total, 1,192 of 4,907 aptamers (deCODE) and 1,860 of 2,923 proteins (UKB) had available \u003cem\u003ecis\u003c/em\u003e-SNPs.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003eMultiple myeloma genome-wide association studies\u003c/h2\u003e \u003cp\u003eFor our MR analyses using deCODE SomaLogic protein GWAS as the exposure, genome-wide summary level data for MM risk were obtained from a meta-analysis of 1,649 cases and 727,247 controls from two GWAS conducted in UK Biobank and FinnGen. In UK Biobank [\u003cspan citationid=\"CR38\" class=\"CitationRef\"\u003e38\u003c/span\u003e] (cases\u0026thinsp;=\u0026thinsp;564; controls\u0026thinsp;=\u0026thinsp;455,784), MM was recorded according to the International Classification of Diseases 10th revision (ICD 10) [\u003cspan citationid=\"CR39\" class=\"CitationRef\"\u003e39\u003c/span\u003e] following mapping to Phecode v.1.2 [\u003cspan citationid=\"CR40\" class=\"CitationRef\"\u003e40\u003c/span\u003e], controls were defined as any individual who had not been diagnosed with MM (those who did not have the MM ICD 10 code), and models were adjusted for age, age\u003csup\u003e2\u003c/sup\u003e, sex, age \u0026times; sex, age\u003csup\u003e2\u003c/sup\u003e \u0026times; sex and the top 20 PCs provided by UK Biobank. In FinnGen [\u003cspan citationid=\"CR41\" class=\"CitationRef\"\u003e41\u003c/span\u003e] (cases\u0026thinsp;=\u0026thinsp;1,085; controls\u0026thinsp;=\u0026thinsp;271,463), MM was recorded according to the International Classification of Diseases (ICD-O-3) following linkage with the Finnish Cancer Registers, controls were defined as any individual without any cancer diagnosis, and models were adjusted for sex, age, top 10 PCs, and genotyping batch. Meta-analysis was performed using METAL (version 2011-03-25) and results were filtered to remove SNPs with a heterogeneity P-value\u0026thinsp;\u0026le;\u0026thinsp;0.05 between the two GWAS [\u003cspan citationid=\"CR42\" class=\"CitationRef\"\u003e42\u003c/span\u003e]. The METAL software was used to combine test statistics and standard errors and control for population stratification as recommended in the METAL documentation [\u003cspan citationid=\"CR43\" class=\"CitationRef\"\u003e43\u003c/span\u003e]. Estimates for each SNP indicates the difference in disease risk for each copy of the effect allele.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003eInstruments for multiple myeloma risk\u003c/h2\u003e \u003cp\u003eWe used all SNPs which met the following requirement to instrument MM risk: a genome-wide significance threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;8\u003c/sup\u003e and an LD R\u003csup\u003e2\u003c/sup\u003e threshold of 0.001 within a 10 kilo-base (kb) window to identify robust and independently associated SNPs. In the meta-analysis of MM GWAS, 1 SNP met the genome-wide significance threshold and was used to instrument MM risk. In the FinnGen MM GWAS no SNPs met the genome-wide significance threshold; instead, a lower threshold of p\u0026thinsp;\u0026lt;\u0026thinsp;5 x 10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e was used and 3 SNPs were identified. Of these, 2 SNPs (rs555992394 and rs8141529) were available in the UKB proteomic GWAS data. In relaxing the p-value threshold for the MM GWAS in FinnGen, we may invalidate a core assumption of MR that the instrument is robustly associated with the exposure. As such, we caution that these analyses were employed to evaluate possible conflicting evidence to the forward MR and should not be interpreted as causal estimates of MM liability on protein levels. We also explored possible pleiotropic effects that may arise from this relaxation via searching the SNP rsIDs in the IEU Open GWAS Project [\u003cspan citationid=\"CR44\" class=\"CitationRef\"\u003e44\u003c/span\u003e], essentially performing a phenome-wide association study (PheWAS).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003eStatistical analysis\u003c/h2\u003e \u003cdiv id=\"Sec9\" class=\"Section3\"\u003e \u003ch2\u003eMendelian randomization analysis\u003c/h2\u003e \u003cp\u003eMR relies upon three core assumptions (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003e): (1) the genetic variant is associated with the exposure, (2) there are no confounders of the genetic variant and outcome association (such as population structure), and (3) the genetic variant is associated with the outcome only via its association with the exposure (i.e., not via alternate pathways) [\u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e16\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eFor all exposures, the following summary-level data were obtained from the original GWAS: rsID, effect allele, other allele, effect allele frequency (EAF), effect estimate, standard error of the effect estimate, p-value of the effect estimate, and where available sample size for each SNP. Where individual SNP sample size was not available the overall sample size was used. Genetic variants were extracted from each outcome GWAS and, where these were not available, proxy SNPs were included if LD was \u0026ge;\u0026thinsp;0.8. For all SNPs, the inclusion of SNPs where the reference strand was ambiguous was allowed and the reference strand was inferred using minor allele frequency (where minor allele frequency was not \u0026ge;\u0026thinsp;0.3, in which case the proxy SNP was excluded). Data were harmonized such that the exposure effect allele was on the increasing scale. As such, MR estimates for the effect of circulating proteins on MM risk are given as the per effect allele normalised SD unit increase in protein concentration whereas estimates for the effect of MM risk on circulating protein levels are given as the normalised SD unit difference in protein per effect allele increase in disease risk. We used F-statistics to assess instrument strength, with an F-statistic\u0026thinsp;\u0026gt;\u0026thinsp;10 indicating a strong instrument [\u003cspan citationid=\"CR45\" class=\"CitationRef\"\u003e45\u003c/span\u003e]. F-statistics were calculated as: F\u0026thinsp;=\u0026thinsp;R\u003csup\u003e2\u003c/sup\u003e x (N-1-k)/((1-R\u003csup\u003e2\u003c/sup\u003e) x k), where k is the number of SNPs in the instrument and N is sample size of the SNP-exposure GWAS. R\u003csup\u003e2\u003c/sup\u003e was calculated as: R\u003csup\u003e2\u003c/sup\u003e= (2(b2) x EAFx (1-EAF))/((2(2) x EAFx (1-EAF)) + ((SE2) x (2 x N) x EAFx (1-EAF))), where b is the SNP-exposure association, EAF is the effect allele frequency of the SNP, SE is the standard error of the SNP-exposure association, and N is sample size of the SNP-exposure GWAS. All exposure data are given in \u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e and \u003cb\u003eSupplementary Table\u0026nbsp;2\u003c/b\u003e for proteins and \u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e for MM.\u003c/p\u003e \u003cp\u003eFor the forward MR analysis for circulating proteins and MM risk, two protein GWAS were used. Where proteins were measured by SomaLogic in deCODE, the MM GWAS used was the meta-analysis of FinnGen and UKB. Where proteins were measured by Olink in UKB, the MM GWAS was in FinnGen alone (to avoid sample overlap). To maximise power for MM risk, and given instrument strength for proteins was high [\u003cspan citationid=\"CR46\" class=\"CitationRef\"\u003e46\u003c/span\u003e], we re-ran analyses for the UKB Olink proteins using the meta-analysis of the MM GWAS. Each protein was instrumented with a single \u003cem\u003ecis\u003c/em\u003e-SNP. As such, the Wald ratio [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e], which is the ratio of the SNP-outcome association divided by the SNP-exposure association, was used to estimate the effect of the protein on MM risk [\u003cspan citationid=\"CR47\" class=\"CitationRef\"\u003e47\u003c/span\u003e]. For the reverse MR, where there was a single SNP the Wald ratio was implemented, and where there were 2 or more (when instrumenting MM) an inverse variance weighted multiplicative random effects (IVW-MRE) model, which combines Wald ratios together in a meta-analysis, adjusting for heterogeneity [\u003cspan citationid=\"CR48\" class=\"CitationRef\"\u003e48\u003c/span\u003e], was used as the primary model. The IVW-MRE model assumes that the strength of association of genetic instruments with the exposure does not correlate with the size of the pleiotropic effects and that the pleiotropic effects have an average of zero.\u003c/p\u003e \u003cp\u003eWe performed Steiger directionality tests to assess whether the direction of effect being tested (either protein-MM risk or MM risk-protein) was supported [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e, \u003cspan citationid=\"CR50\" class=\"CitationRef\"\u003e50\u003c/span\u003e]. The Steiger test calculates the variance explained in the exposure and the variance explained in the outcome by the exposure-related instruments. If more variance is explained in the outcome than the exposure, this may indicate a violation of MR assumption 3, that the genetic instrument is only associated with the outcome via the exposure [\u003cspan citationid=\"CR49\" class=\"CitationRef\"\u003e49\u003c/span\u003e]. Proportion of variance liability was calculated for the UK Biobank MM GWAS using prevalence data for the United Kingdom and for the FinnGen MM GWAS using prevalence data for Finland. Prevalence data were obtained from the World Health Organization [\u003cspan citationid=\"CR51\" class=\"CitationRef\"\u003e51\u003c/span\u003e]. The 5-year prevalence of MM in the United Kingdom in 2022 was 1.4 per 100,000, and for Finland the prevalence was 1.1 per 100,000. These prevalence statistics were used to calculate a weighted prevalence for the meta-analysis of UK Biobank and FinnGen [\u003cspan citationid=\"CR52\" class=\"CitationRef\"\u003e52\u003c/span\u003e].