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

Describing patterns of familial cancer risk in subfertile men using population pedigree data

STUDY QUESTION

Can we simultaneously assess risk for multiple cancers to identify familial multicancer patterns in families of azoospermic and severely oligozoospermic men?

SUMMARY ANSWER

Distinct familial cancer patterns were observed in the azoospermia and severe oligozoospermia cohorts, suggesting heterogeneity in familial cancer risk by both type of subfertility and within subfertility type.

WHAT IS KNOWN ALREADY

Subfertile men and their relatives show increased risk for certain cancers including testicular, thyroid, and pediatric.

STUDY DESIGN, SIZE, DURATION

A retrospective cohort of subfertile men (N = 786) was identified and matched to fertile population controls (N = 5674). Family members out to third-degree relatives were identified for both subfertile men and fertile population controls (N = 337 754). The study period was 1966-2017. Individuals were censored at death or loss to follow-up, loss to follow-up occurred if they left Utah during the study period.

PARTICIPANTS/MATERIALS, SETTING, METHODS

Azoospermic (0 × 106/mL) and severely oligozoospermic (<1.5 × 106/mL) men were identified in the Subfertility Health and Assisted Reproduction and the Environment cohort (SHARE). Subfertile men were age- and sex-matched 5:1 to fertile population controls and family members out to third-degree relatives were identified using the Utah Population Database (UPDB). Cancer diagnoses were identified through the Utah Cancer Registry. Families containing ≥10 members with ≥1 year of follow-up 1966-2017 were included (azoospermic: N = 426 families, 21 361 individuals; oligozoospermic: N = 360 families, 18 818 individuals). Unsupervised clustering based on standardized incidence ratios for 34 cancer phenotypes in the families was used to identify familial multicancer patterns; azoospermia and severe oligospermia families were assessed separately.

MAIN RESULTS AND THE ROLE OF CHANCE

Compared to control families, significant increases in cancer risks were observed in the azoospermia cohort for five cancer types: bone and joint cancers hazard ratio (HR) = 2.56 (95% CI = 1.48-4.42), soft tissue cancers HR = 1.56 (95% CI = 1.01-2.39), uterine cancers HR = 1.27 (95% CI = 1.03-1.56), Hodgkin lymphomas HR = 1.60 (95% CI = 1.07-2.39), and thyroid cancer HR = 1.54 (95% CI = 1.21-1.97). Among severe oligozoospermia families, increased risk was seen for three cancer types: colon cancer HR = 1.16 (95% CI = 1.01-1.32), bone and joint cancers HR = 2.43 (95% CI = 1.30-4.54), and testis cancer HR = 2.34 (95% CI = 1.60-3.42) along with a significant decrease in esophageal cancer risk HR = 0.39 (95% CI = 0.16-0.97). Thirteen clusters of familial multicancer patterns were identified in families of azoospermic men, 66% of families in the azoospermia cohort showed population-level cancer risks, however, the remaining 12 clusters showed elevated risk for 2-7 cancer types. Several of the clusters with elevated cancer risks also showed increased odds of cancer diagnoses at young ages with six clusters showing increased odds of adolescent and young adult (AYA) diagnosis [odds ratio (OR) = 1.96-2.88] and two clusters showing increased odds of pediatric cancer diagnosis (OR = 3.64-12.63). Within the severe oligozoospermia cohort, 12 distinct familial multicancer clusters were identified. All 12 clusters showed elevated risk for 1-3 cancer types. An increase in odds of cancer diagnoses at young ages was also seen in five of the severe oligozoospermia familial multicancer clusters, three clusters showed increased odds of AYA diagnosis (OR = 2.19-2.78) with an additional two clusters showing increased odds of a pediatric diagnosis (OR = 3.84-9.32).

LIMITATIONS, REASONS FOR CAUTION

Although this study has many strengths, including population data for family structure, cancer diagnoses and subfertility, there are limitations. First, semen measures are not available for the sample of fertile men. Second, there is no information on medical comorbidities or lifestyle risk factors such as smoking status, BMI, or environmental exposures. Third, all of the subfertile men included in this study were seen at a fertility clinic for evaluation. These men were therefore a subset of the overall population experiencing fertility problems and likely represent those with the socioeconomic means for evaluation by a physician.

