Citations

Abstract Polygenic risk scores (PRSs), which quantify inherited susceptibility to complex traits and diseases, have emerged as valuable tools for risk stratification and precision medicine. Despite their promise, PRS developed on European cohorts often demonstrate substantially reduced predictive accuracy in non-European populations, due to differences in genetic architecture. The disproportionate representation of European ancestry cohorts in genome-wide association studies (GWAS) leads to inequitable deployment of PRS technologies across diverse populations. Here, we introduce PRANA (Polygenic Risk Adaptation via Neural-network Architecture), a deep learning framework that adapts an existing PRS developed on one population to other ancestries. Unlike methods that require large-scale GWAS in the target population, PRANA leverages pre-trained PRS models derived from European cohorts and adapts them using modestly sized cohorts from the target population. We evaluated PRANA on seven complex traits in South Asian, East Asian and Ashkenazi Jewish populations, as well as in selected smaller East Asian subpopulations where the scarcity of training data poses a particular challenge. PRANA mostly improved predictive performance of the baseline PRS models by 5%-20% in terms of effect size (β) and Nagelkerke’s R², and, in most cases, outperformed existing cross-ancestry multi-PRS approaches. These results highlight PRANA as a scalable and practical strategy to reduce disparities in genomic risk prediction and advance the equitable application of PRS in diverse populations.

Summary Evidence for an association between insulin‐like growth factors (IGF) and multiple myeloma (MM) is inconsistent. We examined total IGF‐I concentrations and risk of MM by combining baseline serological data among UK Biobank participants ( n = 444 187; 732 incident MM) with a two‐sample Mendelian randomisation (MR) analysis using identified genetic variants associated with circulating total IGF‐I and IGF‐binding protein 3 (IGFBP‐3) in the InterLymph consortium (2434 MM and their 2567 controls). Finally, additional lymphoid neoplasm (LN) subtypes were included for comparison with the main hypothesis. Circulating IGF‐I level was positively associated with MM risk Hazard ratio‐HR‐per one standard deviation‐SD‐increase (HR 1‐SD = 1.11, 95% confidence interval [CI]: 1.01–1.22; p ‐value = 0.03), especially closer to diagnosis. Genetically inferred IGF‐I levels were associated with increased MM risk (odds ratio [OR] = 1.27, 95% CI: 1.05–1.54) but not with any other LNs. Genetically inferred IGFBP‐3 levels showed no associations with any LN evaluated. Corroborating previous findings, in a secondary analysis, IGF‐I levels were associated with the risk of chronic lymphocytic leukaemia/small lymphocytic lymphoma (CLL/SLL) in males with higher body mass index (HR 1‐SD in obese male = 1.36, 95% CI: 1.14–1.61). Our serological and MR analyses suggest a contributing role of IGF‐I in the susceptibility of MM; the CLL/SLL findings warrant further investigation considering sex‐specific adiposity.

Background/Objectives: Family history is a known risk factor for multiple myeloma (MM) and its precursor condition, monoclonal gammopathy of undetermined significance (MGUS). Previous genome-wide association studies (GWASs) have identified 35 common loci associated with MM risk and 21 associated with MGUS. The objective of this study was to identify less common and rare genetic loci predisposing to MM/MGUS through whole-exome sequencing (WES)-based linkage analysis. Methods:Multipoint linkage analysis was conducted using the Multipoint Engine for Rapid Likelihood Inference (MERLIN) with the Lander–Green algorithm on germline WES data from 79 pedigrees with 2 or more affected relatives (120 MM, 86 MGUS, and 21 unaffected). Genome-wide linkage was evaluated using 12,946 independent single-nucleotide variants (linkage disequilibrium r2 < 0.05). Results: Significant linkage was observed at chromosome 6q22.33–q24.2 by the non-parametric model (logarithm-of-odds (LOD) = 3.3) and suggestive linkage by the dominant parametric model (heterogeneity LOD (HLOD) = 2.5). Fourteen rare variants within this region were prioritized using family-specific partial LOD scores and in silico functional prediction tools. Nine of these variants, REPS1, THEMIS, TAAR6, AHI1, VNN1, VNN3, MTFR2/FAM54A, LAMA2, and PHACTR2, overlapped immune-regulatory regions in blood cell lines and were not previously identified in GWASs. Conclusions: This study demonstrates the utility of applying a linkage analysis framework to familial WES data for identifying genomic regions and candidate genes that may contribute to MM/MGUS predisposition. These findings provide new insight into the inherited risk and etiology of familial MM and MGUS.

