Polygenic score for body mass index in relation to mortality among patients with renal cell cancer.

BACKGROUND: The association between body mass index (BMI) and mortality among individuals with renal cell cancer (RCC) is debated, with some observational studies suggesting a lower mortality associated with higher BMI. However, methodological issues such as confounding and reverse causation may bias these findings. Using BMI-associated genetic variants can avoid these biases and generate more valid estimates. METHODS: In this prospective cohort study, we included 1264 RCC patients (446 deaths) from the UK Biobank. We created a BMI polygenic score (PGS) based on 336 BMI-associated genetic variants. The association between the PGS and mortality (all-cause and RCC-specific) was evaluated by logistic regression (all RCC cases) and Cox regression (906 incident cases). For comparison, the associations of measured pre-diagnostic BMI and waist-to-hip ratio (WHR) with mortality were quantified by Cox regression among incident cases. We stratified these analyses by time between anthropometric measurement and RCC diagnosis to assess the influence of reverse causation. RESULTS: We did not observe an association between the BMI PGS and all-cause mortality among RCC patients (hazard ratio (HR) per SD increase = 0.98, 95% CI: 0.88,1.10). No association was found for pre-diagnostic BMI (HR per 5 kg/m2 increase = 0.93, 95% CI: 0.83,1.04) or WHR (HR per 0.1 increase = 0.97, 95% CI: 0.83,1.13) with mortality. In patients with anthropometrics measured within 2 years before RCC diagnosis, we observed associations of higher BMI (HR per 5 kg/m2 = 0.76, 95% CI: 0.59,0.98) and WHR (HR = 0.67 per 0.1 increase, 95% CI: 0.45,0.98) with a lower risk of death. Similar patterns were observed for RCC-specific mortality. CONCLUSION: We found no association between either genetic variants for high BMI or measured pre-diagnostic body adiposity and mortality among RCC patients, and our results suggested a role for reverse causation in the association of obesity with lower mortality. Future studies should be designed carefully to produce unbiased estimates that account for confounding and reverse causation.

Investigating the Role of Neighborhood Socioeconomic Status and Germline Genetics on Prostate Cancer Risk.

Background: Genetic factors play an important role in prostate cancer (PCa) development with polygenic risk scores (PRS) predicting disease risk across genetic ancestries. However, there are few convincing modifiable factors for PCa and little is known about their potential interaction with genetic risk. We analyzed incident PCa cases (n=6,155) and controls (n=98,257) of European and African ancestry from the UK Biobank (UKB) cohort to evaluate the role of neighborhood socioeconomic status (nSES)-and how it may interact with PRS-on PCa risk. Methods: We evaluated a multi-ancestry PCa PRS containing 269 genetic variants to understand the association of germline genetics with PCa in UKB. Using the English Indices of Deprivation, a set of validated metrics that quantify lack of resources within geographical areas, we performed logistic regression to investigate the main effects and interactions between nSES deprivation, PCa PRS, and PCa. Results: The PCa PRS was strongly associated with PCa (OR=2.04; 95%CI=2.00-2.09; P<0.001). Additionally, nSES deprivation indices were inversely associated with PCa: employment (OR=0.91; 95%CI=0.86-0.96; P<0.001), education (OR=0.94; 95%CI=0.83-0.98; P<0.001), health (OR=0.91; 95%CI=0.86-0.96; P<0.001), and income (OR=0.91; 95%CI=0.86-0.96; P<0.001). The PRS effects showed little heterogeneity across nSES deprivation indices, except for the Townsend Index (P=0.03). Conclusions: We reaffirmed genetics as a risk factor for PCa and identified nSES deprivation domains that influence PCa detection and are potentially correlated with environmental exposures that are a risk factor for PCa. These findings also suggest that nSES and genetic risk factors for PCa act independently.

Genetic relations between type 1 diabetes, coronary artery disease and leukocyte counts.