WIDER IMPLICATIONS OF THE FINDINGS

This analysis leveraged unique population-level data resources, SHARE and the UPDB, to describe novel multicancer clusters among the families of azoospermic and severely oligozoospermic men. Distinct overall multicancer risk and familial multicancer patterns were observed in the azoospermia and severe oligozoospermia cohorts, suggesting heterogeneity in cancer risk by type of subfertility and within subfertility type. Describing families with similar cancer risk patterns provides a new avenue to increase homogeneity for focused gene discovery and environmental risk factor studies. Such discoveries will lead to more accurate risk predictions and improved counseling for patients and their families.

STUDY FUNDING/COMPETING INTEREST(S)

This work was funded by GEMS: Genomic approach to connecting Elevated germline Mutation rates with male infertility and Somatic health (Eunice Kennedy Shriver National Institute of Child Health and Human Development (NICHD): R01 HD106112). The authors have no conflicts of interest relevant to this work.

TRIAL REGISTRATION NUMBER

N/A.

A common allele increases endometrial Wnt4 expression, with antagonistic implications for pregnancy, reproductive cancers, and endometriosis

The common human SNP rs3820282 is associated with multiple phenotypes including gestational length and likelihood of endometriosis and cancer, presenting a paradigmatic pleiotropic variant. Deleterious pleiotropic mutations cause the co-occurrence of disorders either within individuals, or across population. When adverse and advantageous effects are combined, pleiotropy can maintain high population frequencies of deleterious alleles. To reveal the causal molecular mechanisms of this pleiotropic SNP, we introduced this substitution into the mouse genome by CRISPR/Cas 9. Previous work showed that rs3820282 introduces a high-affinity estrogen receptor alpha-binding site at the Wnt4 locus. Here, we show that this mutation upregulates Wnt4 transcription in endometrial stroma, following the preovulatory estrogen peak. Effects on uterine transcription include downregulation of epithelial proliferation and induction of progesterone-regulated pro-implantation genes. We propose that these changes increase uterine permissiveness to embryo invasion, whereas they decrease resistance to invasion by cancer and endometriotic foci in other estrogen-responsive tissues.

Risk of cancer in relatives of patients with myelodysplastic neoplasia and acute leukemias

BACKGROUND

The risk of cancer among relatives of patients with either myelodysplastic neoplasia (MDS), acute myeloid leukemia (AML) or acute lymphoblastic leukemia (ALL) has not been thoroughly examined.

METHODS

We linked the Danish Civil Registration System with the Danish Cancer Registry, the Danish National Acute Leukemia Registry, and the Danish Myelodysplastic Syndrome Database to estimate the relative risk of cancer among relatives of patients with MDS/AML/ALL. We used standardized incidence ratios (SIRs), i.e., the ratio of observed to expected number of cancers among the relatives as a measure of relative risk.

RESULTS

We identified 13010 first-degree (FDR) and 22051 second-degree (SDR) relatives of 8386 patients with MDS/ALL/AML. Disregarding basal cell carcinoma (BCC), the relative risk for cancer overall was increased in both FDR (SIR=1.3; 95% confidence interval (CI) 1.1-1.4) and SDR (SIR=1.5; 95% CI 1.2-1.8). SIRs among FDRs were statistically significantly increased for malignant melanoma, BCC and for the combined groups of cancers of the male genital organs, urinary tract, and MDS/AML/ALL. Among SDRs, SIRs were statistically significantly increased for malignant melanoma, BCC, and cancers in the digestive organs and peritoneum.

CONCLUSIONS

We observed an increased risk of cancer among FDR and SDR of patients with MDS/AML/ALL.