Abstract Obesity has been associated with non‐Hodgkin lymphoma (NHL), but the evidence is inconclusive. We examined the association between genetically determined adiposity and four common NHL subtypes: diffuse large B‐cell lymphoma (DLBCL), follicular lymphoma, chronic lymphocytic leukemia, and marginal zone lymphoma, using eight genome‐wide association studies of European ancestry ( N = 10,629 cases, 9505 controls) and constructing polygenic scores for body mass index (BMI), waist‐to‐hip ratio (WHR), and waist‐to‐hip ratio adjusted for BMI (WHRadjBMI). Higher genetically determined BMI was associated with an increased risk of DLBCL [odds ratio (OR) per standard deviation (SD) = 1.18, 95% confidence interval (95% CI): 1.05–1.33, p = .005]. This finding was consistent with Mendelian randomization analyses, which demonstrated a similar increased risk of DLBCL with higher genetically determined BMI (OR per SD = 1.12, 95% CI: 1.02–1.23, p = .03). No significant associations were observed with other NHL subtypes. Our study demonstrates a positive link between a genetically determined BMI and an increased risk of DLBCL, providing additional support for increased adiposity as a risk factor for DLBCL.

Abstract Introduction: With over 80, 000 new cases of non-Hodgkin's lymphoma (NHL) diagnosed annually, modifiable risk factors remain unclear. Circadian disruption, linked to cancers like breast and prostate, affects immune cells (e.g., natural killer cells and T-helper cells) by altering their trafficking and proliferation. Some studies (PMID: 30566672, 27611440) suggest long sleep may increase overall NHL risk, including a Mendelian randomization (MR) study (PMID: 32895918). However, no research has examined the causal relationship between sleep traits, including long sleep, and NHL risk, by subtypes. We hypothesize that sleep traits are causally associated with NHL subtypes in European populations, testing this using MR with data from the International Lymphoma Epidemiology Consortium (InterLymph). Methods: We conducted two-sample MR using genome-wide association studies (GWAS) summary data for sleep traits and NHL subtypes. Only independent, genome-wide significant (p &lt; 5×10-8) Single Nucleotide Polymorphisms (SNPs) were selected as valid instruments, sourced from the UK Biobank. Sleep traits included chronotype (153 SNPs), insomnia (48 SNPs), sleep duration (78 SNPs), excessive daytime sleepiness (37 SNPs), short sleep (27 SNPs), and long sleep (8 SNPs). NHL subtype data were from InterLymph: follicular lymphoma (6, 508 cases / 64, 183 controls), mantle cell lymphoma (1, 169 cases / 61, 603 controls), Waldenstrom macroglobulinemia/lymphoplasmacytic lymphoma (WM/LPL: 1, 697 cases / 59, 333 controls), and chronic lymphocytic leukemia (8, 522 cases / 67, 653 controls). The primary analysis used inverse-variance weighted (IVW) random-effects, with MR-Egger for pleiotropy adjustment. Results: Using the IVW method, we did not identify any statistically significant causal association between sleep traits and the risk of NHL subtypes, but did yield several intriguing trends. Long sleep (sleep duration &gt;= 9 h per night) showed a marginally increased NHL risk [Odds ratio (OR) range: 1.51 - 247.9] across each subtype. Short sleep (sleep duration &lt; 7 h per night) showed a similar trend [OR range: 1.25 - 1.81], except for WM/LPL, where we noticed a negative trend. The confidence limit for long sleep was imprecise due to the small number of variants. We did not notice any significant directional horizontal pleiotropy or weak instrument bias. Conclusions: We found no conclusive evidence of a relationship between sleep traits and four NHL subtypes. However, we see suggestive trends of long and short sleep associated with higher risk of some NHL subtypes. These findings align with the previous studies, which showed long sleep was associated with higher risk of NHL overall. Future directions include expanding our analysis to additional NHL subtypes, generating polygenic risk scores, and conducting stratified analyses. Citation Format: Pankhil Shah, Brittany Crawford, Anwar Merchant, Brenda Birmann, Angelica Macauda, Michelle A. Hildebrandt, Aaron Norman, Neil E. Caporaso, Meredith Yeager, Michael Dean, Immaculata De Vivo, Lynn Goldin, Nicola J. Camp, Rosalie Griffin, Delphine Casabonne, Federico