In the present study we examined the degree to which measures of learning and delayed recall, as assessed by the CVLT-2, were influenced by shared as well as variable-specific genetic and environmental influences. Our analyses revealed a highly significant degree of genetic and environmental overlap among these measures. In addition, the best-fitting multivariate model revealed a single higher-order latent factor that accounted for the majority of the phenotypic variance in all of the variables examined. This factor possessed a heritability of .36 (i.e., 36% of the variance was accounted for by latent genetic influences), as well as shared and unique environmental influences of .15 and .49, respectively. At the specific variable level, significant residual genetic influences were observed for only the total of trials 1 through 5, our measure of learning ability. These genetic influences, which by definition were independent of the common genetic factor, accounted for 10% of the variance in the learning measure. A measurement model in which the variable-specific genetic and shared environmental variance components were constrained to be zero, forcing all genetic and shared environmental influences to originate from the latent phenotype, resulted in a significant worsening of model fit. Given that learning involves the acquisition and retrieval of information, whereas delayed recall involves only retrieval, we conclude that the observed variable-specific genetic effects for the total of trials 1 through 5 are likely to reflect genes that are specific to acquisition processes.
As conceptualized by the authors of the CVLT-2 (Delis et al., 2000
), the total of trials 1 through 5 represents an individual’s global verbal learning ability and is a reflection of auditory attention as well as the degree to which information can be immediately stored and added to over repeated trials, all processes that are critical to the acquisition of information. That this variable was found to possess significant residual (i.e., variable-specific) genetic influences, whereas the short and long delay recall conditions did not, lends strong support to the argument that learning does involve some neurobiological processes that are distinct from retrieval. There was also clear evidence for variable-specific unique environment effects on each of the CVLT measures. In our best-fitting model residual unique environmental influences accounted for 17% to 23% of the variance in each of the measures, and in each case the values were significant based on 95% confidence intervals. These variable-specific unique environmental influences are likely to represent the effects of measurement error on each of the phenotypes. The excellent test-retest reliability of the CVLT-2 measures we used, which ranged from .81 to .88 (Delis et al., 2000
), is certainly consistent with this conclusion. As for the other source of unique environmental influences (EL
) the list of potential environmental factors that may influence performance on the CVLT is extensive, and includes level of education, history of head injury, as well as unexpected effects of the individual administering the test (Wiens, Tindall, & Crossen, 1994
To date, a limited number of studies have examined the effects of specific candidate genes on distinct aspects of episodic memory, specifically on measures of immediate and delayed memory performance. Measures of immediate recall typically represent an individual’s ability to reproduce information immediately following its presentation, and like our measure of learning ability involve acquisition and retrieval processes. Genetic variation in the 5-HT2a
receptor, a critical receptor for the functioning of serotonin within the brain, has been found to be associated with delayed but not immediate recall (de Quervain et al., 2003
). Three studies have examined the effects of the KIBRA
gene on immediate and delayed recall; however, these results are somewhat inconsistent, providing evidence for an association with delayed recall but not immediate (Papassotiropoulos et al., 2006
), immediate recall but not delayed (Schaper, Kolsch, Popp, Wagner, & Jessen, 2007
), and both processes simultaneously (Almeida et al., 2008
). The ε4 allele of the Apolipoprotein E (APOE
) gene has also been shown to have discrepant effects on aspects of episodic memory. Using data from the first edition of the CVLT, Bondi and colleagues (1999)
found no effect of the APOE
ε4 allele on the total of trials 1 through 5; however, a significant association was found with long delay free recall, as well as a trend level effect for short delay free recall (Bondi, Salmon, Galasko, Thomas, & Thal, 1999
The results from these gene association studies are largely consistent with our present findings suggesting that some genes influencing acquisition are distinct from those influencing retrieval. Indeed, the findings of the present study help to make sense of why one might observe different genetic associations for learning (or immediate recall) and delayed recall. However, our results also suggest considerable genetic overlap, so that the genetic association findings may not always suggest unique relationships. Inconsistent results from gene association studies may stem from the fact that genes rarely exert their influence in isolation from other genes, or environmental factors for that matter; therefore, discrepancies in the literature may be the result of failing to appropriately account for gene-gene or gene-environment interaction (Moore & Williams, 2002
). It may also be the case that the degree to which learning and delayed recall are influenced by shared genetic and environmental influences changes as a function of increasing age. Longitudinal twin studies have shown that over time new latent genetic and environmental influences on memory performance emerge, and subsequently contribute to the observed changes in memory performance latter in life (McArdle & Plassman, 2009
; Reynolds, Finkel, Gatz, & Pedersen, 2002
). Although these studies have all conceptualized episodic memory as a single variable, their results could imply that later in life new genetic factors begin to exert specific influences on either learning or delayed recall.
