Cancer risk is determined by a complex interplay of genetic and environmental factors. Genome-wide association studies (GWAS) have identified hundreds of common (minor allele frequency [MAF]>0.05) and less common (0.01
Gene-environment interactions; complex phenotypes; genetic epidemiology
To identify common genetic variants that contribute to lung cancer susceptibility, we conducted a multistage genome-wide association study of lung cancer in Asian women who never smoked. We scanned 5,510 never-smoking female lung cancer cases and 4,544 controls drawn from 14 studies from mainland China, South Korea, Japan, Singapore, Taiwan, and Hong Kong. We genotyped the most promising variants (associated at P < 5 × 10-6) in an additional 1,099 cases and 2,913 controls. We identified three new susceptibility loci at 10q25.2 (rs7086803, P = 3.54 × 10-18), 6q22.2 (rs9387478, P = 4.14 × 10-10) and 6p21.32 (rs2395185, P = 9.51 × 10-9). We also confirmed associations reported for loci at 5p15.33 and 3q28 and a recently reported finding at 17q24.3. We observed no evidence of association for lung cancer at 15q25 in never-smoking women in Asia, providing strong evidence that this locus is not associated with lung cancer independent of smoking.
In the National Cancer Institute Cancer Genetic Markers of Susceptibility (CGEMS) genome-wide association study of breast cancer, a single nucleotide polymorphism (SNP) marker, rs999737, in the 14q24.1 interval, was associated with breast cancer risk. In order to fine map this region, we imputed a 3.93MB region flanking rs999737 for Stages 1 and 2 of the CGEMS study (5,692 cases, 5,576 controls) using the combined reference panels of the HapMap 3 and the 1000 Genomes Project. Single-marker association testing and variable-sized sliding-window haplotype analysis were performed, and for both analyses the initial tagging SNP rs999737 retained the strongest association with breast cancer risk. Investigation of contiguous regions did not reveal evidence for an additional independent signal. Therefore, we conclude that rs999737 is an optimal tag SNP for common variants in the 14q24.1 region and thus narrow the candidate variants that should be investigated in follow-up laboratory evaluation.
RAD51L1; breast cancer; genome-wide association study; fine-mapping; imputation
The Lasso shrinkage procedure achieved its popularity, in part, by its tendency to shrink estimated coefficients to zero, and its ability to serve as a variable selection procedure. Using data-adaptive weights, the adaptive Lasso modified the original procedure to increase the penalty terms for those variables estimated to be less important by ordinary least squares. Although this modified procedure attained the oracle properties, the resulting models tend to include a large number of “false positives” in practice. Here, we adapt the concept of local false discovery rates (lFDRs) so that it applies to the sequence, λn, of smoothing parameters for the adaptive Lasso. We define the lFDR for a given λn to be the probability that the variable added to the model by decreasing λn to λn−δ is not associated with the outcome, where δ is a small value. We derive the relationship between the lFDR and λn, show lFDR=1 for traditional smoothing parameters, and show how to select λn so as to achieve a desired lFDR. We compare the smoothing parameters chosen to achieve a specified lFDR and those chosen to achieve the oracle properties, as well as their resulting estimates for model coefficients, with both simulation and an example from a genetic study of prostate specific antigen.
Adaptive Lasso; Local false discovery rate; Smoothing parameter; Variable selection
The genetic regulation of the human epigenome is not fully appreciated. Here we describe the effects of genetic variants on the DNA methylome in human lung based on methylation-quantitative trait loci (meQTL) analyses. We report 34,304 cis- and 585 trans-meQTLs, a genetic-epigenetic interaction of surprising magnitude, including a regulatory hotspot. These findings are replicated in both breast and kidney tissues and show distinct patterns: cis-meQTLs mostly localize to CpG sites outside of genes, promoters, and CpG islands (CGIs), while trans-meQTLs are over-represented in promoter CGIs. meQTL SNPs are enriched in CTCF binding sites, DNaseI hypersensitivity regions and histone marks. Importantly, 4 of the 5 established lung cancer risk loci in European ancestry are cis-meQTLs and, in aggregate, cis-meQTLs are enriched for lung cancer risk in a genome-wide analysis of 11,587 subjects. Thus, inherited genetic variation may affect lung carcinogenesis by regulating the human methylome.