AIMS/HYPOTHESIS: Type 1 diabetes is associated with excess coronary artery disease (CAD) risk even when known cardiovascular risk factors are accounted for. Genetic perturbation of haematopoiesis that alters leukocyte production is a novel independent modifier of CAD risk. We examined whether there are shared genetic determinants and causal relationships between type 1 diabetes, CAD and leukocyte counts. METHODS: Genome-wide association study summary statistics were used to perform pairwise linkage disequilibrium score regression and heritability estimation from summary statistics (ρ-HESS) to respectively estimate the genome-wide and local genetic correlations, and two-sample Mendelian randomisation to estimate the causal relationships between leukocyte counts (335,855 healthy individuals), type 1 diabetes (18,942 cases, 501,638 control individuals) and CAD (122,733 cases, 424,528 control individuals). A latent causal variable (LCV) model was performed to estimate the genetic causality proportion of the genetic correlation between type 1 diabetes and CAD. RESULTS: There was significant genome-wide genetic correlation (rg) between type 1 diabetes and CAD (rg=0.088, p=8.60 × 10-3) and both diseases shared significant genome-wide genetic determinants with eosinophil count (rg for type 1 diabetes [rg(T1D)]=0.093, p=7.20 × 10-3, rg for CAD [rg(CAD)]=0.092, p=3.68 × 10-6) and lymphocyte count (rg(T1D)=-0.052, p=2.76 × 10-2, rg(CAD)=0.176, p=1.82 × 10-15). Sixteen independent loci showed stringent Bonferroni significant local genetic correlations between leukocyte counts, type 1 diabetes and/or CAD. Cis-genetic regulation of the expression levels of genes within shared loci between type 1 diabetes and CAD was associated with both diseases as well as leukocyte counts, including SH2B3, CTSH, MORF4L1, CTRB1, CTRB2, CFDP1 and IFIH1. Genetically predicted lymphocyte, neutrophil and eosinophil counts were associated with type 1 diabetes and CAD (lymphocyte OR for type 1 diabetes [ORT1D]=0.67, p=2.02-19, ORCAD=1.09, p=2.67 × 10-6; neutrophil ORT1D=0.82, p=5.63 × 10-5, ORCAD=1.17, p=5.02 × 10-14; and eosinophil ORT1D=1.67, p=5.45 × 10-25, ORCAD=1.07, p=2.03 × 10-4. The genetic causality proportion between type 1 diabetes and CAD was 0.36 ± 0.16 (pLCV=1.30 × 10-2), suggesting a possible intermediary causal variable. CONCLUSIONS/INTERPRETATION: This study sheds light on shared genetic mechanisms underlying type 1 diabetes and CAD, which may contribute to their co-occurrence through regulation of gene expression and leukocyte counts and identifies cellular and molecular targets for further investigation for disease prediction and potential drug discovery.

Ambient air pollution and urological cancer risk: A systematic review and meta-analysis of epidemiological evidence.

Exposure to ambient air pollution has significant adverse health effects; however, whether air pollution is associated with urological cancer is largely unknown. We conduct a systematic review and meta-analysis with epidemiological studies, showing that a 5 µg/m3 increase in PM2.5 exposure is associated with a 6%, 7%, and 9%, increased risk of overall urological, bladder, and kidney cancer, respectively; and a 10 µg/m3 increase in NO2 is linked to a 3%, 4%, and 4% higher risk of overall urological, bladder, and prostate cancer, respectively. Were these associations to reflect causal relationships, lowering PM2.5 levels to 5.8 µg/m3 could reduce the age-standardized rate of urological cancer by 1.5 ~ 27/100,000 across the 15 countries with the highest PM2.5 level from the top 30 countries with the highest urological cancer burden. Implementing global health policies that can improve air quality could potentially reduce the risk of urologic cancer and alleviate its burden.

Genome-wide Polygenic Risk Scores Predict Risk of Glioma and Molecular Subtypes.