\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003eColocalization\u003c/h2\u003e \u003cp\u003eWe performed genetic colocalization analyses of all circulating proteins performed in the MR and MM risk. We extracted 125kb, 250kb, 500kb and 1Mb windows around the \u003cem\u003ecis\u003c/em\u003e-SNP used in the MR analysis. We extracted all SNPs in these windows from the MM GWAS. We used the 1Mb window as our main analysis and used the other windows to examine sensitivities to the number of SNPs included in the colocalization analysis. Signals present in multiple windows are unlikely to be driven by window size. Colocalization was implemented using the single causal variant assumption of Giambartolomei et al. (2014) [\u003cspan citationid=\"CR53\" class=\"CitationRef\"\u003e53\u003c/span\u003e]. The European population of the 1000 genomes reference panel (phase 3) was used to generate LD matrices. Priors were set based on 5,000 SNPs[\u003cspan citationid=\"CR54\" class=\"CitationRef\"\u003e54\u003c/span\u003e]: p\u003csup\u003e1\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, p\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;6\u003c/sup\u003e, and p\u003csup\u003e12\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;10\u003csup\u003e\u0026minus;\u0026thinsp;7\u003c/sup\u003e; where: p1 is the prior probability that a random SNP in the region is associated with the protein and not MM risk, p2 is the prior probability that a random SNP in the region is associated with MM risk and not the protein, and p12 is the prior probability that a random SNP in the region is associated with the protein and MM risk.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003eIdentifying causal effects\u003c/h2\u003e \u003cp\u003ePotential causal relationships were identified if the effect estimate was consistent in direction across both forward MR analyses, the 95% CI did not overlap the null in both forward MR analyses, a true direction of effect indicated by the Steiger directionality test and there was no evidence for an effect in the reverse MR analysis. We consider evidence for a causal effect to be strongest where there is also evidence from colocalization (h4\u0026thinsp;\u0026gt;\u0026thinsp;0.8).\u003c/p\u003e \u003c/div\u003e"},{"header":"Results","content":"\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003eCausal effects of circulating proteins on MM risk\u003c/h2\u003e \u003cp\u003eIn our MR analyses using two independent protein GWAS to instrument protein levels, a total of 2,565 proteins had suitable genetic instruments \u0026ndash; 994 proteins measured by SomaLogic and 1,570 measured by Olink. 440 unique proteins (based on gene name) were measured and had instruments in both protein GWAS and 36% of these were instrumented by the same SNP. MR estimates for the forward MR are presented as odds ratios for MM risk, which are calculated for a per unit increase in protein. Results for all MR analyses are presented in \u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e and \u003cb\u003eSupplementary Table\u0026nbsp;5\u003c/b\u003e.\u003c/p\u003e \u003cp\u003eIn the MR analysis using data from deCODE (SomaLogic) proteins as the exposure and the FinnGen/UKB meta-analysis of MM risk as the outcome, 53 circulating proteins had 95% CIs that did not cross the null (\u003cb\u003eAdditional File 1; Supplementary Table\u0026nbsp;4;\u003c/b\u003e Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e). There was evidence that higher levels of proteins such as dermatopontin (DPT) and Beta-crystallin 1 (CRYBB1) had an increasing effect on MM risk (ORs per normalised SD unit of protein 1.44 (95% CI 1.18\u0026ndash;1.77) and 1.95 (95% CI 1.30\u0026ndash;2.92), respectively). For all 53 proteins, Steiger directionality tests suggested the tested direction was the true causal direction (\u003cb\u003eAdditional File 1; Supplementary Table\u0026nbsp;6\u003c/b\u003e). In the reverse MR, there was evidence that MM risk may impact levels of 1 of the 53 proteins: matrix metalloproteinase-9 (MMP-9; \u003cb\u003eSupplementary Table\u0026nbsp;7\u003c/b\u003e).\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eIn the MR analysis using data from UK Biobank (Olink) proteins as the exposure and FinnGen myeloma GWAS as the outcome, 78 circulating proteins had MR estimates with 95% CIs that did not cross the null (\u003cb\u003eAdditional File 1; Supplementary Table\u0026nbsp;5\u003c/b\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e) and results were concordant when using the MM meta-analysis (\u003cb\u003eAdditional File 1; Supplementary Table\u0026nbsp;8\u003c/b\u003e). For example, higher levels of proteins such as granulocyte-macrophage colony stimulating factor (CSF2, OR 0.53, 95% CI 0.37 to 0.78), R-spondin-3 (RSPO3, OR 0.41, 95% CI 0.24 to 0.70) and tumour necrosis factor ligand superfamily member 10 (TNFSF10, OR 0.56, 95% CI 0.39 to 0.81) decreased MM risk and higher levels of dermatopontin (DPT) increased MM risk (OR 1.46, 95% CI 1.18 to 1.80). For all 78 proteins, Steiger directionality tests suggested the tested direction was the true causal direction (\u003cb\u003eAdditional File 1; Supplementary Table\u0026nbsp;9\u003c/b\u003e). In the reverse MR, 1 of the 78 proteins (Odorant-binding protein 2b, OBP2B) had evidence for an effect of MM risk on protein levels (\u003cb\u003eAdditional File 1; Supplementary Table\u0026nbsp;10\u003c/b\u003e). Both SNPs used to instrument MM risk were also associated with amyloidosis (rs555992394) and had evidence for having an effect on blood cell counts including lymphocyte count and red cell distribution width (rs8141529) in the pheWAS analysis. These effects may be part of the shared causal pathway rather than pleiotropic given that amyloidosis and MM both share the precursor condition, MGUS [\u003cspan citationid=\"CR55\" class=\"CitationRef\"\u003e55\u003c/span\u003e], and that MM is known to have an effect on blood cell counts through myelosuppression [\u003cspan citationid=\"CR56\" class=\"CitationRef\"\u003e56\u003c/span\u003e].