A General Cognitive Ability Factor for the UK Biobank.

UK Biobank participants do not have a high-quality measure of intelligence or polygenic scores (PGSs) of intelligence to simultaneously examine the genetic and neural underpinnings of intelligence. We created a standardized measure of general intelligence (g factor) relative to the UK population and estimated its quality. After running a GWAS of g on UK Biobank participants with a g factor of good quality and without neuroimaging data (N = 187,288), we derived a g PGS for UK Biobank participants with neuroimaging data. For individuals with at least one cognitive test, the g factor from eight cognitive tests (N = 501,650) explained 29% of the variance in cognitive test performance. The PGS for British individuals with neuroimaging data (N = 27,174) explained 7.6% of the variance in g. We provided high-quality g factor estimates for most UK Biobank participants and g factor PGSs for UK Biobank participants with neuroimaging data.

High intelligence is not associated with a greater propensity for mental health disorders.

BACKGROUND: Studies reporting that highly intelligent individuals have more mental health disorders often have sampling bias, no or inadequate control groups, or insufficient sample size. We addressed these caveats by examining the difference in the prevalence of mental health disorders between individuals with high and average general intelligence (g-factor) in the UK Biobank. METHODS: Participants with g-factor scores standardized relative to the same-age UK population, were divided into two groups: a high g-factor group (g-factor 2 SD above the UK mean; N = 16,137) and an average g-factor group (g-factor within 2 SD of the UK mean; N = 236,273). Using self-report questionnaires and medical diagnoses, we examined group differences in the prevalence of 32 phenotypes, including mental health disorders, trauma, allergies, and other traits. RESULTS: High and average g-factor groups differed across 15/32 phenotypes and did not depend on sex and/or age. Individuals with high g-factors had less general anxiety (odds ratio [OR] = 0.69, 95% CI [0.64;0.74]) and post-traumatic stress disorder (PTSD; OR = 0.67, 95 %CI [0.61;0.74]), were less neurotic (ß = -0.12, 95% CI [-0.15;-0.10]), less socially isolated (OR = 0.85, 95% CI [0.80;0.90]), and were less likely to have experienced childhood stressors and abuse, adulthood stressors, or catastrophic trauma (OR = 0.69-0.90). However, they generally had more allergies (e.g., eczema; OR = 1.13-1.33). CONCLUSIONS: The present study provides robust evidence that highly intelligent individuals do not have more mental health disorders than the average population. High intelligence even appears as a protective factor for general anxiety and PTSD.

Comparing brain asymmetries independently of brain size.

Studies examining cerebral asymmetries typically divide the l-R Measure (e.g., Left-Right Volume) by the L + R Measure to obtain an Asymmetry Index (AI). However, contrary to widespread belief, such a division fails to render the AI independent from the L + R Measure and/or from total brain size. As a result, variations in brain size may bias correlation estimates with the AI or group differences in AI. We investigated how to analyze brain asymmetries in to distinguish global from regional effects, and report unbiased group differences in cerebral asymmetries in the UK Biobank (N = 40, 028). We used 306 global and regional brain measures provided by the UK Biobank. Global gray and white matter volumes were taken from Freesurfer ASEG, subcortical gray matter volumes from Freesurfer ASEG and subsegmentation, cortical gray matter volumes, mean thicknesses, and surface areas from the Destrieux atlas applied on T1-and T2-weighted images, cerebellar gray matter volumes from FAST FSL, and regional white matter volumes from Freesurfer ASEG. We analyzed the extent to which the L + R Measure, Total Cerebral Measure (TCM, e.g., Total Brain Volume), and l-R TCM predict regional asymmetries. As a case study, we assessed the consequences of omitting each of these predictors on the magnitude and significance of sex differences in asymmetries. We found that the L + R Measure, the TCM, and the l-R TCM predicted the AI of more than 89% of regions and that their relationships were generally linear. Removing any of these predictors changed the significance of sex differences in 33% of regions and the magnitude of sex differences across 13-42% of regions. Although we generally report similar sex and age effects on cerebral asymmetries to those of previous large-scale studies, properly adjusting for regional and global brain size revealed additional sex and age effects on brain asymmetry.

Sex differences in the brain are not reduced to differences in body size.

In their comprehensive review of sex differences in the brain, Eliot et al. (2021) conclude that (1) men and women significantly differ in global brain size, but this "mostly parallels the divergence of male/female body size during development" and that (2) "once we account for individual differences in brain size, there is almost no difference in the volume of specific cortical or subcortical structures between men and women". In sum, almost all brain differences would directly or indirectly follow from differences in body size. In a recent study that does not have the same limitations as most studies reviewed by Eliot et al., we find that sex differences in total brain volume are not accounted for by sex differences in height and weight, and that once global brain size is taken into account, there remain numerous regional sex differences in both directions (Williams et al., 2021).

Neuroanatomical norms in the UK Biobank: The impact of allometric scaling, sex, and age.

