Association analysis identifies 65 new breast cancer risk loci

Germline copy number variants and endometrial cancer risk

Known risk loci for endometrial cancer explain approximately one third of familial endometrial cancer. However, the association of germline copy number variants (CNVs) with endometrial cancer risk remains relatively unknown. We conducted a genome-wide analysis of rare CNVs overlapping gene regions in 4115 endometrial cancer cases and 17,818 controls to identify functionally relevant variants associated with disease. We identified a 1.22-fold greater number of CNVs in DNA samples from cases compared to DNA samples from controls (p = 4.4 × 10-63). Under three models of putative CNV impact (deletion, duplication, and loss of function), genome-wide association studies identified 141 candidate gene loci associated (p < 0.01) with endometrial cancer risk. Pathway analysis of the candidate loci revealed an enrichment of genes involved in the 16p11.2 proximal deletion syndrome, driven by a large recurrent deletion (chr16:29,595,483-30,159,693) identified in 0.15% of endometrial cancer cases and 0.02% of control participants. Together, these data provide evidence that rare copy number variants have a role in endometrial cancer susceptibility and that the proximal 16p11.2 BP4-BP5 region contains 25 candidate risk gene(s) that warrant further analysis to better understand their role in human disease.

Development of a Breast Cancer Risk Prediction Model Integrating Monogenic, Polygenic, and Epidemiologic Risk

BACKGROUND

Breast cancer has been associated with monogenic, polygenic, and epidemiologic (clinical, reproductive, and lifestyle) risk factors, but studies evaluating the combined effects of these factors have been limited.

METHODS

We extended previous work in breast cancer risk modeling, incorporating pathogenic variants (PV) in six breast cancer predisposition genes and a 105-SNP polygenic risk score (PRS), to include an epidemiologic risk score (ERS) in a sample of non-Hispanic White women drawn from prospective cohorts and population-based case-control studies, with 23,518 cases and 22,832 controls, from the Cancer Risk Estimates Related to Susceptibility (CARRIERS) Consortium.

RESULTS

The model predicts 4.4-fold higher risk of breast cancer for postmenopausal women with no predisposition PV and median PRS, but with the highest versus lowest ERS. Overall, women with CHEK2 PVs had >20% lifetime risk of breast cancer. However, 15.6% of women with CHEK2 PVs and a family history of breast cancer, and 45.1% of women with CHEK2 PVs but without a family history of breast cancer, had low (<20%) predicted lifetime risk and thus were below the threshold for MRI screening. CHEK2 PV carriers at the 10th percentile of the joint distribution of ERS and PRS, without a family history of breast cancer, had a predicted lifetime risk similar to the general population.

CONCLUSIONS

These results illustrate that an ERS, alone and combined with the PRS, can contribute to clinically relevant risk stratification.

IMPACT

Integrating monogenic, polygenic, and epidemiologic risk factors in breast cancer risk prediction models may inform personalized screening and prevention efforts.

Association between cholelithiasis, cholecystectomy, and risk of breast and gynecological cancers: Evidence from meta-analysis and Mendelian randomization study

BACKGROUND

Observational studies have shown that cholelithiasis and cholecystectomy are associated with the risk of breast cancer (BC) and gynecological cancers, but whether these relationships are causal has not been established and remains controversial.

METHODS

Our study began with a meta-analysis that synthesized data from prior observational studies to examine the association between cholelithiasis, cholecystectomy, and the risk of BC and gynecological cancers. Subsequently, a two-sample Mendelian randomization (MR) analysis was conducted utilizing genetic variant data to investigate the potential causal relationship between cholelithiasis, cholecystectomy, and the aforementioned cancers.

RESULTS

The results of the meta-analysis demonstrated a significant association between cholecystectomy and the risk of BC (risk ratio [RR] = 1.04, 95% confidence interval [CI]: 1.01-1.06, p = 0.002) and endometrial cancer (EC) (RR = 1.26, 95% CI: 1.02-1.56, p = 0.031). Conversely, no significant association was observed between cholelithiasis and the risk of BC, EC, and ovarian cancer. The MR analysis revealed no discernible causal connection between cholelithiasis and overall BC (p = 0.053), as well as BC subtypes (including estrogen receptor-positive/negative). Similarly, there was no causal effect of cholecystectomy on BC risk (p = 0.399) and its subtypes. Furthermore, no causal associations were identified between cholelithiasis, cholecystectomy, and the risk of gynecological cancers (ovarian, endometrial, and cervical cancer [CC]) (all p > 0.05).

CONCLUSION

This study does not support a causal link between cholelithiasis and cholecystectomy and an increased risk of female cancers such as breast, endometrial, ovarian, and CC.

A proteome-wide association study identifies putative causal proteins for breast cancer risk

BACKGROUND

Genome-wide association studies (GWAS) have identified more than 200 breast cancer risk-associated genetic loci, yet the causal genes and biological mechanisms for most loci remain elusive. Proteins, as final gene products, are pivotal in cellular function. In this study, we conducted a proteome-wide association study (PWAS) to identify proteins in breast tissue related to breast cancer risk.

METHODS

We profiled the proteome in fresh frozen breast tissue samples from 120 cancer-free European-ancestry women from the Susan G. Komen Tissue Bank (KTB). Protein expression levels were log2-transformed then normalized via quantile and inverse-rank transformations. GWAS data were also generated for these 120 samples. These data were used to build statistical models to predict protein expression levels via cis-genetic variants using the elastic net method. The prediction models were then applied to the GWAS summary statistics data of 133,384 breast cancer cases and 113,789 controls to assess the associations of genetically predicted protein expression levels with breast cancer risk overall and its subtypes using the S-PrediXcan method.

RESULTS

A total of 6388 proteins were detected in the normal breast tissue samples from 120 women with a high detection false discovery rate (FDR) p value < 0.01. Among the 5820 proteins detected in more than 80% of participants, prediction models were successfully built for 2060 proteins with R > 0.1 and P < 0.05. Among these 2060 proteins, five proteins were significantly associated with overall breast cancer risk at an FDR p value < 0.1. Among these five proteins, the corresponding genes for proteins COPG1, DCTN3, and DDX6 were located at least 1 Megabase away from the GWAS-identified breast cancer risk variants. COPG1 was associated with an increased risk of breast cancer with a p value of 8.54 × 10-4. Both DCTN3 and DDX6 were associated with a decreased risk of breast cancer with p values of 1.01 × 10-3 and 3.25 × 10-4, respectively. The corresponding genes for the remaining two proteins, LSP1 and DNAJA3, were located in previously GWAS-identified breast cancer risk loci. After adjusting for GWAS-identified risk variants, the association for DNAJA3 was still significant (p value of 9.15 × 10-5 and adjusted p value of 1.94 × 10-4). However, the significance for LSP1 became weaker with a p value of 0.62. Stratification analyses by breast cancer subtypes identified three proteins, SMARCC1, LSP1, and NCKAP1L, associated with luminal A, luminal B, and ER-positive breast cancer. NCKAP1L was located at least 1Mb away from the GWAS-identified breast cancer risk variants. After adjusting for GWAS-identified breast cancer risk variants, the association for protein LSP1 was still significant (adjusted p value of 6.43 × 10-3 for luminal B subtype).

CONCLUSION

We conducted the first breast-tissue-based PWAS and identified seven proteins associated with breast cancer, including five proteins not previously implicated. These findings help improve our understanding of the underlying genetic mechanism of breast cancer development.

Two-sample Mendelian Randomization to evaluate the causal relationship between inflammatory arthritis and female-specific cancers

BACKGROUND

There is evidence that inflammatory arthritis in the form of ankylosing spondylitis (AS), psoriatic arthritis (PsA), and rheumatoid arthritis are both positively and negatively associated with certain female-specific cancers. However, the study results are very heterogeneous.

METHODS

Based on up to 375,814 European women, we performed an iterative two-sample Mendelian randomization to assess causal effects of the occurrence of the inflammatory arthritis on the risk of female-specific cancer in form of breast, endometrial, and ovarian cancer sites as well as their subtypes. Evidence was strengthened by using similar exposures for plausibility or by replication with a subsequent meta-analysis. P-values were Bonferroni adjusted.

RESULTS

Genetic liability to AS was associated with ovarian cancer (OR = 1.03; 95% CI: [1.01; 1.04]; [Formula: see text]=0.029) and liability to PsA with breast cancer (OR = 1.02; CI: [1.01; 1.04]; [Formula: see text]=0.002). Subgroup analyses revealed that the high-grade serous ovarian cancer (OR = 1.04; CI: [1.02; 1.06]; [Formula: see text]=0.015) and the ER- breast cancer (OR = 1.04; CI: [1.01; 1.07]; [Formula: see text]=0.118) appeared to drive the observed associations, respectively. No further associations were found between the remaining inflammatory arthritis phenotypes and female-specific cancers.

CONCLUSIONS

This study suggests that AS is a risk factor for ovarian cancer, while PsA is linked to an increased breast cancer risk. These results are important for physicians caring women with inflammatory arthritis to advise their patients on cancer screening and preventive measures.

