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Children who like to read and write tend to be better at it. This association is typically interpreted as enjoyment impacting engagement in literacy activities, which boosts literacy skills. We fitted direction-of-causation models to partial data of 3690 Finnish twins aged 12. Literacy skills were rated by the twins' teachers and literacy enjoyment by the twins themselves. A bivariate twin model showed substantial genetic influences on literacy skills (70%) and literacy enjoyment (35%). In both skills and enjoyment, shared-environmental influences explained about 20% in each. The best-fitting direction-of-causation model showed that skills impacted enjoyment, while the influence in the other direction was zero. The genetic influences on skills influenced enjoyment, likely via the skills[right arrow]enjoyment path. This indicates an active gene-environment correlation: children with an aptitude for good literacy skills are more likely to enjoy reading and seek out literacy activities. To a lesser extent, it was also the shared-environmental influences on children's skills that propagated to influence children's literacy enjoyment. Environmental influences that foster children's literacy skills (e.g., families and schools), also foster children's love for reading and writing. These findings underline the importance of nurturing children's literacy skills.

Understanding the genomic basis of memory processes may help in combating neurodegenerative disorders. Hence, we examined the associations of common genetic variants with verbal short-term memory and verbal learning in adults without dementia or stroke (N = 53,637). We identified novel loci in the intronic region of CDH18, and at 13q21 and 3p21.1, as well as an expected signal in the APOE/APOC1/TOMM40 region. These results replicated in an independent sample. Functional and bioinformatic analyses supported many of these loci and further implicated POC1. We showed that polygenic score for verbal learning associated with brain activation in right parieto-occipital region during working memory task. Finally, we showed genetic correlations of these memory traits with several neurocognitive and health outcomes. Our findings suggest a role of several genomic loci in verbal memory processes.

ObjectiveTo investigate the genetic architecture of internalizing symptoms in childhood and adolescence.MethodIn 22 cohorts, multiple univariate genome-wide association studies (GWASs) were performed using repeated assessments of internalizing symptoms, in a total of 64,561 children and adolescents between 3 and 18 years of age. Results were aggregated in meta-analyses that accounted for sample overlap, first using all available data, and then using subsets of measurements grouped by rater, age, and instrument.ResultsThe meta-analysis of overall internalizing symptoms (INToverall) detected no genome-wide significant hits and showed low single nucleotide polymorphism (SNP) heritability (1.66%, 95% CI = 0.84-2.48%, neffective = 132,260). Stratified analyses indicated rater-based heterogeneity in genetic effects, with self-reported internalizing symptoms showing the highest heritability (5.63%, 95% CI = 3.08%-8.18%). The contribution of additive genetic effects on internalizing symptoms appeared to be stable over age, with overlapping estimates of SNP heritability from early childhood to adolescence. Genetic correlations were observed with adult anxiety, depression, and the well-being spectrum (|rg| > 0.70), as well as with insomnia, loneliness, attention-deficit/hyperactivity disorder, autism, and childhood aggression (range |rg| = 0.42-0.60), whereas there were no robust associations with schizophrenia, bipolar disorder, obsessive-compulsive disorder, or anorexia nervosa.ConclusionGenetic correlations indicate that childhood and adolescent internalizing symptoms share substantial genetic vulnerabilities with adult internalizing disorders and other childhood psychiatric traits, which could partially explain both the persistence of internalizing symptoms over time and the high comorbidity among childhood psychiatric traits. Reducing phenotypic heterogeneity in childhood samples will be key in paving the way to future GWAS success.