Common variants in two of the five genetic regions recently identified from genome-wide association studies (GWAS) of risk of glioma were reported to interact with a history of allergic symptoms. In a pooled analysis of five epidemiologic studies, we evaluated the association between the five GWAS implicated gene variants and allergies and autoimmune conditions (AIC) on glioma risk (851 adult glioma cases and 3,977 controls). We further evaluated the joint effects between allergies and AIC and these gene variants on glioma risk. Risk estimates were calculated as odds ratios (OR) and 95 % confidence intervals (95 % CI), adjusted for age, gender, and study. Joint effects were evaluated by conducting stratified analyses whereby the risk associations (OR and 95 % CI) with the allergy or autoimmune conditions for glioma were evaluated by the presence or absence of the ‘at-risk’ variant, and estimated p interaction by fitting models with the main effects of allergy or autoimmune conditions and genotype and an interaction (product) term between them. Four of the five SNPs previously reported by others were statistically significantly associated with increased risk of glioma in our study (rs2736100, rs4295627, rs4977756, and rs6010620); rs498872 was not associated with glioma in our study. Reporting any allergies or AIC was associated with reduced risks of glioma (allergy: adjusted OR = 0.71, 95 % CI 0.55–0.91; AIC: adjusted OR = 0.65, 95 % CI 0.47–0.90). We did not observe differential association between allergic or autoimmune conditions and glioma by genotype, and there were no statistically significant p interactions. Stratified analysis by glioma grade (low and high grade) did not suggest risk differences by disease grade. Our results do not provide evidence that allergies or AIC modulate the association between the four GWAS-identified SNPs examined and risk of glioma.
Single-nucleotide polymorphisms; Glioma; Allergies; Autoimmune conditions; Gene–environment interaction
Bladder cancer results from the combined effects of environmental and genetic factors, smoking being the strongest risk factor. Evaluating absolute risks resulting from the joint effects of smoking and genetic factors is critical to evaluate the public health relevance of genetic information. Analyses included up to 3,942 cases and 5,680 controls of European background in seven studies. We tested for multiplicative and additive interactions between smoking and 12 susceptibility loci, individually and combined as a polygenic risk score (PRS). Thirty-year absolute risks and risk differences by levels of the PRS were estimated for US-males aged 50-years. Six out of 12 variants showed significant additive gene-environment interactions, most notably NAT2 (P=7×10-4) and UGT1A6 (P=8×10-4). The 30-year absolute risk of bladder cancer in US males was 6.2% for all current smokers. This risk ranged from 2.9% for current smokers in the lowest quartile of the PRS to 9.9% for current smokers in the upper quartile. Risk difference estimates indicated that 8,200 cases would be prevented if elimination of smoking occurred in 100,000 men in the upper PRS quartile, compared to 2,000 cases prevented by a similar effort in the lowest PRS quartile (P-additive =1×10-4). The impact of eliminating smoking the on number of bladder cancer cases prevented is larger for individuals at higher than lower genetic risk. Our findings could have implications for targeted prevention strategies. However, other smoking-related diseases, as well as practical and ethical considerations, need to be considered before any recommendations could be made.