Investigating the genetic causal relationship between breast cancer and endometrial cancer: A two-sample Mendelian randomization study

Observational studies have consistently shown a correlation between breast cancer (BC) and endometrial cancer (EC). Despite these findings, the causal relationship between these cancers has not been clearly defined. This research employed a bidirectional two-sample Mendelian randomization to explore the genetic causality between BC and EC. Genetic instruments for BC were derived from the Breast Cancer Association Consortium genome-wide association studies summary statistics, while for EC, data were sourced from the Endometrial Cancer Association Consortium, the Epidemiology of Endometrial Cancer Consortium, and the UK Biobank. The primary analytical method was inverse-variance weighted. Additional analyses, such as MR-Egger and weighted median, were conducted to validate the robustness of our findings from multiple perspectives. The MR-Egger intercept test was conducted to examine potential pleiotropy, whereas Cochrane Q test was implemented to assess heterogeneity. A leave-one-out analysis was conducted to assess the sensitivity of the observed association. Our analysis identified a bidirectional genetic causal relationship between estrogen receptor-positive breast cancer (ER+BC) and EC. Inverse-variance weighted analysis indicated an odds ratio of 1.0686 (95% confidence interval: 1.0029-1.1386, P = .0403) from ER+BC to EC and an odds ratio of 1.0692 (95% confidence interval: 1.0183-1.1225, P = .0071) from EC to ER+BC. No significant horizontal pleiotropy was detected. This study confirms a bidirectional genetic link between ER+BC and EC, suggesting shared genetic etiologies and possibly linked pathophysiological pathways. Understanding the genetic interplay between ER+BC and EC can enhance strategies for the precise prevention and screening of these prevalent cancers, potentially leading to improved clinical outcomes and management of secondary primary malignancies.

Immunocyte phenotype and breast cancer risk: A Mendel randomization analysis

BACKGROUND

Breast cancer remains a significant global health challenge. Understanding its etiological factors, particularly the role of immune system components, is crucial. This study leverages Mendelian randomization (MR) to investigate the causal relationship between various immune cell features and the risk of developing breast cancer.

METHODS

Utilizing two-sample MR analysis, we examined 731 immune cell features across 7 groups for their potential causal links to breast cancer. We analyzed genome-wide association studies (GWAS) data of 257,730 Europeans, comprising 17,389 cases and 240,341 controls, focusing on 24,133,589 single nucleotide polymorphisms (SNPs). Instrumental variables (IVs) were selected based on genetic associations, with rigorous statistical methods employed, including inverse variance weighting (IVW) and weighted median-based estimation.

RESULTS

Our analysis identified 20 immunophenotypes with significant causal associations with breast cancer risk. Notably, contain B cell, mature T cell, T + B + NK (TBNK) cells, regulatory T (Treg) cell, Classic dendritic cells (cDCs), Monocyte, and Myeloid cell group features displayed positive or negative correlations with breast cancer. For instance, specific B cell phenotypes were found to have both positive and negative causal relationships with breast cancer. Additionally, reverse MR analysis revealed no significant causal effects of breast cancer on these immune characteristics.

CONCLUSIONS

This study underscores the complex interplay between various immune cell phenotypes and breast cancer risk. The identified immunophenotypes could be potential biomarkers or targets for future therapeutic interventions. Our findings contribute to a deeper understanding of the immunological dimensions of breast cancer etiology.

Polygenic risk and rare variant gene clustering enhance cancer risk stratification for breast and prostate cancers

Polygenic risk score (PRS) and rare monogenic variant screening are valuable tools for predicting cancer risk and identifying individuals at high risk. Integrating both common and rare genetic variants is crucial for accurate risk assessment. However, estimating the impacts of rare variants on cancer and combining them with PRS remains challenging. Here, we analyze 454,711 exome sequencing and 487,409 array UK Biobank samples, focusing on breast and prostate cancers. We introduce an expanded PRS (EPRS) approach, yielding a systematic model for more effective risk stratification. By prioritizing and clustering genes with cancer-specific rare variants based on odds ratios and population-attributable fraction, we refine risk stratification by combining both monogenic and polygenic effects. Individuals in high-PRS groups with rare high-impact gene variants show up to 15- and 22-fold higher risk for breast and prostate cancers, respectively, compared to those in the intermediate-PRS groups without rare variants. Combined risk profiles vary across distinct rare variant clusters within the same PRS group for both cancers. Our EPRS approach enhances risk stratification for breast and prostate cancers, offering important insights for future research and potential applications to other cancer types.

Multi-ancestry GWAS meta-analyses of lung cancer reveal susceptibility loci and elucidate smoking-independent genetic risk

Lung cancer remains the leading cause of cancer mortality, despite declining smoking rates. Previous lung cancer GWAS have identified numerous loci, but separating the genetic risks of lung cancer and smoking behavioral susceptibility remains challenging. Here, we perform multi-ancestry GWAS meta-analyses of lung cancer using the Million Veteran Program cohort (approximately 95% male cases) and a previous study of European-ancestry individuals, jointly comprising 42,102 cases and 181,270 controls, followed by replication in an independent cohort of 19,404 cases and 17,378 controls. We then carry out conditional meta-analyses on cigarettes per day and identify two novel, replicated loci, including the 19p13.11 pleiotropic cancer locus in squamous cell lung carcinoma. Overall, we report twelve novel risk loci for overall lung cancer, lung adenocarcinoma, and squamous cell lung carcinoma, nine of which are externally replicated. Finally, we perform PheWAS on polygenic risk scores for lung cancer, with and without conditioning on smoking. The unconditioned lung cancer polygenic risk score is associated with smoking status in controls, illustrating a reduced predictive utility in non-smokers. Additionally, our polygenic risk score demonstrates smoking-independent pleiotropy of lung cancer risk across neoplasms and metabolic traits.

Germline polygenic risk scores are associated with immune gene expression signature and immune cell infiltration in breast cancer

The tumor immune microenvironment (TIME) plays key roles in tumor progression and response to immunotherapy. Previous studies have identified individual germline variants associated with differences in TIME. Here, we hypothesize that common variants associated with breast cancer risk or cancer-related traits, represented by polygenic risk scores (PRSs), may jointly influence immune features in TIME. We derived 154 immune traits from bulk gene expression profiles of 764 breast tumors and 598 adjacent normal tissue samples from 825 individuals with breast cancer in the Nurses' Health Study (NHS) and NHSII. Immunohistochemical staining of four immune cell markers were available for a subset of 205 individuals. Germline PRSs were calculated for 16 different traits including breast cancer, autoimmune diseases, type 2 diabetes, ages at menarche and menopause, body mass index (BMI), BMI-adjusted waist-to-hip ratio, alcohol intake, and tobacco smoking. Overall, we identified 44 associations between germline PRSs and immune traits at false discovery rate q < 0.25, including 3 associations with q < 0.05. We observed consistent inverse associations of inflammatory bowel disease (IBD) and Crohn disease (CD) PRSs with interferon signaling and STAT1 scores in breast tumor and adjacent normal tissue; these associations were replicated in a Norwegian cohort. Inverse associations were also consistently observed for IBD PRS and B cell abundance in normal tissue. We also observed positive associations between CD PRS and endothelial cell abundance in tumor. Our findings suggest that the genetic mechanisms that influence immune-related diseases are also associated with TIME in breast cancer.

Associations between serum metabolites and female cancers: A bidirectional two-sample mendelian randomization study

Female cancers, especially breast, ovarian, cervical, and endometrial cancers, constitute a major threat to women's health worldwide. In view of the complex genetic background of cancers cannot be fully explained with current genetic information, we used a bidirectional two-sample mendelian randomization approach to explore the causal associations between serum metabolites and four major female cancers-breast, ovarian, cervical, and endometrial cancers. We analyzed the metabolites dataset from the Canadian Longitudinal Study of Aging and cancer datasets from the 10th round of the Finngen project. Replication analyses was performed with Cancer Association Consortium and Leo's studies. Instrumental variables were analyzed using methods including the Wald ratio, inverse-variance weighted, MR-Egger, and weighted median. To ensure robustness, sensitivity analyses were performed using Cochrane's Q, Egger's intercept, MR-PRESSO, and leave-one-out methods. After meticulous analysis, we obtained levels of 3-hydroxyoleoylcarnitine, hexadecanedioate, tetradecanedioate, and carnitine C14 with robust causal associations with breast cancer, levels of 5alpha-androstan-3alpha,17beta-diol monosulfate (1), androstenediol (3beta,17beta) monosulfate (1), androsterone sulfate, and 5alpha-androstan-3beta,17beta-diol disulfate causal associations with endometrial cancer. The reverse analysis showed that breast, ovarian, and endometrial cancer and survival of breast and ovarian cancer were found to have causal relationships with 8, 5, 2, 6, and 3 metabolites, respectively. These insights underscore the potential roles of specific metabolites in the etiology of female cancers, providing new biomarkers for early detection, risk stratification, and disease progression monitoring. Further research could elucidate how these metabolites influence specific pathways in cancer development, offering theoretical foundations for prevention and treatment strategies.

Systematic prioritization of functional variants and effector genes underlying colorectal cancer risk

Genome-wide association studies of colorectal cancer (CRC) have identified 170 autosomal risk loci. However, for most of these, the functional variants and their target genes are unknown. Here, we perform statistical fine-mapping incorporating tissue-specific epigenetic annotations and massively parallel reporter assays to systematically prioritize functional variants for each CRC risk locus. We identify plausible causal variants for the 170 risk loci, with a single variant for 40. We link these variants to 208 target genes by analyzing colon-specific quantitative trait loci and implementing the activity-by-contact model, which integrates epigenomic features and Micro-C data, to predict enhancer-gene connections. By deciphering CRC risk loci, we identify direct links between risk variants and target genes, providing further insight into the molecular basis of CRC susceptibility and highlighting potential pharmaceutical targets for prevention and treatment.

