The Shared Genetic Basis of Educational Attainment and Cerebral Cortical Morphology

Polygenic Resilience Scores are Associated with Lower Penetrance of Schizophrenia Risk Genes, Protection Against Psychiatric and Medical Disorders, and Enhanced Mental Well-Being and Cognition

In the past decade, significant advances have been made in finding genomic risk loci for schizophrenia (SCZ). This, in turn, has enabled the search for SCZ resilience loci that mitigate the impact of SCZ risk genes. Recently, we discovered the first genomic resilience profile for SCZ, completely independent from the established risk loci for SCZ. We posited that these resilience loci protect against SCZ for those having a heighted genomic risk for SCZ. Nevertheless, our understanding of genetic resilience remains limited. It remains unclear whether resilience loci foster protection against adverse states associated with SCZ risk related to clinical, cognitive, and brain-structural phenotypes. To address this knowledge gap, we analyzed data from 487,409 participants from the UK Biobank, and found that resilience loci for SCZ afforded protection against lifetime psychiatric (schizophrenia, bipolar disorder, anxiety, and depression) and non-psychiatric medical disorders (such as asthma, cardiovascular disease, digestive disorders, metabolic disorders, and external causes of morbidity and mortality). Resilience loci also protected against self-harm behaviors, improved fluid intelligence, and larger whole-brain and brain-regional sizes. Overall, this study sheds light on the range of phenotypes that are significantly associated with resilience loci within the general population, revealing distinct patterns separate from those associated with SCZ risk loci. Our findings indicate that resilience loci may offer protection against serious psychiatric and medical outcomes, co-morbidities, and cognitive impairment. Therefore, it is conceivable that resilience loci facilitate adaptive processes linked to improved health and life expectancy.

Clinical predictors of Alzheimer's disease-like brain atrophy in individuals with memory complaints

OBJECTIVES

The definition and assessment methods for subjective cognitive decline (SCD) vary among studies. We aimed to investigate which features or assessment methods of SCD best predict Alzheimer's disease (AD)-related structural atrophy patterns.

METHODS

We assessed 104 individuals aged 55+ with memory complaints but normal cognitive screening. Our research questions were as follows: To improve the prediction of AD related morphological changes, (1) Would the use of a standardized cognitive screening scale be beneficial? (2) Is conducting a thorough neuropsychological evaluation necessary instead of relying solely on cognitive screening tests? (3) Should we apply SCD-plus research criteria, and if so, which criterion would be the most effective? (4) Is it necessary to consider medical and psychiatric comorbidities, vitamin deficiencies, vascular burden on MRI, and family history? We utilized Freesurfer to analyze cortical thickness and regional brain volume meta-scores linked to AD or predicting its development. We employed multiple linear regression models for each variable, with morphology as the dependent variable.

RESULTS

AD-like morphology was associated with subjective complaints in males, individuals with advanced age, and higher education. Later age of onset for complaints, complaints specifically related to memory, excessive deep white matter vascular lesions, and using medications that have negative implications for cognitive health (according to the Beers criteria) were predictive of AD-related morphology. The subjective cognitive memory questionnaire scores were found to be a better predictor of reduced volumes than a single-question assessment. It is important to note that not all SCD-plus criteria were evaluated in this study, particularly the APOE genotype, amyloid, and tau status, due to resource limitations.

CONCLUSIONS

The detection of AD-related structural changes is impacted by demographics and assessment methods. Standardizing SCD assessment methods can enhance predictive accuracy.

Human cortical neurogenesis is altered via glucocorticoid-mediated regulation of ZBTB16 expression

Glucocorticoids are important for proper organ maturation, and their levels are tightly regulated during development. Here, we use human cerebral organoids and mice to study the cell-type-specific effects of glucocorticoids on neurogenesis. We show that glucocorticoids increase a specific type of basal progenitors (co-expressing PAX6 and EOMES) that has been shown to contribute to cortical expansion in gyrified species. This effect is mediated via the transcription factor ZBTB16 and leads to increased production of neurons. A phenome-wide Mendelian randomization analysis of an enhancer variant that moderates glucocorticoid-induced ZBTB16 levels reveals causal relationships with higher educational attainment and altered brain structure. The relationship with postnatal cognition is also supported by data from a prospective pregnancy cohort study. This work provides a cellular and molecular pathway for the effects of glucocorticoids on human neurogenesis that relates to lasting postnatal phenotypes.