A pleiotropic ATM variant (rs1800057 C>G) is associated with risk of multiple cancers

ATM (ataxia-telangiectasia mutated) is an important cell-cycle checkpoint kinase required for cellular response to DNA damage. Activated by DNA double strand breaks, ATM regulates the activities of many downstream proteins involved in various carcinogenic events. Therefore, ATM or its genetic variants may have a pleiotropic effect in cancer development. We conducted a pleiotropic analysis to evaluate associations between genetic variants of ATM and risk of multiple cancers. With genotyping data extracted from previously published genome-wide association studies of various cancers, we performed multivariate logistic regression analysis, followed by a meta-analysis for each cancer site, to identify cancer risk-associated single-nucleotide polymorphisms (SNPs). In the ASSET two-sided analysis, we found that two ATM SNPs were significantly associated with risk of multiple cancers. One tagging SNP (rs1800057 C>G) was associated with risk of multiple cancers (two-sided P=5.27×10 -7). Because ATM rs1800057 is a missense variant, we also explored the intermediate phenotypes through which this variant may confer risk of multiple cancers and identified a possible immune-mediated effect of this variant. Our findings indicate that genetic variants of ATM may have a pleiotropic effect on cancer risk and thus provide an important insight into common mechanisms of carcinogenesis.

Partitioned glioma heritability shows subtype-specific enrichment in immune cells

BACKGROUND

Epidemiological studies of adult glioma have identified genetic syndromes and 25 heritable risk loci that modify individual risk for glioma, as well increased risk in association with exposure to ionizing radiation and decreased risk in association with allergies. In this analysis, we assess whether there is a shared genome-wide genetic architecture between glioma and atopic/autoimmune diseases.

METHODS

Using summary statistics from a glioma genome-wide association studies (GWAS) meta-analysis, we identified significant enrichment for risk variants associated with gene expression changes in immune cell populations. We also estimated genetic correlations between glioma and autoimmune, atopic, and hematologic traits using linkage disequilibrium score regression (LDSC), which leverages genome-wide single-nucleotide polymorphism (SNP) associations and patterns of linkage disequilibrium.

RESULTS

Nominally significant negative correlations were observed for glioblastoma (GB) and primary biliary cirrhosis (rg = -0.26, P = .0228), and for non-GB gliomas and celiac disease (rg = -0.32, P = .0109). Our analyses implicate dendritic cells (GB pHM = 0.0306 and non-GB pHM = 0.0186) in mediating both GB and non-GB genetic predisposition, with GB-specific associations identified in natural killer (NK) cells (pHM = 0.0201) and stem cells (pHM = 0.0265).

CONCLUSIONS

This analysis identifies putative new associations between glioma and autoimmune conditions with genomic architecture that is inversely correlated with that of glioma and that T cells, NK cells, and myeloid cells are involved in mediating glioma predisposition. This provides further evidence that increased activation of the acquired immune system may modify individual susceptibility to glioma.

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.

Sequencing at lymphoid neoplasm susceptibility loci maps six myeloma risk genes

Inherited genetic risk factors play a role in multiple myeloma (MM), yet considerable missing heritability exists. Rare risk variants at genome-wide association study (GWAS) loci are a new avenue to explore. Pleiotropy between lymphoid neoplasms (LNs) has been suggested in family history and genetic studies, but no studies have interrogated sequencing for pleiotropic genes or rare risk variants. Sequencing genetically enriched cases can help discover rarer variants. We analyzed exome sequencing in familial or early-onset MM cases to identify rare, functionally relevant variants near GWAS loci for a range of LNs. A total of 149 distinct and significant LN GWAS loci have been published. We identified six recurrent, rare, potentially deleterious variants within 5 kb of significant GWAS single nucleotide polymorphisms in 75 MM cases. Mutations were observed in BTNL2, EOMES, TNFRSF13B, IRF8, ACOXL and TSPAN32. All six genes replicated in an independent set of 255 early-onset MM or familial MM or precursor cases. Expansion of our analyses to the full length of these six genes resulted in a list of 39 rare and deleterious variants, seven of which segregated in MM families. Three genes also had significant rare variant burden in 733 sporadic MM cases compared with 935 control individuals: IRF8 (P = 1.0 × 10-6), EOMES (P = 6.0 × 10-6) and BTNL2 (P = 2.1 × 10-3). Together, our results implicate six genes in MM risk, provide support for genetic pleiotropy between LN subtypes and demonstrate the utility of sequencing genetically enriched cases to identify functionally relevant variants near GWAS loci.