Identification of candidate causal variants and target genes at 41 breast cancer risk loci through differential allelic expression analysis

Understanding breast cancer genetic risk relies on identifying causal variants and candidate target genes in risk loci identified by genome-wide association studies (GWAS), which remains challenging. Since most loci fall in active gene regulatory regions, we developed a novel approach facilitated by pinpointing the variants with greater regulatory potential in the disease's tissue of origin. Through genome-wide differential allelic expression (DAE) analysis, using microarray data from 64 normal breast tissue samples, we mapped the variants associated with DAE (daeQTLs). Then, we intersected these with GWAS data to reveal candidate risk regulatory variants and analysed their cis-acting regulatory potential. Finally, we validated our approach by extensive functional analysis of the 5q14.1 breast cancer risk locus. We observed widespread gene expression regulation by cis-acting variants in breast tissue, with 65% of coding and noncoding expressed genes displaying DAE (daeGenes). We identified over 54 K daeQTLs for 6761 (26%) daeGenes, including 385 daeGenes harbouring variants previously associated with BC risk. We found 1431 daeQTLs mapped to 93 different loci in strong linkage disequilibrium with risk-associated variants (risk-daeQTLs), suggesting a link between risk-causing variants and cis-regulation. There were 122 risk-daeQTL with stronger cis-acting potential in active regulatory regions with protein binding evidence. These variants mapped to 41 risk loci, of which 29 had no previous report of target genes and were candidates for regulating the expression levels of 65 genes. As validation, we identified and functionally characterised five candidate causal variants at the 5q14.1 risk locus targeting the ATG10 and ATP6AP1L genes, likely acting via modulation of alternative transcription and transcription factor binding. Our study demonstrates the power of DAE analysis and daeQTL mapping to identify causal regulatory variants and target genes at breast cancer risk loci, including those with complex regulatory landscapes. It additionally provides a genome-wide resource of variants associated with DAE for future functional studies.

Implementing the Risk Stratification and Clinical Management of Breast Cancer Families Using Polygenic Risk Score Evaluation: A Pilot Study

BACKGROUND

The identification of women at high risk of breast cancer (BC) is crucial for personalized screening strategies. Pathogenic and likely pathogenic variants (PVs/LPVs) in susceptibility risk genes explain part of the individual risk. Moreover, a polygenic background, summarized as a polygenic risk score (PRS), contributes to the risk of BC and may modify the individual risk in carrier and non-carrier members of BC families.

METHODS

We performed a retrospective pilot study evaluating PRS in women from a subset of high- (BRCA1 and BRCA2) and moderate-risk (PALB2 and ATM) BC families. We included PVs/LPVs carriers and non-carriers and evaluated a PRS based on 577,113 BC-associated variants. Using BOADICEA, we calculated the adjusted lifetime BC risk.

RESULTS

Our data showed that in BRCA1/BRCA2 carriers, PVs have a major role in stratifying the lifetime risk, while PRS improves risk estimation in non-carriers of these families. A different scenario may be observed in PALB2 and ATM families where PRS combined with PV/LPV carrier status gives a more informative lifetime risk.

CONCLUSIONS

This study showed that in BC families, the PRS might help to quantify the weight of the genetic familial background, improving the individual risk stratification and contributing to personalized clinical management for carrier and non-carrier women.

RNA Sequencing of Sperm from Healthy Cattle and Horses Reveals the Presence of a Large Bacterial Population

RNA molecules within ejaculated sperm can be characterized through whole-transcriptome sequencing, enabling the identification of pivotal transcripts that may influence reproductive success. However, the profiling of sperm transcriptomes through next-generation sequencing has several limitations impairing the identification of functional transcripts. In this study, we explored the nature of the RNA sequences present in the sperm transcriptome of two livestock species, cattle and horses, using RNA sequencing (RNA-seq) technology. Through processing of transcriptomic data derived from bovine and equine sperm cell preparations, low mapping rates to the reference genomes were observed, mainly attributed to the presence of ribosomal RNA and bacteria in sperm samples, which led to a reduced sequencing depth of RNAs of interest. To explore the presence of bacteria, we aligned the unmapped reads to a complete database of bacterial genomes and identified bacteria-associated transcripts which were characterized. This analysis examines the limitations associated with sperm transcriptome profiling by reporting the nature of the RNA sequences among which bacterial RNA was found. These findings can aid researchers in understanding spermatozoal RNA-seq data and pave the way for the identification of molecular markers of sperm performance.

Improving on polygenic scores across complex traits using select and shrink with summary statistics (S4) and LDpred2

BACKGROUND

As precision medicine advances, polygenic scores (PGS) have become increasingly important for clinical risk assessment. Many methods have been developed to create polygenic models with increased accuracy for risk prediction. Our select and shrink with summary statistics (S4) PGS method has previously been shown to accurately predict the polygenic risk of epithelial ovarian cancer. Here, we applied S4 PGS to 12 phenotypes for UK Biobank participants, and compared it with the LDpred2 and a combined S4 + LDpred2 method.

RESULTS

The S4 + LDpred2 method provided overall improved PGS accuracy across a variety of phenotypes for UK Biobank participants. Additionally, the S4 + LDpred2 method had the best estimated PGS accuracy in Finnish and Japanese populations. We also addressed the challenge of limited genotype level data by developing the PGS models using only GWAS summary statistics.

CONCLUSIONS

Taken together, the S4 + LDpred2 method represents an improvement in overall PGS accuracy across multiple phenotypes and populations.

Innate immunity gene Nod2 protects mice from orthotopic breast cancer

BACKGROUND

Nod2 is involved in innate immune responses to bacteria, regulation of metabolism, and sensitivity to cancer. A Nod2 polymorphism is associated with breast cancer, but the role of Nod2 in the development and progression of breast cancer is unknown.

METHODS

Here, we tested the hypothesis that Nod2 protects mice from breast cancer using the 4T1 orthotopic model of mammary tumorigenesis. WT and Nod2-/- mice were injected with 4T1 mammary carcinoma cells and the development of tumors was monitored. A detailed analysis of the tumor transcriptome was performed and genes that were differentially expressed and pathways that were predicted to be altered between WT and Nod2-/- mice were identified. The activation of key signaling molecules involved in metabolism and development of cancer was studied.

RESULTS

Our data demonstrate that Nod2-/- mice had a higher incidence and larger tumors than WT mice. Nod2-/- mice had increased expression of genes that promote DNA replication and cell division, and decreased expression of genes required for lipolysis, lipogenesis, and steroid biosynthesis compared with WT mice. Nod2-/- mice also had lower expression of genes required for adipogenesis and reduced levels of lipids compared with WT mice. The tumors in Nod2-/- mice had decreased expression of genes associated with PPARα/γ signaling, increased activation of STAT3, decreased activation of STAT5, and no change in the activation of ERK compared with WT mice.

CONCLUSIONS

We conclude that Nod2 protects mice from the 4T1 orthotopic breast tumor, and that tumors in Nod2-/- mice are predicted to have increased DNA replication and cell proliferation and decreased lipid metabolism compared with WT mice.

Polygenic scores and their applications in kidney disease

Genome-wide association studies (GWAS) have uncovered thousands of risk variants that individually have small effects on the risk of human diseases, including chronic kidney disease, type 2 diabetes, heart diseases and inflammatory disorders, but cumulatively explain a substantial fraction of disease risk, underscoring the complexity and pervasive polygenicity of common disorders. This complexity poses unique challenges to the clinical translation of GWAS findings. Polygenic scores combine small effects of individual GWAS risk variants across the genome to improve personalized risk prediction. Several polygenic scores have now been developed that exhibit sufficiently large effects to be considered clinically actionable. However, their clinical use is limited by their partial transferability across ancestries and a lack of validated models that combine polygenic, monogenic, family history and clinical risk factors. Moreover, prospective studies are still needed to demonstrate the clinical utility and cost-effectiveness of polygenic scores in clinical practice. Here, we discuss evolving methods for developing polygenic scores, best practices for validating and reporting their performance, and the study designs that will empower their clinical implementation. We specifically focus on the polygenic scores relevant to nephrology and other chronic, complex diseases and review their key limitations, necessary refinements and potential clinical applications.

An observational and genetic investigation into the association between psoriasis and risk of malignancy

The relationship between psoriasis and site-specific cancers remains unclear. Here, we aim to investigate whether psoriasis is causally associated with site-specific cancers. We use observational and genetic data from the UK Biobank, obtaining GWAS summary data, eQTL analysis data, TCGA data, and GTEx data from public datasets. We perform PheWAS, polygenic risk score analysis, and one-sample and two-sample Mendelian randomization analyses to investigate the potential causal associations between psoriasis and cancers. In the unselected PheWAS analysis, psoriasis is associated with higher risks of 16 types of cancer. Using one-sample Mendelian randomization analyses, it is found that genetically predicted psoriasis is associated with higher risks of anal canal cancer, breast cancer, follicular non-Hodgkin's lymphoma and nonmelanoma skin cancer in women; and lung cancer and kidney cancer in men. Our two-sample Mendelian randomization analysis indicates that psoriasis is causally associated with breast cancer and lung cancer. Gene annotation shows that psoriasis-related genes, such as ERAP1, are significantly different in lung and breast cancer tissues. Taken together, clinical attention to lung cancer and breast cancer may be warranted among patients with psoriasis.

Investigation of the α9-nicotinic receptor single nucleotide polymorphisms induced oncogenic properties and molecular mechanisms in breast cancer

α9-nAChR, a subtype of nicotinic acetylcholine receptor, is significantly overexpressed in female breast cancer tumor tissues compared to normal tissues. Previous studies have proposed that specific single nucleotide polymorphisms (SNPs) in the CHRNA9 (α9-nAChR) gene are associated with an increased risk of breast cancer in interaction with smoking. The study conducted a breast cancer risk assessment of the α9-nAChR SNP rs10009228 (NM_017581.4:c.1325A > G) in the Taiwanese female population, including 308 breast cancer patients and 198 healthy controls revealed that individuals with the heterozygous A/G or A/A wild genotype have an increased susceptibility to developing breast cancer in the presence of smoking compared to carriers of the G/G variant genotype. Our investigation confirmed the presence of this missense variation, resulting in an alteration of the amino acid sequence from asparagine (N442) to serine (S442) to facilitate phosphorylation within the α9-nAchR protein. Additionally, overexpression of N442 (A/A) in breast cancer cells significantly enhanced cell survival, migration, and cancer stemness compared to S442 (G/G). Four-line triple-negative breast cancer patient-derived xenograft (TNBC-PDX) models with distinct α9-nAChR rs10009228 SNP genotypes (A/A, A/G, G/G) further demonstrated that chronic nicotine exposure accelerated tumor growth through sustained activation of the α9-nAChR downstream oncogenic AKT/ERK/STAT3 pathway, particularly in individuals with the A/G or A/A genotype. Collectively, our study established the links between genetic variations in α9-nAChR and smoking exposure in promoting breast tumor development. This emphasizes the need to consider gene-environment interactions carefully while developing effective breast cancer prevention and treatment strategies.