Genomic Structural Equation Modeling Reveals Latent Phenotypes in the Human Cortex with Distinct Genetic Architecture

Genetic contributions to human cortical structure manifest pervasive pleiotropy. This pleiotropy may be harnessed to identify unique genetically-informed parcellations of the cortex that are neurobiologically distinct from functional, cytoarchitectural, or other cortical parcellation schemes. We investigated genetic pleiotropy by applying genomic structural equation modeling (SEM) to map the genetic architecture of cortical surface area (SA) and cortical thickness (CT) for the 34 brain regions recently reported in the ENIGMA cortical GWAS. Genomic SEM uses the empirical genetic covariance estimated from GWAS summary statistics with LD score regression (LDSC) to discover factors underlying genetic covariance, which we are denoting genetically informed brain networks (GIBNs). Genomic SEM can fit a multivariate GWAS from summary statistics for each of the GIBNs, which can subsequently be used for LD score regression (LDSC). We found the best-fitting model of cortical SA identified 6 GIBNs and CT identified 4 GIBNs. The multivariate GWASs of these GIBNs identified 74 genome-wide significant (GWS) loci (p<5×10-8), including many previously implicated in neuroimaging phenotypes, behavioral traits, and psychiatric conditions. LDSC of GIBN GWASs found that SA-derived GIBNs had a positive genetic correlation with bipolar disorder (BPD), and cannabis use disorder, indicating genetic predisposition to a larger SA in the specific GIBN is associated with greater genetic risk of these disorders. A negative genetic correlation was observed with attention deficit hyperactivity disorder (ADHD), major depressive disorder (MDD), and insomnia, indicating genetic predisposition to a larger SA in the specific GIBN is associated with lower genetic risk of these disorders. CT GIBNs displayed a negative genetic correlation with alcohol dependence. Jointly modeling the genetic architecture of complex traits and investigating multivariate genetic links across phenotypes offers a new vantage point for mapping the cortex into genetically informed networks.

Structural architecture and brain network efficiency link polygenic scores to intelligence

Intelligence is highly heritable. Genome-wide association studies (GWAS) have shown that thousands of alleles contribute to variation in intelligence with small effect sizes. Polygenic scores (PGS), which combine these effects into one genetic summary measure, are increasingly used to investigate polygenic effects in independent samples. Whereas PGS explain a considerable amount of variance in intelligence, it is largely unknown how brain structure and function mediate this relationship. Here, we show that individuals with higher PGS for educational attainment and intelligence had higher scores on cognitive tests, larger surface area, and more efficient fiber connectivity derived by graph theory. Fiber network efficiency as well as the surface of brain areas partly located in parieto-frontal regions were found to mediate the relationship between PGS and cognitive performance. These findings are a crucial step forward in decoding the neurogenetic underpinnings of intelligence, as they identify specific regional networks that link polygenic predisposition to intelligence.

A platform for phenotyping disease progression and associated longitudinal risk factors in large-scale EHRs, with application to incident diabetes complications in the UK Biobank

Objective

Modern healthcare data reflect massive multi-level and multi-scale information collected over many years. The majority of the existing phenotyping algorithms use case-control definitions of disease. This paper aims to study the time to disease onset and progression and identify the time-varying risk factors that drive them.

Materials and Methods

We developed an algorithmic approach to phenotyping the incidence of diseases by consolidating data sources from the UK Biobank (UKB), including primary care electronic health records (EHRs). We focused on defining events, event dates, and their censoring time, including relevant terms and existing phenotypes, excluding generic, rare, or semantically distant terms, forward-mapping terminology terms, and expert review. We applied our approach to phenotyping diabetes complications, including a composite cardiovascular disease (CVD) outcome, diabetic kidney disease (DKD), and diabetic retinopathy (DR), in the UKB study.

Results

We identified 49 049 participants with diabetes. Among them, 1023 had type 1 diabetes (T1D), and 40 193 had type 2 diabetes (T2D). A total of 23 833 diabetes subjects had linked primary care records. There were 3237, 3113, and 4922 patients with CVD, DKD, and DR events, respectively. The risk prediction performance for each outcome was assessed, and our results are consistent with the prediction area under the ROC (receiver operating characteristic) curve (AUC) of standard risk prediction models using cohort studies.

Discussion and Conclusion

Our publicly available pipeline and platform enable streamlined curation of incidence events, identification of time-varying risk factors underlying disease progression, and the definition of a relevant cohort for time-to-event analyses. These important steps need to be considered simultaneously to study disease progression.