Childhood aggressive behavior (AGG) has a substantial heritability of around 50%. Here we present a genome-wide association meta-analysis (GWAMA) of childhood AGG, in which all phenotype measures across childhood ages from multiple assessors were included. We analyzed phenotype assessments for a total of 328 935 observations from 87 485 children aged between 1.5 and 18 years, while accounting for sample overlap. We also meta-analyzed within subsets of the data, i.e., within rater, instrument and age. SNP-heritability for the overall meta-analysis (AGGoverall) was 3.31% (SE = 0.0038). We found no genome-wide significant SNPs for AGGoverall. The gene-based analysis returned three significant genes: ST3GAL3 (P = 1.6E-06), PCDH7 (P = 2.0E-06), and IPO13 (P = 2.5E-06). All three genes have previously been associated with educational traits. Polygenic scores based on our GWAMA significantly predicted aggression in a holdout sample of children (variance explained = 0.44%) and in retrospectively assessed childhood aggression (variance explained = 0.20%). Genetic correlations (rg) among rater-specific assessment of AGG ranged from rg = 0.46 between self- and teacher-assessment to rg = 0.81 between mother- and teacher-assessment. We obtained moderate-to-strong rgs with selected phenotypes from multiple domains, but hardly with any of the classical biomarkers thought to be associated with AGG. Significant genetic correlations were observed with most psychiatric and psychological traits (range [Formula: see text]: 0.19-1.00), except for obsessive-compulsive disorder. Aggression had a negative genetic correlation (rg = ~-0.5) with cognitive traits and age at first birth. Aggression was strongly genetically correlated with smoking phenotypes (range [Formula: see text]: 0.46-0.60). The genetic correlations between aggression and psychiatric disorders were weaker for teacher-reported AGG than for mother- and self-reported AGG. The current GWAMA of childhood aggression provides a powerful tool to interrogate the rater-specific genetic etiology of AGG.

Handedness has been extensively studied because of its relationship with language and the over-representation of left-handers in some neurodevelopmental disorders. Using data from the UK Biobank, 23andMe and the International Handedness Consortium, we conducted a genome-wide association meta-analysis of handedness (N = 1,766,671). We found 41 loci associated (P

Degree of memory impairment is often used to infer early versus late amnestic mild cognitive impairment (aMCI). Previously, 318 Alzheimer's Disease Neuroimaging Initiative participants with aMCI-determined by a single memory test-were divided based on Rey Auditory Verbal Learning Task (AVLT) delayed recall: AVLT-impaired (n = 225) and AVLT-normal (n = 93). Equally consistent with differential progression or differential diagnosis, the AVLT-impaired group had more abnormal Alzheimer's disease (AD) biomarkers, more neurodegeneration over time, and was more likely to develop AD. In the present study, higher AD polygenic risk scores were associated with increased odds of being AVLT-impaired (odds ratio 1.8, p < 0.001). Thus, impairment severity does not necessarily reflect early versus late aMCI because disease progression cannot alter polygenic risk. Presumed early MCI is likely a heterogeneous category that includes excess false-positives. The additional cognitive test improved diagnostic precision by reducing false positives. Impairment severity may reflect differences in underlying disease risk but cannot be used to infer early versus late MCI based on cross-sectional data alone.

Uncertainty exists regarding the relation of body size and weight change with dementia risk. As populations continue to age and the global obesity epidemic shows no sign of waning, reliable quantification of such associations is important. We examined the relationship of body mass index, waist circumference, and annual percent weight change with risk of dementia and its subtypes by pooling data from 19 prospective cohort studies and four clinical trials using meta-analysis. Compared with body mass index-defined lower-normal weight (18.5-22.4 kg/m2 ), the risk of all-cause dementia was higher among underweight individuals but lower among those with upper-normal (22.5-24.9 kg/m2 ) levels. Obesity was associated with higher risk in vascular dementia. Similarly, relative to the lowest fifth of waist circumference, those in the highest fifth had nonsignificant higher vascular dementia risk. Weight loss was associated with higher all-cause dementia risk relative to weight maintenance. Weight gain was weakly associated with higher vascular dementia risk. The relationship between body size, weight change, and dementia is complex and exhibits non-linear associations depending on dementia subtype under scrutiny. Weight loss was associated with an elevated risk most likely due to reverse causality and/or pathophysiological changes in the brain, although the latter remains speculative.