Single-cell chromatin accessibility reveals malignant regulatory programs in primary human cancers

To identify cancer-associated gene regulatory changes, we generated single-cell chromatin accessibility landscapes across eight tumor types as part of The Cancer Genome Atlas. Tumor chromatin accessibility is strongly influenced by copy number alterations that can be used to identify subclones, yet underlying cis-regulatory landscapes retain cancer type-specific features. Using organ-matched healthy tissues, we identified the "nearest healthy" cell types in diverse cancers, demonstrating that the chromatin signature of basal-like-subtype breast cancer is most similar to secretory-type luminal epithelial cells. Neural network models trained to learn regulatory programs in cancer revealed enrichment of model-prioritized somatic noncoding mutations near cancer-associated genes, suggesting that dispersed, nonrecurrent, noncoding mutations in cancer are functional. Overall, these data and interpretable gene regulatory models for cancer and healthy tissue provide a framework for understanding cancer-specific gene regulation.

Transcriptome- and proteome-wide association studies identify genes associated with renal cell carcinoma

We performed a series of integrative analyses including transcriptome-wide association studies (TWASs) and proteome-wide association studies (PWASs) of renal cell carcinoma (RCC) to nominate and prioritize molecular targets for laboratory investigation. On the basis of a genome-wide association study (GWAS) of 29,020 affected individuals and 835,670 control individuals and prediction models trained in transcriptomic reference models, our TWAS across four kidney transcriptomes (GTEx kidney cortex, kidney tubules, TCGA-KIRC [The Cancer Genome Atlas kidney renal clear-cell carcinoma], and TCGA-KIRP [TCGA kidney renal papillary cell carcinoma]) identified 38 gene associations (false-discovery rate <5%) in at least two of four transcriptomic panels and identified 12 genes that were independent of GWAS susceptibility regions. Analyses combining TWAS associations across 48 tissues from GTEx identified associations that were replicable in tumor transcriptomes for 23 additional genes. Analyses by the two major histologic types (clear-cell RCC and papillary RCC) revealed subtype-specific associations, although at least three gene associations were common to both subtypes. PWAS identified 13 associated proteins, all mapping to GWAS-significant loci. TWAS-identified genes were enriched for active enhancer or promoter regions in RCC tumors and hypoxia-inducible factor binding sites in relevant cell lines. Using gene expression correlation, common cancers (breast and prostate) and RCC risk factors (e.g., hypertension and BMI) display genetic contributions shared with RCC. Our work identifies potential molecular targets for RCC susceptibility for downstream functional investigation.

Causality between autoimmune diseases and breast cancer: a two-sample Mendelian randomization study in a European population

BACKGROUND

The incidence of autoimmune diseases and breast cancer is significantly higher in women compared to men. Previous observational studies have not conclusively determined the relationship between these two conditions. This study utilizes the Mendelian randomization approach to investigate the genetic association between autoimmune diseases and breast cancer.

METHOD

Two-sample Mendelian randomization was conducted on a European population using the GWAS database. The inverse variance-weighted method served as the primary analytical approach. The MR-PRESSO test was applied to detect horizontal pleiotropy. To ensure result robustness, the FDR correction method was used.

RESULT

The study revealed that Sjögren's syndrome lowers the overall risk of breast cancer (OR 0.96, 95% CI [0.93-0.99], p = 0.011). Idiopathic inflammatory myopathy shows a protective effect against overall breast cancer (OR 0.98, 95% CI [0.97-0.99], p = 0.035). An association was identified between rheumatoid arthritis and overall breast cancer (OR 0.98, 95% CI [0.96-1.00], p = 0.050). No causal link was found between systemic lupus erythematosus, systemic sclerosis, and overall breast cancer. The study also suggests that Sjögren's syndrome, rheumatoid arthritis, and idiopathic inflammatory myopathy might reduce the risk of developing HER + breast cancer. Specifically, Sjögren's syndrome (OR = 0.90, 95% CI [0.83-0.98], p = 0.02), rheumatoid arthritis (OR = 0.94, 95% CI [0.91-0.98], p = 0.006), and idiopathic inflammatory myopathy (OR = 0.96, 95% CI [0.93-0.99], p = 0.036). Additionally, systemic lupus erythematosus was found to lower the risk of HER- breast cancer (OR = 0.95, 95% CI [0.91-0.99], p = 0.046). The study did not establish a causal relationship between these five autoimmune diseases and ER + or ER- breast cancer.

CONCLUSION

This study found that autoimmune diseases may act as protective factors against breast cancer risk.

Population-specific validation and comparison of the performance of 77- and 313-variant polygenic risk scores for breast cancer risk prediction

BACKGROUND

The polygenic risk score (PRS) allows the quantification of the polygenic effect of many low-penetrance alleles on the risk of breast cancer (BC). This study aimed to evaluate the performance of two sets comprising 77 or 313 low-penetrance loci (PRS77 and PRS313) in patients with BC in the Czech population.

METHODS

In a retrospective case-control study, variants were genotyped from both the PRS77 and PRS313 sets in 1329 patients with BC and 1324 noncancer controls, all women without germline pathogenic variants in BC predisposition genes. Odds ratios (ORs) were calculated according to the categorical PRS in individual deciles. Weighted Cox regression analysis was used to estimate the hazard ratio (HR) per standard deviation (SD) increase in PRS.

RESULTS

The distributions of standardized PRSs in patients and controls were significantly different (p < 2.2 × 10-16) with both sets. PRS313 outperformed PRS77 in categorical and continuous PRS analyses. For patients in the highest 2.5% of PRS313, the risk reached an OR of 3.05 (95% CI, 1.66-5.89; p = 1.76 × 10-4). The continuous risk was estimated as an HRper SD of 1.64 (95% CI, 1.49-1.81; p < 2.0 × 10-16), which resulted in an absolute risk of 21.03% at age 80 years for individuals in the 95th percentile of PRS313. Discordant categorization into PRS deciles was observed in 248 individuals (9.3%).

CONCLUSIONS

Both PRS77 and PRS313 are able to stratify individuals according to their BC risk in the Czech population. PRS313 shows better discriminatory ability. The results support the potential clinical utility of using PRS313 in individualized BC risk prediction.

Global impact of unproductive splicing on human gene expression

Alternative splicing (AS) in human genes is widely viewed as a mechanism for enhancing proteomic diversity. AS can also impact gene expression levels without increasing protein diversity by producing 'unproductive' transcripts that are targeted for rapid degradation by nonsense-mediated decay (NMD). However, the relative importance of this regulatory mechanism remains underexplored. To better understand the impact of AS-NMD relative to other regulatory mechanisms, we analyzed population-scale genomic data across eight molecular assays, covering various stages from transcription to cytoplasmic decay. We report threefold more unproductive splicing compared with prior estimates using steady-state RNA. This unproductive splicing compounds across multi-intronic genes, resulting in 15% of transcript molecules from protein-coding genes being unproductive. Leveraging genetic variation across cell lines, we find that GWAS trait-associated loci explained by AS are as often associated with NMD-induced expression level differences as with differences in protein isoform usage. Our findings suggest that much of the impact of AS is mediated by NMD-induced changes in gene expression rather than diversification of the proteome.

Genetic evidence supporting potential causal roles of EIF4 family in breast cancer: a two-sample randomized Mendelian study

Translational control plays a crucial role in the regulation of apoptosis, with the EIF4 family serving as one of the mRNA translation factors that modulate the process of mRNA translation based on mRNA characteristics. To address this potential causal role of EIF4 family proteins and breast cancer, Mendelian randomization was employed. The study incorporated four sets of genetics instrumental variables, namely EIF4E, EIF4B, EIF4A, and EIF4EBP2. The outcome variables selected for analysis were the BCAC consortium, which included estrogen receptor positive (ER+) and estrogen receptor negative (ER-) samples. To assess the potential violations of the MR assumption, the primary MR analysis employed inverse variance weighted (IVW), and several sensitivity analyses were conducted. The findings of the two-sample MR analysis indicate that EIF4E has an adverse effect on breast cancer risk (p = 0.028). However, the evidence for the relationship between EIF4E and ER status of breast cancer suggests a weak association with ER+ breast cancer (p = 0.054), but not with ER- breast cancer (p > 0.05). The study findings indicate that EIF4A is not causally linked to the risk of ER+ breast cancer, but is significantly associated with an elevated risk of ER- breast cancer (p = 0.028). However, the evidence is inadequate to support the effects of EIF4B and EIF4EBP2 on breast cancer (p > 0.05). Our results suggest that EIF4 may be a potential factor in the occurrence and development of breast cancer, which may lead to a better understanding of its causes and prevention.

