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Breast cancer (BrCa) is the most common cancer for women globally. BrCa incidence varies by age and differs between racial groups, with Black women having an earlier age of onset and higher mortality compared to White women. The underlying biological mechanisms of this disparity remain uncertain. Here, we address this knowledge gap by examining the association between overall epigenetic age acceleration and BrCa initiation as well as the mediating role of race. We measured whole-genome methylation (866,238 CpGs) using the Illumina EPIC array in blood DNA extracted from 209 women recruited from University Hospitals Cleveland Medical Center. Overall and intrinsic epigenetic age acceleration was calculated-accounting for the estimated white blood cell distribution-using the second-generation biological clock GrimAge. After quality control, 149 BrCa patients and 42 disease-free controls remained. The overall chronological mean age at BrCa diagnosis was 57.4 ± 11.4 years and nearly one-third of BrCa cases were self-reported Black women (29.5%). When comparing BrCa cases to disease-free controls, GrimAge acceleration was 2.48 years greater (p-value = 0.0056), while intrinsic epigenetic age acceleration was 1.72 years higher (p-value = 0.026) for cases compared to controls. After adjusting for known BrCa risk factors, we observed BrCa risk increased by 14% [odds ratio (OR) = 1.14; 95% CI: 1.05, 1.25] for a one-year increase in GrimAge acceleration. The stratified analysis by self-reported race revealed differing ORs for GrimAge acceleration: White women (OR = 1.17; 95% CI: 1.03, 1.36), and Black women (OR = 1.08; 95% CI: 0.96, 1.23). However, our limited sample size failed to detect a statistically significant interaction for self-reported race (p-value >0.05) when examining GrimAge acceleration with BrCa risk. Our study demonstrated that epigenetic age acceleration is associated with BrCa risk, and the association suggests variation by self-reported race. Although our sample size is limited, these results highlight a potential biological mechanism for BrCa risk and identifies a novel research area of BrCa health disparities requiring further inquiry.

While novel statistical methods quantifying the shared heritability of traits and diseases between ancestral distinct populations have been recently proposed, a thorough evaluation of these approaches under differing circumstances remain elusive. Brown et al.2016 proposed the method Popcorn to estimate the shared heritability, i.e. genetic correlation, using only summary statistics. Here, we evaluate Popcorn under several parameters and circumstances: sample size, number of SNPs, sample size of external reference panel, various population pairs, inappropriate external reference panel, and admixed population involved. Our results determined the minimum sample size of the external reference panel, summary statistics, and number of SNPs required to accurately estimate both the genetic correlation and heritability. Moreover, the number of individuals and SNPs required to produce accurate and stable estimates was directly proportional with heritability in Popcorn. Misrepresentation of the reference panel overestimated the genetic correlation by 20% and heritability by 60%. Lastly, applying Popcorn to homogeneous (EUR) and admixed (ASW) populations underestimated the genetic correlation by 15%. Although statistical approaches estimating the shared heritability between ancestral populations will provide novel etiologic insight, caution is required ensuring results are based on the appropriate sample size, number of SNPs, and the generalizability of the reference panel to the discovery populations.

The vast majority of genome-wide association study (GWAS) findings reported to date are from populations with European Ancestry (EA), and it is not yet clear how broadly the genetic associations described will generalize to populations of diverse ancestry. The Population Architecture Using Genomics and Epidemiology (PAGE) study is a consortium of multi-ancestry, population-based studies formed with the objective of refining our understanding of the genetic architecture of common traits emerging from GWAS. In the present analysis of five common diseases and traits, including body mass index, type 2 diabetes, and lipid levels, we compare direction and magnitude of effects for GWAS-identified variants in multiple non-EA populations against EA findings. We demonstrate that, in all populations analyzed, a significant majority of GWAS-identified variants have allelic associations in the same direction as in EA, with none showing a statistically significant effect in the opposite direction, after adjustment for multiple testing. However, 25% of tagSNPs identified in EA GWAS have significantly different effect sizes in at least one non-EA population, and these differential effects were most frequent in African Americans where all differential effects were diluted toward the null. We demonstrate that differential LD between tagSNPs and functional variants within populations contributes significantly to dilute effect sizes in this population. Although most variants identified from GWAS in EA populations generalize to all non-EA populations assessed, genetic models derived from GWAS findings in EA may generate spurious results in non-EA populations due to differential effect sizes. Regardless of the origin of the differential effects, caution should be exercised in applying any genetic risk prediction model based on tagSNPs outside of the ancestry group in which it was derived. Models based directly on functional variation may generalize more robustly, but the identification of functional variants remains challenging.