How and when education improves cognitive capacity is an issue of profound societal importance. Education and later-life education-related factors, such as occupational complexity and engagement in cognitive-intellectual activities, are frequently considered indices of cognitive reserve, but whether their effects are truly causal remains unclear. In this study, after accounting for general cognitive ability (GCA) at an average age of 20 y, additional education, occupational complexity, or engagement in cognitive-intellectual activities accounted for little variance in late midlife cognitive functioning in men age 56-66 (n = 1009). Age 20 GCA accounted for 40% of variance in the same measure in late midlife and approximately 10% of variance in each of seven cognitive domains. The other factors each accounted for

Height and general cognitive ability are positively associated, but the underlying mechanisms of this relationship are not well understood. Both height and general cognitive ability are positively associated with brain size. Still, the neural substrate of the height-cognitive ability association is unclear. We used a sample of 515 middle-aged male twins with structural magnetic resonance imaging data to investigate whether the association between height and cognitive ability is mediated by cortical size. In addition to cortical volume, we used genetically, ontogenetically and phylogenetically distinct cortical metrics of total cortical surface area and mean cortical thickness. Height was positively associated with general cognitive ability and total cortical volume and cortical surface area, but not with mean cortical thickness. Mediation models indicated that the well-replicated height-general cognitive ability association is accounted for by individual differences in total cortical volume and cortical surface area (highly heritable metrics related to global brain size), and that the genetic association between cortical surface area and general cognitive ability underlies the phenotypic height-general cognitive ability relationship.

Two basic neuroimaging-based characterizations of white matter tracts are the magnitude of water diffusion along the principal tract orientation (axial diffusivity, AD) and water diffusion perpendicular to the principal orientation (radial diffusivity, RD). It is generally accepted that decreases in AD reflect disorganization, damage, or loss of axons, whereas increases in RD are indicative of disruptions to the myelin sheath. Previous reports have detailed the heritability of individual AD and RD measures, but have not examined the extent to which the same or different genetic or environmental factors influence these two phenotypes (except for corpus callosum). We implemented bivariate twin analyses to examine the shared and independent genetic influences on AD and RD. In the Vietnam Era Twin Study of Aging, 393 men (mean age = 61.8 years, SD = 2.6) underwent diffusion-weighted magnetic resonance imaging. We derived fractional anisotropy (FA), mean diffusivity (MD), AD, and RD estimates for 11 major bilateral white matter tracts and the mid-hemispheric corpus callosum, forceps major, and forceps minor. Separately, AD and RD were each highly heritable. In about three-quarters of the tracts, genetic correlations between AD and RD were >.50 (median = .67) and showed both unique and common variance. Genetic variance of FA and MD were predominately explained by RD over AD. These findings are important for informing genetic association studies of axonal coherence/damage and myelination/demyelination. Thus, genetic studies would benefit from examining the shared and unique contributions of AD and RD.

There is evidence that differences among individuals in white matter microstructure, as measured with diffusion tensor imaging (DTI), are under genetic control. However, little is known about the relative contribution of genetic and environmental effects on different diffusivity indices among late middle-aged adults. Here, we examined the magnitude of genetic influences for fractional anisotropy (FA), and mean (MD), axial (AD), and radial (RD) diffusivities in male twins aged 56-66 years old. Using an atlas-based registration approach to delineate individual white matter tracts, we investigated mean DTI-based indices within the corpus callosum, 12 bilateral tracts and all these regions of interest combined. All four diffusivity indices had high heritability at the global level (72%-80%). The magnitude of genetic effects in individual tracts varied from 0% to 82% for FA, 0% to 81% for MD, 8% to 77% for AD, and 0% to 80% for RD with most of the tracts showing significant heritability estimates. Despite the narrow age range of this community-based sample, age was correlated with all four diffusivity indices at the global level. In sum, all diffusion indices proved to have substantial heritability for most of the tracts and the heritability estimates were similar in magnitude for different diffusivity measures. Future studies could aim to discover the particular set of genes that underlie the significant heritability of white matter microstructure. Hum Brain Mapp 38:2026-2036, 2017. © 2017 Wiley Periodicals, Inc.