Colocalised Genetic Associations Reveal Alternative Splicing Variants as Candidate Causal Links for Breast Cancer Risk in 10 Loci

Genome-wide association studies (GWASs) have revealed numerous loci associated with breast cancer risk, yet the precise causal variants, their impact on molecular mechanisms, and the affected genes often remain elusive. We hypothesised that specific variants exert their influence by affecting cis-regulatory alternative splice elements. An analysis of splicing quantitative trait loci (sQTL) in healthy breast tissue from female individuals identified multiple variants linked to alterations in splicing ratios. Through colocalisation analysis, we pinpointed 43 variants within twelve genes that serve as candidate causal links between sQTL and GWAS findings. In silico splice analysis highlighted a potential mechanism for three genes-FDPS, SGCE, and MRPL11-where variants in proximity to or on the splice site modulate usage, resulting in alternative splice transcripts. Further in vitro/vivo studies are imperative to fully understand how these identified changes contribute to breast oncogenesis. Moreover, investigating their potential as biomarkers for breast cancer risk could enhance screening strategies and early detection methods for breast cancer.

Association of telomerase reverse transcriptase gene rs10069690 variant with cancer risk: an updated meta-analysis

OBJECTIVE

Existing evidence suggests telomerase activation is a crucial step in tumorigenesis. The telomerase reverse transcriptase (TERT), encoded by the human TERT gene, is critical for telomerase expression. The TERT rs10069690 (C > T) variant was identified to be associated with the risk of cancer, however, there have been inconsistent results. Therefore, we performed a comprehensive meta-analysis aiming to clarify the association between this variant and cancer susceptibility.

METHODS

We conducted literature search in PubMed, EMbase, MEDLINE and Cochrane Library up to April 30, 2024. Overall, there are 55 studies involving 334,196 patients with cancer and 741,187 controls included in the present study. All statistical analyses were performed by STATA software (version 11.0).

RESULTS

The pooled results showed a significant association between rs10069690 and an increased risk of cancer under allele model (OR = 1.10, 95% CI: 1.07-1.13, P < 0.001), especially in European and Asian populations. When stratified by cancer types, this variant was associated with elevated risk of breast cancer (OR = 1.11, 95% CI: 1.07-1.15, P < 0.001), ovarian cancer (OR = 1.14, 95% CI: 1.10-1.19, P < 0.001), lung cancer (OR = 1.20, 95% CI: 1.07-1.35, P = 0.003), thyroid cancer (OR = 1.23, 95% CI: 1.15-1.32, P < 0.001), gastric cancer (OR = 1.31, 95% CI: 1.19-1.45, P < 0.001), and renal cell carcinoma (OR = 1.29, 95% CI: 1.07-1.55, P = 0.007), while decreased risk was found for hepatocellular carcinoma, prostate cancer and pancreatic cancer. Our results also indicated that this variant was significantly associated with solid cancer (OR = 1.11, 95% CI: 1.07-1.14, P < 0.001), but not with hematological tumor.

CONCLUSION

This systematic meta-analysis demonstrated that the TERT rs10069690 variant was a risk factor for cancer. However, the effects of this variant may vary in different types of cancer and differ across ethnic populations.

Polygenic Risk Score (PRS) Combined with NGS Panel Testing Increases Accuracy in Hereditary Breast Cancer Risk Estimation

Breast cancer (BC) is the most prominent tumor type among women, accounting for 32% of newly diagnosed cancer cases. BC risk factors include inherited germline pathogenic gene variants and family history of disease. However, the etiology of the disease remains occult in most cases. Therefore, in the absence of high-risk factors, a polygenic basis has been suggested to contribute to susceptibility. This information is utilized to calculate the Polygenic Risk Score (PRS) which is indicative of BC risk. This study aimed to evaluate retrospectively the clinical usefulness of PRS integration in BC risk calculation, utilizing a group of patients who have already been diagnosed with BC. The study comprised 105 breast cancer patients with hereditary genetic analysis results obtained by NGS. The selection included all testing results: high-risk gene-positive, intermediate/low-risk gene-positive, and negative. PRS results were obtained from an external laboratory (Allelica). PRS-based BC risk was computed both with and without considering additional risk factors, including gene status and family history. A significantly different PRS percentile distribution consistent with higher BC risk was observed in our cohort compared to the general population. Higher PRS-based BC risks were detected in younger patients and in those with FH of cancers. Among patients with a pathogenic germline variant detected, reduced PRS values were observed, while the BC risk was mainly determined by a monogenic etiology. Upon comprehensive analysis encompassing FH, gene status, and PRS, it was determined that 41.90% (44/105) of the patients demonstrated an elevated susceptibility for BC. Moreover, 63.63% of the patients with FH of BC and without an inherited pathogenic genetic variant detected showed increased BC risk by incorporating the PRS result. Our results indicate a major utility of PRS calculation in women with FH in the absence of a monogenic etiology detected by NGS. By combining high-risk strategies, such as inherited disease analysis, with low-risk screening strategies, such as FH and PRS, breast cancer risk stratification can be improved. This would facilitate the development of more effective preventive measures and optimize the allocation of healthcare resources.

Large-Scale Alternative Polyadenylation-Wide Association Studies to Identify Putative Cancer Susceptibility Genes

Alternative polyadenylation (APA) modulates mRNA processing in the 3'-untranslated regions (3' UTR), affecting mRNA stability and translation efficiency. Research into genetically regulated APA has the potential to provide insights into cancer risk. In this study, we conducted large APA-wide association studies to investigate associations between APA levels and cancer risk. Genetic models were built to predict APA levels in multiple tissues using genotype and RNA sequencing data from 1,337 samples from the Genotype-Tissue Expression project. Associations of genetically predicted APA levels with cancer risk were assessed by applying the prediction models to data from large genome-wide association studies of six common cancers among European ancestry populations: breast, ovarian, prostate, colorectal, lung, and pancreatic cancers. A total of 58 risk genes (corresponding to 76 APA sites) were associated with at least one type of cancer, including 25 genes previously not linked to cancer susceptibility. Of the identified risk APAs, 97.4% and 26.3% were supported by 3'-UTR APA quantitative trait loci and colocalization analyses, respectively. Luciferase reporter assays for four selected putative regulatory 3'-UTR variants demonstrated that the risk alleles of 3'-UTR variants, rs324015 (STAT6), rs2280503 (DIP2B), rs1128450 (FBXO38), and rs145220637 (LDHA), significantly increased the posttranscriptional activities of their target genes compared with reference alleles. Furthermore, knockdown of the target genes confirmed their ability to promote proliferation and migration. Overall, this study provides insights into the role of APA in the genetic susceptibility to common cancers. Significance: Systematic evaluation of associations of alternative polyadenylation with cancer risk reveals 58 putative susceptibility genes, highlighting the contribution of genetically regulated alternative polyadenylation of 3'UTRs to genetic susceptibility to cancer.

Comprehensive mendelian randomization reveals atrial fibrillation-breast cancer relationship and explores common druggable targets

Background

Atrial fibrillation (AF) and breast cancer pose significant risks to human health. The reasons behind the concurrent occurrence of AF and breast cancer remain unclear, leading to complex treatment approaches. Mendelian Randomization (MR) analyses aim to offer genetic evidence supporting the causation of AF and breast cancer and to investigate common druggable genes associated with both conditions.

Methods

We used two-samples of MR to sequentially explore the causal relationship between atrial fibrillation and breast cancer, and between atrial fibrillation and breast cancer therapeutic drugs, and verified the stability of the results through colocalization analysis. We utilized the Connectivity map database to infer the direction of drug effects on disease. Finally, we explored druggable genes that play a role in AF and breast cancer and performed a Phenome-wide MR analysis to analyze the potential side effects of drug targets.

Results

We found 15 breast cancer therapeutic drugs that significantly support a causal association between AF and breast cancer through expression in blood and/or atrial appendage tissue. Among these, activation of ANXA5 by Docetaxel, inhibition of EIF5A by Fulvestrant, and inhibition of GNA12 by Tamoxifen increased the risk of AF, while inhibition of ANXA5 by Gemcitabine and Vinorebine and inhibition of PCGF6 by Paclitaxel reduced the risk of AF. Inhibition of MSH6 and SF3B1 by Cyclophosphamide, as well as inhibition of SMAD4 and PSMD2 and activation of ASAH1 and MLST8 by Doxorubicin can have bidirectional effects on AF occurrence. XBP1 can be used as a common druggable gene for AF and breast cancer, and there are no potential side effects of treatment against this target.

Conclusion

This study did not find a direct disease causality between AF and breast cancer but identified 40 target genes for 15 breast cancer therapeutic drugs associated with AF, clarified the direction of action of 8 breast cancer therapeutic drugs on AF, and finally identified one common druggable target for AF and breast cancer.

GRIEVOUS: your command-line general for resolving cross-dataset genotype inconsistencies

SUMMARY

Harmonizing variant indexing and allele assignments across datasets is crucial for data integrity in cross-dataset studies such as multi-cohort genome-wide association studies, meta-analyses, and the development, validation, and application of polygenic risk scores. Ensuring this indexing and allele consistency is a laborious, time-consuming, and error-prone process requiring a certain degree of computational proficiency. Here, we introduce GRIEVOUS, a command-line tool for cross-dataset variant homogenization. By means of an internal database and a custom indexing methodology, GRIEVOUS identifies, formats, and aligns all biallelic single nucleotide polymorphisms (SNPs) across all summary statistic and genotype files of interest. Upon completion of dataset harmonization, GRIEVOUS can also be used to extract the maximal set of biallelic SNPs common to all datasets.

AVAILABILITY AND IMPLEMENTATION

GRIEVOUS and all supporting documentation and tutorials can be found at https://github.com/jvtalwar/GRIEVOUS. It is freely and publicly available under the MIT license and can be installed via pip.

Statins inhibit paclitaxel-induced PD-L1 expression and increase CD8+ T cytotoxicity for better prognosis in breast cancer

BACKGROUND

In recent years, the widespread use of lipid-lowering drugs, especially statins, has attracted people's attention. Statin use may be potentially associated with a reduced risk of breast cancer.

