Quantifying the Genetic Correlation between Multiple Cancer Types

Complex Traits Heritability is Highly Clustered in the eQTL Bipartite Network

Single Nucleotide Polymorphisms (SNPs) associated with traits typically explain a small part of the trait genetic heritability-with the remainder thought to be distributed throughout the genome. Such SNPs are likely to alter expression levels of biologically relevant genes. Expression Quantitative Trait Locus (eQTL) networks analysis has helped to functionally characterize such variants. We systematically analyze the distribution of SNP heritability for ten traits across 29 tissue-specific eQTL networks. We find that heritability is clustered in a small number or tissue-specific, functionally relevant SNP-gene modules and that the greatest occurs in local "hubs" that are both the cornerstone of the network's modules and tissue-specific regulatory elements. The network structure could thus both amplify the genotype-phenotype connection and buffer the deleterious effect of the genetic variations on other traits. Together, these results define a conceptual framework for understanding complex trait architecture and identifying key mutations carrying most of the heritability.

A scalable approach to characterize pleiotropy across thousands of human diseases and complex traits using GWAS summary statistics

Genome-wide association studies (GWASs) across thousands of traits have revealed the pervasive pleiotropy of trait-associated genetic variants. While methods have been proposed to characterize pleiotropic components across groups of phenotypes, scaling these approaches to ultra-large-scale biobanks has been challenging. Here, we propose FactorGo, a scalable variational factor analysis model to identify and characterize pleiotropic components using biobank GWAS summary data. In extensive simulations, we observe that FactorGo outperforms the state-of-the-art (model-free) approach tSVD in capturing latent pleiotropic factors across phenotypes while maintaining a similar computational cost. We apply FactorGo to estimate 100 latent pleiotropic factors from GWAS summary data of 2,483 phenotypes measured in European-ancestry Pan-UK BioBank individuals (N = 420,531). Next, we find that factors from FactorGo are more enriched with relevant tissue-specific annotations than those identified by tSVD (p = 2.58E-10) and validate our approach by recapitulating brain-specific enrichment for BMI and the height-related connection between reproductive system and muscular-skeletal growth. Finally, our analyses suggest shared etiologies between rheumatoid arthritis and periodontal condition in addition to alkaline phosphatase as a candidate prognostic biomarker for prostate cancer. Overall, FactorGo improves our biological understanding of shared etiologies across thousands of GWASs.

Spatial Difference Boundary Detection for Multiple Outcomes Using Bayesian Disease Mapping

Regional aggregates of health outcomes over delineated administrative units (e.g., states, counties, and zip codes), or areal units, are widely used by epidemiologists to map mortality or incidence rates and capture geographic variation. To capture health disparities over regions, we seek "difference boundaries" that separate neighboring regions with significantly different spatial effects. Matters are more challenging with multiple outcomes over each unit, where we capture dependence among diseases as well as across the areal units. Here, we address multivariate difference boundary detection for correlated diseases. We formulate the problem in terms of Bayesian pairwise multiple comparisons and seek the posterior probabilities of neighboring spatial effects being different. To achieve this, we endow the spatial random effects with a discrete probability law using a class of multivariate areally referenced Dirichlet process models that accommodate spatial and interdisease dependence. We evaluate our method through simulation studies and detect difference boundaries for multiple cancers using data from the Surveillance, Epidemiology, and End Results Program of the National Cancer Institute.

Polygenic scores in cancer

Since the publication of the first genome-wide association study for cancer in 2007, thousands of common alleles that are associated with the risk of cancer have been identified. The relative risk associated with individual variants is small and of limited clinical significance. However, the combined effect of multiple risk variants as captured by polygenic scores (PGSs) may be much greater and therefore provide risk discrimination that is clinically useful. We review the considerable research efforts over the past 15 years for developing statistical methods for PGSs and their application in large-scale genome-wide association studies to develop PGSs for various cancers. We review the predictive performance of these PGSs and the multiple challenges currently limiting the clinical application of PGSs. Despite this, PGSs are beginning to be incorporated into clinical multifactorial risk prediction models to stratify risk in both clinical trials and clinical implementation studies.

Pan-cancer and cross-population genome-wide association studies dissect shared genetic backgrounds underlying carcinogenesis

Integrating genomic data of multiple cancers allows de novo cancer grouping and elucidating the shared genetic basis across cancers. Here, we conduct the pan-cancer and cross-population genome-wide association study (GWAS) meta-analysis and replication studies on 13 cancers including 250,015 East Asians (Biobank Japan) and 377,441 Europeans (UK Biobank). We identify ten cancer risk variants including five pleiotropic associations (e.g., rs2076295 at DSP on 6p24 associated with lung cancer and rs2525548 at TRIM4 on 7q22 nominally associated with six cancers). Quantifying shared heritability among the cancers detects positive genetic correlations between breast and prostate cancer across populations. Common genetic components increase the statistical power, and the large-scale meta-analysis of 277,896 breast/prostate cancer cases and 901,858 controls identifies 91 newly genome-wide significant loci. Enrichment analysis of pathways and cell types reveals shared genetic backgrounds across said cancers. Focusing on genetically correlated cancers can contribute to enhancing our insights into carcinogenesis.

Genome-wide analyses characterize shared heritability among cancers and identify novel cancer susceptibility regions

BACKGROUND

The shared inherited genetic contribution to risk of different cancers is not fully known. In this study, we leverage results from 12 cancer genome-wide association studies (GWAS) to quantify pairwise genome-wide genetic correlations across cancers and identify novel cancer susceptibility loci.

METHODS

We collected GWAS summary statistics for 12 solid cancers based on 376 759 participants with cancer and 532 864 participants without cancer of European ancestry. The included cancer types were breast, colorectal, endometrial, esophageal, glioma, head and neck, lung, melanoma, ovarian, pancreatic, prostate, and renal cancers. We conducted cross-cancer GWAS and transcriptome-wide association studies to discover novel cancer susceptibility loci. Finally, we assessed the extent of variant-specific pleiotropy among cancers at known and newly identified cancer susceptibility loci.