Detection and measurement of amyloid‐beta (Aβ) in the brain is a key factor for early identification and diagnosis of Alzheimer's disease (AD). We aimed to develop a deep learning model to predict Aβ cerebrospinal fluid (CSF) concentration directly from amyloid PET images, independent of tracers, brain reference regions or preselected regions of interest. We used 1870 Aβ PET images and CSF measurements to train and validate a convolutional neural network ("ArcheD"). We evaluated the ArcheD performance in relation to episodic memory and the standardized uptake value ratio (SUVR) of cortical Aβ. We also compared the brain region's relevance for the model's CSF prediction within clinical‐based and biological‐based classifications. ArcheD‐predicted Aβ CSF values correlated with measured Aβ CSF values (r = 0.92; q < 0.01), SUVR (rAV45 = −0.64, rFBB = −0.69; q < 0.01) and episodic memory measures (0.33 < r < 0.44; q < 0.01). For both classifications, cerebral white matter significantly contributed to CSF prediction (q < 0.01), specifically in non‐symptomatic and early stages of AD. However, in late‐stage disease, the brain stem, subcortical areas, cortical lobes, limbic lobe and basal forebrain made more significant contributions (q < 0.01). Considering cortical grey matter separately, the parietal lobe was the strongest predictor of CSF amyloid levels in those with prodromal or early AD, while the temporal lobe played a more crucial role for those with AD. In summary, ArcheD reliably predicted Aβ CSF concentration from Aβ PET scans, offering potential clinical utility for Aβ level determination. [ABSTRACT FROM AUTHOR]

General cognitive ability (GCA) has substantial explanatory power for behavioral and health outcomes, but its cortical substrate is still not fully established. GCA is highly polygenic and research to date strongly suggests that its cortical substrate is highly polyregional. We show in map-based and region-of-interest-based analyses of adult twins that a complex cortical configuration underlies GCA. Having relatively greater surface area in evolutionary and developmentally high-expanded prefrontal, lateral temporal, and inferior parietal regions is positively correlated with GCA, whereas relatively greater surface area in low-expanded occipital, medial temporal, and motor cortices is negatively correlated with GCA. Essentially the opposite pattern holds for relative cortical thickness. The phenotypic positive-to-negative gradients in our cortical-GCA association maps were largely driven by a similar pattern of genetic associations. The patterns are consistent with regional cortical stretching whereby relatively greater surface area is related to relatively thinner cortex in high-expanded regions. Thus, the typical "bigger is better" view does not adequately capture cortical-GCA associations. Rather, cognitive ability is influenced by complex configurations of cortical development patterns that are strongly influenced by genetic factors. Optimal cognitive ability appears to be driven both by the absolute size and the polyregional configuration of the entire cortex rather than by small, circumscribed regions.

Extraversion is a relatively stable and heritable personality trait associated with numerous psychosocial, lifestyle and health outcomes. Despite its substantial heritability, no genetic variants have been detected in previous genome-wide association (GWA) studies, which may be due to relatively small sample sizes of those studies. Here, we report on a large meta-analysis of GWA studies for extraversion in 63,030 subjects in 29 cohorts. Extraversion item data from multiple personality inventories were harmonized across inventories and cohorts. No genome-wide significant associations were found at the single nucleotide polymorphism (SNP) level but there was one significant hit at the gene level for a long non-coding RNA site (LOC101928162). Genome-wide complex trait analysis in two large cohorts showed that the additive variance explained by common SNPs was not significantly different from zero, but polygenic risk scores, weighted using linkage information, significantly predicted extraversion scores in an independent cohort. These results show that extraversion is a highly polygenic personality trait, with an architecture possibly different from other complex human traits, including other personality traits. Future studies are required to further determine which genetic variants, by what modes of gene action, constitute the heritable nature of extraversion.