Comparison of adaptive multiple phenotype association tests using summary statistics in genome-wide association studies

Genome-wide association studies have been successful mapping loci for individual phenotypes, but few studies have comprehensively interrogated evidence of shared genetic effects across multiple phenotypes simultaneously. Statistical methods have been proposed for analyzing multiple phenotypes using summary statistics, which enables studies of shared genetic effects while avoiding challenges associated with individual-level data sharing. Adaptive tests have been developed to maintain power against multiple alternative hypotheses because the most powerful single-alternative test depends on the underlying structure of the associations between the multiple phenotypes and a single nucleotide polymorphism (SNP). Here we compare the performance of six such adaptive tests: two adaptive sum of powered scores (aSPU) tests, the unified score association test (metaUSAT), the adaptive test in a mixed-models framework (mixAda) and two principal-component-based adaptive tests (PCAQ and PCO). Our simulations highlight practical challenges that arise when multivariate distributions of phenotypes do not satisfy assumptions of multivariate normality. Previous reports in this context focus on low minor allele count (MAC) and omit the aSPU test, which relies less than other methods on asymptotic and distributional assumptions. When these assumptions are not satisfied, particularly when MAC is low and/or phenotype covariance matrices are singular or nearly singular, aSPU better preserves type I error, sometimes at the cost of decreased power. We illustrate this trade-off with multiple phenotype analyses of six quantitative electrocardiogram traits in the Population Architecture using Genomics and Epidemiology (PAGE) study.

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.

Primo: integration of multiple GWAS and omics QTL summary statistics for elucidation of molecular mechanisms of trait-associated SNPs and detection of pleiotropy in complex traits

To provide a comprehensive mechanistic interpretation of how known trait-associated SNPs affect complex traits, we propose a method, Primo, for integrative analysis of GWAS summary statistics with multiple sets of omics QTL summary statistics from different cellular conditions or studies. Primo examines association patterns of SNPs to complex and omics traits. In gene regions harboring known susceptibility loci, Primo performs conditional association analysis to account for linkage disequilibrium. Primo allows for unknown study heterogeneity and sample correlations. We show two applications using Primo to examine the molecular mechanisms of known susceptibility loci and to detect and interpret pleiotropic effects.

Genetic variant rs2494938 of LRFN2 gene is associated with non-small cell lung cancer risk in North-Indian population

Various Genome-wide association studies (GWAS) have reported the association of variant rs2494938 with lung cancer. However, genetic association of LRFN2 genetic variation with non-small cell lung cancer (NSCLC) in North Indian population remained unexplored. We conducted a case-control association study using TaqMan-based chemistry in which a total of 619 individuals, 189 NSCLC cases and 430 controls, were genotyped to explore the association of rs2494938 genetic variant of the LRFN2 gene with NSCLC patients from North India. The allele 'G' (risk allele) of the genetic variant rs2494938 was significantly associated with the NSCLC [OR = 1.51 (1.18-1.93 at 95% CI); p value = 0.0009]. Genetic association was also explored by applying different genetic models (Dominant, Additive). These results suggest that rs2494938 polymorphism of the LRFN2 gene is a risk factor in the North Indian populations to develop NSCLC. The LD (Linkage Disequilibrium) plot demonstrates the variant and its LD SNPs (r ² > 0.8) and the variant has direct regulatory effect, which could affect the overall physiology of the gene. These findings could be used as diagnostic and prognostic markers in clinical studies of lung cancer patients in North Indian population groups. The present study also provides an important evidence on the genetic etiology of NSCLC in North Indian populations and further expounds GWAS findings on the role of LRFN2 in lung cancer risk. This study provides the holistic view about the non-small cell lung cancer in Jammu and Kashmir, North Indian population and it can be a hallmark of cancer if verified on a very large sample size (cohort).

