Ziv, Elad

Most genetic variants associated with complex traits are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterized 14,324 RNA-sequencing samples from the Trans-Omics for Precision Medicine program and performed expression and splicing quantitative trait locus (e/sQTL) analyses in six tissues and cell types, including whole blood ( n = 6454) and lung ( n = 1291). We detected tens of thousands of secondary cis-e/sQTLs, showing that secondary cis-e/sQTL discovery remains unsaturated. We fine-mapped UK Biobank–derived genome-wide association study (GWAS) signals from 164 traits and identified e/sQTL colocalizations for 10,611 GWAS signals, including 7096 that colocalize with secondary e/sQTLs. Our results suggest that even larger e/sQTL analyses will uncover additional secondary e/sQTLs, further benefiting GWAS interpretation.

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.

Abstract Background Breast cancer genome-wide association studies (GWAS) have identified more than 200 susceptibility loci, but most studies are dominated by European and East Asian populations. Methods We analyzed breast cancer GWAS summary statistics from African (AFR), East Asian (EAS), European (EUR), and Hispanic/Latina (H/L) samples (159,297 cases and 212,102 controls). We estimated logit-scale SNP-based heritability, polygenicity, and cross-ancestry genetic correlation, partitioned heritability across functional annotations, and integrated GWAS results with the Tabula Sapiens single-cell atlas using scDRS+. Results The logit-scale heritability of breast cancer ranged from ℎ 2 =0.47 (SE = 0.07) in EAS to AFR ℎ 2 =0.61 (SE = 0.10), with no significant differences across ancestries (p=0.63). The estimated number of susceptibility markers in a sparse normal-mixture effects model also varied from 4,446 (SE = 3,100) in EAS to 8,308 (SE = 2,751) in AFR, but differences were not significant across ancestries (p=0.55). Cross-sample genetic correlations varied, with the strongest correlation between EUR and EAS (𝜌 = 0.79, SE = 0.08) and weakest between AFR and H/L (𝜌 = 0.26, SE = 0.24). Regulatory annotations were enriched for breast cancer heritability across samples. Integration with single-cell expression profiles implicated ancestry-shared associations with innate immune, secretory epithelial, and stromal cell types. Conclusion These results indicate substantial cross-ancestry sharing of breast cancer polygenic architecture, highlight a consistent contribution of regulatory variation, and identify convergent cellular contexts that motivate functional follow-up and inform expectations for the transferability and attainable performance of common-variant risk prediction across populations.

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 < 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 >= 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 < 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.