Total gray matter volume is associated with general cognitive ability (GCA), an association mediated by genetic factors. It is expectable that total neocortical volume should be similarly associated with GCA. Neocortical volume is the product of thickness and surface area, but global thickness and surface area are unrelated phenotypically and genetically in humans. The nature of the genetic association between GCA and either of these 2 cortical dimensions has not been examined. Humans possess greater cognitive capacity than other species, and surface area increases appear to be the primary driver of the increased size of the human cortex. Thus, we expected neocortical surface area to be more strongly associated with cognition than thickness. Using multivariate genetic analysis in 515 middle-aged twins, we demonstrated that both the phenotypic and genetic associations between neocortical volume and GCA are driven primarily by surface area rather than thickness. Results were generally similar for each of 4 specific cognitive abilities that comprised the GCA measure. Our results suggest that emphasis on neocortical surface area, rather than thickness, could be more fruitful for elucidating neocortical-GCA associations and identifying specific genes underlying those associations.

ImportanceNeuroticism is a pervasive risk factor for psychiatric conditions. It genetically overlaps with major depressive disorder (MDD) and is therefore an important phenotype for psychiatric genetics. The Genetics of Personality Consortium has created a resource for genome-wide association analyses of personality traits in more than 63,000 participants (including MDD cases).ObjectivesTo identify genetic variants associated with neuroticism by performing a meta-analysis of genome-wide association results based on 1000 Genomes imputation; to evaluate whether common genetic variants as assessed by single-nucleotide polymorphisms (SNPs) explain variation in neuroticism by estimating SNP-based heritability; and to examine whether SNPs that predict neuroticism also predict MDD.Design, setting, and participantsGenome-wide association meta-analysis of 30 cohorts with genome-wide genotype, personality, and MDD data from the Genetics of Personality Consortium. The study included 63,661 participants from 29 discovery cohorts and 9786 participants from a replication cohort. Participants came from Europe, the United States, or Australia. Analyses were conducted between 2012 and 2014.Main outcomes and measuresNeuroticism scores harmonized across all 29 discovery cohorts by item response theory analysis, and clinical MDD case-control status in 2 of the cohorts.ResultsA genome-wide significant SNP was found on 3p14 in MAGI1 (rs35855737; P = 9.26 × 10-9 in the discovery meta-analysis). This association was not replicated (P = .32), but the SNP was still genome-wide significant in the meta-analysis of all 30 cohorts (P = 2.38 × 10-8). Common genetic variants explain 15% of the variance in neuroticism. Polygenic scores based on the meta-analysis of neuroticism in 27 cohorts significantly predicted neuroticism (1.09 × 10-12

ImportanceNeuroticism is a pervasive risk factor for psychiatric conditions. It genetically overlaps with major depressive disorder (MDD) and is therefore an important phenotype for psychiatric genetics. The Genetics of Personality Consortium has created a resource for genome-wide association analyses of personality traits in more than 63,000 participants (including MDD cases).ObjectivesTo identify genetic variants associated with neuroticism by performing a meta-analysis of genome-wide association results based on 1000 Genomes imputation; to evaluate whether common genetic variants as assessed by single-nucleotide polymorphisms (SNPs) explain variation in neuroticism by estimating SNP-based heritability; and to examine whether SNPs that predict neuroticism also predict MDD.Design, setting, and participantsGenome-wide association meta-analysis of 30 cohorts with genome-wide genotype, personality, and MDD data from the Genetics of Personality Consortium. The study included 63,661 participants from 29 discovery cohorts and 9786 participants from a replication cohort. Participants came from Europe, the United States, or Australia. Analyses were conducted between 2012 and 2014.Main outcomes and measuresNeuroticism scores harmonized across all 29 discovery cohorts by item response theory analysis, and clinical MDD case-control status in 2 of the cohorts.ResultsA genome-wide significant SNP was found on 3p14 in MAGI1 (rs35855737; P = 9.26 × 10-9 in the discovery meta-analysis). This association was not replicated (P = .32), but the SNP was still genome-wide significant in the meta-analysis of all 30 cohorts (P = 2.38 × 10-8). Common genetic variants explain 15% of the variance in neuroticism. Polygenic scores based on the meta-analysis of neuroticism in 27 cohorts significantly predicted neuroticism (1.09 × 10-12