A few epidemiologic studies have found that use of nonsteroidal anti-inflammatory drugs (NSAIDs) is associated with reduced risk of bladder cancer. However, the effects of specific NSAID use and individual variability in risk have not been well studied. We examined the association between NSAIDs use and bladder cancer risk, and its modification by 39 candidate genes related to NSAID metabolism. A population-based case–control study was conducted in northern New England, enrolling 1,171 newly diagnosed cases and 1,418 controls. Regular use of nonaspirin, nonselective NSAIDs was associated with reduced bladder cancer risk, with a statistically significant inverse trend in risk with duration of use (ORs of 1.0, 0.8, 0.6 and 0.6 for <5, 5–9, 10–19 and 201 years, respectively; ptrend = 0.015). This association was driven mainly by ibuprofen; significant inverse trends in risk with increasing duration and dose of ibuprofen were observed (ptrend = 0.009 and 0.054, respectively). The reduced risk from ibuprofen use was limited to individuals carrying the T allele of a single nucleotide polymorphism (rs4646450) compared to those who did not use ibuprofen and did not carry the T allele in the CYP3A locus, providing new evidence that this association might be modified by polymorphisms in genes that metabolize ibuprofen. Significant positive trends in risk with increasing duration and cumulative dose of selective cyclooxygenase (COX-2) inhibitors were observed. Our results are consistent with those from previous studies linking use of NSAIDs, particularly ibuprofen, with reduced risk. We observed a previously unrecognized risk associated with use of COX-2 inhibitors, which merits further evaluation.
bladder cancer; nonsteroidal anti-inflammatory drugs; gene–drug interaction; CYP3A
Neuronal nicotinic acetylcholine receptor (nAChR) genes (CHRNA5/CHRNA3/CHRNB4) have been reproducibly associated with nicotine dependence, smoking behaviors, and lung cancer risk. Of the few reports that have focused on early smoking behaviors, association results have been mixed. This meta-analysis examines early smoking phenotypes and SNPs in the gene cluster to determine: (1) whether the most robust association signal in this region (rs16969968) for other smoking behaviors is also associated with early behaviors, and/or (2) if additional statistically independent signals are important in early smoking. We focused on two phenotypes: age of tobacco initiation (AOI) and age of first regular tobacco use (AOS). This study included 56,034 subjects (41 groups) spanning nine countries and evaluated five SNPs including rs1948, rs16969968, rs578776, rs588765, and rs684513. Each dataset was analyzed using a centrally generated script. Meta-analyses were conducted from summary statistics. AOS yielded significant associations with SNPs rs578776 (beta = 0.02, P = 0.004), rs1948 (beta = 0.023, P = 0.018), and rs684513 (beta = 0.032, P = 0.017), indicating protective effects. There were no significant associations for the AOI phenotype. Importantly, rs16969968, the most replicated signal in this region for nicotine dependence, cigarettes per day, and cotinine levels, was not associated with AOI (P = 0.59) or AOS (P = 0.92). These results provide important insight into the complexity of smoking behavior phenotypes, and suggest that association signals in the CHRNA5/A3/B4 gene cluster affecting early smoking behaviors may be different from those affecting the mature nicotine dependence phenotype.
CHRNA5; CHRNA3; CHRNB4; meta-analysis; nicotine; smoke
Primary analysis of case–control studies focuses on the relationship between disease D and a set of covariates of interest (Y, X). A secondary application of the case–control study, which is often invoked in modern genetic epidemiologic association studies, is to investigate the interrelationship between the covariates themselves. The task is complicated owing to the case–control sampling, where the regression of Y on X is different from what it is in the population. Previous work has assumed a parametric distribution for Y given X and derived semiparametric efficient estimation and inference without any distributional assumptions about X. We take up the issue of estimation of a regression function when Y given X follows a homoscedastic regression model, but otherwise the distribution of Y is unspecified. The semiparametric efficient approaches can be used to construct semiparametric efficient estimates, but they suffer from a lack of robustness to the assumed model for Y given X. We take an entirely different approach. We show how to estimate the regression parameters consistently even if the assumed model for Y given X is incorrect, and thus the estimates are model robust. For this we make the assumption that the disease rate is known or well estimated. The assumption can be dropped when the disease is rare, which is typically so for most case–control studies, and the estimation algorithm simplifies. Simulations and empirical examples are used to illustrate the approach.