RESULTS

We observed widespread but modest genome-wide genetic correlations across cancers. In cross-cancer GWAS and transcriptome-wide association studies, we identified 15 novel cancer susceptibility loci. Additionally, we identified multiple variants at 77 distinct loci with strong evidence of being associated with at least 2 cancer types by testing for pleiotropy at known cancer susceptibility loci.

CONCLUSIONS

Overall, these results suggest that some genetic risk variants are shared among cancers, though much of cancer heritability is cancer-specific and thus tissue-specific. The increase in statistical power associated with larger sample sizes in cross-disease analysis allows for the identification of novel susceptibility regions. Future studies incorporating data on multiple cancer types are likely to identify additional regions associated with the risk of multiple cancer types.

A scalable variational approach to characterize pleiotropic components across thousands of human diseases and complex traits using GWAS summary statistics

Genome-wide association studies (GWAS) across thousands of traits have revealed the pervasive pleiotropy of trait-associated genetic variants. While methods have been proposed to characterize pleiotropic components across groups of phenotypes, scaling these approaches to ultra large-scale biobanks has been challenging. Here, we propose FactorGo, a scalable variational factor analysis model to identify and characterize pleiotropic components using biobank GWAS summary data. In extensive simulations, we observe that FactorGo outperforms the state-of-the-art (model-free) approach tSVD in capturing latent pleiotropic factors across phenotypes, while maintaining a similar computational cost. We apply FactorGo to estimate 100 latent pleiotropic factors from GWAS summary data of 2,483 phenotypes measured in European-ancestry Pan-UK BioBank individuals (N=420,531). Next, we find that factors from FactorGo are more enriched with relevant tissue-specific annotations than those identified by tSVD (P=2.58E-10), and validate our approach by recapitulating brain-specific enrichment for BMI and the height-related connection between reproductive system and muscular-skeletal growth. Finally, our analyses suggest novel shared etiologies between rheumatoid arthritis and periodontal condition, in addition to alkaline phosphatase as a candidate prognostic biomarker for prostate cancer. Overall, FactorGo improves our biological understanding of shared etiologies across thousands of GWAS.

Canonical and Noncanonical ER Stress-Mediated Autophagy Is a Bite the Bullet in View of Cancer Therapy

Cancer cells adapt multiple mechanisms to counter intense stress on their way to growth. Tumor microenvironment stress leads to canonical and noncanonical endoplasmic stress (ER) responses, which mediate autophagy and are engaged during proteotoxic challenges to clear unfolded or misfolded proteins and damaged organelles to mitigate stress. In these conditions, autophagy functions as a cytoprotective mechanism in which malignant tumor cells reuse degraded materials to generate energy under adverse growing conditions. However, cellular protection by autophagy is thought to be complicated, contentious, and context-dependent; the stress response to autophagy is suggested to support tumorigenesis and drug resistance, which must be adequately addressed. This review describes significant findings that suggest accelerated autophagy in cancer, a novel obstacle for anticancer therapy, and discusses the UPR components that have been suggested to be untreatable. Thus, addressing the UPR or noncanonical ER stress components is the most effective approach to suppressing cytoprotective autophagy for better and more effective cancer treatment.

Assessment of genetic susceptibility to multiple primary cancers through whole-exome sequencing in two large multi-ancestry studies

BACKGROUND

Up to one of every six individuals diagnosed with one cancer will be diagnosed with a second primary cancer in their lifetime. Genetic factors contributing to the development of multiple primary cancers, beyond known cancer syndromes, have been underexplored.

METHODS

To characterize genetic susceptibility to multiple cancers, we conducted a pan-cancer, whole-exome sequencing study of individuals drawn from two large multi-ancestry populations (6429 cases, 165,853 controls). We created two groupings of individuals diagnosed with multiple primary cancers: (1) an overall combined set with at least two cancers across any of 36 organ sites and (2) cancer-specific sets defined by an index cancer at one of 16 organ sites with at least 50 cases from each study population. We then investigated whether variants identified from exome sequencing were associated with these sets of multiple cancer cases in comparison to individuals with one and, separately, no cancers.

RESULTS

We identified 22 variant-phenotype associations, 10 of which have not been previously discovered and were significantly overrepresented among individuals with multiple cancers, compared to those with a single cancer.

CONCLUSIONS

Overall, we describe variants and genes that may play a fundamental role in the development of multiple primary cancers and improve our understanding of shared mechanisms underlying carcinogenesis.

Hierarchical multivariate directed acyclic graph autoregressive models for spatial diseases mapping

Disease mapping is an important statistical tool used by epidemiologists to assess geographic variation in disease rates and identify lurking environmental risk factors from spatial patterns. Such maps rely upon spatial models for regionally aggregated data, where neighboring regions tend to exhibit similar outcomes than those farther apart. We contribute to the literature on multivariate disease mapping, which deals with measurements on multiple (two or more) diseases in each region. We aim to disentangle associations among the multiple diseases from spatial autocorrelation in each disease. We develop multivariate directed acyclic graphical autoregression models to accommodate spatial and inter-disease dependence. The hierarchical construction imparts flexibility and richness, interpretability of spatial autocorrelation and inter-disease relationships, and computational ease, but depends upon the order in which the cancers are modeled. To obviate this, we demonstrate how Bayesian model selection and averaging across orders are easily achieved using bridge sampling. We compare our method with a competitor using simulation studies and present an application to multiple cancer mapping using data from the Surveillance, Epidemiology, and End Results program.