\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003cp\u003eA total of 440 aptamers/proteins were shared across both MR analyses exploring the effect of circulating proteins on MM risk, therefore resulting in two MR estimates. For 157 of these aptamers/proteins, the cis-SNP identified for each from the Olink GWAS was also the cis-SNP identified from the SomaLogic GWAS. Where proteins were included in MR analyses using instruments from both protein GWAS, effect estimates from both forward MR analyses are available in \u003cb\u003eSupplementary Table\u0026nbsp;11\u003c/b\u003e. Of these shared proteins, a total of 302 had consistent directions of effect (both negative or both positive beta coefficients) across MR analyses, seven of which had 95% CIs which did not overlap the null in both analyses. In the reverse MR, MM risk had little evidence for an effect on all seven of these circulating proteins (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e). Of these seven circulating proteins, an increase in abundance of four proteins (dermatopontin (DPT), beta-crystallin B1 (CRYBB1), interleukin-18-binding protein (IL18BP) and vascular endothelial growth factor receptor 2 (KDR)) was associated with an increase in MM risk, while an increase in the abundance of 3 proteins (odorant-binding protein 2b (OBP2B), glutamate-cysteine ligase regulatory subunit (GCLM) and gamma-crystallin D (CRYGD)) was associated with decreased MM risk.\u003c/p\u003e \u003cp\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003eColocalization\u003c/h2\u003e \u003cp\u003eIn colocalization analyses for the seven proteins with MR evidence for a causal effect on MM risk (Fig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e), evidence to support colocalization was limited across all windows (h4\u0026thinsp;\u0026lt;\u0026thinsp;0.8 \u003cb\u003eSupplementary Table\u0026nbsp;12, Supplementary Table\u0026nbsp;13\u003c/b\u003e). There was evidence for a shared causal variant between one protein, uncharacterized family 31 glucosidase KIAA1161, and multiple myeloma (h4\u0026thinsp;=\u0026thinsp;0.84 across all colocalization windows). This protein was detected and quantified in deCODE (SomaLogic), where there was MR evidence to support a causal relationship, but it was not measured in UKB (Olink).\u003c/p\u003e \u003c/div\u003e"},{"header":"Discussion","content":"\u003cp\u003eIn this study, bidirectional MR and genetic colocalization analyses were performed to identify whether circulating proteins causally impact MM risk. There was evidence using two protein GWAS that higher levels of four circulating proteins may increase MM risk and higher levels of three circulating proteins may decrease MM risk, however none of these results were supported by genetic colocalization, possibly indicative of low power or that estimates may not be robust to horizontal pleiotropy. A single protein, KIAA1161, measured only by SomaLogic, with evidence of an increasing effect on MM risk was supported by evidence from genetic colocalization.\u003c/p\u003e \u003cp\u003eTwo previous MR studies have explored the effect of circulating proteins on MM risk. The first focused on inflammatory proteins alone [\u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e26\u003c/span\u003e], whereas the second used GWAS data from protein levels measured by the SomaScan in a smaller sample (3,301 participants) from the INTERVAL study [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e]. Four of the 13 proteins with evidence for a causal relationship with MM risk by Wang et al. were also instrumented in our analysis. Our MR evidence (also using SomaScan) only supported a causal relationship for one of these proteins and MM risk (follistatin-related protein 1, FSTL1). We did not find evidence for a causal effect for the other three proteins, this may be due to the use of \u003cem\u003etrans\u003c/em\u003e SNPs by Wang et al. which likely included pleiotropic pathways [\u003cspan citationid=\"CR57\" class=\"CitationRef\"\u003e57\u003c/span\u003e].