BACKGROUND: Polygenic risk scores (PRS) aggregate the contribution of many risk variants to provide a personalized genetic susceptibility profile. Since sample sizes of glioma genome-wide association studies (GWAS) remain modest, there is a need to efficiently capture genetic risk using available data. METHODS: We applied a method based on continuous shrinkage priors (PRS-CS) to model the joint effects of over 1 million common variants on disease risk and compared this to an approach (PRS-CT) that only selects a limited set of independent variants that reach genome-wide significance (P<5×10-8). PRS models were trained using GWAS stratified by histological (10,346 cases, 14,687 controls) and molecular subtype (2,632 cases, 2,445 controls), and validated in two independent cohorts. RESULTS: PRS-CS was generally more predictive than PRS-CT with a median increase in explained variance (R2) of 24% (interquartile range=11-30%) across glioma subtypes. Improvements were pronounced for glioblastoma (GBM), with PRS-CS yielding larger odds ratios (OR) per standard deviation (OR=1.93, P=2.0×10-54 vs. OR=1.83, P=9.4×10-50) and higher explained variance (R2=2.82% vs. R2=2.56%). Individuals in the 80th percentile of the PRS-CS distribution had significantly higher risk of GBM (0.107%) at age 60 compared to those with average PRS (0.046%, P=2.4×10-12). Lifetime absolute risk reached 1.18% for glioma and 0.76% for IDH wildtype tumors for individuals in the 95th PRS percentile. PRS-CS augmented the classification of IDH mutation status in cases when added to demographic factors (AUC=0.839 vs. AUC=0.895, PΔAUC=6.8×10-9). CONCLUSIONS: Genome-wide PRS has potential to enhance the detection of high-risk individuals and help distinguish between prognostic glioma subtypes.

Transcriptome-wide association analysis identifies candidate susceptibility genes for prostate-specific antigen levels in men without prostate cancer.

Deciphering the genetic basis of prostate-specific antigen (PSA) levels may improve their utility for prostate cancer (PCa) screening. Using genome-wide association study (GWAS) summary statistics from 95,768 PCa-free men, we conducted a transcriptome-wide association study (TWAS) to examine impacts of genetically predicted gene expression on PSA. Analyses identified 41 statistically significant (p < 0.05/12,192 = 4.10 × 10-6) associations in whole blood and 39 statistically significant (p < 0.05/13,844 = 3.61 × 10-6) associations in prostate tissue, with 18 genes associated in both tissues. Cross-tissue analyses identified 155 statistically significantly (p < 0.05/22,249 = 2.25 × 10-6) genes. Out of 173 unique PSA-associated genes across analyses, we replicated 151 (87.3%) in a TWAS of 209,318 PCa-free individuals from the Million Veteran Program. Based on conditional analyses, we found 20 genes (11 single tissue, nine cross-tissue) that were associated with PSA levels in the discovery TWAS that were not attributable to a lead variant from a GWAS. Ten of these 20 genes replicated, and two of the replicated genes had colocalization probability of >0.5: CCNA2 and HIST1H2BN. Six of the 20 identified genes are not known to impact PCa risk. Fine-mapping based on whole blood and prostate tissue revealed five protein-coding genes with evidence of causal relationships with PSA levels. Of these five genes, four exhibited evidence of colocalization and one was conditionally independent of previous GWAS findings. These results yield hypotheses that should be further explored to improve understanding of genetic factors underlying PSA levels.

Improved multi-ancestry fine-mapping identifies cis-regulatory variants underlying molecular traits and disease risk.