ObjectiveIn an effort to address earliest detection of mild cognitive impairment (MCI), we examined hippocampal volumes and atrophy in middle-aged men to explore neuroanatomical support for different neuropsychological definitions of MCI.Methods460 men aged 51-60 years underwent neuropsychological testing and MRI. MCI was defined according to five criteria sets. MRI-derived hippocampal volume and hippocampal occupancy (HOC) were obtained via FreeSurfer. Statistical analyses were performed using linear mixed models.ResultsDifferences in HOC between normal cognitive functioning, amnestic, and non-amnestic MCI were observed using MCI criteria that required one impaired (>1.5 SD) cognitive measure in a given cognitive domain or a cognitive composite score method with a cut-point 2 SD below the mean. Differences in standard hippocampal volume were only found between normal and amnestic presentations and only when using the composite score method.ConclusionResults provide empirical support for detection of pre-MCI in younger cohorts. Convergence of neuropsychological and neuroanatomical data, particularly HOC (as opposed to standard cross-sectional volume), supports early identification of MCI as defined by some neuropsychological criteria.

ObjectivesAttachment theory has become a key framework for understanding responses to and consequences of trauma across the life course. We predicted that more severe post-traumatic stress (PTS) symptoms at age 37 years would be associated with insecure attachment at age 55 and with worse PTS symptoms 24 years later at age 61, and that age 55 attachment would mediate the influence of earlier PTS symptoms on later symptoms.DesignData on PTS self-reported symptoms were available for 975 community-dwelling participants from the longitudinal Vietnam Era Twin Study of Aging at ages 37 and 61 years. At age 55, participants completed the Experiences in Close Relationships Inventory, a measure of adult attachment.ResultsPTS symptoms at ages 37 and 61 correlated (r = 0.43; p

Right-left regional cerebral differences are a feature of the human brain linked to functional abilities, aging, and neurodevelopmental and mental disorders. The role of genetic factors in structural asymmetry has been incompletely studied. We analyzed data from 515 individuals (130 monozygotic twin pairs, 97 dizygotic pairs, and 61 unpaired twins) from the Vietnam Era Twin Study of Aging to answer three questions about genetic determinants of brain structural asymmetry: First, does the magnitude of heritability differ for homologous regions in each hemisphere? Despite adequate power to detect regional differences, heritability estimates were not significantly larger in one hemisphere versus the other, except left > right inferior lateral ventricle heritability. Second, do different genetic factors influence left and right hemisphere size in homologous regions? Interhemispheric genetic correlations were high and significant; in only two subcortical regions (pallidum and accumbens) did the estimate statistically differ from 1.0. Thus, there was little evidence for different genetic influences on left and right hemisphere regions. Third, to what extent do genetic factors influence variability in left-right size differences? There was no evidence that variation in asymmetry (i.e., the size difference) of left and right homologous regions was genetically determined, except in pallidum and accumbens. Our findings suggest that genetic factors do not play a significant role in determining individual variation in the degree of regional cortical size asymmetries measured with MRI, although they may do so for volume of some subcortical structures. Despite varying interpretations of existing data, we view the present results as consistent with previous findings.