\u003c/p\u003e \u003cp\u003eAll seven proteins with consistent evidence across our two protein datasets have limited evidence in the literature of having previously been implicated in the pathogenesis or progression to MM [\u003cspan citationid=\"CR58\" class=\"CitationRef\"\u003e58\u003c/span\u003e]. The strongest evidence for an effect was with dermatopontin on MM risk, where higher levels were associated with an increase in MM risk. DPT is an extracellular matrix protein and has been shown to promote adherence of whole bone marrow to ECM proteins in mice [\u003cspan citationid=\"CR59\" class=\"CitationRef\"\u003e59\u003c/span\u003e]. As this protein may have a role in the bone marrow microenvironment, it is possible that dysregulation of this protein could contribute to the MM pathology. The involvement of DPT in MM pathology needs to be further characterised, such as through mouse models of MM and by exploring whether DPT is dysregulated in the bone marrow in patients with MM. In the current study, MR evidence suggested that higher levels of KDR (VEGFR2) increased risk of MM, and there was no evidence for an effect in the reverse direction (MM risk on levels of VEGFR2). VEGFR2 is involved in endothelial migration and proliferation and is implicated in liver, renal and thyroid cancers, where it is now exploited as a drug target [\u003cspan citationid=\"CR60\" class=\"CitationRef\"\u003e60\u003c/span\u003e]. The role of VEGFR2 in the progression from healthy, through the precursors of MM (MGUS and smouldering myeloma), and to MM, should be further characterised, for instance, by generating proteomic data on patient samples. MR evidence suggested that higher levels of GCLM may result in a decrease in MM risk. GCLM is a subunit of an enzyme involved in the cellular glutathione (GSH) biosynthetic pathway, which is critical to cell survival. Treating MM cells with a proteasome inhibitor, bortezomib (an approved MM treatment), has been shown to lead to higher levels of GCLM. This is directionally consistent with the MR results, where higher levels of GCLM had a lowering effect on MM risk [\u003cspan citationid=\"CR61\" class=\"CitationRef\"\u003e61\u003c/span\u003e]. In addition, there was one protein with evidence of genetic colocalization: uncharacterized family 31 glucosidase KIAA1161, it is unclear how this protein might be involved in MM risk; MR analysis should be replicated using a large independent protein GWAS using SomaScan to investigate this further.\u003c/p\u003e \u003cp\u003eOur results point towards putative causal relationships between circulating proteins and MM risk. However, there are limitations to these analyses that need to be considered and results should be interpreted with caution. Firstly, we did not adjust for multiple testing in each individual MR analyses. As we attempted to perform a discovery and replication approach, we believe that adjusting for multiple testing would be too conservative, especially given that the MM GWAS used are not highly powered. Suitable genetic instruments were also not available for all proteins, therefore some potentially important protein-MM or MM-protein effects will inevitably be missed. Additionally, perturbations in one protein do not occur in isolation. Effects of a single protein may be because of its role in one or more pathways, and therefore it is likely that there is a much more complex interaction between circulating proteins and risk of MM, rather than one or a few proteins being solely responsible for the change in risk. Currently, exploring the contribution of proteins together (as opposed to performing univariable analyses) to MM risk remains a challenge. These analyses were performed in participants only of European ancestry living in the UK, Iceland and Finland, therefore findings may not be generalisable to participants of other ancestries or in other contexts. More highly powered GWAS are required in non-European ancestries in order to evaluate the role of circulating proteins in MM risk more broadly. Another possible limitation is that there may be some misclassification, where participants who were deemed as controls could include those with undetected MGUS or smouldering myeloma, and this may lead to estimates being biased (towards or away from the null).\u003c/p\u003e \u003cp\u003eWe identified seven proteins which have consistent MR evidence across two proteomic datasets for a role in MM risk. Some of these proteins have previously been implicated in the other cancer types (VEGFR2 and GCLM), however relatively little is known about these seven proteins in relation to MM risk. Generating proteomic data from patients with myeloma (or its precursor conditions) and characterising these proteins further represent important steps in further understanding MM development.