The number of Endometrial Carcinoma (EC) diagnoses is projected to increase substantially in coming decades. Although most ECs have a favorable prognosis, the aggressive, non-endometrioid subtypes are disproportionately concentrated in Black women and spread rapidly, making treatment difficult and resulting in poor outcomes. Therefore, this study offers an exploratory spatial epidemiological investigation of EC patients within a U.S.-based health system’s institutional cancer registry (n = 1748) to search for and study geographic patterns. Clinical, demographic, and geographic characteristics were compared by histotype using chi-square tests for categorical and t-tests for continuous variables. Multivariable logistic regression evaluated the impact of risks on these histotypes. Cox proportional hazard models measured risks in overall and cancer-specific death. Cluster detection indicated that patients with the EC non-endometrioid histotypes exhibit geographic clustering in their home address, such that congregate buildings can be identified for targeted outreach. Furthermore, living in a high social vulnerability area was independently associated with non-endometrioid histotypes, as continuous and categorical variables. This study provides a methodological framework for early, geographically targeted intervention; social vulnerability associations require further investigation. We have begun to fill the knowledge gap of geography in gynecologic cancers, and geographic clustering of aggressive tumors may enable targeted intervention to improve prognoses.

PurposePrevious literature shows that more bladder cancer patients overall die from causes other than the primary malignancy. Given known disparities in bladder cancer outcomes by race and sex, we aimed to characterize differences in cause-specific mortality for bladder cancer patients by these demographics.MethodsWe identified 215,252 bladder cancer patients diagnosed with bladder cancer from 2000 to 2017 in the SEER 18 database. We calculated cumulative incidence of death from seven causes (bladder cancer, COPD, diabetes, heart disease, external, other cancer, other) to assess differences in cause-specific mortality between race and sex subgroups. We used multivariable Cox proportional hazards regression and Fine-Gray competing risk models to compare risk of bladder cancer-specific mortality between race and sex subgroups overall and stratified by cancer stage.Results17% of patients died from bladder cancer (n = 36,923), 30% died from other causes (n = 65,076), and 53% were alive (n = 113,253). Among those who died, the most common cause of death was bladder cancer, followed by other cancer and diseases of the heart. All race-sex subgroups were more likely than white men to die from bladder cancer. Compared to white men, white women (HR: 1.20, 95% CI: 1.17–1.23) and Black women (HR: 1.57, 95% CI: 1.49–1.66) had a higher risk of dying from bladder cancer, overall and stratified by stage.ConclusionAmong bladder cancer patients, death from other causes especially other cancer and heart disease contributed a large proportion of mortality. We found differences in cause-specific mortality by race-sex subgroups, with Black women having a particularly high risk of dying from bladder cancer.

Background Racial/ethnic disparities in metastatic colorectal cancer (mCRC) survival are well documented as is the impact that tumor mutation of KRAS and BRAF has on prognosis. It has been suggested that frequency differences of KRAS- and BRAF- mutated tumors may partially explain this disparity. Demographic differences in mutation frequency are not well established nor whether mutation and microsatellite instability (MSI) differentially impact survival among groups. Methods Using data for 11,117 patients diagnosed with de-novo mCRC from an electronic health record-derived database we estimated adjusted odds ratios (aOR) to characterize the association between demographics and MSI and KRAS/NRAS/BRAF -mutation status. Stratified Cox models were used to identify differences in overall survival (OS), adjusting for treatment and demographics. Results Being female, compared to male, (aOR KRAS :1.33 (1.23–1.44); aOR BRAF :1.84 (1.56–2.16)), and non-Hispanic Black race (NHB), compared to non-Hispanic White (NHW) (aOR KRAS :1.62 (1.42–1.85); aOR BRAF : 0.55 (0.38–0.77)) were associated with KRAS- or BRAF- mutant tumors. MSI prevalence was similar across race/ethnicity but higher in women. BRAF- mutant tumors were associated with poorer prognosis overall, especially among non-white patients. Among patients who had KRAS/NRAS/BRAF- WT tumors we observed no difference in OS by race or MSI. Among patients with KRAS -mutant tumors, Hispanic patients had more favorable prognosis adjusted hazards ratio (aHR) = 0.76 (0.65–0.89)) than their NHW counterparts. Among those with BRAF- mutant tumors, NHB patients had poorer prognosis than NHW patients (aHR:1.78 (1.08–2.93)). Conclusion MSI and frequency of KRAS and BRAF mutations differed by demographics. Racial/ethnic disparities in OS differed by mutation. Future studies should explore biological and/or social determinants underlying these differences.