Biased samples; Homoscedastic regression; Secondary data; Secondary phenotypes; Semiparametric inference; Two-stage samples
A recent genome-wide association study (GWAS) of subjects from Japan and South Korea reported a novel association between the TP63 locus on chromosome 3q28 and risk of lung adenocarcinoma (p = 7.3 × 10−12); however, this association did not achieve genome-wide significance (p < 10−7) among never-smoking males or females. To determine if this association with lung cancer risk is independent of tobacco use, we genotyped the TP63 SNPs reported by the previous GWAS (rs10937405 and rs4488809) in 3,467 never-smoking female lung cancer cases and 3,787 never-smoking female controls from 10 studies conducted in Taiwan, Mainland China, South Korea, and Singapore. Genetic variation in rs10937405 was associated with risk of lung adenocarcinoma [n = 2,529 cases; p = 7.1 × 10−8; allelic risk = 0.80, 95% confidence interval (CI) = 0.74–0.87]. There was also evidence of association with squamous cell carcinoma of the lung (n = 302 cases; p = 0.037; allelic risk = 0.82, 95% CI = 0.67–0.99). Our findings provide strong evidence that genetic variation in TP63 is associated with the risk of lung adenocarcinoma among Asian females in the absence of tobacco smoking.
We present a Bayesian approach to modeling dynamic smoking addiction behavior processes when cure is not directly observed due to censoring. Subject-specific probabilities model the stochastic transitions among three behavioral states: smoking, transient quitting, and permanent quitting (absorbent state). A multivariate normal distribution for random effects is used to account for the potential correlation among the subject-specific transition probabilities. Inference is conducted using a Bayesian framework via Markov chain Monte Carlo simulation. This framework provides various measures of subject-specific predictions, which are useful for policy-making, intervention development, and evaluation. Simulations are used to validate our Bayesian methodology and assess its frequentist properties. Our methods are motivated by, and applied to, the Alpha-Tocopherol, Beta-Carotene Lung Cancer Prevention study, a large (29,133 individuals) longitudinal cohort study of smokers from Finland.
Cure model; MCMC; Mixed-effects model; Prediction; Recurrent events; Smoking cessation
There has been a long-standing controversy in epidemiology with regard to an appropriate risk scale for testing interactions between genes (G) and environmental exposure (E ). Although interaction tests based on the logistic model—which approximates the multiplicative risk for rare diseases—have been more widely applied because of its convenience in statistical modeling, interactions under additive risk models have been regarded as closer to true biologic interactions and more useful in intervention-related decision-making processes in public health. It has been well known that exploiting a natural assumption of G-E independence for the underlying population can dramatically increase statistical power for detecting multiplicative interactions in case-control studies. However, the implication of the independence assumption for tests for additive interaction has not been previously investigated. In this article, the authors develop a likelihood ratio test for detecting additive interactions for case-control studies that incorporates the G-E independence assumption. Numerical investigation of power suggests that incorporation of the independence assumption can enhance the efficiency of the test for additive interaction by 2- to 2.5-fold. The authors illustrate their method by applying it to data from a bladder cancer study.
additive risk model; case-control studies; gene-environment independence; gene-environment interaction; multiplicative risk model
Pulmonary inflammation may contribute to lung cancer etiology. We conducted a broad evaluation of the association of single nucleotide polymorphisms (SNPs) in innate immunity and inflammation pathways with lung cancer risk, and conducted comparisons with a lung cancer genome wide association study (GWAS).
We included 378 lung cancer cases and 450 controls from the Prostate, Lung, Colorectal and Ovarian (PLCO) Cancer Screening Trial. An Illumina GoldenGate oligonucleotide pool assay was used to genotype 1,429 SNPs. Odds ratios (ORs) and 95% confidence intervals (CIs) were estimated for each SNP, and p-values for trend were calculated. For statistically significant SNPs (p-trend<0.05), we replicated our results with genotyped or imputed SNPs in the GWAS, and adjusted p-values for multiple testing.
