Berndt, Sonja

Abstract Background Physical activity (PA) is an established protective factor for colorectal cancer (CRC), but it is unclear if genetic variants modify this effect. To investigate this possibility, we conducted a genome-wide gene–PA interaction analysis. Methods Using logistic regression and two-step and joint tests, we analyzed interactions between common genetic variants across the genome and PA in relation to CRC risk. Self-reported PA levels were categorized as active (≥ 8.75 MET-h/wk) vs. inactive (< 8.75 MET-h/wk) and as study- and sex-specific quartiles of activity. Results PA had an overall protective effect on CRC (OR [active vs. inactive] = 0.85; 95%CI = 0.81–0.90). The two-step GxE method identified an interaction between rs4779584, an intergenic variant near the GREM1 and SCG5 genes, and PA for CRC risk (p-interaction = 2.6×10 − 8 ). Stratification by genotype at this locus showed a significant reduction in CRC risk by 20% in active vs. inactive participants with the CC genotype (OR = 0.80; 95%CI = 0.75–0.85), but no significant PA–CRC association among CT or TT carriers. When PA was modeled as quartiles, the 1-d.f. GxE test identified that rs56906466, an intergenic variant near the KCNG1 gene, modified the association between PA and CRC (p-interaction = 3.5×10 − 8 ). Stratification at this locus showed that increase in PA (highest vs. lowest quartile) was associated with a lower CRC risk solely among TT carriers (OR = 0.77; 95%CI = 0.72–0.82). Conclusions In summary, we identified two genetic variants that modified the association between PA and CRC risk. One of them, related to GREM1 and SCG5 , suggests that the bone morphogenetic protein (BMP)-related, inflammatory, and/or insulin signaling pathways may be associated with the protective influence of PA on colorectal carcinogenesis.

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.

Abstract Background Although 20 pancreatic cancer susceptibility loci have been identified through genome-wide association studies in individuals of European ancestry, much of its heritability remains unexplained and the genes responsible largely unknown. Methods To discover novel pancreatic cancer risk loci and possible causal genes, we performed a pancreatic cancer transcriptome-wide association study in Europeans using three approaches: FUSION, MetaXcan, and Summary-MulTiXcan. We integrated genome-wide association studies summary statistics from 9040 pancreatic cancer cases and 12 496 controls, with gene expression prediction models built using transcriptome data from histologically normal pancreatic tissue samples (NCI Laboratory of Translational Genomics [n = 95] and Genotype-Tissue Expression v7 [n = 174] datasets) and data from 48 different tissues (Genotype-Tissue Expression v7, n = 74–421 samples). Results We identified 25 genes whose genetically predicted expression was statistically significantly associated with pancreatic cancer risk (false discovery rate &lt; .05), including 14 candidate genes at 11 novel loci (1p36.12: CELA3B; 9q31.1: SMC2, SMC2-AS1; 10q23.31: RP11-80H5.9; 12q13.13: SMUG1; 14q32.33: BTBD6; 15q23: HEXA; 15q26.1: RCCD1; 17q12: PNMT, CDK12, PGAP3; 17q22: SUPT4H1; 18q11.22: RP11-888D10.3; and 19p13.11: PGPEP1) and 11 at six known risk loci (5p15.33: TERT, CLPTM1L, ZDHHC11B; 7p14.1: INHBA; 9q34.2: ABO; 13q12.2: PDX1; 13q22.1: KLF5; and 16q23.1: WDR59, CFDP1, BCAR1, TMEM170A). The association for 12 of these genes (CELA3B, SMC2, and PNMT at novel risk loci and TERT, CLPTM1L, INHBA, ABO, PDX1, KLF5, WDR59, CFDP1, and BCAR1 at known loci) remained statistically significant after Bonferroni correction. Conclusions By integrating gene expression and genotype data, we identified novel pancreatic cancer risk loci and candidate functional genes that warrant further investigation.