Canzian, Pelin Unal, Elad Ziv, Catherine R. Marinac, Alexandra Nieters, Stephen J. Chanock, Mitchell J. Machiela, Michael Conry, Charlie Zhong, Hanla A. Park, Simon Cheah, Jonathan N. Hofmann, Elizabeth E. Brown, Celine Vachon, Susan Slager, Sonja Berndt, Sophia S. Wang, Vijai Joseph, James McKay, Henrik Hjalgrim, Lara Sucheston-Campbell, Karl Smith-Byrne, Alyssa Clay-Gilmour. Association between sleep traits and risk of non-Hodgkin's lymphoma subtypes: a mendelian randomization study in the International Lymphoma Epidemiology (InterLymph) Consortium [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2025; Part 1 (Regular Abstracts); 2025 Apr 25-30; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2025;85(8_Suppl_1):Abstract nr 7415.

Introduction: WM is a rare subtype of lymphoma, identified by the presence of IgM paraprotein, infiltration of clonal, small lymphoplasmacytic B cells in the bone marrow, and the somatic mutation, MYD88 L265P, which is observed in over 90% of WM patients. We previously identified two rare variants at 6p25.3 (EXOC2) and 14q32.13 (TCL1) that were significantly associated with an increased risk of WM/LPL (PMID: 30305637). However, the genetic basis for WM/LPL predisposition remains largely unknown. We conducted the largest WM/LPL GWAS to discover further germline susceptibility loci for WM/LPL. Methods: Our meta-analysis discovery set included 1,707 cases and 58,328 controls of European ancestry from the InterLymph Consortium, genotyped on a high-density SNP array and imputed to TOPMed, providing over 10 million common variants for association testing. Germline genetic loci with suggestive significance (P&lt;5x10-6) were selected for replication in four independent sets (570 cases and 771,770 controls; UK Biobank, Copenhagen Hospital Biobank, FinnGen, Mayo Clinic), giving us a total of 2,277 WM/LPL cases and 831,098 controls. Conditional and sensitivity analyses were performed considering family history and sex. A transcriptome-wide association study (TWAS) was performed using FUSION with expression quantitative trait loci (eQTL) data from GTEx and Young Finns Study in whole blood, lymphocytes, and other tissues. Functional annotations were done using FUMA, F-MAGMA, FORGE, and OpenTarget. Colocalization was measured using CoLoC with whole blood gene expression data. Results & Discussion: We identified 11 germline genetic loci associated with WM/LPL risk that replicated and reached genome-wide significance (P&lt; 5x10-8), including the two known loci on 6p25.3 and 14q32.13 and nine novel loci in the following regions: 6p25.3 (n=2 independent loci), 2q37.1, 9p13.2, 6p21.32, 10q23.31, 16q21, 1p31.1, and 15q15.1. Four of the discovered variants showed strong associations with WM/LPL [odds ratios (ORs) &gt;10]. Enrichment was observed for enhancer and promoter elements in B-cells (FDR q &lt; 0.01), and gene-based and colocalization analyses identified several potential biological pathways. Colocalization analyses show the intronic PAX5 risk variant on 9p (OR=1.5, P= 2.3x10-25) associated with whole blood eQTL expression of PAX5, which plays an essential role in regulating lymphoid progenitors to the B-lymphocyte lineage, as well as RP11-297B17.3 (RNA gene, antisense to PAX5). In addition, GWAS have shown this variant is in linkage disequilibrium (LD) with GWAS loci for CD20 on IgD+ CD38- B cell that represents a specific immunophenotype that helps identify and characterize B cells involved with WM and other similar conditions. The 2q37.1 (SP140) locus (OR=1.6, P=1.2x10-33) contains variants linked to chronic lymphocytic leukemia (CLL) and autoimmune diseases, including multiple sclerosis and Crohn's disease. This locus is important in chromatin-mediated gene regulation, suggesting a significant role in the immune system. The specific variant influences exon-skipping and potentially protein expression, altering the gene's function. The variants on 10q23.31 in FAS (OR=1.3, P=4.4x10-13) and 15q15.1 (OR=1.2, P=8.5x10-11) in BCL2 modifying factor (BMF), are in strong LD with known CLL susceptibility variants. The 15q variant is also linked with multiple lymphoid leukemia GW significant SNPs. Sex-specific analyses identified one novel locus at 20q13.33 associated with the expression of ZGPAT and UCKL1 in males (OR=1.9, P=9.5x10-10). TWAS identified associations with CRHR1 