Adult height is associated with increased risk of ovarian cancer: a Mendelian randomisation study

Background Observational studies suggest greater height is associated with increased ovarian cancer risk, but cannot exclude bias and/or confounding as explanations for this. Mendelian randomisation (MR) can provide evidence which may be less prone to bias. Methods We pooled data from 39 Ovarian Cancer Association Consortium studies (16,395 cases; 23,003 controls). We applied two-stage predictor-substitution MR, using a weighted genetic risk score combining 609 single-nucleotide polymorphisms. Study-specific odds ratios (OR) and 95% confidence intervals (CI) for the association between genetically predicted height and risk were pooled using random-effects meta-analysis. Results Greater genetically predicted height was associated with increased ovarian cancer risk overall (pooled-OR (pOR) = 1.06; 95% CI: 1.01–1.11 per 5 cm increase in height), and separately for invasive (pOR = 1.06; 95% CI: 1.01–1.11) and borderline (pOR = 1.15; 95% CI: 1.02–1.29) tumours. Conclusions Women with a genetic propensity to being taller have increased risk of ovarian cancer. This suggests genes influencing height are involved in pathways promoting ovarian carcinogenesis.


INTRODUCTION
Observational studies have reported a positive association between adult height and ovarian cancer risk. [1][2][3][4] However, these studies were subject to the biases inherent in conventional observational studies, including selection bias, differential and non-differential reporting bias and confounding. The degree to which these factors could account for the observed association is uncertain. Mendelian randomisation (MR) uses genetic markers as proxies for environmental exposures and, due to the singular qualities of genotype data, can provide complementary evidence by overcoming many biases affecting conventional studies. 5 We used MR to examine the relationship between height and ovarian cancer risk in the Ovarian Cancer Association Consortium (OCAC), 6 aiming to provide more certainty about the relationship between height and ovarian cancer risk. We hypothesised that greater genetically predicted height would be associated with increased risk.

Study population and outcomes
We pooled data from 16,395 genetically European women with primary ovarian/fallopian tube/peritoneal cancer and 23,003 controls from 39 OCAC studies (Table 1; Supplementary Table 1). The data set and methods have been described previously. 7 Participants were genotyped via the Collaborative Oncological Gene-Environment Study. 8 Twenty-two studies provided height data (16 provided parity, oral contraceptive (OC) use, education and age at menarche information) for >50% of their participants. We first considered all cases, then stratified by tumour behaviour. Secondary analyses stratified by histologic subtype/behaviour. Genetic risk score The Genetic Investigation of ANthropometric Traits (GIANT) Consortium had previously identified 697 single-nucleotide polymorphisms (SNPs) significantly associated with height. 9 In our sample, 92 of these SNPs had been genotyped and the remainder were imputed using 1000 Genome Project data. 8,10 After excluding poorly-imputed SNPs (quality r 2 < 0.6), 609 remained (92 genotyped/517 imputed) (Supplementary Table 2). In controls, minor allele frequencies (MAFs) were >5% (except for 16 SNPs, MAFs 1.7-4.9%).
We constructed a weighted genetic risk score (GRS) for height by summing height-increasing alleles across the 609 SNPs ('GRS-609'/'the GRS'), weighting alleles by β-coefficients for their association with height reported by GIANT. The score represents predicted additional height conferred by these variants, compared to having no height-increasing alleles. We report results for 5 cm increments.
Statistical analysis Statistical methods have been described previously. 7 Briefly, we used individual-level OCAC data for two-stage predictor-substitution MR 11,12 : first, we predicted height from the weighted GRS for all participants using coefficients from linear regression in 17,649 controls with height data; second, within each study, we used logistic regression to model disease status on GRS-predicted height. Models adjusted for age and five principal components for population structure. 8 We combined study-specific estimates using meta-analysis, 13 generating pooled odds ratios (pOR) and 95% confidence intervals (CI) for the trend in risk per 5 cm increase in predicted height. We had 97% power to detect an OR of 1.10 (mRnd tool). 14 Sensitivity analyses included removing 16 SNPs with MAFs <5%, and restricting to SNPs with imputation r 2 ≥ 0.9 ('GRS-363'), SNPs representing distinct loci 9 ('GRS-377'), and directly-genotyped and compared results with MR-estimates among the same women.
Analyses were performed using SAS 9.2 (SAS Institute Inc., Cary, NC) and Stata 13.0 (StataCorp LP, College Station, TX). This work and each contributing study was approved by the appropriate institutional review board/ethics committee. All participants provided informed consent.

Secondary outcomes
After stratifying by subtype/behaviour, the strongest associations were seen for clear cell (OR = 1.20, 95% CI: 1.04-1.38) and lowgrade/borderline serous cancers (OR = 1.15, 95% CI: 1.01-1.30) ( Table 2). However, CIs were wide and overlapping due to lower statistical power in these subgroup analyses. The estimate for clear cell cancers was also significantly elevated in our conventional analyses (Supplementary Table 4).

DISCUSSION
We used a 609-SNP GRS to examine the relationship between height and ovarian cancer risk for women of European ancestry. Our data indicate a modest positive association between genetically predicted height and ovarian cancer risk, which may be stronger for borderline cancers. Height may be relevant to cancer risk as a marker for lifetime growth-factor levels (e.g. IGF-1) and/or early-life exposures (socio-economic/environmental/nutritional). 3,21,22 Observational studies are subject to biases (reverse causality, selection bias, differential/non-differential reporting, confounding) which cannot be ruled out as possible explanations for observed associations. By using genotype, the MR technique can overcome some of these biases, given three assumptions. We confirmed the two verifiable assumptions: the GRS was associated with height, and not with most known confounders. The GRS-menarche age association is unlikely to explain the observed association, because age at menarche is only weakly associated with ovarian cancer, and women with later menarche have if anything lower ovarian cancer risk, so if this affected our results, we would expect the true effect to be at least as strong as the reported association. Also, removing hormone-related SNPs, or adjusting for menarche age, did not attenuate estimates. Owing to the limited current biological understanding of all 609 SNPs, we could not conclusively exclude the presence of alternate pathways from height genes to ovarian cancer (assumption three). However, MR-Egger and sensitivity analyses excluding pathway-specific SNPs provided some evidence for their absence, minimising the likelihood that our observed association is explained by pathways separate from height/growth. Although height data were not available for the entire population, this is unlikely to have affected our results as we used these data only to refine the height predictions from the GRS, and there is no reason to believe the GRS-height relationship would be different for women with and without height data. Further strengths of our analysis include the large number of SNPs and power to detect modest differences. Despite potential limitations of conventional observational studies, our MR-estimate is almost identical to previously reported associations, suggesting previous estimates were not appreciably biased. The World Cancer Research Fund/American Institute for Cancer Research meta-analysis of 24 prospective studies, and a study pooling 47 prospective/case-control studies, both reported a significant 7-8% increase in risk (combining invasive/borderline cancers) per 5 cm height increase. 3,4 The lack of association seen in the OCAC conventional height analysis reflects the greater potential for bias in case-control studies and illustrates the value of MR in overcoming these biases. Few previous studies have examined borderline cancers separately, a strength of our analysis. Previous observational studies have not reported consistent patterns by histologic subtype 2, 4, 23 ; our secondary analyses were under-powered to resolve this question.
Using MR, we have established that the previously observed association between height and ovarian cancer risk is unlikely to have been explained by bias, and that genetic factors influencing height play roles in ovarian cancer development. Height could therefore be used, with other risk factors, to identify women at elevated risk. Further research should continue to explore mechanisms underpinning this association.