In our PLCO analysis, we observed a significant association between 81 SNPs located in 44 genes and lung cancer (p-trend<0.05). Of these 81 SNPS, there was evidence for confirmation in the GWAS for 10 SNPs. However, after adjusting for multiple comparisons, the only SNP that remained significantly associated with lung cancer in the replication phase was rs4648127 (NFKB1; multiple testing adjusted p-trend=0.02). The CT/TT genotype of NFKB1 was associated with reduced odds of lung cancer in the PLCO study (OR=0.56; 95% CI 0.37–0.86) and the GWAS (OR=0.79; 95% CI 0.69–0.90).
We found a significant association between a variant in the NFKB1 gene and lung cancer risk. Our findings add to evidence implicating inflammation and immunity in lung cancer etiology.
lung cancer; genetics; inflammation; immunity; epidemiology
The interest in performing gene-environment interaction studies has seen a significant increase with the increase of advanced molecular genetics techniques. Practically, it became possible to investigate the role of environmental factors in disease risk and hence to investigate their role as genetic effect modifiers. The understanding that genetics is important in the uptake and metabolism of toxic substances is an example of how genetic profiles can modify important environmental risk factors to disease. Several rationales exist to set up gene-environment interaction studies and the technical challenges related to these studies – when the number of environmental or genetic risk factors is relatively small – has been described before.
In the post-genomic era, it is now possible to study thousands of genes and their interaction with the environment. This brings along a whole range of new challenges and opportunities. Despite a continuing effort in developing efficient methods and optimal bioinformatics infrastructures to deal with the available wealth of data, the challenge remains how to best present and analyze Genome-Wide Environmental Interaction (GWEI) studies involving multiple genetic and environmental factors. Since GWEIs are performed at the intersection of statistical genetics, bioinformatics and epidemiology, usually similar problems need to be dealt with as for Genome-Wide Association gene-gene Interaction (GWAI) studies. However, additional complexities need to be considered which are typical for large-scale epidemiological studies, but are also related to “joining” two heterogeneous types of data in explaining complex disease trait variation or for prediction purposes.
Genome-wide association studies; gene-environment interaction; post-GWAS analysis; association tests; exploratory methods
We report a new model to project the predictive performance of polygenic models based on the number and distribution of effect sizes for the underlying susceptibility alleles and the size of the training dataset. Using estimates of effect-size distribution and heritability derived from current studies, we project that while 45% of the variance of height has been attributed to common tagging Single Nucleotide Polymorphisms (SNP), a model trained on one million people may only explain 33.4% of variance of the trait. Current studies can identify 3.0%, 1.1%, and 7.0%, of the populations who are at two-fold or higher than average risk for Type 2 diabetes, coronary artery disease and prostate cancer, respectively. Tripling of sample sizes could elevate the percentages to 18.8%, 6.1%, and 12.2%, respectively. The utility of future polygenic models will depend on achievable sample sizes, underlying genetic architecture and information on other risk-factors, including family history.
Gliomas account for approximately 80% of all primary malignant brain tumors, and despite improvements in clinical care over the last 20 years remain among the most lethal tumors, underscoring the need for gaining new insights that could translate into clinical advances. Recent genome-wide association studies (GWAS) have identified seven new susceptibility regions. We conducted a new independent GWAS of glioma using 1,856 cases and 4,955 controls (from 14 cohort studies, 3 casecontrol studies, and 1 population-based case only study) and found evidence of strong replication for three of the seven previously reported associations at 20q13.33 (RTEL), 5p15.33 (TERT), and 9p21.3 (CDKN2BAS), and consistent association signals for the remaining four at 7p11.2 (EGFR both loci), 8q24.21 (CCDC26) and 11q23.3 (PHLDB1). The direction and magnitude of the signal were consistent for samples from cohort and case-control studies, but the strength of the association was more pronounced for loci rs6010620 (20q,13.33; RTEL) and rs2736100 (5p15.33, TERT) in cohort studies despite the smaller number of cases in this group, likely due to relatively more higher grade tumors being captured in the cohort studies. We further examined the 85 most promising single nucleotide polymorphism (SNP) markers identified in our study in three replication sets (5,015 cases and 11,601 controls), but no new markers reached genome-wide significance. Our findings suggest that larger studies focusing on novel approaches as well as specific tumor subtypes or subgroups will be required to identify additional common susceptibility loci for glioma risk.