and CDKN2A; overexpression of both are shown to be associated with an increased risk of WM/LPL. Expression of CDKN2A has been linked to childhood acute lymphoblastic leukemia, while CRHR1 has been associated with breast cancer risk. Conclusions: Our study, the largest GWAS of WM to date, has identified 11 susceptibility loci, including two previously known and nine novel regions. The findings underscore the genetic complexity of WM, with the PAX5 and SP140 loci implicated in both chromatin-mediated gene regulation and immune system functions. These insights not only enhance our understanding of WM predisposition but also pave the way for future research focused on functional understanding of these loci, potentially leading to new therapeutic strategies.

Introduction: Follicular lymphoma (FL) is the most common indolent subtype of non-Hodgkin lymphoma. Family history is a risk factor. Genome-wide association studies (GWAS) have identified several germline variants, especially within the HLA region, to be associated with an increased risk of FL, but these variants only explain a fraction (16%) of heritability. Here, we present novel findings from GWAS of FL uncovering multiple germline genetic variants contributing to disease susceptibility. Methods: A GWAS was performed comprised of a discovery analysis in 6,508 FL cases and 64,183 controls of European ancestry followed by a replication of the most significant variants (P&lt;5x10-7) in 2,745 FL cases and 782,226 controls from the biobanks of UK, Copenhagen Hospital, FinnGen and Mayo Clinic . At 9,253 FL cases and 846,409 FL free controls imputed genome-wide, this is the largest FL GWAS of European genetic ancestry to date. HLA alleles were imputed, and Class I and Class II signals were analysed at the amino acid level. A transcriptome wide association study (TWAS) was performed using FUSION investigate relationship to expression in GTEx and Young Finns Study in whole blood and cross-tissue . Functional annotations were done using FUMA, F-MAGMA, Open Targets and FORGE. Colocalization was conducted using CoLoC with whole blood gene expression data to understand causal variants. Mendelian randomization (MR) was performed on blood cell traits and leukocyte telomere lengths (LTL) to infer their relationships with FL genetic risk. Results: We replicated the previously published susceptibility loci within and outside the HLA region, finding two additional independent SNPs at two known loci (P &lt; 5 x10-8) and discovered thirteen novel, genome-wide significant loci located at chromosome 2q13, 3p13, 3p24.1,3q13, 3q24.1, 4q31.21, 6p21, 6p22, 7q31, 10p14, 10q22, 10q23, 17q12, 19p13 and 20p11 with P &lt; 5 x10-8. These SNPs were located near genes that show relevance for B-cell biology, such as BCL2L11, FOXP1, CD86, EOMES, IZKF3, PRF1, KLF2 NKX2-2 and HLA Class I and Class II genes. TWAS uncovered a role for H2AX, IGKV2-29, IMP, UBE4A and IKZF3 and provided directionality of effect for associated transcripts. Colocalization analysis corroborated a positive association with IKZF3 expression showing a robust correlation with FL risk variants (posterior probability PP4&gt;0.7). In addition, several genome-wide significant variants in IKZF3 colocalize with eQTLs across tissues. IKZF3 (Aiolos) is part of the Ikaros family of transcription factors, critically important to the regulation of B-cell development and function. Therapeutic strategies leveraging Immunomodulatory imide drugs (IMiDs) to degrade IKZF3 have shown efficacy in multiple myeloma, and we speculate may also have potential benefits in FL by enhancing anti-tumor immunity and disrupting malignant B-cell function. Gene-set analysis showed blood and spleen to be the most relevant tissues for FL risk variants with lymphocyte activation and immune cell type differentiation genes demonstrating enrichment based on gene-ontology terms. Associated traits included allergies, respiratory diseases (e.g., asthma phenotypes), inflammatory bowel diseases, type 1 diabetes (T1D), lipid levels and commonly measured blood cell traits, with some SNPs showing evidence of colocalization with select blood traits. Interestingly, no causal association to blood traits was observed using MR; however, MR analyses did reveal a strong association with LTL. For the HLA loci, FL shows a diverse set of amino acid associations