\u003c/p\u003e"},{"header":"Abbreviations","content":"\u003cdiv class=\"DefinitionList\"\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRYBB1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eBeta-crystallin 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCRYGD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGamma-crystallin D\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eCSF2\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGranulocyte-macrophage colony stimulating factor 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eDPT\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eDermatopontin\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eFSTL1\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eFollistatin-related protein 1\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGCLM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGlutamate-cysteine ligase regulatory subunit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eGWAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eGenome-wide association study\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIL18BP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eInterleukin 18 binding protein\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eIVW\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMRE-Inverse variance weighted multiplicative random effects\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKb\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eKilobase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eKDR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eVascular endothelial growth factor 2\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eLD\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eLinkage disequilibrium\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMb\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMegabase\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMGUS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMonoclonal gammopathy of unknown significance\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMM\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMultiple myeloma\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMMP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003e9-Matrix metalloproteinase-9\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eMR\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eMendelian randomization\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eNPX\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eNormalised protein expression\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eOBP2b\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eOdorant-binding protein 2b\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003ePheWAS\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003ePhenome-wide association study\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRFU\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eRelative fluorescence unit\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eEDTA\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eEthylenediaminetetraacetic acid\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eRSPO3\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eR-spondin-3\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eSNP\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eSingle nucleotide polymorphism\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eTNFSF10\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eTumour necrosis factor ligand superfamily member 10\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003cdiv class=\"DefinitionListEntry\"\u003e \u003cdiv class=\"Term\"\u003eUKB\u003c/div\u003e \u003cdiv class=\"Description\"\u003e \u003cp\u003eUK Biobank\u003c/p\u003e \u003c/div\u003e \u003c/div\u003e \u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003eWhere authors are identified as personnel of the International Agency for Research on Cancer/World Health Organization, the authors alone are responsible for the views expressed in this article and they do not necessarily represent the decisions, policy or views of the International Agency for Research on Cancer/World Health Organization.\u003c/p\u003e\u003ch2\u003eFunding\u003c/h2\u003e \u003cp\u003eLJG is supported by a Cancer Research UK 25 (C18281/A29019) programme grant (the Integrative Cancer Epidemiology Programme). KB, NIHR Academic Clinical Lecturer, is funded by Health Education England (HEE)/NIHR. EH is supported by a Cancer Research UK Population Research Committee Studentship (C18281/A30905), is supported by the CRUK Integrative Cancer Epidemiology Programme (C18281/A29019) and is part of the Medical Research Council Integrative Epidemiology Unit at the University of Bristol which is supported by the Medical Research Council (MC_UU_00032/03) and the University of Bristol.\u003c/p\u003e\u003ch2\u003eAuthor Contribution\u003c/h2\u003e\u003cp\u003eMAL: analysis, interpretation of results, writing, study management. KLB: interpretation of results, writing. ELH: interpretation of results, writing, analysis. SM: interpretation of results, writing. SJL: interpretation of results, writing. LJG: study conceptualization, analysis, interpretation of results, writing, study management.\u003c/p\u003e\u003ch2\u003eData Availability\u003c/h2\u003e\u003cp\u003eAll scripts are archived on Zenodo (https://zenodo.org/records/12784512) and available on GitHub (https://github.com/mattlee821/protein_myeloma). Meta-analysis was performed following the METAL online documentation (https://genome.sph.umich.edu/wiki/METAL_Documentation). All analyses were performed using R version 4.1.2. MR analyses were performed using TwoSampleMR (version 0.4.22). Colocalisation was performed using coloc (version 5.2.0). Weighted prevalence was calculated using the metaprop() function from the meta package (version 6.5-0).