Beta Carotene

We conducted a meta-analysis of five GWASs within five cohorts (Table 1) with prospectively collected 25(OH)D levels and replicated the findings in three prospective case–control studies. Analyses were restricted to subjects of European ancestry. The five GWASs were: a case–control study of lung cancer in the Alpha-Tocopherol, Beta-Carotene Cancer Prevention Study (ATBC) (7 (link)); a case–control study of prostate cancer [Cancer Genetic Markers of Susceptibility (CGEMS)] in the Prostate, Lung, Colorectal, Ovarian Cancer Screening Trial (PLCO) (8 (link)); three case–control studies of pancreatic cancer nested within ATBC, Cancer Prevention Study-II (CPS-II) (9 (link)) and Give Us a Clue to Cancer and Heart Disease Study (CLUE II) (10 (link)); and case–control studies of breast cancer (CGEMS) and type 2 diabetes (T2D) nested within the Nurses' Health Study (NHS) (11 (link)). GWAS genotyping used the Illumina 550K (or higher version) platform with the exception of the T2D study, which was genotyped using the Affymetrix 6.0 platform. Genotypes for markers that were on the Illumina 550K platform but not the Affymetrix 6.0 were imputed in the T2D study using the hidden-Markov model algorithm implemented in the MACH and the HapMap CEU reference panel (Rel 22). Quality-control assessment of genotypes, including sample completion and SNP call rates, concordance rates, deviation from fitness for the Hardy–Weinberg proportions in control DNA and final sample selection for association analyses, are described elsewhere (26 (link)–29 ). For the majority (92%) of the PLCO, ATBC, CPS-II and CLUE II samples, serum 25(OH)D concentrations were measured by competitive chemiluminescence immunoassay (CLIA) in a single laboratory (Heartland Assays, Ames, IA, USA) (30 (link)), with coefficients of variation (CV) for 25(OH)D in the blinded duplicate quality-control samples of 9.3% (intrabatch) and 12.7% (interbatch). Previously available 25(OH)D measurements for 492 ATBC subjects from other serologic substudies showed similar CVs [9.5% (intrabatch) and 13.6% (interbatch)]. For the NHS-CGEMS samples, plasma 25(OH)D levels were measured by radioimmunoassay (RIA) (30 (link)) in three batches (CVs 8.7–17.6%): two in the laboratory of Dr Michael Holick at Boston University School of Medicine (31 (link)) and the third in the laboratory of Dr Bruce Hollis at the Medical University of South Carolina in Charleston, SC (31 (link)). Plasma levels of 25(OH)D in the NHS-T2D samples were measured in the Nutrition Evaluation Laboratory in the Human Nutrition Research Center on Aging at Tufts University (with CV = 8.7%) by rapid extraction followed by an equilibrium I-125 RIA procedure (DiaSorin Inc., Stillwater, MN, USA) as specified by the manufacturer's procedural documentation and analyzed on a gamma counter (Cobra II, Packard).
Four markers selected for replication were genotyped by TaqMan in three case–control samples: NHS colon polyp study (13 (link)) (n = 403/407 cases/controls), colorectal cancer study (12 (link)) (173/371 cases/controls) and a study of prostate cancer in the Health Professionals Follow-up Study (14 (link)) (431/436 cases/controls). There was no overlap of participants in the NHS-CGEMS, T2D, colon polyp or colorectal cancer studies. Plasma 25(OH)D concentrations in these studies were measured by RIA in the laboratory of Dr Bruce Hollis [CVs 7.5, 11.8 and 5.4–5.6% (two-batch), respectively].
Concentrations of 25(OH)D were similar across CPS-II, CLUE II and PLCO (Tables 1 and 2). In ATBC, the average population concentration was lower, likely due to reduced UVB solar radiation exposure at that northern latitude and no blood collection during July and some of June and August. The NHS had overall higher values, likely the result of having had blood samples analyzed in a different laboratory using a different assay. Nonetheless, there was a substantial overlap with the observed range of 25(OH)D values across all studies.
We conducted a pooled analysis (1 ) of four GWASs (ATBC, CPS-II, CLUE II and PLCO) and tested the association between 593 253 SNP markers that passed quality-control filters and the square-root-transformed value of circulating 25(OH)D using linear regression under an additive genetic model. We adjusted for age, vitamin D assay batch, study, case–control status, sex, body mass index (<20, 20–25, 25–30, 30+ kg/m²), season of blood collection (December–February; March–May; June–August; September–November), vitamin D supplement intake (missing, 0, 0–400 and 400+ IU/day), dietary vitamin D intake (missing, <100, 100–200, 200–300, 300–400 and 400+ IU/day), region/latitude and three eigenvectors to control for population stratification. Usual dietary intake and other covariate information were collected through self-administered food frequency questionnaires and baseline risk factor questionnaires, respectively. The square-root transformation was very close to the most optimal transformation identified by the Box-Cox procedure and was used to ensure normality of the residuals. The Wald test was used for testing the association between each SNP and the outcome. A similar approach was used for analysis of each of the following two GWASs, NHS-CGEMS (2 (link)) and NHS-T2D (3 (link)). Imputed markers in the NHS-T2D study were analyzed using genotype dosages (expected allele counts). We conducted the meta-analysis of the GWASs, and of the GWAS and replication studies combined, by averaging the signed Wald statistics weighted by the square root of the corresponding sample sizes; this analysis is robust to differences in scale across different techniques for measuring circulating 25(OH)D (32 (link)). We also used the random-effect model to estimate the common effect size and to assess heterogeneity among results from different studies. The quantile–quantile plot of P-values from the GWAS meta-analysis showed no evidence of systematic type-I error inflation (λ_GC = 1.0007; Fig. 2).