Identification of pleiotropic genes between risk factors of stroke by multivariate metaCCA analysis

Genome-wide association studies (GWASs) have identified more than 20 genetic loci as risk predictors associated with stroke. However, these studies were generally performed for single-trait and failed to consider the pleiotropic effects of these risk genes among the multiple risk factors for stroke. In this study, we applied a novel metaCCA method followed by gene-based VEGAS2 analysis to identify the risk genes for stroke that may overlap between seven correlated risk factors (including atrial fibrillation, hypertension, coronary artery disease, heart failure, diabetes, body mass index, and total cholesterol level) by integrating seven corresponding GWAS data. We detected 20 potential pleiotropic genes that may be associated with multiple risk factors of stroke. Furthermore, using gene-to-trait pathway analysis, we suggested six potential risk genes (FUT8, GMIP, PLA2G6, PDE3A, SMARCA4, SKAPT) that may affect ischemic or hemorrhage stroke through multiple intermediate factors such as MAPK family. These findings provide novel insight into the genetic determinants contributing to the concurrent development of biological conditions that may influence stroke susceptibility, and also indicate some potential therapeutic targets that can be further studied for the prevention of cerebrovascular disease.

MassARRAY analysis of twelve cancer related SNPs in esophageal squamous cell carcinoma in J&K, India

Background

MassARRAY (Agena Bioscience™) combines competitive PCR with MALDI-TOF mass spectrometry (MS) analysis that gives highly accurate, sensitive, and high-throughput methods for the quantitative analysis of variation of gene expression in multiple samples. SNPs (Single Nucleotide Polymorphisms) have a very high potential of discovering disease-gene relationships. SNP-genotyping through MassARRAY is not only a cost-effective genotyping method but also provides a platform to validate variants observed through a high-throughput Next-generation sequencing (NGS).

Methods

In the present study, we have incorporated the use of matrix-assisted laser desorption/ionization-time of flight, mass spectrometry (MALDI-TOF) as a tool for differentiating genotypes based on the mass of variant. We have performed multiplex PCR and genotyped 12 SNPs in 758 samples (166 cases and 592 controls). The 12 studied SNPs were chosen with a rationale for their association with multiple cancers in literature.

Results

This is the first study to explore these SNPs with esophageal cancer within the J&K population. Out of 12 SNPs, two SNPs rs12190287 of TCF21 and rs10046 of CYP19A1 were significantly associated with esophageal cancer with Odds Ratio (OR) 1.412 (1.09–1.8 at 95% CI, p = 0.008) and 1.54 (1.21–2.072 at 95% CI, p = 0.0007) within the population of Jammu and Kashmir.

Conclusion

We explored 12 SNPs that were found to be associated with multiple cancers in literature with esophageal cancer within the population of J&K. This is the first study to find the relation of these SNPs with ESCC within the studied population. This study explores the relation of genetic and environmental factors with the ESCC susceptibility.

Harnessing Population Pedigree Data and Machine Learning Methods to Identify Patterns of Familial Bladder Cancer Risk

BACKGROUND

Relatives of patients with bladder cancer have been shown to be at increased risk for kidney, lung, thyroid, and cervical cancer after correcting for smoking-related behaviors that may concentrate in some families. We demonstrate a novel approach to simultaneously assess risks for multiple cancers to identify distinct multicancer configurations (multiple different cancer types that cluster in relatives) surrounding patients with familial bladder cancer.

METHODS

This study takes advantage of a unique population-level data resource, the Utah Population Database (UPDB), containing vast genealogy and statewide cancer data. Familial risk is measured using standardized incidence risk (SIR) ratios that account for sex, age, birth cohort, and person-years of the pedigree members.

RESULTS

We identify 1,023 families with a significantly higher bladder cancer rate than population controls (familial bladder cancer). Familial SIRs are then calculated across 25 cancer types, and a weighted Gower distance with K-medoids clustering is used to identify familial multicancer configurations (FMC). We found five FMCs, each exhibiting a different pattern of cancer aggregation. Of the 25 cancer types studied, kidney and prostate cancers were most commonly enriched in the familial bladder cancer clusters. Laryngeal, lung, stomach, acute lymphocytic leukemia, Hodgkin disease, soft-tissue carcinoma, esophageal, breast, lung, uterine, thyroid, and melanoma cancers were the other cancer types with increased incidence in familial bladder cancer families.