Considering hormone-sensitive cancers as a single disease in the UK biobank reveals shared aetiology

Hormone-related cancers, including cancers of the breast, prostate, ovaries, uterine, and thyroid, globally contribute to the majority of cancer incidence. We hypothesize that hormone-sensitive cancers share common genetic risk factors that have rarely been investigated by previous genomic studies of site-specific cancers. Here, we show that considering hormone-sensitive cancers as a single disease in the UK Biobank reveals shared genetic aetiology. We observe that a significant proportion of variance in disease liability is explained by the genome-wide single nucleotide polymorphisms (SNPs), i.e., SNP-based heritability on the liability scale is estimated as 10.06% (SE 0.70%). Moreover, we find 55 genome-wide significant SNPs for the disease, using a genome-wide association study. Pair-wise analysis also estimates positive genetic correlations between some pairs of hormone-sensitive cancers although they are not statistically significant. Our finding suggests that heritable genetic factors may be a key driver in the mechanism of carcinogenesis shared by hormone-sensitive cancers.

Associations of genetic susceptibility to 16 cancers with risk of breast cancer overall and by intrinsic subtypes

Certain genetic variants are associated with risks of multiple cancers. We investigated breast cancer risk with overall genetic susceptibility to each of 16 other cancers. We constructed polygenic risk scores (PRS) for 16 cancers using risk variants identified by genome-wide association studies. We evaluated the associations of these PRSs with breast cancer risk (overall and by subtypes) using Breast Cancer Association Consortium data, including 106,278 cases and 91,477 controls of European ancestry. Odds ratios (OR) and 95% confidence intervals (CIs) were estimated to measure the association of each PRS with breast cancer risk. Data from the UK Biobank, including 4,337 cases and 209,983 non-cases, were used to replicate the findings. A 5%–8% significantly elevated risk of overall breast cancer was associated with per unit increase of the PRS for glioma and cancers of the corpus uteri, stomach, or colorectum. Analyses by subtype revealed that the PRS for corpus uteri cancer (OR = 1.09; 95% CI, 1.03–1.15) and stomach cancer (OR = 1.07; 95% CI, 1.03–1.12) were associated with estrogen receptor-positive breast cancer, while ovarian cancer PRS was associated with triple-negative breast cancer (OR = 1.25; 95% CI, 1.01–1.55). UK Biobank data supported the positive associations of overall breast cancer risk with PRS for melanoma and cancers of the stomach, colorectum, and ovary. Our study provides strong evidence for shared genetic susceptibility of breast cancer with several other cancers. Results from our study help uncover the genetic basis for breast and other cancers and identify individuals at high risk for multiple cancers.

Large-scale cross-cancer fine-mapping of the 5p15.33 region reveals multiple independent signals

Genome-wide association studies (GWASs) have identified thousands of cancer risk loci revealing many risk regions shared across multiple cancers. Characterizing the cross-cancer shared genetic basis can increase our understanding of global mechanisms of cancer development. In this study, we collected GWAS summary statistics based on up to 375,468 cancer cases and 530,521 controls for fourteen types of cancer, including breast (overall, estrogen receptor [ER]-positive, and ER-negative), colorectal, endometrial, esophageal, glioma, head/neck, lung, melanoma, ovarian, pancreatic, prostate, and renal cancer, to characterize the shared genetic basis of cancer risk. We identified thirteen pairs of cancers with statistically significant local genetic correlations across eight distinct genomic regions. Specifically, the 5p15.33 region, harboring the TERT and CLPTM1L genes, showed statistically significant local genetic correlations for multiple cancer pairs. We conducted a cross-cancer fine-mapping of the 5p15.33 region based on eight cancers that showed genome-wide significant associations in this region (ER-negative breast, colorectal, glioma, lung, melanoma, ovarian, pancreatic, and prostate cancer). We used an iterative analysis pipeline implementing a subset-based meta-analysis approach based on cancer-specific conditional analyses and identified ten independent cross-cancer associations within this region. For each signal, we conducted cross-cancer fine-mapping to prioritize the most plausible causal variants. Our findings provide a more in-depth understanding of the shared inherited basis across human cancers and expand our knowledge of the 5p15.33 region in carcinogenesis.

Genetic correlation and causal relationships between cardio-metabolic traits and lung function impairment

BACKGROUND

Associations of low lung function with features of poor cardio-metabolic health have been reported. It is, however, unclear whether these co-morbidities reflect causal associations, shared genetic heritability or are confounded by environmental factors.

METHODS

We performed three analyses: (1) cardio-metabolic health to lung function association tests in Northern Finland Birth cohort 1966, (2) cross-trait linkage disequilibrium score regression (LDSC) to compare genetic backgrounds and (3) Mendelian randomisation (MR) analysis to assess the causal effect of cardio-metabolic traits and disease on lung function, and vice versa (bidirectional MR). Genetic associations were obtained from the UK Biobank data or published large-scale genome-wide association studies (N > 82,000).

RESULTS

We observed a negative genetic correlation between lung function and cardio-metabolic traits and diseases. In Mendelian Randomisation analysis (MR), we found associations between type 2 diabetes (T2D) instruments and forced vital capacity (FVC) as well as FEV1/FVC. Body mass index (BMI) instruments were associated to all lung function traits and C-reactive protein (CRP) instruments to FVC. These genetic associations provide evidence for a causal effect of cardio-metabolic traits on lung function. Multivariable MR suggested independence of these causal effects from other tested cardio-metabolic traits and diseases. Analysis of lung function specific SNPs revealed a potential causal effect of FEV1/FVC on blood pressure.

CONCLUSIONS

The present study overcomes many limitations of observational studies by using Mendelian Randomisation. We provide evidence for an independent causal effect of T2D, CRP and BMI on lung function with some of the T2D effect on lung function being attributed to inflammatory mechanisms. Furthermore, this analysis suggests a potential causal effect of FEV1/FVC on blood pressure. Our detailed analysis of the interplay between cardio-metabolic traits and impaired lung function provides the opportunity to improve the quality of existing intervention strategies.