We conducted a GWAS of serum alpha-tocopherol concentrations within the Alpha-Tocopherol, Beta-Carotene Cancer Prevention (ATBC) Study cohort and replicated the findings in a combined meta-analysis with the Prostate, Lung, Colorectal and Ovarian Cancer Screening Trial (PLCO) Study and the Nurses’ Health Study (NHS) (Table 1). Similarly, a separate GWAS was performed for gamma-tocopherol concentrations using available data from the PLCO Study.
Briefly, the ATBC Study was a randomized, double-blind, placebo-controlled intervention trial conducted to determine whether supplementation with alpha-tocopherol, beta-carotene or both could prevent cancer (37 (link)). The participants were all male smokers at study entry, aged 50–69 years, and residents of southwestern Finland. Men were not eligible for study inclusion if they reported a history of cancer, had severe diseases limiting long-term participation or took supplements of vitamins E (>20 mg/day) or A (>20 000 IU/day) or beta-carotene (>6 mg/day). Fasting blood samples were collected at baseline and stored at −70°C until analyzed. Serum alpha-tocopherol levels were measured by high-performance liquid chromatography (38 (link)), with a coefficient of variation (CV) of 2.2%. Gamma-tocopherol was not measured at study baseline. Serum cholesterol levels were measured with an enzymatic assay by the CHOD-PAP method (Boehringer Mannhein) (39 (link)). GWAS data from the Illumina 550K platform were available for 4014 men that were also previously analyzed with respect to circulating vitamin D levels (12 (link)).
The PLCO Study was a multi-center trial conducted in the US to evaluate the effectiveness of cancer screening and examine early markers of cancer (40 (link)). PLCO male participants of Caucasian descent, aged 55–74 years, were included in the present GWAS (n= 992). Plasma concentrations of alpha- and gamma-tocopherol were measured by CLIA. The CVs for alpha- and gamma-tocopherol concentrations were 5.8 and 8.9%, respectively. Cholesterol was measured enzymatically by a standard procedure at 37°C on a Hitachi 912 autoanalyzer. GWAS genotyping used both the Illumina 317K and 240K platforms, and as a result, SNP coverage (relative to the Illumina 550K used for ATBC) for two of the loci associated with circulating alpha-tocopherol, was incomplete. Genotype imputation was therefore performed for rs964184 and rs11057830 using IMPUTE2 to identify SNPs with the 1000 genomes project June 2010 release and HapMap 3 release 2 as the reference set. The imputed SNPs had a high imputation quality score.
Data from the NHS, a cohort of US women, was also used to replicate the most significant findings (approximately 100 SNPs with P < 1 × 10⁻⁵ or higher) obtained in the original GWAS. For the NHS samples, plasma tocopherol levels were measured using reversed-phase, high-performance liquid chromatography. The CVs for each batch were ≤13% with the exception of one batch which had a CV of 22%. Total cholesterol was assayed from plasma using the enzymatic methods described by Allain et al. (41 (link)). The Affymetrix 6.0 platform was used for nested case–control studies of coronary heart disease (CHD; n= 425) and type 2 diabetes (T2D; n= 394), and Illumina 550K for breast cancer [Cancer Genetic Markers of Susceptibility (CGEMS); n= 1929]. Each study sample used the MACH to impute up to approximately 2.5 million autosomal SNPs with NCBI build 36 of Phase II HapMap CEU data (release 22) as the reference panel. All of the imputed SNPs had a high imputation quality score.
Prior to analysis, tocopherol levels were log-transformed to normalize the distributions. A linear model adjusted for age, BMI and cancer status was used to relate the log-transformed outcomes to a SNP by assuming an additive mode of inheritance. Furthermore, because it is well established that vitamin E levels are affected by circulating lipids, we further adjusted the analyses for total cholesterol. Because HDL cholesterol was available in the ATBC Study, we performed a sensitivity GWAS analysis that adjusted for ‘non-HDL’ cholesterol levels (total minus HDL, or essentially LDL + VLDL, which we would expect to more closely reflect triglycerides) and yielded SNP findings identical to those adjusted for total cholesterol. Additional models that included both total cholesterol and HDL also provided similar results. The likelihood ratio test was used to detect the association between the tocopherol levels and the SNPs, adjusting for the above covariates. To identify the independent effect of other SNPs in the region of the most significant SNP, the likelihood ratio test was again used in the initial GWAS sample with the most significant SNP and those covariates involved in the basic model. We used a fixed effects meta-analysis on the GWAS and replication studies. The meta-analysis was conducted by combining the study-specific beta-estimates weighted by the inverse of the corresponding variances. We performed a sensitivity analysis on the alpha-tocopherol GWAS, excluding subjects who reported any use of vitamin E supplements (including those from multivitamins); the identified SNPs remained significant and accentuated among non-users. Additionally adjusting the GWAS for HDL levels did not change study findings. Study protocols for ATBC, PLCO and NHS were approved by their respective institutional review boards and eligible participants provided written consent.