Recent studies have identified common genetic variants that are unequivocally associated with central adiposity, BMI, and/or fasting plasma glucose among individuals of European descent. Our objective was to evaluate these associations in a population of Asian-Indians. We examined 16 single-nucleotide polymorphisms (SNPs) from loci previously linked to waist circumference, BMI, or fasting glucose in 1,129 Asian-Indians from New Delhi and Trivandrum. Trained medical staff measured waist circumference, height, and weight. Fasting plasma glucose was measured from collected blood specimens. Genotype–phenotype associations were evaluated using linear regression, with adjustments for age, gender, religion, and study region. For gene–environment interaction tests, total physical activity (PA) during the past 7 days was assessed by the International Physical Activity Questionnaire (IPAQ). The T allele at the FTO rs3751812 locus was associated with increased waist circumference (per allele effect of +1.58 cm, Ptrend = 0.0015) after Bonferroni adjustment for multiple testing (Padj = 0.04). We also found a nominally statistically significant FTO–PA interaction (Pinteraction = 0.008). Among participants with <81 metabolic equivalent (MET)-h/wk of PA, the rs3751812 variant was associated with increased waist size (+2.68 cm; 95% confidence interval (CI) = 1.24, 4.12), but not among those with 212+ MET-h/wk (−1.79 cm; 95% CI = −4.17, 0.58). No other variant had statistically significant associations, although statistical power was modest. In conclusion, we confirmed that an FTO variant associated with central adiposity in European populations is associated with central adiposity among Asian-Indians and corroborated prior reports indicating that high PA attenuates FTO-related genetic susceptibility to adiposity.
Background Some, but not all, observational studies have suggested that taller stature is associated with a significant increased risk of glioma. In a pooled analysis of observational studies, we investigated the strength and consistency of this association, overall and for major sub-types, and investigated effect modification by genetic susceptibility to the disease.
Methods We standardized and combined individual-level data on 1354 cases and 4734 control subjects from 13 prospective and 2 case–control studies. Pooled odds ratios (ORs) and 95% confidence intervals (CIs) for glioma and glioma sub-types were estimated using logistic regression models stratified by sex and adjusted for birth cohort and study. Pooled ORs were additionally estimated after stratifying the models according to seven recently identified glioma-related genetic variants.
Results Among men, we found a positive association between height and glioma risk (≥190 vs 170–174 cm, pooled OR = 1.70, 95% CI: 1.11–2.61; P-trend = 0.01), which was slightly stronger after restricting to cases with glioblastoma (pooled OR = 1.99, 95% CI: 1.17–3.38; P-trend = 0.02). Among women, these associations were less clear (≥175 vs 160–164 cm, pooled OR for glioma = 1.06, 95% CI: 0.70–1.62; P-trend = 0.22; pooled OR for glioblastoma = 1.36, 95% CI: 0.77–2.39; P-trend = 0.04). In general, we did not observe evidence of effect modification by glioma-related genotypes on the association between height and glioma risk.
Conclusion An association of taller adult stature with glioma, particularly for men and stronger for glioblastoma, should be investigated further to clarify the role of environmental and genetic determinants of height in the etiology of this disease.
Height; brain cancer; glioma; cancer; epidemiology
Recent studies have shown an association between cigarettes per day (CPD) and a nonsynonymous single-nucleotide polymorphism in CHRNA5, rs16969968.
To determine whether the association between rs16969968 and smoking is modified by age at onset of regular smoking.
Available genetic studies containing measures of CPD and the genotype of rs16969968 or its proxy.