BackgroundIdentifying mild cognitive impairment (MCI) in midlife could improve early identification of Alzheimer's disease (AD). Also, AD is highly heritable, but the heritability of MCI has not been established. We estimated prevalence rates, association with premorbid general cognitive ability (GCA) and heritability for different definitions of neuropsychologically defined MCI in adults in their 50s.MethodWe examined 1126 twins aged 51-59 years when recruited into the Vietnam Era Twin Study of Aging (VETSA). Six neurocognitive domains were assessed using tests designed to avoid ceiling effects. To differentiate MCI from low overall ability, criteria included adjustment for GCA measured at approximately age 20 years.ResultsAs in older adults, prevalence rates varied widely. Among the lower prevalence rates were some definitions of multiple-domain MCI and single-domain amnestic MCI, which may be less likely than other MCI categories to revert to normal on follow-up. Low prevalence rates in middle-aged adults are also more likely to be valid. MCI was also associated with lower premorbid GCA. Heritability estimates for any MCI and amnestic MCI averaged .40-.48.ConclusionsBy testing multiple cognitive domains and avoiding ceiling effects, MCI can be identified before age 60 years. Premorbid GCA is a risk/protective factor, but deficits after adjusting for early adult GCA suggest additional processes leading to declining trajectories. Heritabilities were comparable to AD, suggesting MCI as an appropriate phenotype for genetic association studies. Full validation will require follow-up assessments (currently under way). Community-based studies are important for this early identification because adults of this age are unlikely to present in clinics.

Animal data show that cortical development is initially patterned by genetic gradients largely along three orthogonal axes. We previously reported differences in genetic influences on cortical surface area along an anterior-posterior axis using neuroimaging data of adult human twins. Here, we demonstrate differences in genetic influences on cortical thickness along a dorsal-ventral axis in the same cohort. The phenomenon of orthogonal gradations in cortical organization evident in different structural and functional properties may originate from genetic gradients. Another emerging theme of cortical patterning is that patterns of genetic influences recapitulate the spatial topography of the cortex within hemispheres. The genetic patterning of both cortical thickness and surface area corresponds to cortical functional specializations. Intriguingly, in contrast to broad similarities in genetic patterning, two sets of analyses distinguish cortical thickness and surface area genetically. First, genetic contributions to cortical thickness and surface area are largely distinct; there is very little genetic correlation (i.e., shared genetic influences) between them. Second, organizing principles among genetically defined regions differ between thickness and surface area. Examining the structure of the genetic similarity matrix among clusters revealed that, whereas surface area clusters showed great genetic proximity with clusters from the same lobe, thickness clusters appear to have close genetic relatedness with clusters that have similar maturational timing. The discrepancies are in line with evidence that the two traits follow different mechanisms in neurodevelopment. Our findings highlight the complexity of genetic influences on cortical morphology and provide a glimpse into emerging principles of genetic organization of the cortex.

In this longitudinal study we examined the stability of general cognitive ability (GCA), as well as heterogeneity and genetic and environmental influences underlying individual differences in change. We investigated GCA from young adulthood through late midlife in 1,288 Vietnam Era Twin Study of Aging participants at ages ~20, ~56, and ~62 years. The correlations among the 3 occasions ranged from 0.73 to 0.85, reflecting substantial stability. The heritability was significant on each of the 3 occasions and ranged from 0.59 to 0.66. The influence of the shared environment was not significant at any of the ages. The genetic correlations across the 3 occasions ranged from 0.95 to 0.99 and did not differ significantly from 1.0. The nonshared environmental correlations ranged from 0.21 to 0.47. Latent growth curve analysis was applied to characterize trajectories over the 42-year period. Slope was significantly different from 0 and indicated that there was modest change over time. There was a significant genetic influence on initial level of GCA (h[superscript 2] = 0.67), but not change (h[superscript 2] = 0.23). Genetic factors primarily contribute to stability, while change reflects the influence of nonshared environmental influences. There was a significant negative correlation between initial level of GCA and change (r = -0.31). Latent class growth analysis identified 4 trajectories. In general, the 4 groups followed parallel trajectories and were differentiated mainly by differences in AFQT performance level at the time of military induction.