The Shared Genetic Architectures Between Lung Cancer and Multiple Polygenic Phenotypes in Genome-Wide Association Studies

BACKGROUND

Prior genome-wide association studies have identified numerous lung cancer risk loci and reveal substantial etiologic heterogeneity across histologic subtypes. Analyzing the shared genetic architecture underlying variation in complex traits can elucidate common genetic etiologies across phenotypes. Exploring pairwise genetic correlations between lung cancer and other polygenic traits can reveal the common genetic etiology of correlated phenotypes.

METHODS

Using cross-trait linkage disequilibrium score regression, we estimated the pairwise genetic correlation and heritability between lung cancer and multiple traits using publicly available summary statistics. Identified genetic relationships were also examined after excluding genomic regions known to be associated with smoking behaviors, a major risk factor for lung cancer.

RESULTS

We observed several traits showing moderate single nucleotide polymorphism-based heritability and significant genetic correlations with lung cancer. We observed highly significant correlations between the genetic architectures of lung cancer and emphysema/chronic bronchitis across all histologic subtypes, as well as among lung cancer occurring among smokers. Our analyses revealed highly significant positive correlations between lung cancer and paternal history of lung cancer. We also observed a strong negative correlation with parental longevity. We observed consistent directions in genetic patterns after excluding genomic regions associated with smoking behaviors.

CONCLUSIONS

This study identifies numerous phenotypic traits that share genomic architecture with lung carcinogenesis and are not fully accounted for by known smoking-associated genomic loci.

IMPACT

These findings provide new insights into the etiology of lung cancer by identifying traits that are genetically correlated with increased risk of lung cancer.

Cross-cancer evaluation of polygenic risk scores for 16 cancer types in two large cohorts

Even distinct cancer types share biological hallmarks. Here, we investigate polygenic risk score (PRS)-specific pleiotropy across 16 cancers in European ancestry individuals from the Genetic Epidemiology Research on Adult Health and Aging cohort (16,012 cases, 50,552 controls) and UK Biobank (48,969 cases, 359,802 controls). Within cohorts, each PRS is evaluated in multivariable logistic regression models against all other cancer types. Results are then meta-analyzed across cohorts. Ten positive and one inverse cross-cancer associations are found after multiple testing correction. Two pairs show bidirectional associations; the melanoma PRS is positively associated with oral cavity/pharyngeal cancer and vice versa, whereas the lung cancer PRS is positively associated with oral cavity/pharyngeal cancer, and the oral cavity/pharyngeal cancer PRS is inversely associated with lung cancer. Overall, we validate known, and uncover previously unreported, patterns of pleiotropy that have the potential to inform investigations of risk prediction, shared etiology, and precision cancer prevention strategies.

A powerful method for pleiotropic analysis under composite null hypothesis identifies novel shared loci between Type 2 Diabetes and Prostate Cancer

There is increasing evidence that pleiotropy, the association of multiple traits with the same genetic variants/loci, is a very common phenomenon. Cross-phenotype association tests are often used to jointly analyze multiple traits from a genome-wide association study (GWAS). The underlying methods, however, are often designed to test the global null hypothesis that there is no association of a genetic variant with any of the traits, the rejection of which does not implicate pleiotropy. In this article, we propose a new statistical approach, PLACO, for specifically detecting pleiotropic loci between two traits by considering an underlying composite null hypothesis that a variant is associated with none or only one of the traits. We propose testing the null hypothesis based on the product of the Z-statistics of the genetic variants across two studies and derive a null distribution of the test statistic in the form of a mixture distribution that allows for fractions of variants to be associated with none or only one of the traits. We borrow approaches from the statistical literature on mediation analysis that allow asymptotic approximation of the null distribution avoiding estimation of nuisance parameters related to mixture proportions and variance components. Simulation studies demonstrate that the proposed method can maintain type I error and can achieve major power gain over alternative simpler methods that are typically used for testing pleiotropy. PLACO allows correlation in summary statistics between studies that may arise due to sharing of controls between disease traits. Application of PLACO to publicly available summary data from two large case-control GWAS of Type 2 Diabetes and of Prostate Cancer implicated a number of novel shared genetic regions: 3q23 (ZBTB38), 6q25.3 (RGS17), 9p22.1 (HAUS6), 9p13.3 (UBAP2), 11p11.2 (RAPSN), 14q12 (AKAP6), 15q15 (KNL1) and 18q23 (ZNF236).

We propose a new approach PLACO that uses aggregate-level genotype-phenotype association statistics—commonly referred to as GWAS summary statistics—to identify genetic variants that influence risk of two traits or diseases. It allows correlation in summary statistics between studies that may arise due to sharing of controls between disease traits. We demonstrate that PLACO can achieve major power gain over alternative methods that are typically used. We applied PLACO to Type 2 Diabetes and Prostate Cancer summary data from two large case-control studies. Many previous studies have reported an inverse association of these two chronic diseases suggesting shared risk factors; however, shared genetic mechanisms underlying this association is poorly understood. PLACO identified a number of novel shared genetic regions that are not detected by individual trait analysis. Many of the loci implicated by PLACO increase risk for one disease while decreasing risk for the other. PLACO can similarly be used on other traits to shed light on shared genetic risk factors.

Association between inflammatory bowel disease and prostate cancer: A large-scale, prospective, population-based study

Inflammatory bowel disease (IBD) is an established risk factor for colorectal cancer. Recent reports suggesting IBD is also a risk factor for prostate cancer (PC) require further investigation. We studied 218 084 men in the population-based UK Biobank cohort, aged 40 to 69 at study entry between 2006 and 2010, with follow-up through mid-2015. We assessed the association between IBD and subsequent PC using multivariable Cox regression analyses, adjusting for age at assessment, ethnic group, UK region, smoking status, alcohol drinking frequency, body mass index, Townsend Deprivation Index, family history of PC and previous prostate-specific antigen testing. Mean age at study entry was 56 years, 94% of the men were white, and 1.1% (n = 2311) had a diagnosis of IBD. After a median follow-up of 78 months, men with IBD had an increased risk of PC (adjusted hazard ratio [aHR] = 1.31, 95% confidence interval [CI] = 1.03-1.67, P = .029). The association with PC was only among men with the ulcerative colitis (UC; aHR = 1.47, 95% CI = 1.11-1.95, P = .0070), and not Crohn's disease (aHR 1.06, 95% CI = 0.63-1.80, P = .82). Results are limited by lack of data on frequency of health care interactions. In a large-scale, prospective cohort study, we detected an association between IBD, and UC specifically, with incident PC diagnosis.