OBJECTIVE

To explore the relationship between statin use and cancer risk. And further explore the potential role of statins in the adjuvant treatment of breast cancer.

METHODS

Data for the Mendelian randomization portion of the study were obtained from genome-wide association studies of common cancers in the UK Biobank and FinnGen studies and from the Global Lipid Genetics Consortium's low density lipoprotein (LDL). In addition, the impacts of statins and chemotherapy drugs on breast cancer were examined using both in vitro and in vivo models, with particular attention to the expression levels of the immune checkpoint protein PD-L1 and its potential to suppress tumor growth.

RESULTS

Data from about 3.8 million cancer patients and ~1.3 million LDL-measuring individuals were analyzed. Genetically proxied HMGCR inhibition (statins) was associated with breast cancer risk reduction (P=0.0005). In vitro experiments showed that lovastatin significantly inhibited paclitaxel-induced PD-L1 expression and assisted paclitaxel in suppressing tumor cell growth. Furthermore, the combination therapy involving lovastatin and paclitaxel amplified CD8+ T-cell infiltration, bolstering their tumor-killing capacity and enhancing in vivo efficacy.

CONCLUSION

The utilization of statins is correlated with improved prognoses for breast cancer patients and may play a role in facilitating the transition from cold to hot tumors. Combination therapy with lovastatin and paclitaxel enhances CD8+ T-cell activity and leads to better prognostic characteristics.

The relationship between 11 different polygenic longevity scores, parental lifespan, and disease diagnosis in the UK Biobank

Large-scale genome-wide association studies (GWAS) strongly suggest that most traits and diseases have a polygenic component. This observation has motivated the development of disease-specific "polygenic scores (PGS)" that are weighted sums of the effects of disease-associated variants identified from GWAS that correlate with an individual's likelihood of expressing a specific phenotype. Although most GWAS have been pursued on disease traits, leading to the creation of refined "Polygenic Risk Scores" (PRS) that quantify risk to diseases, many GWAS have also been pursued on extreme human longevity, general fitness, health span, and other health-positive traits. These GWAS have discovered many genetic variants seemingly protective from disease and are often different from disease-associated variants (i.e., they are not just alternative alleles at disease-associated loci) and suggest that many health-positive traits also have a polygenic basis. This observation has led to an interest in "polygenic longevity scores (PLS)" that quantify the "risk" or genetic predisposition of an individual towards health. We derived 11 different PLS from 4 different available GWAS on lifespan and then investigated the properties of these PLS using data from the UK Biobank (UKB). Tests of association between the PLS and population structure, parental lifespan, and several cancerous and non-cancerous diseases, including death from COVID-19, were performed. Based on the results of our analyses, we argue that PLS are made up of variants not only robustly associated with parental lifespan, but that also contribute to the genetic architecture of disease susceptibility, morbidity, and mortality.

Case-Case Genome-Wide Analyses Identify Subtype-Informative Variants That Confer Risk for Breast Cancer

Breast cancer includes several subtypes with distinct characteristic biological, pathologic, and clinical features. Elucidating subtype-specific genetic etiology could provide insights into the heterogeneity of breast cancer to facilitate the development of improved prevention and treatment approaches. In this study, we conducted pairwise case-case comparisons among five breast cancer subtypes by applying a case-case genome-wide association study (CC-GWAS) approach to summary statistics data of the Breast Cancer Association Consortium. The approach identified 13 statistically significant loci and eight suggestive loci, the majority of which were identified from comparisons between triple-negative breast cancer (TNBC) and luminal A breast cancer. Associations of lead variants in 12 loci remained statistically significant after accounting for previously reported breast cancer susceptibility variants, among which, two were genome-wide significant. Fine mapping implicated putative functional/causal variants and risk genes at several loci, e.g., 3q26.31/TNFSF10, 8q22.3/NACAP1/GRHL2, and 8q23.3/LINC00536/TRPS1, for TNBC as compared with luminal cancer. Functional investigation further identified rs16867605 at 8q22.3 as a SNP that modulates the enhancer activity of GRHL2. Subtype-informative polygenic risk scores (PRS) were derived, and patients with a high subtype-informative PRS had an up to two-fold increased risk of being diagnosed with TNBC instead of luminal cancers. The CC-GWAS PRS remained statistically significant after adjusting for TNBC PRS derived from traditional case-control GWAS in The Cancer Genome Atlas and the African Ancestry Breast Cancer Genetic Consortium. The CC-GWAS PRS was also associated with overall survival and disease-specific survival among patients with breast cancer. Overall, these findings have advanced our understanding of the genetic etiology of breast cancer subtypes, particularly for TNBC. Significance: The discovery of subtype-informative genetic risk variants for breast cancer advances our understanding of the etiologic heterogeneity of breast cancer, which could accelerate the identification of targets and personalized strategies for prevention and treatment.

Investigation of the causal relationship between breast cancer and thyroid cancer: a set of two-sample bidirectional Mendelian randomization study

PURPOSE

A potential association between breast (BC) and thyroid cancer (TC) has been observed. We investigated if the relationship between BC and TC is causal using bidirectional Mendelian randomization (MR) in Asian and European populations.

METHODS

BC-linked single nucleotide polymorphisms (SNPs) were acquired from a genome-wide association study (GWAS) conducted by the Breast Cancer Association Consortium and Biobank Japan. The most recent TC GWAS data were obtained from the FinnGen Project and National Biobank of Korea. We assessed the potential causal relationship between BC and TC using various MR methods, including inverse-variance-weighting (IVW). Sensitivity, heterogeneity, and pleiotropic tests were performed to assess reliability.

RESULTS

We found a bidirectional causal association between BC and TC within Europeans (IVW, TC on BC: odds ratio [OR] 1.090, 95% confidence interval [CI]: 1.012-1.173, P = 0.023; BC on TC: OR 1.265, 95% CI: 1.158-1.381, P < 0.001). A one-way causal relationship between BC susceptibility and TC risk was found in Asians (IVW BC on TC: OR 2.274, 95% CI: 2.089-2.475, P < 0.001). Subsequently, we identified a noteworthy bidirectional causal relationship between estrogen receptor (ER)-positive BC and TC (IVW, TC on ER-positive BC: OR 1.104, 95% CI: 1.001-1.212, P = 0.038; ER-positive BC on TC: OR 1.223, 95%CI: 1.072-1.395, P = 0.003), but not ER-negative BC and TC in Europeans.

CONCLUSION

We revealed a reciprocal causal association between ER-positive BC and TC. These findings establish a theoretical framework for the simultaneous surveillance and treatment of BC and TC.

Causal association of dietary factors with five common cancers: univariate and multivariate Mendelian randomization studies

Background

Daily dietary habits are closely related to human health, and long-term unhealthy dietary intake, such as excessive consumption of alcohol and pickled foods, may promote the development of cancers. However, comprehensive research on the causal relationship between dietary habits and cancer is lacking. Therefore, this study aimed to reveal the potential causal link between dietary risk factors and the prognosis of cancer-related to genetic susceptibility.

Methods

GWAS (Genome-Wide Association Studies) summary data on dietary habits and five common types of cancer and their pathological subtypes were obtained from the UK Biobank and various cancer association consortia. A univariable two-sample Mendelian randomization (UVMR) and FDR correction analysis was conducted to explore the causal relationships between 45 dietary habits and five common types of cancer and their histopathological subtypes. In addition, multivariable Mendelian randomization analysis (MVMR) was performed to adjust for traditional risk factors for dietary habits, and the direct or indirect effects of diet on cancer were evaluated. Finally, the prognostic impact of selected instrumental variables on cancer was analyzed using an online data platform.

Results

In the UVMR analysis, four dietary habits were identified as risk factors for cancer, while five dietary habits were identified as protective factors. Among the latter, one dietary habit showed a significant association with cancer even after FDR correction, indicating a potential causal relationship. The MVMR analysis revealed that weekly beer and cider intake, may act as an independent risk factor for cancer development. Other causal associations between dietary habits and cancer risk may be mediated by intermediate factors. In the prognostic analysis, the SNPs (Single Nucleotide Polymorphisms) of average weekly beer and cider intake were set as independent risk factors and were found to significantly impact overall survival (OS) and cancer-specific survival (CSS) in lung cancer.

Conclusion

This causal relationship study supports the notion that adjusting daily dietary habits and specific dietary interventions may decrease the risk of cancer.

Generalizability of PGS313 for breast cancer risk in a Los Angeles biobank

Polygenic scores (PGSs) summarize the combined effect of common risk variants and are associated with breast cancer risk in patients without identifiable monogenic risk factors. One of the most well-validated PGSs in breast cancer to date is PGS313, which was developed from a Northern European biobank but has shown attenuated performance in non-European ancestries. We further investigate the generalizability of the PGS313 for American women of European (EA), African (AFR), Asian (EAA), and Latinx (HL) ancestry within one institution with a singular electronic health record (EHR) system, genotyping platform, and quality control process. We found that the PGS313 achieved overlapping areas under the receiver operator characteristic (ROC) curve (AUCs) in females of HL (AUC = 0.68, 95% confidence interval [CI] = 0.65-0.71) and EA ancestry (AUC = 0.70, 95% CI = 0.69-0.71) but lower AUCs for the AFR and EAA populations (AFR: AUC = 0.61, 95% CI = 0.56-0.65; EAA: AUC = 0.64, 95% CI = 0.60-0.680). While PGS313 is associated with hormone-receptor-positive (HR+) disease in EA Americans (odds ratio [OR] = 1.42, 95% CI = 1.16-1.64), this association is lost in African, Latinx, and Asian Americans. In summary, we found that PGS313 was significantly associated with breast cancer but with attenuated accuracy in women of AFR and EAA descent within a singular health system in Los Angeles. Our work further highlights the need for additional validation in diverse cohorts prior to the clinical implementation of PGSs.