Low neighborhood deprivation buffers against hippocampal neurodegeneration, white matter hyperintensities, and poorer cognition

There is increasing recognition that socioeconomic inequalities contribute to disparities in brain and cognitive health in older adults. However, whether neighborhood socioeconomic status (SES) buffers individuals with low individual SES against neurodegeneration, cerebrovascular disease, and poorer cognitive function is not well understood. Here, we evaluated whether neighborhood deprivation (Townsend deprivation index) interacted with individual SES (composite household income and education levels) on hippocampus volume, regional cortical thickness, white matter hyperintensities, and cognition in 19,638 individuals (mean age = 54.8) from the UK Biobank. We found that individuals with low individual SES had the smallest hippocampal volumes, greatest white matter hyperintensity burden, and poorest cognition if they were living in high deprivation neighborhoods but that these deleterious effects on brain and cognitive function were attenuated if they were living in low deprivation neighborhoods (p for interactions < .05). While neighborhood deprivation did not interact with individual SES to influence regional cortical thickness, higher neighborhood deprivation was independently associated with lower cortical thickness in 16 regions (false discovery rate q < .05). Across multiple brain indices and cognitive function analyses, we found converging evidence suggesting that low neighborhood deprivation may have a neuroprotective effect against neurodegeneration, cerebrovascular pathology, and cognitive impairment, particularly in vulnerable individuals with low household income and education levels.

Neuroanatomical Correlates Underlying the Association Between Maternal Interleukin 6 Concentration During Pregnancy and Offspring Fluid Reasoning Performance in Early Childhood

BACKGROUND

Maternal inflammation during pregnancy can alter offspring brain development and influence risk for disorders commonly accompanied by deficits in cognitive functioning. We therefore examined associations between maternal interleukin 6 (IL-6) concentrations during pregnancy and offspring cognitive ability and concurrent magnetic resonance imaging-based measures of brain anatomy in early childhood. We further examined newborn brain anatomy in secondary analyses to consider whether effects are evident soon after birth and to increase capacity to differentiate effects of pre- versus postnatal exposures.

METHODS

IL-6 concentrations were quantified in early (12.6 ± 2.8 weeks), mid (20.4 ± 1.5 weeks), and late (30.3 ± 1.3 weeks) pregnancy. Offspring nonverbal fluid intelligence (Gf) was assessed at 5.2 ± 0.6 years using a spatial reasoning task (Wechsler Preschool and Primary Scale of Intelligence-Matrix) (n = 49). T1-weighted magnetic resonance imaging scans were acquired at birth (n = 89, postmenstrual age = 42.9 ± 2.0 weeks) and in early childhood (n = 42, scan age = 5.1 ± 1.0 years). Regional cortical volumes were examined for a joint association between maternal IL-6 and offspring Gf performance.

RESULTS

Average maternal IL-6 concentration during pregnancy was inversely associated with offspring Gf performance after adjusting for socioeconomic status and the quality of the caregiving and learning environment (R² = 13%; p = .02). Early-childhood pars triangularis volume was jointly associated with maternal IL-6 and childhood Gf (p_corrected < .001). An association also was observed between maternal IL-6 and newborn pars triangularis volume (R² = 6%; p = .02).

CONCLUSIONS

These findings suggest that the origins of variation in child cognitive ability can, in part, trace back to maternal conditions during the intrauterine period of life and support the role of inflammation as an important component of this putative biological pathway.

Genome-wide gene-diet interaction analysis in the UK Biobank identifies novel effects on hemoglobin A1c

Diet is a significant modifiable risk factor for type 2 diabetes (T2D), and its effect on disease risk is under partial genetic control. Identification of specific gene-diet interactions (GDIs) influencing risk biomarkers such as glycated hemoglobin (HbA1c) is a critical step towards precision nutrition for T2D prevention, but progress has been slow due to limitations in sample size and accuracy of dietary exposure measurement. We leveraged the large UK Biobank (UKB) cohort and a diverse group of dietary exposures, including 30 individual dietary traits and 8 empirical dietary patterns, to conduct genome-wide interaction studies in ~340 000 European-ancestry participants to identify novel GDIs influencing HbA1c. We identified five variant-dietary trait pairs reaching genome-wide significance (P < 5 × 10-8): two involved dietary patterns (meat pattern with rs147678157 and a fruit & vegetable-based pattern with rs3010439) and three involved individual dietary traits (bread consumption with rs62218803, dried fruit consumption with rs140270534 and milk type [dairy vs. other] with 4:131148078_TAGAA_T). These were affected minimally by adjustment for geographical and lifestyle-related confounders, and four of the five variants lacked genetic main effects that would have allowed their detection in a traditional genome-wide association study for HbA1c. Notably, multiple loci near transient receptor potential subfamily M genes (TRPM2 and TRPM3) interacted with carbohydrate-containing food groups. These interactions were further characterized using non-European UKB subsets and alternative measures of glycaemia (fasting glucose and follow-up HbA1c measurements). Our results highlight GDIs influencing HbA1c for future investigation, while reinforcing known challenges in detecting and replicating GDIs.