\u003c/p\u003e\u003cp\u003e \u003ch2\u003eConflicts of interest\u003c/h2\u003e \u003cp\u003eAll authors declare no conflicts of interest.\u003c/p\u003e "},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eCRUK. \u003cem\u003eCancer research UK: Myeloma statistics\u003c/em\u003e. 2023; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/myeloma\u003c/span\u003e\u003cspan address=\"https://www.cancerresearchuk.org/health-professional/cancer-statistics/statistics-by-cancer-type/myeloma\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAtkin, C., A. Richter, and E. Sapey, \u003cem\u003eWhat is the significance of monoclonal gammopathy of undetermined significance?\u003c/em\u003e Clin Med (Lond), 2018. 18(5): p. 391\u0026ndash;396.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eUK, C.R., \u003cem\u003eMyeloma statistics\u003c/em\u003e. 2023.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eNakaya, A., et al., \u003cem\u003eImpact of CRAB Symptoms in Survival of Patients with Symptomatic Myeloma in Novel Agent Era\u003c/em\u003e. Hematol Rep, 2017. 9(1): p. 6887.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDe Stefano, V., et al., \u003cem\u003eThrombosis in multiple myeloma: risk stratification, antithrombotic prophylaxis, and management of acute events. A consensus-based position paper from an\u003c/em\u003e. Haematologica, 2022. 107(11): p. 2536\u0026ndash;2547.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBlimark, C., et al., \u003cem\u003eMultiple myeloma and infections: a population-based study on 9253 multiple myeloma patients\u003c/em\u003e. Haematologica, 2015. 100(1): p. 107\u0026ndash;13.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eTerpos, E., et al., \u003cem\u003eManagement of patients with multiple myeloma in the era of COVID-19 pandemic: a consensus paper from the European Myeloma Network (EMN)\u003c/em\u003e. Leukemia, 2020. 34(8): p. 2000\u0026ndash;2011.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eRajkumar, S.V., \u003cem\u003eMultiple myeloma: 2022 update on diagnosis, risk stratification, and management\u003c/em\u003e. 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Hemani, \u003cem\u003eMendelian randomization: genetic anchors for causal inference in epidemiological studies\u003c/em\u003e. Hum Mol Genet, 2014. 23(R1): p. R89-98.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDavies, N.M., M.V. Holmes, and G. Davey Smith, \u003cem\u003eReading Mendelian randomisation studies: a guide, glossary, and checklist for clinicians\u003c/em\u003e. BMJ, 2018. 362: p. k601.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eSmith, G.D. and S. Ebrahim, \u003cem\u003e'Mendelian randomization': can genetic epidemiology contribute to understanding environmental determinants of disease?\u003c/em\u003e Int J Epidemiol, 2003. 32(1): p. 1\u0026ndash;22.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBowden, J. and M.V. Holmes, \u003cem\u003eMeta-analysis and Mendelian randomization: A review\u003c/em\u003e. Res Synth Methods, 2019. 10(4): p. 486\u0026ndash;496.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLawlor, D.A., \u003cem\u003eCommentary: Two-sample Mendelian randomization: opportunities and challenges\u003c/em\u003e. Int J Epidemiol, 2016. 45(3): p. 908\u0026ndash;15.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWent, M., et al., \u003cem\u003eSearch for multiple myeloma risk factors using Mendelian randomization\u003c/em\u003e. Blood Adv, 2020. 4(10): p. 2172\u0026ndash;2179.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLandegren, U. and M. Hammond, \u003cem\u003eCancer diagnostics based on plasma protein biomarkers: hard times but great expectations\u003c/em\u003e. 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Br J Haematol, 2008. 141(4): p. 494\u0026ndash;503.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, M.A. \u003cem\u003eExploring the role of circulating proteins in multiple myeloma risk: a Mendelian randomization study - Zenodo archived scripts\u003c/em\u003e 2024 [cited 2024 22nd July]; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://zenodo.org/records/12784512\u003c/span\u003e\u003cspan address=\"https://zenodo.org/records/12784512\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eLee, M.A. \u003cem\u003eExploring the role of circulating proteins in multiple myeloma risk: a Mendelian randomization study - scripts on GitHub\u003c/em\u003e. 2024 [cited 2024 22nd July]; Available from: \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://github.com/mattlee821/protein_myeloma\u003c/span\u003e\u003cspan address=\"https://github.com/mattlee821/protein_myeloma\" targettype=\"URL\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Proteomics, multiple myeloma, Mendelian randomization, genetic colocalization","lastPublishedDoi":"10.21203/rs.3.rs-4800219/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-4800219/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003ch2\u003eBackground\u003c/h2\u003e \u003cp\u003eMultiple myeloma (MM) is an incurable blood cancer with unclear aetiology. Proteomics, the high-throughput measurement of circulating proteins, is a valuable tool in exploring mechanisms of disease. We investigated the causal relationship between circulating proteins and MM risk, using two of the largest cohorts with proteomics data to-date.