Cancer PRSweb: An Online Repository with Polygenic Risk Scores for Major Cancer Traits and Their Evaluation in Two Independent Biobanks

To facilitate scientific collaboration on polygenic risk scores (PRSs) research, we created an extensive PRS online repository for 35 common cancer traits integrating freely available genome-wide association studies (GWASs) summary statistics from three sources: published GWASs, the NHGRI-EBI GWAS Catalog, and UK Biobank-based GWASs. Our framework condenses these summary statistics into PRSs using various approaches such as linkage disequilibrium pruning/p value thresholding (fixed or data-adaptively optimized thresholds) and penalized, genome-wide effect size weighting. We evaluated the PRSs in two biobanks: the Michigan Genomics Initiative (MGI), a longitudinal biorepository effort at Michigan Medicine, and the population-based UK Biobank (UKB). For each PRS construct, we provide measures on predictive performance and discrimination. Besides PRS evaluation, the Cancer-PRSweb platform features construct downloads and phenome-wide PRS association study results (PRS-PheWAS) for predictive PRSs. We expect this integrated platform to accelerate PRS-related cancer research.

Pan-cancer study detects genetic risk variants and shared genetic basis in two large cohorts

Deciphering the shared genetic basis of distinct cancers has the potential to elucidate carcinogenic mechanisms and inform broadly applicable risk assessment efforts. Here, we undertake genome-wide association studies (GWAS) and comprehensive evaluations of heritability and pleiotropy across 18 cancer types in two large, population-based cohorts: the UK Biobank (408,786 European ancestry individuals; 48,961 cancer cases) and the Kaiser Permanente Genetic Epidemiology Research on Adult Health and Aging cohorts (66,526 European ancestry individuals; 16,001 cancer cases). The GWAS detect 21 genome-wide significant associations independent of previously reported results. Investigations of pleiotropy identify 12 cancer pairs exhibiting either positive or negative genetic correlations; 25 pleiotropic loci; and 100 independent pleiotropic variants, many of which are regulatory elements and/or influence cross-tissue gene expression. Our findings demonstrate widespread pleiotropy and offer further insight into the complex genetic architecture of cross-cancer susceptibility.

The relationship between circulating lipids and breast cancer risk: A Mendelian randomization study

BACKGROUND

A number of epidemiological and genetic studies have attempted to determine whether levels of circulating lipids are associated with risks of various cancers, including breast cancer (BC). However, it remains unclear whether a causal relationship exists between lipids and BC. If alteration of lipid levels also reduced risk of BC, this could present a target for disease prevention. This study aimed to assess a potential causal relationship between genetic variants associated with plasma lipid traits (high-density lipoprotein, HDL; low-density lipoprotein, LDL; triglycerides, TGs) with risk for BC using Mendelian randomization (MR).

METHODS AND FINDINGS

Data from genome-wide association studies in up to 215,551 participants from the Million Veteran Program (MVP) were used to construct genetic instruments for plasma lipid traits. The effect of these instruments on BC risk was evaluated using genetic data from the BCAC (Breast Cancer Association Consortium) based on 122,977 BC cases and 105,974 controls. Using MR, we observed that a 1-standard-deviation genetically determined increase in HDL levels is associated with an increased risk for all BCs (HDL: OR [odds ratio] = 1.08, 95% confidence interval [CI] = 1.04-1.13, P < 0.001). Multivariable MR analysis, which adjusted for the effects of LDL, TGs, body mass index (BMI), and age at menarche, corroborated this observation for HDL (OR = 1.06, 95% CI = 1.03-1.10, P = 4.9 × 10-4) and also found a relationship between LDL and BC risk (OR = 1.03, 95% CI = 1.01-1.07, P = 0.02). We did not observe a difference in these relationships when stratified by breast tumor estrogen receptor (ER) status. We repeated this analysis using genetic variants independent of the leading association at core HDL pathway genes and found that these variants were also associated with risk for BCs (OR = 1.11, 95% CI = 1.06-1.16, P = 1.5 × 10-6), including locus-specific associations at ABCA1 (ATP Binding Cassette Subfamily A Member 1), APOE-APOC1-APOC4-APOC2 (Apolipoproteins E, C1, C4, and C2), and CETP (Cholesteryl Ester Transfer Protein). In addition, we found evidence that genetic variation at the ABO locus is associated with both lipid levels and BC. Through multiple statistical approaches, we minimized and tested for the confounding effects of pleiotropy and population stratification on our analysis; however, the possible existence of residual pleiotropy and stratification remains a limitation of this study.

CONCLUSIONS

We observed that genetically elevated plasma HDL and LDL levels appear to be associated with increased BC risk. Future studies are required to understand the mechanism underlying this putative causal relationship, with the goal of developing potential therapeutic strategies aimed at altering the cholesterol-mediated effect on BC risk.

