We next used quantile normalization to convert each trait to approximate normality, and fitted a simple model with two variance components (a heritable additive polygenic component and an individual specific environmental component) and five covariates (sex, age, age2, and the terms for the interaction of sex with age and age2) [23,24]. The results of this variance component analysis (summarized under the headings Effect of Covariates and Basic Model in Tables 1 and 2) were consistent with preliminary analyses using mid-parent regression [25] and untransformed data (unpublished data). Further details, including estimates of individual variance components and likelihoods for each model are available online (http://www.sph.umich.edu/csg/chen/public/sardinia). When sex was modeled as covariate, sex differences explained 6.7% of the variance on average for blood test results, 16.2% for anthropometric measures, 4.3% for cardiovascular traits, and 2.4% for personality traits. When age and age2 were modeled as covariates, age differences typically explained a smaller proportion of the variance for blood tests results (5.1%) and personality traits (5.6%) than for anthropometric measures (20.4%) and for cardiovascular traits (25.3%). On average, the interaction between age and