Multi-ancestry statistical fine-mapping of cis-molecular quantitative trait loci (cis-molQTL) aims to improve the precision of distinguishing causal cis-molQTLs from tagging variants. However, existing approaches fail to reflect shared genetic architectures. To solve this limitation, we present the Sum of Shared Single Effects (SuShiE) model, which leverages LD heterogeneity to improve fine-mapping precision, infer cross-ancestry effect size correlations, and estimate ancestry-specific expression prediction weights. We apply SuShiE to mRNA expression measured in PBMCs (n=956) and LCLs (n=814) together with plasma protein levels (n=854) from individuals of diverse ancestries in the TOPMed MESA and GENOA studies. We find SuShiE fine-maps cis-molQTLs for 16% more genes compared with baselines while prioritizing fewer variants with greater functional enrichment. SuShiE infers highly consistent cis-molQTL architectures across ancestries on average; however, we also find evidence of heterogeneity at genes with predicted loss-of-function intolerance, suggesting that environmental interactions may partially explain differences in cis-molQTL effect sizes across ancestries. Lastly, we leverage estimated cis-molQTL effect-sizes to perform individual-level TWAS and PWAS on six white blood cell-related traits in AOU Biobank individuals (n=86k), and identify 44 more genes compared with baselines, further highlighting its benefits in identifying genes relevant for complex disease risk. Overall, SuShiE provides new insights into the cis-genetic architecture of molecular traits.

Association of immunoglobulin E levels with glioma risk and survival.

Background: Previous epidemiological studies have reported an association of serum immunoglobulin E (IgE) levels with reduced glioma risk, but the association between IgE and glioma prognosis is not well characterized. This study aimed to examine how sex, tumor subtype, and IgE class modulate the association of serum IgE levels with glioma risk and survival. Methods: We conducted a case-control study using participants from the University of California, San Francisco Adult Glioma Study (1997-2010). Serum IgE levels for total, respiratory and food allergy were measured in adults diagnosed with glioma (n=1,696) and cancer-free controls (n=1,135) matched based on age, sex, and race/ethnicity. Logistic regression was adjusted for patient demographics to assess the association between IgE levels and glioma risk. Multivariable Cox regression adjusted for patient-specific and tumor-specific factors compared survival between the elevated and normal IgE groups. Results: Elevated total IgE was associated with reduced risk of IDH wildtype (OR=0.65, 95% CI: 0.54-0.78) and IDH mutant glioma (OR=0.65, 95% CI: 0.50-0.85). In multivariable Cox regression, elevated respiratory IgE was associated with improved survival for IDH wildtype glioma (HR=0.78, 95% CI: 0.67-0.91). The reduction in mortality risk was more pronounced in females (HR=0.71, 95% CI: 0.53-0.96) than in males (HR=0.80, 95% CI: 0.66-0.97), with improvements in median survival of 6.2 months (P<.001) and 1.6 months (P=0.003), respectively. Conclusion: Elevated serum IgE was associated with improved prognosis for IDH wildtype glioma, with a more pronounced protective effect in females. These results suggest a possible sexual dimorphism and antitumor activity of IgE-mediated immune responses.

The full spectrum of SLC22 OCT1 mutations illuminates the bridge between drug transporter biophysics and pharmacogenomics.

Mutations in transporters can impact an individual's response to drugs and cause many diseases. Few variants in transporters have been evaluated for their functional impact. Here, we combine saturation mutagenesis and multi-phenotypic screening to dissect the impact of 11,213 missense single-amino-acid deletions, and synonymous variants across the 554 residues of OCT1, a key liver xenobiotic transporter. By quantifying in parallel expression and substrate uptake, we find that most variants exert their primary effect on protein abundance, a phenotype not commonly measured alongside function. Using our mutagenesis results combined with structure prediction and molecular dynamic simulations, we develop accurate structure-function models of the entire transport cycle, providing biophysical characterization of all known and possible human OCT1 polymorphisms. This work provides a complete functional map of OCT1 variants along with a framework for integrating functional genomics, biophysical modeling, and human genetics to predict variant effects on disease and drug efficacy.

Polygenic risk score for ulcerative colitis predicts immune checkpoint inhibitor-mediated colitis.