Integrating muti-omics data to identify tissue-specific DNA methylation biomarkers for cancer risk

The relationship between tissue-specific DNA methylation and cancer risk remains inadequately elucidated. Leveraging resources from the Genotype-Tissue Expression consortium, here we develop genetic models to predict DNA methylation at CpG sites across the genome for seven tissues and apply these models to genome-wide association study data of corresponding cancers, namely breast, colorectal, renal cell, lung, ovarian, prostate, and testicular germ cell cancers. At Bonferroni-corrected P < 0.05, we identify 4248 CpGs that are significantly associated with cancer risk, of which 95.4% (4052) are specific to a particular cancer type. Notably, 92 CpGs within 55 putative novel loci retain significant associations with cancer risk after conditioning on proximal signals identified by genome-wide association studies. Integrative multi-omics analyses reveal 854 CpG-gene-cancer trios, suggesting that DNA methylation at 309 distinct CpGs might influence cancer risk through regulating the expression of 205 unique cis-genes. These findings substantially advance our understanding of the interplay between genetics, epigenetics, and gene expression in cancer etiology.

Evaluation of polygenic scoring methods in five biobanks shows larger variation between biobanks than methods and finds benefits of ensemble learning

Methods of estimating polygenic scores (PGSs) from genome-wide association studies are increasingly utilized. However, independent method evaluation is lacking, and method comparisons are often limited. Here, we evaluate polygenic scores derived via seven methods in five biobank studies (totaling about 1.2 million participants) across 16 diseases and quantitative traits, building on a reference-standardized framework. We conducted meta-analyses to quantify the effects of method choice, hyperparameter tuning, method ensembling, and the target biobank on PGS performance. We found that no single method consistently outperformed all others. PGS effect sizes were more variable between biobanks than between methods within biobanks when methods were well tuned. Differences between methods were largest for the two investigated autoimmune diseases, seropositive rheumatoid arthritis and type 1 diabetes. For most methods, cross-validation was more reliable for tuning hyperparameters than automatic tuning (without the use of target data). For a given target phenotype, elastic net models combining PGS across methods (ensemble PGS) tuned in the UK Biobank provided consistent, high, and cross-biobank transferable performance, increasing PGS effect sizes (β coefficients) by a median of 5.0% relative to LDpred2 and MegaPRS (the two best-performing single methods when tuned with cross-validation). Our interactively browsable online-results and open-source workflow prspipe provide a rich resource and reference for the analysis of polygenic scoring methods across biobanks.

Polygenic embryo screening: quo vadis?

Recently, the use of polygenic risk scores in embryo screening (PGT-P) has been introduced on the premise of reducing polygenic disease risk through embryo selection. However, it has been met with extensive critique: considered "technology-driven" rather than "evidence-based", concerns exist about its validity, utility, ethics, and societal effects. Its scientific foundations and criticisms thus need to be carefully considered. However, seeing as PGT-P is already offered in some settings, further questions need to be addressed, in order to give due diligence to various aspects of PGT-P. By examining the complexities of clinical introduction of PGT-P, we discuss whether PGT-P could be responsibly implemented in the first place, what elements need to be addressed if PGT-P is clinically implemented, and subsequently how counselling and decision-making of its users could be envisaged. By dissecting these elements, we provide an overview of important practical questions of PGT-P and emphasize elements of PGT-P that we think have yet to be given sufficient attention. These questions and elements are for example related to the potential target group, scope, and decision-making possibilities of PGT-P. The aspects we raise are crucial to consider by the scientific community and policy makers for the development of guidelines and/or an ethical framework for PGT-P.

DeepRisk: A deep learning approach for genome-wide assessment of common disease risk

The potential for being able to identify individuals at high disease risk solely based on genotype data has garnered significant interest. Although widely applied, traditional polygenic risk scoring methods fall short, as they are built on additive models that fail to capture the intricate associations among single nucleotide polymorphisms (SNPs). This presents a limitation, as genetic diseases often arise from complex interactions between multiple SNPs. To address this challenge, we developed DeepRisk, a biological knowledge-driven deep learning method for modeling these complex, nonlinear associations among SNPs, to provide a more effective method for scoring the risk of common diseases with genome-wide genotype data. Evaluations demonstrated that DeepRisk outperforms existing PRS-based methods in identifying individuals at high risk for four common diseases: Alzheimer's disease, inflammatory bowel disease, type 2 diabetes, and breast cancer.

Genetic drivers and cellular selection of female mosaic X chromosome loss

Mosaic loss of the X chromosome (mLOX) is the most common clonal somatic alteration in leukocytes of female individuals1,2, but little is known about its genetic determinants or phenotypic consequences. Here, to address this, we used data from 883,574 female participants across 8 biobanks; 12% of participants exhibited detectable mLOX in approximately 2% of leukocytes. Female participants with mLOX had an increased risk of myeloid and lymphoid leukaemias. Genetic analyses identified 56 common variants associated with mLOX, implicating genes with roles in chromosomal missegregation, cancer predisposition and autoimmune diseases. Exome-sequence analyses identified rare missense variants in FBXO10 that confer a twofold increased risk of mLOX. Only a small fraction of associations was shared with mosaic Y chromosome loss, suggesting that distinct biological processes drive formation and clonal expansion of sex chromosome missegregation. Allelic shift analyses identified X chromosome alleles that are preferentially retained in mLOX, demonstrating variation at many loci under cellular selection. A polygenic score including 44 allelic shift loci correctly inferred the retained X chromosomes in 80.7% of mLOX cases in the top decile. Our results support a model in which germline variants predispose female individuals to acquiring mLOX, with the allelic content of the X chromosome possibly shaping the magnitude of clonal expansion.

Genetic susceptibility to breast cancer increases the risk of neutropenia and agranulocytosis: insights from Mendelian randomization

PURPOSE

The causal relationship between breast cancer and its estrogen receptor (ER) subtypes and neutropenia and agranulocytosis is unclear.

METHODS

In two-sample Mendelian randomization (MR), we used inverse variance weighting (IVW), Bayesian weighted MR (BWMR), MR-Egger, weighted median, simple mode, and weighted mode methods to analyze causality for ER-positive breast cancer, ER-negative breast cancer, overall breast cancer, and drug-induced neutropenia and agranulocytosis. To validate the results, we performed the analysis again using GWAS data on neutropenia from different databases. In multivariable MR (MVMR), we assessed the independent effects of ER-positive and ER-negative breast cancer on causality.

RESULTS

Two-sample MR analysis showed a causal relationship between ER-positive breast cancer (IVW odds ratio (OR) = 1.319, P = 7.580 × 10-10), ER-negative breast cancer (OR = 1.285, P = 1.263 × 10-4), overall breast cancer (OR = 1.418, P = 2.123 × 10-13), and drug-induced neutropenia and a causal relationship between ER-positive breast cancer (OR = 1.349, P = 1.402 × 10-7), ER-negative breast cancer (OR = 1.235, P = 7.615 × 10-3), overall breast cancer (OR = 1.429, P = 9.111 × 10-10), and neutropenia. Similarly, ER-positive breast cancer (OR = 1.213, P = 5.350 × 10-8), ER-negative breast cancer (OR = 1.179, P = 1.300 × 10-3), and overall breast cancer (OR = 1.275, P = 8.642 × 10-11) also had a causal relationship with agranulocytosis. MVMR analysis showed that ER-positive breast cancer remained causally associated with drug-induced neutropenia (OR = 1.233, P = 4.188 × 10-4), neutropenia (OR = 1.283, P = 6.363 × 10-4), and agranulocytosis (OR = 1.142, P = 4.549 × 10-3). Heterogeneity analysis and pleiotropy test showed that our results were reliable.

CONCLUSION

Our study provides genetic evidence for a causal association between breast cancer and its estrogen receptor subtypes and neutropenia. In clinical practice, in addition to focusing on therapeutic factors, additional attention should be given to breast cancer patients to avoid severe neutropenia.

Association of ESR1 Germline Variants with TP53 Somatic Variants in Breast Tumors in a Genome-wide Study

In breast tumors, somatic mutation frequencies in TP53 and PIK3CA vary by tumor subtype and ancestry. Emerging data suggest tumor mutation status is associated with germline variants and genetic ancestry. We aimed to identify germline variants that are associated with somatic TP53 or PIK3CA mutation status in breast tumors. A genome-wide association study was conducted in 2,850 women of European ancestry with breast cancer using TP53 and PIK3CA mutation status (positive or negative) as well as specific functional categories [e.g., TP53 gain-of-function (GOF) and loss-of-function, PIK3CA activating] as phenotypes. Germline variants showing evidence of association were selected for validation analyses and tested in multiple independent datasets. Discovery association analyses found five variants associated with TP53 mutation status with P values <1 × 10-6 and 33 variants with P values <1 × 10-5. Forty-four variants were associated with PIK3CA mutation status with P values <1 × 10-5. In validation analyses, only variants at the ESR1 locus were associated with TP53 mutation status after multiple comparisons corrections. Combined analyses in European and Malaysian populations found ESR1 locus variants rs9383938 and rs9479090 associated with the presence of TP53 mutations overall (P values 2 × 10-11 and 4.6 × 10-10, respectively). rs9383938 also showed association with TP53 GOF mutations (P value 6.1 × 10-7). rs9479090 showed suggestive evidence (P value 0.02) for association with TP53 mutation status in African ancestry populations. No other variants were significantly associated with TP53 or PIK3CA mutation status. Larger studies are needed to confirm these findings and determine if additional variants contribute to ancestry-specific differences in mutation frequency.