The UK Biobank imaging enhancement of 100,000 participants: rationale, data collection, management and future directions

UK Biobank is a population-based cohort of half a million participants aged 40-69 years recruited between 2006 and 2010. In 2014, UK Biobank started the world's largest multi-modal imaging study, with the aim of re-inviting 100,000 participants to undergo brain, cardiac and abdominal magnetic resonance imaging, dual-energy X-ray absorptiometry and carotid ultrasound. The combination of large-scale multi-modal imaging with extensive phenotypic and genetic data offers an unprecedented resource for scientists to conduct health-related research. This article provides an in-depth overview of the imaging enhancement, including the data collected, how it is managed and processed, and future directions.

The interrelation of sleep and mental and physical health is anchored in grey-matter neuroanatomy and under genetic control

Humans need about seven to nine hours of sleep per night. Sleep habits are heritable, associated with brain function and structure, and intrinsically related to well-being, mental, and physical health. However, the biological basis of the interplay of sleep and health is incompletely understood. Here we show, by combining neuroimaging and behavioral genetic approaches in two independent large-scale datasets (HCP (n = 1106), age range: 22-37, eNKI (n = 783), age range: 12-85), that sleep, mental, and physical health have a shared neurobiological basis in grey matter anatomy; and that these relationships are driven by shared genetic factors. Though local associations between sleep and cortical thickness were inconsistent across samples, we identified two robust latent components, highlighting the multivariate interdigitation of sleep, intelligence, BMI, depression, and macroscale cortical structure. Our observations provide a system-level perspective on the interrelation of sleep, mental, and physical conditions, anchored in grey-matter neuroanatomy.

Comprehensive genomic analysis of dietary habits in UK Biobank identifies hundreds of genetic associations

Unhealthful dietary habits are leading risk factors for life-altering diseases and mortality. Large-scale biobanks now enable genetic analysis of traits with modest heritability, such as diet. We perform a genomewide association on 85 single food intake and 85 principal component-derived dietary patterns from food frequency questionnaires in UK Biobank. We identify 814 associated loci, including olfactory receptor associations with fruit and tea intake; 136 associations are only identified using dietary patterns. Mendelian randomization suggests our top healthful dietary pattern driven by wholemeal vs. white bread consumption is causally influenced by factors correlated with education but is not strongly causal for coronary artery disease or type 2 diabetes. Overall, we demonstrate the value in complementary phenotyping approaches to complex dietary datasets, and the utility of genomic analysis to understand the relationships between diet and human health.

Accelerated estimation and permutation inference for ACE modeling

There are a wealth of tools for fitting linear models at each location in the brain in neuroimaging analysis, and a wealth of genetic tools for estimating heritability for a small number of phenotypes. But there remains a need for computationally efficient neuroimaging genetic tools that can conduct analyses at the brain‐wide scale. Here we present a simple method for heritability estimation on twins that replaces a variance component model‐which requires iterative optimisation‐with a (noniterative) linear regression model, by transforming data to squared twin‐pair differences. We demonstrate that the method has comparable bias, mean squared error, false positive risk, and power to best practice maximum‐likelihood‐based methods, while requiring a small fraction of the computation time. Combined with permutation, we call this approach “Accelerated Permutation Inference for the ACE Model (APACE)” where ACE refers to the additive genetic (A) effects, and common (C), and unique (E) environmental influences on the trait. We show how the use of spatial statistics like cluster size can dramatically improve power, and illustrate the method on a heritability analysis of an fMRI working memory dataset.

Age and cognitive decline in the UK Biobank

OBJECTIVES

Age-related cognitive decline is a well-known phenomenon after age 65 but little is known about earlier changes and prior studies are based on relatively small samples. We investigated the impact of age on cognitive decline in the largest population sample to date including young to old adults.

METHOD

Between 100,352 and 468,534 participants aged 38-73 years from UK Biobank completed at least one of seven self-administered cognitive functioning tests: prospective memory (PM), pairs matching (Pairs), fluid intelligence (FI), reaction time (RT), symbol digit substitution, trail making A and B. Up to 26,005 participants completed at least one of two follow-up assessments of PM, Pairs, FI and RT. Multivariable regression models examined the association between age (<45[reference], 45-49, 50-54, 55-59, 60-64, 65+) and cognition scores at baseline. Mixed models estimated the impact of age on cognitive decline over follow-up (~5.1 years).

RESULTS

FI was higher between ages 50 and 64 and lower at 65+ compared to <45 at baseline. Performance on all other baseline tests was lower with older age: with increasing age category, difference in test scores ranged from 2.5 to 7.8%(P<0.0001). Compared to <45 at baseline, RT and Pairs performance declined faster across all older age cohorts (3.0 and 1.2% change, respectively, with increasing age category, P<0.0001). Cross-sectional results yielded 8 to 12-fold higher differences in RT and Pairs with age compared to longitudinal results.

CONCLUSIONS

Our findings suggest that declines in cognitive abilities <65 are small. The cross-sectional differences in cognition scores for middle to older adult years may be due in part to age cohort effects.