Cross-Cancer Pleiotropic Analysis Reveals Novel Susceptibility Loci for Lung Cancer

Genome-wide association studies (GWASs) have identified hundreds of single nucleotide polymorphisms (SNPs) associated with cancer risk, several of which have shown pleiotropic effects across cancers. Therefore, we performed a systematic cross-cancer pleiotropic analysis to detect the effects of GWAS-identified variants from non-lung cancers on lung cancer risk in 12,843 cases and 12,639 controls from four lung cancer GWASs. The overall association between variants in each cancer and risk of lung cancer was explored using sequential kernel association test (SKAT) analysis. For single variant analysis, we combined the result of specific study using fixed-effect meta-analysis. We performed functional exploration of significant associations based on features from public databases. To further detect the biological mechanism underlying identified observations, pathway enrichment analysis were conducted with R package “clusterProfiler.” SNP-set analysis revealed the overall associations between variants of 8 cancer types and lung cancer risk. Single variant analysis identified 6 novel SNPs related to lung cancer risk after multiple correction (P_fdr < 0.10), including rs1707302 (1p34.1, OR = 0.93, 95% CI: 0.90–0.97, P = 7.60 × 10⁻⁴), rs2516448 (6p21.33, OR = 1.07, 95% CI: 1.03–1.11, P = 1.00 × 10⁻³), rs3869062 (6p22.1, OR = 0.91, 95% CI: 0.86–0.96, P = 7.10 × 10⁻⁴), rs174549 (11q12.2, OR = 0.90, 95% CI: 0.87–0.94, P = 1.00 × 10⁻⁷), rs7193541 (16q23.1, OR = 0.93, 95% CI: 0.90–0.96, P = 1.20 × 10⁻⁴), and rs8064454 (17q12, OR = 1.07, 95% CI: 1.03–1.11, P = 4.30 × 10⁻⁴). The eQTL analysis and functional annotation suggested that these variants might modify lung cancer susceptibility through regulating the expression of related genes. Pathway enrichment analysis showed that genes modulated by these variants play important roles in cancer carcinogenesis. Our findings demonstrate the pleiotropic associations between non-lung cancer susceptibility loci and lung cancer risk, providing important insights into the shared mechanisms of carcinogenesis across cancers.

Shared Genetic Etiology of Obesity-Related Traits and Barrett's Esophagus/Adenocarcinoma: Insights from Genome-Wide Association Studies

BACKGROUND

Obesity is a major risk factor for esophageal adenocarcinoma (EA) and its precursor Barrett's esophagus (BE). Research suggests that individuals with high genetic risk to obesity have a higher BE/EA risk. To facilitate understanding of biological factors that lead to progression from BE to EA, the present study investigated the shared genetic background of BE/EA and obesity-related traits.

METHODS

Cross-trait linkage disequilibrium score regression was applied to summary statistics from genome-wide association meta-analyses on BE/EA and on obesity traits. Body mass index (BMI) was used as a proxy for general obesity, and waist-to-hip ratio (WHR) for abdominal obesity. For single marker analyses, all genome-wide significant risk alleles for BMI and WHR were compared with summary statistics of the BE/EA meta-analyses.

RESULTS

Sex-combined analyses revealed a significant genetic correlation between BMI and BE/EA (r_g = 0.13, P = 2 × 10^-04) and a r_g of 0.12 between WHR and BE/EA (P = 1 × 10^-02). Sex-specific analyses revealed a pronounced genetic correlation between BMI and EA in females (r_g = 0.17, P = 1.2 × 10^-03), and WHR and EA in males (r_g = 0.18, P = 1.51 × 10^-02). On the single marker level, significant enrichment of concordant effects was observed for BMI and BE/EA risk variants (P = 8.45 × 10^-03) and WHR and BE/EA risk variants (P = 2 × 10^-02).

CONCLUSIONS

Our study provides evidence for sex-specific genetic correlations that might reflect specific biological mecha-nisms. The data demonstrate that shared genetic factors are particularly relevant in progression from BE to EA.

IMPACT

Our study quantifies the genetic correlation between BE/EA and obesity. Further research is now warranted to elucidate these effects and to understand the shared pathophysiology.

Is Schizophrenia a Risk Factor for Breast Cancer?-Evidence From Genetic Data

Observational epidemiological studies have found an association between schizophrenia and breast cancer, but it is not known if the relationship is a causal one. We used summary statistics from very large genome-wide association studies of schizophrenia (n = 40675 cases and 64643 controls) and breast cancer (n = 122977 cases and 105974 controls) to investigate whether there is evidence that the association is partly due to shared genetic risk factors and whether there is evidence of a causal relationship. Using LD-score regression, we found that there is a small but significant genetic correlation (rG) between the 2 disorders (rG = 0.14, SE = 0.03, P = 4.75 × 10-8), indicating shared genetic risk factors. Using 142 genetic variants associated with schizophrenia as instrumental variables that are a proxy for having schizophrenia, we estimated a causal effect of schizophrenia on breast cancer on the observed scale as bxy = 0.032 (SE = 0.009, P = 2.3 × 10-4). A 1 SD increase in liability to schizophrenia increases risk of breast cancer 1.09-fold. In contrast, the estimated causal effect of breast cancer on schizophrenia from 191 instruments was not significantly different from zero (bxy = -0.005, SE = 0.012, P = .67). No evidence for pleiotropy was found and adjusting for the effects of smoking or parity did not alter the results. These results provide evidence that the previously observed association is due to schizophrenia causally increasing risk for breast cancer. Genetic variants may provide an avenue to elucidating the mechanism underpinning this relationship.

Moving from one to many: insights from the growing list of pleiotropic cancer risk genes

Pleiotropy, a phenomenon in which a single gene affects multiple phenotypes, is becoming very common among different cancer types and cancer-related phenotypes, such as those in hormonal, cardiometabolic and inflammatory/immune conditions. The discovery of pleiotropic associations can improve our understanding of cancer and help to target investigation of genes with greater clinical relevance.

Cross-Cancer Pleiotropic Associations with Lung Cancer Risk in African Americans

BACKGROUND

Identifying genetic variants with pleiotropic associations across multiple cancers can reveal shared biologic pathways. Prior pleiotropic studies have primarily focused on European-descent individuals. Yet population-specific genetic variation can occur, and potential pleiotropic associations among diverse racial/ethnic populations could be missed. We examined cross-cancer pleiotropic associations with lung cancer risk in African Americans.