Immune checkpoint inhibitor-mediated colitis (IMC) is a common adverse event of treatment with immune checkpoint inhibitors (ICI). We hypothesize that genetic susceptibility to Crohn's disease (CD) and ulcerative colitis (UC) predisposes to IMC. In this study, we first develop a polygenic risk scores for CD (PRSCD) and UC (PRSUC) in cancer-free individuals and then test these PRSs on IMC in a cohort of 1316 patients with ICI-treated non-small cell lung cancer and perform a replication in 873 ICI-treated pan-cancer patients. In a meta-analysis, the PRSUC predicts all-grade IMC (ORmeta=1.35 per standard deviation [SD], 95% CI = 1.12-1.64, P = 2×10-03) and severe IMC (ORmeta=1.49 per SD, 95% CI = 1.18-1.88, P = 9×10-04). PRSCD is not associated with IMC. Furthermore, PRSUC predicts severe IMC among patients treated with combination ICIs (ORmeta=2.20 per SD, 95% CI = 1.07-4.53, P = 0.03). Overall, PRSUC can identify patients receiving ICI at risk of developing IMC and may be useful to monitor patients and improve patient outcomes.

A noncoding regulatory variant in IKZF1 increases acute lymphoblastic leukemia risk in Hispanic/Latino children.

Hispanic/Latino children have the highest risk of acute lymphoblastic leukemia (ALL) in the US compared to other racial/ethnic groups, yet the basis of this remains incompletely understood. Through genetic fine-mapping analyses, we identified a new independent childhood ALL risk signal near IKZF1 in self-reported Hispanic/Latino individuals, but not in non-Hispanic White individuals, with an effect size of ∼1.44 (95% confidence interval = 1.33-1.55) and a risk allele frequency of ∼18% in Hispanic/Latino populations and <0.5% in European populations. This risk allele was positively associated with Indigenous American ancestry, showed evidence of selection in human history, and was associated with reduced IKZF1 expression. We identified a putative causal variant in a downstream enhancer that is most active in pro-B cells and interacts with the IKZF1 promoter. This variant disrupts IKZF1 autoregulation at this enhancer and results in reduced enhancer activity in B cell progenitors. Our study reveals a genetic basis for the increased ALL risk in Hispanic/Latino children.

Genome-wide Polygenic Risk Scores Predict Risk of Glioma and Molecular Subtypes.

Background: Polygenic risk scores (PRS) aggregate the contribution of many risk variants to provide a personalized genetic susceptibility profile. Since sample sizes of glioma genome-wide association studies (GWAS) remain modest, there is a need to find efficient ways of capturing genetic risk factors using available germline data. Methods: We developed a novel PRS (PRS-CS) that uses continuous shrinkage priors to model the joint effects of over 1 million polymorphisms on disease risk and compared it to an approach (PRS-CT) that selects a limited set of independent variants that reach genome-wide significance (P<5×10-8). PRS models were trained using GWAS results stratified by histological (10,346 cases, 14,687 controls) and molecular subtype (2,632 cases, 2,445 controls), and validated in two independent cohorts. Results: PRS-CS was consistently more predictive than PRS-CT across glioma subtypes with an average increase in explained variance (R2) of 21%. Improvements were particularly pronounced for glioblastoma tumors, with PRS-CS yielding larger effect sizes (odds ratio (OR)=1.93, P=2.0×10-54 vs. OR=1.83, P=9.4×10-50) and higher explained variance (R2=2.82% vs. R2=2.56%). Individuals in the 95th percentile of the PRS-CS distribution had a 3-fold higher lifetime absolute risk of IDH mutant (0.63%) and IDH wildtype (0.76%) glioma relative to individuals with average PRS. PRS-CS also showed high classification accuracy for IDH mutation status among cases (AUC=0.895). Conclusions: Our novel genome-wide PRS may improve the identification of high-risk individuals and help distinguish between prognostic glioma subtypes, increasing the potential clinical utility of germline genetics in glioma patient management.