After data reduction, further phenotype selection may be necessary. If data cannot be aggregated, selecting those measurements with the highest heritabilities may increase the power of genetic association studies; this has been highlighted as a key avenue of research in recent studies (e.g., Doyle et al.71). Selection may be at the individual measurement level (e.g., measurements of RT variability show higher heritabilities than do measurements of mean RT62) or at the aggregation level, with twin analysis having indicated higher heritabilities (or higher familial variance) for latent factor scores over mean measurements.62,72 Whether this approach will translate into a “real-world effect” remains an empirical question. Similarly, understanding the etiology of the covariance between endophenotype measurements and ADHD will help researchers to select measurements that covary with ADHD for reasons other than being due to a general underlying deficit. This is a newer line of research, but data from larger-scale twin studies, for example, have indicated that the covariance between RT data and ADHD scores in the general population is independent of the covariation between ADHD scores and lowered IQ.62 To date,
