Studies of twins and families also provide an empirical basis for combining data across measurements, which decreases the number of phenotypes to be analyzed. This decrease mitigates the loss of power incurred when correcting for multiple testing. Recent analyses have highlighted that, although phenotypic correlations across RT data collected across different tasks may be low, genetic correlations can approach unity.62 Findings for activity-level data are similar: mechanical assessments of activity level across situations show modest phenotypic correlations, in the region of 0.5 to 0.6, but the genetic factors underlying laboratory-based tests and those from a “free play” session are very highly correlated, indicating that the two situations measure the same underlying genetic liability.69,70 Twin and factor analysis further indicate that mean RT and RT variability measure the same underlying construct or liability in the general population,62 indicating that only one cognitive construct need be analyzed in gene-finding studies. Thus twin studies can identify which measurements can be dropped, or preferably combined, because, despite modest phenotypic correlations, the genetic factors underlying the measurements may be largely identical.