\u003c/p\u003e\u003ch2\u003eMethods\u003c/h2\u003e \u003cp\u003eWe performed bidirectional two-sample Mendelian randomization (MR; forward MR\u0026thinsp;=\u0026thinsp;causal effect estimation of proteins and MM risk; reverse MR\u0026thinsp;=\u0026thinsp;causal effect estimation of MM risk and proteins). Summary statistics for plasma proteins were obtained from genome-wide association studies performed using SomaLogic (N\u0026thinsp;=\u0026thinsp;35,559; deCODE) and Olink (N\u0026thinsp;=\u0026thinsp;34,557; UK Biobank; UKB) proteomic platforms and for MM risk from a meta-analysis of UKB and FinnGen (case\u0026thinsp;=\u0026thinsp;1,649; control\u0026thinsp;=\u0026thinsp;727,247) or FinnGen only (case\u0026thinsp;=\u0026thinsp;1,085; control\u0026thinsp;=\u0026thinsp;271,463). \u003cem\u003eCis-\u003c/em\u003eSNPs associated with protein levels were used to instrument circulating proteins. We evaluated proteins for the consistency of directions of effect across MR analyses (with 95% confidence intervals not overlapping the null) and corroborating evidence from genetic colocalization.\u003c/p\u003e\u003ch2\u003eResults\u003c/h2\u003e \u003cp\u003eIn the forward MR, 994 (SomaLogic) and 1,570 (Olink) proteins were instrumentable. 440 proteins were analysed in both deCODE and UKB; 302 (69%) of these showed consistent directions of effect in the forward MR. Seven proteins had 95% confidence intervals (CIs) that did not overlap the null in both forward MR analyses and did not have evidence for an effect in the reverse direction. MR evidence was strongest for the effect of dermatopontin on MM risk (deCODE) OR: 1.49 per SD higher protein levels, 95% CI 1.06\u0026ndash;2.09; (UKB) OR: 1.47; 95% CI 1.14\u0026ndash;1.90). Evidence from genetic colocalization did not meet our threshold for a shared causal signal between this protein and MM risk (h4\u0026thinsp;\u0026lt;\u0026thinsp;0.8).\u003c/p\u003e\u003ch2\u003eConclusions\u003c/h2\u003e \u003cp\u003eOur results highlight seven circulating proteins which may be involved in MM risk. Although evidence from genetic colocalization suggests these associations may not be robust to horizontal pleiotropy, these proteins may be useful markers of MM risk. Future work should explore the utility of these proteins in disease prediction or prevention using proteomic data from patients with MM or precursor conditions.\u003c/p\u003e","manuscriptTitle":"Exploring the role of circulating proteins in multiple myeloma risk: a Mendelian randomization study","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-08-02 16:05:41","doi":"10.21203/rs.3.rs-4800219/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-12-10T16:38:25+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-11-23T23:24:06+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"35816464029741392881597883781373692405","date":"2024-11-15T19:38:57+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-27T20:52:17+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"77693977684274983323180065661094480441","date":"2024-10-24T20:36:30+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-03T16:43:12+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"249050638834870238374455682971050726592","date":"2024-09-27T07:32:50+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"257227948959491679987741524881551435743","date":"2024-09-25T23:56:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"162312639024810593644830148168198383810","date":"2024-09-25T14:44:25+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-09-25T04:54:39+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-08-29T08:47:14+00:00","index":"","fulltext":""},{"type":"editorInvited","content":"","date":"2024-08-07T02:31:50+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-08-02T03:57:40+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2024-07-25T08:25:36+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"975e2bdf-d385-4c64-a03b-d5f267f6e362","owner":[],"postedDate":"August 2nd, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":35479533,"name":"Biological sciences/Cancer/Haematological cancer/Myeloma"},{"id":35479534,"name":"Biological sciences/Genetics"}],"tags":[],"updatedAt":"2025-02-03T15:59:58+00:00","versionOfRecord":{"articleIdentity":"rs-4800219","link":"https://doi.org/10.1038/s41598-025-86222-5","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-01-30 15:56:53","publishedOnDateReadable":"January 30th, 2025"},"versionCreatedAt":"2024-08-02 16:05:41","video":"","vorDoi":"10.1038/s41598-025-86222-5","vorDoiUrl":"https://doi.org/10.1038/s41598-025-86222-5","workflowStages":[]},"version":"v1","identity":"rs-4800219","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-4800219","identity":"rs-4800219","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}