METHODS

We conducted a pleiotropic analysis among 1,410 African American lung cancer cases and 2,843 controls. We examined 36,958 variants previously associated (or in linkage disequilibrium) with cancer in prior genome-wide association studies. Logistic regression analyses were conducted, adjusting for age, sex, global ancestry, study site, and smoking status.

RESULTS

We identified three novel genomic regions significantly associated (FDR-corrected P <0.10) with lung cancer risk (rs336958 on 5q14.3, rs7186207 on 16q22.2, and rs11658063 on 17q12). On chromosome16q22.2, rs7186207 was significantly associated with reduced risk [OR = 0.43; 95% confidence interval (CI), 0.73-0.89], and functional annotation using GTEx showed rs7186207 modifies DHODH gene expression. The minor allele at rs336958 on 5q14.3 was associated with increased lung cancer risk (OR = 1.47; 95% CI, 1.22-1.78), whereas the minor allele at rs11658063 on 17q12 was associated with reduced risk (OR = 0.80; 95% CI, 0.72-0.90).

CONCLUSIONS

We identified novel associations on chromosomes 5q14.3, 16q22.2, and 17q12, which contain HNF1B, DHODH, and HAPLN1 genes, respectively. SNPs within these regions have been previously associated with multiple cancers. This is the first study to examine cross-cancer pleiotropic associations for lung cancer in African Americans.

IMPACT

Our findings demonstrate novel cross-cancer pleiotropic associations with lung cancer risk in African Americans.

GWAS of smoking behaviour in 165,436 Japanese people reveals seven new loci and shared genetic architecture

Cigarette smoking is a risk factor for a wide range of human diseases¹. To investigate the genetic components associated with smoking behaviours in the Japanese population, we conducted a genome-wide association study of four smoking-related traits using up to 165,436 individuals. In total, we identified seven new loci, including three loci associated with the number of cigarettes per day (EPHX2-CLU, RET and CUX2-ALDH2), three loci associated with smoking initiation (DLC1, CXCL12-TMEM72-AS1 and GALR1-SALL3) and LINC01793-MIR4432HG, associated with the age of smoking initiation. Of these, three loci (LINC01793-MIR4432HG, CXCL12-TMEM72-AS1 and GALR1-SALL3) were found by conducting an additional sex-stratified genome-wide association study. This additional analysis showed heterogeneity of effects between sexes. The cross-sex linkage disequilibrium score regression^2,3 analysis also indicated that the genetic component of smoking initiation was significantly different between the sexes. Cross-trait linkage disequilibrium score regression analysis and trait-relevant tissue analysis showed that the number of cigarettes per day has a specific genetic background distinct from those of the other three smoking behaviours. We also report 11 diseases that share genetic basis with smoking behaviours. Although the current study should be carefully considered owing to the lack of replication samples, our findings characterized the genetic architecture of smoking behaviours. Further studies in East Asian populations are warranted to confirm our findings.

Family history of cancer, Ashkenazi Jewish ancestry, and pancreatic cancer risk

Background

Individuals with a family history of cancer may be at increased risk of pancreatic cancer. Ashkenazi Jewish (AJ) individuals carry increased risk for pancreatic cancer and other cancer types.

Methods

We examined the association between family history of cancer, AJ heritage, and incident pancreatic cancer in 49 410 male participants of the prospective Health Professionals Follow-up Study. Hazard ratios (HRs) were estimated using multivariable-adjusted Cox proportional hazards models.

Results

During 1.1 million person-years (1986–2016), 452 participants developed pancreatic cancer. Increased risk of pancreatic cancer was observed in individuals with a family history of pancreatic (HR, 2.79; 95% confidence interval [CI], 1.28–6.07) or breast cancer (HR, 1.40; 95% CI, 1.01–1.94). There was a trend towards higher risk of pancreatic cancer in relation to a family history of colorectal cancer (HR, 1.21; 95% CI, 0.95–1.55) or AJ heritage (HR, 1.29; 95% CI, 0.94–1.77). The risk was highly elevated among AJ men with a family history of breast or colorectal cancer (HR, 2.61 [95% CI, 1.41–4.82] and 1.92 [95% CI, 1.05–3.49], respectively).

Conclusion

Family history of pancreatic cancer was associated with increased risk of this malignancy. Family history of breast or colorectal cancer was associated with the increased risk among AJ men.

Shared heritability and functional enrichment across six solid cancers

Quantifying the genetic correlation between cancers can provide important insights into the mechanisms driving cancer etiology. Using genome-wide association study summary statistics across six cancer types based on a total of 296,215 cases and 301,319 controls of European ancestry, here we estimate the pair-wise genetic correlations between breast, colorectal, head/neck, lung, ovary and prostate cancer, and between cancers and 38 other diseases. We observed statistically significant genetic correlations between lung and head/neck cancer (rg = 0.57, p = 4.6 × 10-8), breast and ovarian cancer (rg = 0.24, p = 7 × 10-5), breast and lung cancer (rg = 0.18, p =1.5 × 10-6) and breast and colorectal cancer (rg = 0.15, p = 1.1 × 10-4). We also found that multiple cancers are genetically correlated with non-cancer traits including smoking, psychiatric diseases and metabolic characteristics. Functional enrichment analysis revealed a significant excess contribution of conserved and regulatory regions to cancer heritability. Our comprehensive analysis of cross-cancer heritability suggests that solid tumors arising across tissues share in part a common germline genetic basis.

Genetic Evidence for the Association between Schizophrenia and Breast Cancer

OBJECTIVE

To estimate the potential effect of schizophrenia on breast cancer risk in women, we performed a two-sample Mendelian randomization (MR) study.