Peter Scacheri and colleagues identify ‘outside’ SNPs that physically interact with GWAS risk SNPs as part of a target gene's regulatory circuitry. Their findings suggest a model whereby outside variants and GWAS SNPs that physically interact collude to influence target transcript levels as well as clinical risk. SNPs associated with disease susceptibility often reside in enhancer clusters, or super-enhancers. Constituents of these enhancer clusters cooperate to regulate target genes and often extend beyond the linkage disequilibrium (LD) blocks containing risk SNPs identified in genome-wide association studies (GWAS). We identified 'outside variants', defined as SNPs in weak LD with GWAS risk SNPs that physically interact with risk SNPs as part of a target gene's regulatory circuitry. These outside variants further explain variation in target gene expression beyond that explained by GWAS-associated SNPs. Additionally, the clinical risk associated with GWAS SNPs is considerably modified by the genotype of outside variants. Collectively, these findings suggest a potential model in which outside variants and GWAS SNPs that physically interact in 3D chromatin collude to influence target transcript levels as well as clinical risk. This model offers an additional hypothesis for the source of missing heritability for complex traits.

Monogenic causes account for up to 20% of nephrolithiasis instances and are crucial for developing targeted treatments. Whole-exome sequencing, genome-wide association, candidate gene, and in vitro and animal functional studies are crucial to identify these mutations. Therapies targeting monogenic variants, such as RNA-interference-based treatments, have been successfully used to treat monogenic disorders.

Using computational tools, bulk transcriptomics can be deconvoluted to estimate the abundance of constituent cell types. However, existing deconvolution methods are conditioned on the assumption that the whole study population is served by a single reference panel, ignoring person-to-person heterogeneity. Here, we present imply , a novel algorithm to deconvolute cell type proportions using personalized reference panels. Simulation studies demonstrate reduced bias compared with existing methods. Real data analyses on longitudinal consortia show disparities in cell type proportions are associated with several disease phenotypes in Type 1 diabetes and Parkinson’s disease. imply is available through the R/Bioconductor package ISLET at https://bioconductor.org/packages/ISLET/ .

Wei Zheng and colleagues report the results of a large-scale genome-wide association study of colorectal cancer in East Asians. They identify six new susceptibility loci, including variants near TCF7L2 and TGFB1 . Known genetic loci explain only a small proportion of the familial relative risk of colorectal cancer (CRC). We conducted a genome-wide association study of CRC in East Asians with 14,963 cases and 31,945 controls and identified 6 new loci associated with CRC risk ( P = 3.42 × 10 −8 to 9.22 × 10 −21 ) at 10q22.3, 10q25.2, 11q12.2, 12p13.31, 17p13.3 and 19q13.2. Two of these loci map to genes ( TCF7L2 and TGFB1 ) with established roles in colorectal tumorigenesis. Four other loci are located in or near genes involved in transcriptional regulation ( ZMIZ1 ), genome maintenance ( FEN1 ), fatty acid metabolism ( FADS1 and FADS2 ), cancer cell motility and metastasis ( CD9 ), and cell growth and differentiation ( NXN ). We also found suggestive evidence for three additional loci associated with CRC risk near genome-wide significance at 8q24.11, 10q21.1 and 10q24.2. Furthermore, we replicated 22 previously reported CRC-associated loci. Our study provides insights into the genetic basis of CRC and suggests the involvement of new biological pathways.