in HLA Class I and Class II genes that extends beyond what is known from autoimmune disease associations, such as rheumatoid arthritis or T1D. Our study also reveals interesting biological insights about FL susceptibility, such as a novel locus intragenic to FOXP1, an activated oncogene preferentially expressed in FL and DLBCL tumors, but rarely observed in Hodgkin's lymphoma. High levels of FOXP1 are known to result in poor survival in FL. Conclusion: This large GWAS of FL has identified multiple susceptibility variants, uncovering interesting gene candidates for further pathophysiological characterization of FL risk and B-cell development. It suggests new biological pathways and genes that could be potential leads for drug development and targeting treatment, in addition to identifying genetically high-risk populations for FL susceptibility.

Introduction: Over 80,000 new cases of Non-Hodgkin's Lymphoma (NHL) are diagnosed annually, but modifiable risk factors are largely unknown. Circadian disruption has been identified as a risk factor for several cancers, including breast and prostate cancer, and affects immune cells such as Natural Killer (NK) cells and T-helper cells. Disruptions in circadian rhythms can lead to aberrant immune cell trafficking and proliferation. Despite this biological connection, evidence linking circadian disruptions to NHL remains inconclusive. Only single Mendelian Randomization (MR) study (PMID: 32895918) has explored the causal relationship between sleep traits and overall NHL risk, without examining NHL subtypes. Studies have shown distinct risk factor profiles among NHL subtypes. Therefore, this study aims to investigate the causal relationship between sleep traits and NHL subtypes in European populations using MR. Methods: We conducted a two-sample MR analyses using genome-wide association studies (GWAS) summary data on sleep traits and NHL subtypes. The GWAS summary statistics on sleep traits were obtained from publicly available UKBiobank datasets. The sleep traits analyzed included chronotype (N=449,734), insomnia (N=237,627), sleep duration (N=446,118), daytime sleepiness (N=452,071), short sleep (N=411,934), and long sleep (N=339,926). Only independent, genome-wide significant SNPs (p &lt; 5×10-8) were considered as valid instruments. The NHL subtype-specific summary statistics came from the InterLymph including follicular lymphoma (FL: N=6,508 cases / 64,183 controls), mantle cell lymphoma (MCL: N=1,169 cases / 61,603 controls), and Waldenström macroglobulinemia/lymphoplasmacytic lymphoma (WM/LPL: N=1,697 cases / 59,333 controls). Our primary analysis used the random-effects inverse-variance weighted (IVW) method. In addition, we employed MR-Egger to adjust for directional pleiotropy. Heterogeneity in the MR results was evaluated with Cochran's Q statistic. Results: The IVW method did not identify any statistically significant causal association between sleep traits and the risk of NHL subtypes (FL,MCL,WM/LPL), but did yield several intriguing associations. Chronotype (early rising) showed a tendency towards a marginally an increased NHL risk across subtypes [Odds ratio (OR) range: 1.07 - 2.56]. Conversely, a decreased risk was noted for long sleep (≥ 9 hours vs. 7-8 hours) [OR range: 0.19 - 0.62], insomnia [OR range: 0.72 - 0.88], and short sleep (excludes MCL) [OR range: 0.12 - 0.72]. Confidence limit for long sleep was wide due to small number of variants (N=8). The MR-Egger regression analysis did not provide evidence of significant directional horizontal pleiotropy. The F-statistics for all sleep traits were greater than 10 indicating absences of weak instrument bias. Conclusions: Our study found no statistically significant associations between sleep traits and three NHL subtypes (FL, MCL, WM/LPL) in European populations. However, we observed potential influences of chronotype, long and short sleep, and insomnia on NHL risk. These findings align with and extend previous MR study by providing subtype-specific insights. Future directions include expanding our analysis to additional NHL subtypes, generating polygenic risk scores, and conducting stratified analyses using the InterLymph population. These efforts aim to further elucidate the complex relationship between sleep characteristics and NHL risk across diverse subtypes and populations.