Abstract Introduction: Breast cancer genome-wide association studies (GWAS) have identified over 200 susceptibility loci, many replicated in diverse populations. However, cross-ancestry evaluation of breast cancer genetic architecture remains limited. We examined breast cancer genetic architecture using GWAS summary results from European (EUR; cases (ca) = 188,474, controls (co) = 96,201), East Asian (EAS; ca = 20,393, co = 86,329), African American (AA; ca = 9,235, co = 10,184), and US Hispanic/Latina and Latin American (H/L; ca = 2,396, co =7,468) studies. Methods: GWAS results were derived from the Breast Cancer Association Consortium, the African Ancestry Breast Cancer Genetics Consortium (AABCG), Biobank Japan, and a meta-analysis of five studies of H/L women with breast cancer. Linkage disequilibrium (LD) scores were generated for EUR, EAS, and AMR using the 1000 Genomes Project, while AA LD scores were derived from a subset of AABCG controls. Heritability was estimated for each ancestry group using LD score regression. Genetic correlations between populations were assessed via Popcorn. Polygenicity was analyzed using GENESIS; however, due to the limited sample size in the H/L studies, these estimates were restricted to AA, EAS, and EUR. Enrichment of heritability across regulatory elements was evaluated using stratified LD score regression across all populations. Results: The logit-scale heritability and corresponding standard error (SE) were 0.466 (0.066) for EAS, 0.501 (0.050) for EUR, 0.588 (0.360) for H/L, and 0.614 (0.095) for AA. The estimated number of independent susceptibility loci was 4,446 for EAS, 5,235 for EUR, and 8,308 for AA. Using a clumping and thresholding approach, an optimal set of common variants were projected to explain 38.6% (EUR), 39.4% (EAS), and 26.2% (AA) of genetic variance for samples of 100,000 cases and 100,000 controls with AUCs from corresponding estimated polygenic risk scores (PRSs) of 0.621 (EUR), 0.634 (EAS), and 0.611 (AA). Genetic correlations were strongest between EUR and EAS (ρ = 0.79, SE = 0.08) and EUR and H/L (ρ = 0.68, SE = 0.21), and weakest between AA and EAS (ρ = 0.42, SE = 0.14) and AA and H/L (ρ = 0.26, SE = 0.24). Among 73 genomic features, we found significant (P < 0.05/73) enrichment in heritability in EUR for ‘ancient promoter’ regions, transcription factor binding sites, H3K4me3, ‘super-enhancers’, H3K4me1, H3K27ac; in EAS for ‘super-enhancers’; and in AA for H3K27ac. No genomic features were significantly enriched in H/L, likely due to limited power. Conclusion: These findings suggest a shared breast cancer genetic architecture across diverse populations, as well as the potential for a similar level of breast cancer risk stratification by PRS in these populations. Expanding GWAS in underrepresented populations is essential to improve genetic risk predictions and foster equitable cancer prevention. Citation Format: James L. Li, Maria Zanti, Jacob Williams, Om Jahagirdar, Guochong Jia, Qiang Hu, Jean-Tristan Brandenburg, Li Yan, Weang-Kee Ho, Jingmei Li, José P. Miranda, Devika Godbole, Julie-Alexia Dias, Leila Dorling, Wenlong C. Chen, Nicholas Boddicker, Ying Wang, Alicia Martin, Martin J. Zhang, Yan Zhang, Joe Dennis, Esther M. John, Gabriela Torres-Mejia, Larry Kushi, Jeffrey Weitzel, Susan L. Neuhausen, Luis Carvajal-Carmona, Christopher Haiman, Elad Ziv, Laura Fejerman, Wei Zheng, Dezheng Huo, Douglas Easton, Nilanjan Chatterjee, Peter Kraft, Montserrat Garcia-Closas, Wendy Wong, Kyriaki Michailidou, Qianqian Zhu, Diptavo Dutta, Thomas U. Ahearn, Haoyu Zhang. Genetic architecture of breast cancer across diverse populations: Assessing heritability, genetic correlation, and polygenicity [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 2282.

Cancer immunotherapy—including immune checkpoint inhibition (ICI) and adoptive cell therapy (ACT)—has become a standard, potentially curative treatment for a subset of advanced solid and liquid tumors. However, most patients with cancer do not benefit from the rapidly evolving improvements in the understanding of principal mechanisms determining cancer immune responsiveness (CIR); including patient-specific genetically determined and acquired factors, as well as intrinsic cancer cell biology. Though CIR is multifactorial, fundamental concepts are emerging that should be considered for the design of novel therapeutic strategies and related clinical studies. Recent advancements as well as novel approaches to address the limitations of current treatments are discussed here, with a specific focus on ICI and ACT.

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 < 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.