CONCLUSIONS

This study identified five familial bladder cancer FMCs showing unique risk patterns for cancers of other organs, suggesting phenotypic heterogeneity familial bladder cancer.

IMPACT

FMC configurations could permit better definitions of cancer phenotypes (subtypes or multicancer) for gene discovery and environmental risk factor studies.

Genetic determinants of telomere length and cancer risk

The relationship of telomere length with cancer risk has been the source of much debate within epidemiological studies, which have produced inconsistent finding both between and within different cancer types. Over recent years, genome-wide association studies of increasing size have identified variants that determine human telomere length. These variants have subsequently been utilised as instrumental variables in Mendelian randomisation based studies, allowing the investigation of potential causal relationships between telomere length and cancer. Here we discuss recent advances in both genomic discovery, studies that give increasing evidence towards a causal role for telomere length in cancer risk and considerations for future studies.

Mendelian Randomization and mediation analysis of leukocyte telomere length and risk of lung and head and neck cancers

BACKGROUND

Evidence from observational studies of telomere length (TL) has been conflicting regarding its direction of association with cancer risk. We investigated the causal relevance of TL for lung and head and neck cancers using Mendelian Randomization (MR) and mediation analyses.

METHODS

We developed a novel genetic instrument for TL in chromosome 5p15.33, using variants identified through deep-sequencing, that were genotyped in 2051 cancer-free subjects. Next, we conducted an MR analysis of lung (16 396 cases, 13 013 controls) and head and neck cancer (4415 cases, 5013 controls) using eight genetic instruments for TL. Lastly, the 5p15.33 instrument and distinct 5p15.33 lung cancer risk loci were evaluated using two-sample mediation analysis, to quantify their direct and indirect, telomere-mediated, effects.

RESULTS

The multi-allelic 5p15.33 instrument explained 1.49-2.00% of TL variation in our data (p = 2.6 × 10-9). The MR analysis estimated that a 1000 base-pair increase in TL increases risk of lung cancer [odds ratio (OR) = 1.41, 95% confidence interval (CI): 1.20-1.65] and lung adenocarcinoma (OR = 1.92, 95% CI: 1.51-2.22), but not squamous lung carcinoma (OR = 1.04, 95% CI: 0.83-1.29) or head and neck cancers (OR = 0.90, 95% CI: 0.70-1.05). Mediation analysis of the 5p15.33 instrument indicated an absence of direct effects on lung cancer risk (OR = 1.00, 95% CI: 0.95-1.04). Analysis of distinct 5p15.33 susceptibility variants estimated that TL mediates up to 40% of the observed associations with lung cancer risk.

CONCLUSIONS

Our findings support a causal role for long telomeres in lung cancer aetiology, particularly for adenocarcinoma, and demonstrate that telomere maintenance partially mediates the lung cancer susceptibility conferred by 5p15.33 loci.

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.

ELL2 Influences Transcription Elongation, Splicing, Ig Secretion and Growth

ELL2 was previously discovered as a member of the Super Elongation Complex. It is involved in driving the maturation of B cells to plasma cells through shifting patterns of RNA processing, favoring generation of the secretory form of heavy chain immunoglobulin (IgH) associated with plasma cells. ELL2 influences the expression and splicing patterns of more than 4,000 genes in antibody secreting cells. The ELL2 gene has been implicated in cancers such as multiple myeloma and salivary gland carcinoma. A member of the ELL family (ELL1) was recently proven to act as an E3 ubiquitin ligase to known proto-oncogene, c-Myc, through a highly conserved cysteine residue in the C-terminal CEYLH region. Comparison of sequence homology shows this region is conserved between the three members of the ELL family, leading us to hypothesize that the other two ELLs (2 and 3) could serve the same role. In this review, we summarize what is known about ELL2 with respect to its role in driving B cell to plasma cell differentiation as well as its potential role in tumor suppression.