Background Polygenic risk score (PRS), calculated based on genome-wide association studies (GWASs), can improve breast cancer (BC) risk assessment. To date, most BC GWASs have been performed in individuals of European (EUR) ancestry, and the generalisation of EUR-based PRS to other populations is a major challenge. In this study, we examined the performance of EUR-based BC PRS models in Ashkenazi Jewish (AJ) women. Methods We generated PRSs based on data on EUR women from the Breast Cancer Association Consortium (BCAC). We tested the performance of the PRSs in a cohort of 2161 AJ women from Israel (1437 cases and 724 controls) from BCAC (BCAC cohort from Israel (BCAC-IL)). In addition, we tested the performance of these EUR-based BC PRSs, as well as the established 313-SNP EUR BC PRS, in an independent cohort of 181 AJ women from Hadassah Medical Center (HMC) in Israel. Results In the BCAC-IL cohort, the highest OR per 1 SD was 1.56 (±0.09). The OR for AJ women at the top 10% of the PRS distribution compared with the middle quintile was 2.10 (±0.24). In the HMC cohort, the OR per 1 SD of the EUR-based PRS that performed best in the BCAC-IL cohort was 1.58±0.27. The OR per 1 SD of the commonly used 313-SNP BC PRS was 1.64 (±0.28). Conclusions Extant EUR GWAS data can be used for generating PRSs that identify AJ women with markedly elevated risk of BC and therefore hold promise for improving BC risk assessment in AJ women.

Abstract BACKGROUND: Multiple Myeloma (MM) is an incurable disease with no known germline high penetrant risk gene. The higher risk of MM among affected relatives suggests a genetic contribution to the etiology. To date, 24 common risk variants with low effect sizes were identified using genome-wide association studies, which account for 17% of heritability. However, a systematic analysis of the known rare alleles in cancer predisposition genes has not been undertaken so far in MM. We collaborated with six other academic centers across the United States to identify and select cases for analysis of germline predisposition of MM. METHODS: We performed whole exome, next-generation sequencing of 2,387 familial or early onset germline MM cases from the myeloma sequencing consortium (MMSEQ). We called 1.5 million variants and performed standard quality control of the data. We also obtained exome data on 1,285 cases from UK Biobank (UKBB) and 344,513 non-cancer controls (total=3,672 cases). We analyzed the ultra-rare, coding variants in 90 clinically relevant or putative candidates for cancer predisposition. We then used the automated variant curator PathoMAN to annotate and assert pathogenicity of each variant. We contrasted our results with assertions of pathogenicity from ClinVar and tested for gene association with MM. RESULTS: Overall, we observed several pathogenic or likely pathogenic variants, both singleton and recurrent founder mutations. In the MMSEQ, 8.7% of MM cases harbored a pathogenic variant in these known/putative cancer predisposition genes. Predominant group of pathogenic variants were observed within CHEK2 (19%), TP53 (8%), and ATM (5%). A case-control analysis revealed strong association with TP53 and weakly with ATM. In the familial/early onset MMSEQ cases, TP53 mutations were enriched 68 times more than in the UKBB controls. Our preliminary results suggest that known genes of cancer susceptibility such as TP53 and ATM may also play a role in myeloma risk. CONCLUSION: In a large study of multi-center MM cases, we identified a significant proportion of individuals who are carriers of known (solid) cancer predisposition genes. This study suggests that these known cancer predisposition genes may be relevant in a subset of MM cases. Citation Format: Michael T. Conry, Nicola Camp, Celine Vachon, Michelle Hildebrandt, Elizabeth E. Brown, Steven M. Lipkin, Judy Garber, Susan L. Slager, Samantha Stokes, Aaron D. Norman, Aalin Izhar, Sita Dandiker, Kylee Maclachlan, Kenneth Offit, Saad Usmani, Vijai Joseph. Pathogenic germline mutations and risk of multiple myeloma. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5229.