We must acknowledge some potential limitations of the present study. First, the all-male relatively homogenous nature of the VETSA sample limits our ability to generalize these findings to female populations or ethnic minorities. Similarly, it is unclear whether the present results will replicate in child or adolescent populations due to the substantial degree of cognitive and brain development that is likely to be taking place. These data are of course cross-sectional and provide no insight into whether the observed relationships are long standing, or whether they are the result of age-related changes. Longitudinal data are currently being collected as part of wave 2 of the VETSA, and will allow for the examination of how the genetic and environmental influences of learning and recall change as these late midlife men enter later life. Because information can only be recalled if it has first been learned, it is possible that the degree of genetic overlap between our measures of learning and delayed recall was somewhat inflated. If that were the case, however, it would also strengthen the conclusion that there were genetic influences that were specific to learning and independent of recall. Lastly, there remain aspects of episodic memory that we are unable to examine with our current study design. Clinical and experimental memory testing typically involves delays of anywhere from 20 to 40 minutes, however, there is evidence from functional and structural neuroimaging studies to suggest that the retrieval of information over longer delay periods (e.g., days or weeks) may utilize distinct neurocognitive processes that are not accessed with traditional testing paradigms (Takashima et al., 2006
; Walhovd et al., 2006
). Consolidation and retrieval over an extended period of time could, therefore, represent yet another aspect of episodic memory that may or may not be genetically related to the constructs examined in the present study.
Delis and colleagues have argued that the use of phenotypic shared-variance procedures (i.e., data analytic techniques that are based on the correlations between variables) may obscure otherwise distinct cognitive constructs when used on data collected from normal populations (Delis et al., 2003
). Although multivariate twin analyses are essentially an elaborate form of a shared-variance procedure, we believe that the unique properties of this method, specifically the ability to decompose the covariance as well as residual variance into genetic and environmental components, allows for insights into cognition that are beyond the scope of phenotypic shared-variance procedures. In previous studies of neurocognitive processes we have demonstrated the ability of multivariate twin analyses to isolate the genetic influences specific to executive functioning on tests of verbal working memory (Kremen et al., 2007
; Kremen et al., 2008
), as well as differentiate genetic influences for speed and efficiency on a test of planning and problem solving (Kremen et al., 2009
). In some cases, specific effects or different factors were observed even when there was only a single phenotypic factor. As with the present results, these findings were produced using samples of cognitively normal participants and with measures that possessed a substantial amount of method variance, two features that have been argued to be potential confounds for differentiating cognitive constructs in non-patient populations (Delis et al., 2003
). Thus, the examination of the genetic relationships between variables does appear capable of differentiating cognitive processes when phenotypic shared-variance techniques fail to do so. Future studies will ultimately need to determine whether the genetic relationships observed in the present study remain stable or change later in life. Furthermore, focusing on how the genetic and environmental relationships between learning and delayed recall change or stay the same may enhance our ability to find specific determinants of cognition, cognitive aging, and aging-related disease processes—such as mild cognitive impairment or Alzheimer’s disease—that substantially affect episodic memory.