Height, age at menarche, and risk of epithelial ovarian cancer

Objective: We hypothesized that the hormonal changes of adolescence influence ovarian cancer risk particularly in younger women. We investigated this possibility by examining the relationship between ovarian cancer and adult height and age at menarche as both factors reflect pubertal hormonal levels. Methods: Participants were a population-based sample of women with incident ovarian cancer (n = 794) and control women randomly selected from the Australian Electoral Roll (n = 855). The women provided comprehensive reproductive and lifestyle data during a standard interview. Results: Although neither height nor age at menarche was significantly related to the risk of ovarian cancer overall, increasing height was associated with increasing risk of the subgroup of mucinous borderline ovarian cancer (odds ratio, 5.3; 95% confidence interval, 1.5-19.1 for women 175 cm compared with women < 160 cm, P-trend = 0.02). Similarly, later age at menarche was associated with increasing risk of mucinous borderline cancers (odds ratio, 3.8; 95% confidence interval, 1.3-11.4 for those with age at menarche >= 44 years compared with those < 12 years, P-trend = 0.003). Women with mucinous borderline cancers were significantly younger than the women diagnosed with invasive cancers (mean 44 versus 57 years; P < 0.0001). Conclusions: Development of mucinous borderline ovarian cancers, predominantly diagnosed in women ages under 50 years, seems to be associated with age at menarche and attained adult height. These results are consistent with our original hypothesis that pubertal levels of reproductive hormones and insulin-like growth factor-I influence ovarian cancer risk in younger women.

Bias, precision and heritability of self-reported and clinically measured height in Australian twins

Many studies of quantitative and disease traits in human genetics rely upon self-reported measures. Such measures are based on questionnaires or interviews and are often cheaper and more readily available than alternatives. However, the precision and potential bias cannot usually be assessed. Here we report a detailed quantitative genetic analysis of stature. We characterise the degree of measurement error by utilising a large sample of Australian twin pairs (857 MZ, 815 DZ) with both clinical and self-reported measures of height. Self-report height measurements are shown to be more variable than clinical measures. This has led to lowered estimates of heritability in many previous studies of stature. In our twin sample the heritability estimate for clinical height exceeded 90%. Repeated measures analysis shows that 2-3 times as many self-report measures are required to recover heritability estimates similar to those obtained from clinical measures. Bivariate genetic repeated measures analysis of self-report and clinical height measures showed an additive genetic correlation > 0.98. We show that the accuracy of self-report height is upwardly biased in older individuals and in individuals of short stature. By comparing clinical and self-report measures we also showed that there was a genetic component to females systematically reporting their height incorrectly; this phenomenon appeared to not be present in males. The results from the measurement error analysis were subsequently used to assess the effects of error on the power to detect linkage in a genome scan. Moderate reduction in error (through the use of accurate clinical or multiple self-report measures) increased the effective sample size by 22%; elimination of measurement error led to increases in effective sample size of 41%.