Multiple myeloma (MM) arises following malignant proliferation of plasma cells in the bone marrow, that secrete high amounts of specific monoclonal immunoglobulins or light chains, resulting in the massive production of unfolded or misfolded proteins. Autophagy can have a dual role in tumorigenesis, by eliminating these abnormal proteins to avoid cancer development, but also ensuring MM cell survival and promoting resistance to treatments. To date no studies have determined the impact of genetic variation in autophagy-related genes on MM risk. We performed meta-analysis of germline genetic data on 234 autophagy-related genes from three independent study populations including 13,387 subjects of European ancestry (6863 MM patients and 6524 controls) and examined correlations of statistically significant single nucleotide polymorphisms (SNPs; p < 1 × 10−9) with immune responses in whole blood, peripheral blood mononuclear cells (PBMCs), and monocyte-derived macrophages (MDM) from a large population of healthy donors from the Human Functional Genomic Project (HFGP). We identified SNPs in six loci, CD46, IKBKE, PARK2, ULK4, ATG5, and CDKN2A associated with MM risk (p = 4.47 × 10−4−5.79 × 10−14). Mechanistically, we found that the ULK4rs6599175 SNP correlated with circulating concentrations of vitamin D3 (p = 4.0 × 10−4), whereas the IKBKErs17433804 SNP correlated with the number of transitional CD24+CD38+ B cells (p = 4.8 × 10−4) and circulating serum concentrations of Monocyte Chemoattractant Protein (MCP)-2 (p = 3.6 × 10−4). We also found that the CD46rs1142469 SNP correlated with numbers of CD19+ B cells, CD19+CD3− B cells, CD5+IgD− cells, IgM− cells, IgD−IgM− cells, and CD4−CD8− PBMCs (p = 4.9 × 10−4−8.6 × 10−4) and circulating concentrations of interleukin (IL)-20 (p = 0.00082). Finally, we observed that the CDKN2Ars2811710 SNP correlated with levels of CD4+EMCD45RO+CD27− cells (p = 9.3 × 10−4). These results suggest that genetic variants within these six loci influence MM risk through the modulation of specific subsets of immune cells, as well as vitamin D3−, MCP-2−, and IL20-dependent pathways.

AbstractPleiotropy, which consists of a single gene or allelic variant affecting multiple unrelated traits, is common across cancers, with evidence for genome‐wide significant loci shared across cancer and noncancer traits. This feature is particularly relevant in multiple myeloma (MM) because several susceptibility loci that have been identified to date are pleiotropic. Therefore, the aim of this study was to identify novel pleiotropic variants involved in MM risk using 28 684 independent single nucleotide polymorphisms (SNPs) from GWAS Catalog that reached a significant association (P < 5 × 10−8) with their respective trait. The selected SNPs were analyzed in 2434 MM cases and 3446 controls from the International Lymphoma Epidemiology Consortium (InterLymph). The 10 SNPs showing the strongest associations with MM risk in InterLymph were selected for replication in an independent set of 1955 MM cases and 1549 controls from the International Multiple Myeloma rESEarch (IMMEnSE) consortium and 418 MM cases and 147 282 controls from the FinnGen project. The combined analysis of the three studies identified an association between DNAJB4‐rs34517439‐A and an increased risk of developing MM (OR = 1.22, 95%CI 1.13‐1.32, P = 4.81 × 10−7). rs34517439‐A is associated with a modified expression of the FUBP1 gene, which encodes a multifunctional DNA and RNA‐binding protein that it was observed to influence the regulation of various genes involved in cell cycle regulation, among which various oncogenes and oncosuppressors. In conclusion, with a pleiotropic scan approach we identified DNAJB4‐rs34517439 as a potentially novel MM risk locus.

Abstract Background: Genome-wide association studies (GWAS) of multiple myeloma in populations of European ancestry (EA) identified and confirmed 24 susceptibility loci. For other cancers (e.g., colorectum and melanoma), risk loci have also been associated with patient survival. Methods: We explored the possible association of all the known risk variants and their polygenic risk score (PRS) with multiple myeloma overall survival (OS) in multiple populations of EA [the International Multiple Myeloma rESEarch (IMMEnSE) consortium, the International Lymphoma Epidemiology consortium, CoMMpass, and the German GWAS] for a total of 3,748 multiple myeloma cases. Cox proportional hazards regression was used to assess the association between each risk SNP with OS under the allelic and codominant models of inheritance. All analyses were adjusted for age, sex, country of origin (for IMMEnSE) or principal components (for the others) and disease stage (ISS). SNP associations were meta-analyzed. Results: SNP associations were meta-analyzed. From the meta-analysis, two multiple myeloma risk SNPs were associated with OS (P &lt; 0.05), specifically POT1-AS1-rs2170352 [HR = 1.37; 95% confidence interval (CI) = 1.09–1.73; P = 0.007] and TNFRSF13B-rs4273077 (HR = 1.19; 95% CI = 1.01–1.41; P = 0.04). The association between the combined 24 SNP MM-PRS and OS, however, was not significant. Conclusions: Overall, our results did not support an association between the majority of multiple myeloma risk SNPs and OS. Impact: This is the first study to investigate the association between multiple myeloma PRS and OS in multiple myeloma.