SIGNIFICANCE

Emerging data show ancestry-specific differences in TP53 and PIK3CA mutation frequency in breast tumors suggesting that germline variants may influence somatic mutational processes. This study identified variants near ESR1 associated with TP53 mutation status and identified additional loci with suggestive association which may provide biological insight into observed differences.

Causal effect of sarcopenia-related traits on the occurrence and prognosis of breast cancer - A bidirectional and multivariable Mendelian randomization study

Introduction

Background and aims: although sarcopenia is associated with several types of cancer, there is limited research regarding its effect on breast cancer. We aimed to explore the causality between sarcopenia-related traits and the incidence and prognosis of breast cancer. Methods: two-sample bidirectional and multivariate Mendelian randomization (MR) analyses were utilized in this study. Genome-wide association studies were used to genetically identify sarcopenia-related traits, such as appendicular lean mass, grip strength of both hands, and walking pace. Data on the incidence and prognosis of breast cancer were collected from two extensive cohort studies. Multivariate MR analysis was used to adjust for body mass index, waist circumference, and whole-body fat mass. The primary method used for analysis was inverse-variance weighted analysis. Results: a significant association was found between appendicular lean mass and ER- breast cancer (OR = 0.873, 95 % CI: 0.817-0.933, p = 6.570 × 10-5). Increased grip strength of the left hand was associated with a reduced risk of ER- breast cancer (OR = 0.744, 95 % CI: 0.579-0.958, p = 0.022). Stronger grip strength of the right hand was associated with prolonged survival time of ER+ breast cancer patients (OR = 0.463, 95 % CI: 0.242-0.882, p = 0.019). In the multivariable MR analysis, appendicular lean mass, grip strength of both hands, and walking pace were still genetically associated with the development of total breast cancer and ER-/+ breast cancer. Conclusions: several sarcopenia-related traits were genetically associated with the occurrence and prognosis of breast cancer. It is crucial for elderly women to increase their strength and muscle mass to help prevent breast cancer.

Association between genetically proxied glucosamine and risk of cancer and non-neoplastic disease: A Mendelian randomization study

Introduction

Observational investigations have examined the impact of glucosamine use on the risk of cancer and non-neoplastic diseases. However, the findings from these studies face limitations arising from confounding variables, reverse causation, and conflicting reports. Consequently, the establishment of a causal relationship between habitual glucosamine consumption and the risk of cancer and non-neoplastic diseases necessitates further investigation.

Methods

For Mendelian randomization (MR) investigation, we opted to employ single-nucleotide polymorphisms (SNPs) as instruments that exhibit robust associations with habitual glucosamine consumption. We obtained the corresponding effect estimates of these SNPs on the risk of cancer and non-neoplastic diseases by extracting summary data for genetic instruments linked to 49 varied cancer types amounting to 378,284 cases and 533,969 controls, as well as 20 non-neoplastic diseases encompassing 292,270 cases and 842,829 controls. Apart from the primary analysis utilizing inverse-variance weighted MR, we conducted two supplementary approaches to account for potential pleiotropy (MR-Egger and weighted median) and assessed their respective MR estimates. Furthermore, the results of the leave-one-out analysis revealed that there were no outlying instruments.

Results

Our results suggest divergence from accepted biological understanding, suggesting that genetically predicted glucosamine utilization may be linked to an increased vulnerability to specific illnesses, as evidenced by increased odds ratios and confidence intervals (95% CI) for diseases, such as malignant neoplasm of the eye and adnexa (2.47 [1.34-4.55]), benign neoplasm of the liver/bile ducts (2.12 [1.32-3.43]), benign neoplasm of the larynx (2.01 [1.36-2.96]), melanoma (1.74 [1.17-2.59]), follicular lymphoma (1.50 [1.06-2.11]), autoimmune thyroiditis (2.47 [1.49-4.08]), and autoimmune hyperthyroidism (1.93 [1.17-3.18]). In contrast to prior observational research, our genetic investigations demonstrate a positive correlation between habitual glucosamine consumption and an elevated risk of sigmoid colon cancer, lung adenocarcinoma, and benign neoplasm of the thyroid gland.

Conclusion

Casting doubt on the purported purely beneficial association between glucosamine ingestion and prevention of neoplastic and non-neoplastic diseases, habitual glucosamine ingestion exhibits dichotomous effects on disease outcomes. Endorsing the habitual consumption of glucosamine as a preventative measure against neoplastic and non-neoplastic diseases cannot be supported.

Shared genetics between breast cancer and predisposing diseases identifies novel breast cancer treatment candidates

Background

Current effective breast cancer treatment options have severe side effects, highlighting a need for new therapies. Drug repurposing can accelerate improvements to care, as FDA-approved drugs have known safety and pharmacological profiles. Some drugs for other conditions, such as metformin, an antidiabetic, have been tested in clinical trials for repurposing for breast cancer. Here, we exploit the genetics of breast cancer and linked predisposing diseases to propose novel drug repurposing. We hypothesize that if a predisposing disease contributes to breast cancer pathology, identifying the pleiotropic genes related to the risk of cancer could prioritize drug targets, among all drugs treating a predisposing disease. We aim to develop a method to not only prioritize drug repurposing, but also to highlight shared etiology explaining repurposing.

Methods

We compile breast cancer's predisposing diseases from literature. For each predisposing disease, we use GWAS summary statistics to identify genes in loci showing genetic correlation with breast cancer. Then, we use a network approach to link these shared genes to canonical pathways, and similarly for all drugs treating the predisposing disease, we link their targets to pathways. In this manner, we are able to prioritize a list of drugs based on each predisposing disease, with each drug linked to a set of implicating pathways. Finally, we evaluate our recommendations against drugs currently under investigation for breast cancer.

Results

We identify 84 loci harboring mutations with positively correlated effects between breast cancer and its predisposing diseases; these contain 194 identified shared genes. Out of the 112 drugs indicated for the predisposing diseases, 76 drugs can be linked to shared genes via pathways (candidate drugs for repurposing). Fifteen out of these candidate drugs are already in advanced clinical trial phases or approved for breast cancer (OR = 9.28, p = 7.99e-03, one-sided Fisher's exact test), highlighting the ability of our approach to identify likely successful candidate drugs for repurposing.

Conclusions

Our novel approach accelerates drug repurposing for breast cancer by leveraging shared genetics with its known risk factors. The result provides 59 novel candidate drugs alongside biological insights supporting each recommendation.

Relationship between age at menarche and breast cancer in individuals, as well as in first-degree kin and estrogen receptor status: a Mendelian randomization study

Target

We executed a Mendelian randomization (MR) investigation employing two distinct cohorts of genetic instrumental variables to elucidate the causal nexus between age at menarche (AAM) and the incidence of disparate breast cancer (BC) subtypes, in addition to the incidence of BC among first-degree kin.

Methods

We aggregated statistical data pertaining to AAM and BC from various consortia representing a homogenous population cohort. MR analysis was conducted employing inverse variance weighted (IVW) methodology as the principal approach, complemented by weighted median and MR-Egger regression techniques for an exhaustive evaluation. To evaluate the presence of pleiotropy, we applied the MR-Egger intercept test, MR-PRESSO, and leave-one-out sensitivity analysis.

Results

Upon exclusion of confounding SNP, an increment of one standard deviation in AAM was inversely correlated with the incidence of BC. (odds ratio [OR] 0.896, 95% confidence interval [CI] 0.831-0.968)/(OR 0.998, 95% CI 0.996-0.999) and estrogen receptor-positive (ER+) BC incidence (OR 0.895, 95% CI 0.814-0.983). It was also associated with reducing the risk of maternal BC incidence (OR 0.995, 95% CI 0.990-0.999) and sibling BC incidence (OR 0.997, 95% CI 0.994-0.999). No significant association was found between AAM and estrogen receptor-negative (ER-) BC incidence (OR 0.936, 95% CI 0.845-1.037).

Conclusion

Our study substantiated the causal relationship between a delayed AAM and a diminished risk of BC in probands, as well as in their maternal progenitors and siblings. Furthermore, the analysis suggests that AAM exerts a considerable potential causal influence on the risk of developing Luminal-a/b subtype of BC.

Inflammatory bowel disease and breast cancer: A two-sample bidirectional Mendelian randomization study

There is a correlation between IBD and breast cancer according to previous observational studies. However, so far there is no evidence to support if there is a causal relationship between these 2 diseases. We acquired comprehensive Genome-Wide Association Study (GWAS) summary data on IBD (including ulcerative colitis [UC] and Crohn disease [CD]) as well as breast cancer of completely European descent from the IEU GWAS database. The estimation of bidirectional causality between IBD (including UC and CD) and breast cancer was achieved through the utilization of 2-sample Mendelian randomization (MR). The MR results were also assessed for any potential bias caused by heterogeneity and pleiotropy through sensitivity analyses. Our study found a bidirectional causal effect between IBD and breast cancer. Genetic susceptibility to IBD was associated with an increased risk of breast cancer (OR = 1.053, 95% CI: 1.016-1.090, P = .004). Similarly, the presence of breast cancer may increase the risk of IBD (OR = 1.111, 95% CI: 1.035-1.194, P = .004). Moreover, the bidirectional causal effect between IBD and breast cancer can be confirmed by another GWAS of IBD. Subtype analysis showed that CD was associated with breast cancer (OR = 1.050, 95% CI: 1.020-1.080, P < .001), but not UC and breast cancer. There was a suggestive association between breast cancer and UC (OR = 1.106, 95% CI: 1.011-1.209, P = .028), but not with CD. This study supports a bidirectional causal effect between IBD and breast cancer. There appear to be considerable differences in the specific associations of UC and CD with AD. Understanding that IBD including its specific subtypes and breast cancer constitute common risk factors can contribute to the clinical management of both diseases.