Uniform statistical analysis scripts were run locally. Starting with 94 050 ever-smokers from 43 studies, we extracted the heavy smokers (CPD >20) and light smokers (CPD ≤10) with age-at-onset information, reducing the sample size to 33 348. Each study was stratified into early-onset smokers (age at onset ≤16 years) and late-onset smokers (age at onset >16 years), and a logistic regression of heavy vs light smoking with the rs16969968 genotype was computed for each stratum. Meta-analysis was performed within each age-at-onset stratum.
Individuals with 1 risk allele at rs16969968 who were early-onset smokers were significantly more likely to be heavy smokers in adulthood (odds ratio [OR]=1.45; 95% CI, 1.36–1.55; n=13 843) than were carriers of the risk allele who were late-onset smokers (OR = 1.27; 95% CI, 1.21–1.33, n = 19 505) (P = .01).
These results highlight an increased genetic vulnerability to smoking in early-onset smokers.
Epidemiological studies have yielded inconsistent associations between vitamin D status and prostate cancer risk, and few studies have evaluated whether the associations vary by disease aggressiveness. We investigated the association between vitamin D status, as determined by serum 25-hydroxy-vitamin D [25(OH)D] level, and risk of prostate cancer in a case–control study nested within the Prostate, Lung, Colorectal, and Ovarian (PLCO) Cancer Screening Trial.
The study included 749 case patients with incident prostate cancer who were diagnosed 1 to 8 years after blood draw and 781 control subjects who were frequency-matched by age at cohort entry, time since initial screening, and calendar year of cohort entry. All study participants were selected from the trial screening arm (which includes annual standardized prostate cancer screening). Conditional logistic regression was used to estimate odds ratios (ORs) with 95% confidence intervals (CIs) by quintile of 25(OH)D. Statistical tests were two-sided.
No statistically significant trend in overall prostate cancer risk was observed with increasing serum season-standardized 25(OH)D level. However, serum 25(OH)D concentrations greater than the lowest quintile (Q1) associated with increased risk of aggressive (Gleason sum ≥7 or clinical stage III or IV) disease (ORs for Q2 vs Q1 = 1.20, 95% CI = 0.80 to 1.81, for Q3 vs Q1 =1.96, 95% CI = 1.34 to 2.87, for Q4 vs Q1 = 1.61, 95% CI = 1.09 to 2.38, and for Q5 vs Q1 = 1.37, 95% CI = 0.92 to 2.05; Ptrend = .05). The rates of aggressive prostate cancer for increasing quintiles of serum 25(OH)2D were 406, 479, 780, 633, and 544 per 100,000 person-years. In exploratory analyses, these associations with aggressive disease were consistent across subgroups defined by age, family history of prostate cancer, diabetes, body mass index, vigorous physical activity, calcium intake, study center, season of blood collection, and assay batch.
The findings of this large prospective study do not support the hypothesis that vitamin D is associated with decreased risk of prostate cancer; indeed, higher circulating 25(OH)D concentrations may be associated with increased risk of aggressive disease.
25-hydroxy-vitamin D; prostate cancer
We show how to use reports of cancer in family members to discover additional genetic associations or confirm previous findings in genome-wide association (GWA) studies conducted in case-control, cohort, or cross-sectional studies. Our novel family-history-based approach allows economical association studies for multiple cancers, without genotyping of relatives (as required in family studies), follow-up of participants (as required in cohort studies), or oversampling of specific cancer cases, (as required in case-control studies). We empirically evaluate the performance of the proposed family-history-based approach in studying associations with prostate and ovarian cancers, using data from GWA studies previously conducted within the Prostate, Lung, Colorectal, and Ovarian Cancer Screening Trial. The family-history-based method may be particularly useful for investigating genetic susceptibility to rare diseases, for which accruing cases may be very difficult, by using disease information from non-genotyped relatives of participants in multiple case-control and cohort studies designed primarily for other purposes.