Few neuroimaging studies are sufficiently large to adequately describe population-wide variations. This study's primary aim was to generate neuroanatomical norms and individual markers that consider age, sex, and brain size, from 629 cerebral measures in the UK Biobank (N = 40,028). The secondary aim was to examine the effects and interactions of sex, age, and brain allometry-the nonlinear scaling relationship between a region and brain size (e.g., total brain volume)-across cerebral measures. Allometry was a common property of brain volumes, thicknesses, and surface areas (83%) and was largely stable across age and sex. Sex differences occurred in 67% of cerebral measures (median |ß| = .13): 37% of regions were larger in males and 30% in females. Brain measures (49%) generally decreased with age, although aging effects varied across regions and sexes. While models with an allometric or linear covariate adjustment for brain size yielded similar significant effects, omitting brain allometry influenced reported sex differences in variance. Finally, we contribute to the reproducibility of research on sex differences in the brain by replicating previous studies examining cerebral sex differences. This large-scale study advances our understanding of age, sex, and brain allometry's impact on brain structure and provides data for future UK Biobank studies to identify the cerebral regions that covary with specific phenotypes, independently of sex, age, and brain size.

Combining multivariate genomic approaches to elucidate the comorbidity between autism spectrum disorder and attention deficit hyperactivity disorder.

BACKGROUND: Attention deficit hyperactivity disorder (ADHD) and autism spectrum disorder (ASD) are two highly heritable neurodevelopmental disorders. Several lines of evidence point towards the presence of shared genetic factors underlying ASD and ADHD. We conducted genomic analyses of common risk variants (i.e. single nucleotide polymorphisms, SNPs) shared by ASD and ADHD, and those specific to each disorder. METHODS: With the summary data from two GWAS, one on ASD (N = 46,350) and another on ADHD (N = 55,374) individuals, we used genomic structural equation modelling and colocalization analysis to identify SNPs shared by ASD and ADHD and SNPs specific to each disorder. Functional genomic analyses were then conducted on shared and specific common genetic variants. Finally, we performed a bidirectional Mendelian randomization analysis to test whether the shared genetic risk between ASD and ADHD was interpretable in terms of reciprocal relationships between ASD and ADHD. RESULTS: We found that 37.5% of the SNPs associated with ASD (at p < 1e-6) colocalized with ADHD SNPs and that 19.6% of the SNPs associated with ADHD colocalized with ASD SNPs. We identified genes mapped to SNPs that are specific to ASD or ADHD and that are shared by ASD and ADHD, including two novel genes INSM1 and PAX1. Our bidirectional Mendelian randomization analyses indicated that the risk of ASD was associated with an increased risk of ADHD and vice versa. CONCLUSIONS: Using multivariate genomic analyses, the present study uncovers shared and specific genetic variants associated with ASD and ADHD. Further functional investigation of genes mapped to those shared variants may help identify pathophysiological pathways and new targets for treatment.

Adjusting for allometric scaling in ABIDE I challenges subcortical volume differences in autism spectrum disorder.

Inconsistencies across studies investigating subcortical correlates of autism spectrum disorder (ASD) may stem from small sample size, sample heterogeneity, and omitting or linearly adjusting for total brain volume (TBV). To properly adjust for TBV, brain allometry-the nonlinear scaling relationship between regional volumes and TBV-was considered when examining subcortical volumetric differences between typically developing (TD) and ASD individuals. Autism Brain Imaging Data Exchange I (ABIDE I; N = 654) data was analyzed with two methodological approaches: univariate linear mixed effects models and multivariate multiple group confirmatory factor analyses. Analyses were conducted on the entire sample and in subsamples based on age, sex, and full scale intelligence quotient (FSIQ). A similar ABIDE I study was replicated and the impact of different TBV adjustments on neuroanatomical group differences was investigated. No robust subcortical allometric or volumetric group differences were observed in the entire sample across methods. Exploratory analyses suggested that allometric scaling and volume group differences may exist in certain subgroups defined by age, sex, and/or FSIQ. The type of TBV adjustment influenced some reported volumetric and scaling group differences. This study supports the absence of robust volumetric differences between ASD and TD individuals in the investigated volumes when adjusting for brain allometry, expands the literature by finding no group difference in allometric scaling, and further suggests that differing TBV adjustments contribute to the variability of reported neuroanatomical differences in ASD.

Neuroanatomy of dyslexia: An allometric approach.

Despite evidence for a difference in total brain volume between dyslexic and good readers, no previous neuroimaging study examined differences in allometric scaling (i.e. differences in the relationship between regional and total brain volumes) between dyslexic and good readers. The present study aims to fill this gap by testing differences in allometric scaling and regional brain volume differences in dyslexic and good readers. Object-based morphometry analysis was used to determine grey and white matter volumes of the four lobes, the cerebellum and limbic structures in 130 dyslexic and 106 good readers aged 8-14 years. Data were collected across three countries (France, Poland and Germany). Three methodological approaches were used as follows: principal component analysis (PCA), linear regression and multiple-group confirmatory factor analysis (MGCFA). Difference in total brain volume between good and dyslexic readers was Cohen's d = 0.39. We found no difference in allometric scaling, nor in regional brain volume between dyslexic and good readers. Results of our three methodological approaches (PCA, linear regression and MGCFA) were consistent. This study provides evidence for total brain volume differences between dyslexic and control children, but no evidence for differences in the volumes of the four lobes, the cerebellum or limbic structures, once allometry is taken into account. It also finds no evidence for a difference in allometric relationships between the groups. We highlight the methodological interest of the MGCFA approach to investigate such research issues.