METHODS

The instrumental variables comprised 170 uncorrelated and non-pleiotropic single nucleotide polymorphisms (SNPs) that are significantly associated with schizophrenia risk in genome-wide association studies in 105,000 European descent individuals of the Psychiatric Genomics Consortium (http://www.med.unc.edu/pgc/) and the United Kingdom Clozapine Clinic. The association between these SNPs determined schizophrenia and breast cancer risk was estimated in approximately 229,000 European descent females from the Breast Cancer Association Consortium using the inverse-variance weighted and the weighted median MR methods.

RESULTS

We found that the genetically-predicted risk of schizophrenia was associated with increased breast cancer risk (under a random-effects model: odds ratio per 1 unit increase in log odds of schizophrenia = 1.04, 95% confidence interval: 1.02-1.06, p = 5.6 × 10-5). Similar significant associations were observed in analyses using a weighted median model and sensitivity analysis excluding six SNPs with genotype imputation score of less than 0.8, as well as analyses stratified by estrogen receptor status of breast cancer.

CONCLUSION

Our findings implicate a modest increased risk for breast cancer in genetically determined schizophrenic females.

Genetic susceptibility markers for a breast-colorectal cancer phenotype: Exploratory results from genome-wide association studies

BACKGROUND

Clustering of breast and colorectal cancer has been observed within some families and cannot be explained by chance or known high-risk mutations in major susceptibility genes. Potential shared genetic susceptibility between breast and colorectal cancer, not explained by high-penetrance genes, has been postulated. We hypothesized that yet undiscovered genetic variants predispose to a breast-colorectal cancer phenotype.

METHODS

To identify variants associated with a breast-colorectal cancer phenotype, we analyzed genome-wide association study (GWAS) data from cases and controls that met the following criteria: cases (n = 985) were women with breast cancer who had one or more first- or second-degree relatives with colorectal cancer, men/women with colorectal cancer who had one or more first- or second-degree relatives with breast cancer, and women diagnosed with both breast and colorectal cancer. Controls (n = 1769), were unrelated, breast and colorectal cancer-free, and age- and sex- frequency-matched to cases. After imputation, 6,220,060 variants were analyzed using the discovery set and variants associated with the breast-colorectal cancer phenotype at P<5.0E-04 (n = 549, at 60 loci) were analyzed for replication (n = 293 cases and 2,103 controls).

RESULTS

Multiple correlated SNPs in intron 1 of the ROBO1 gene were suggestively associated with the breast-colorectal cancer phenotype in the discovery and replication data (most significant; rs7430339, Pdiscovery = 1.2E-04; rs7429100, Preplication = 2.8E-03). In meta-analysis of the discovery and replication data, the most significant association remained at rs7429100 (P = 1.84E-06).

CONCLUSION

The results of this exploratory analysis did not find clear evidence for a susceptibility locus with a pleiotropic effect on hereditary breast and colorectal cancer risk, although the suggestive association of genetic variation in the region of ROBO1, a potential tumor suppressor gene, merits further investigation.

Identification of Pleiotropic Cancer Susceptibility Variants from Genome-Wide Association Studies Reveals Functional Characteristics

Background: There exists compelling evidence that some genetic variants are associated with the risk of multiple cancer sites (i.e., pleiotropy). However, the biological mechanisms through which the pleiotropic variants operate are unclear.Methods: We obtained all cancer risk associations from the National Human Genome Research Institute-European Bioinformatics Institute GWAS Catalog, and correlated cancer risk variants were clustered into groups. Pleiotropic variant groups and genes were functionally annotated. Associations of pleiotropic cancer risk variants with noncancer traits were also obtained.Results: We identified 1,431 associations between variants and cancer risk, comprised of 989 unique variants associated with 27 unique cancer sites. We found 20 pleiotropic variant groups (2.1%) composed of 33 variants (3.3%), including novel pleiotropic variants rs3777204 and rs56219066 located in the ELL2 gene. Relative to single-cancer risk variants, pleiotropic variants were more likely to be in genes (89.0% vs. 65.3%, P = 2.2 × 10-16), and to have somewhat larger risk allele frequencies (median RAF = 0.49 versus 0.39, P = 0.046). The 27 genes to which the pleiotropic variants mapped were suggestive for enrichment in response to radiation and hypoxia, alpha-linolenic acid metabolism, cell cycle, and extension of telomeres. In addition, we observed that 8 of 33 pleiotropic cancer risk variants were associated with 16 traits other than cancer.Conclusions: This study identified and functionally characterized genetic variants showing pleiotropy for cancer risk.Impact: Our findings suggest biological pathways common to different cancers and other diseases, and provide a basis for the study of genetic testing for multiple cancers and repurposing cancer treatments. Cancer Epidemiol Biomarkers Prev; 27(1); 75-85. ©2017 AACR.

Association of Pancreatic Cancer Susceptibility Variants with Risk of Breast Cancer in Women of European and African Ancestry

Background: Pancreatic cancer mutation signatures closely resemble breast cancer, suggesting that both cancers may have common predisposition mechanisms that may include commonly inherited SNPs.Methods: We examined 23 genetic variants known to be associated with pancreatic cancer as breast cancer risk factors in the Root genome-wide association study (GWAS; 1,657 cases and 2,029 controls of African diaspora) and GAME-ON/DRIVE GWAS (16,003 cases and 41,335 controls of European ancestry).Results: None of the pancreatic cancer susceptibility variants were individually associated with breast cancer risk after adjustment for multiple testing (at α = 0.002) in the two populations. In Root GWAS, a change by one SD in the polygenic risk score (PRS) was not significantly associated with breast cancer. In addition, we did not observe a trend in the relationship between PRS percentiles and breast cancer risk.Conclusions: The association between reported pancreatic cancer genetic susceptibility variants and breast cancer development in women of African or European ancestry is likely weak, if it does exist.Impact: Known GWAS-derived susceptibility variants of pancreatic cancer do not explain its shared genetic etiology with breast cancer. Cancer Epidemiol Biomarkers Prev; 27(1); 116-8. ©2017 AACR.