Biology subjects

Caspi, A.

Publications and source records attributed to Caspi, A..

At least 19 recordsLinked to original sources

bioRxiv2026

p38α MAPK integrates opposing oncogenic APC/β-catenin/TCF and GUCY2C tumor-suppressor axes in colorectal cancer

Introduction: Sporadic colorectal cancers (CRCs) form following initiating APC mutations, inducing constitutive {beta}-catenin/TCF signaling. Similarly, the guanylyl cyclase C (GUCY2C) tumor suppressor axis is universally silenced in CRC. While the GUCY2C receptor is retained, loss of its paracrine hormones guanylin (GUCA2A) and uroguanylin (GUCA2B) is among the earliest events in transformation. Mutant APC/{beta}-catenin/TCF signaling driving CRC transcriptionally silences GUCY2C hormone expression while suppressing that oncogenic pathway reconstitutes hormone production opposing tumorigenesis. p38 opposes tumor initiation, and its loss promotes transformation in chemical and genetic mouse models of CRC. In healthy intestine, p38 supports a gradient of epithelial differentiation along the crypt-surface axis. Here, we reveal that p38 links regulation of oncogenic APC/{beta}-catenin/TCF signaling and GUCY2C hormone expression. Methods: Regulation of p38 by APC/{beta}-catenin/TCF signaling was defined in four genetically engineered human CRC cell lines with conditional oncogenic APC/{beta}-catenin/TCF signaling and healthy human colon organoids. Expression of p38 was eliminated from conditional human CRC cell lines by pharmacological inhibition, RNA knockdown, and CRISPR/Cas9 gene editing. Transcriptomic analysis of these cells by RNA sequencing defined the set of genes regulated by APC/{beta}-catenin/TCF signaling controlled through p38. Results: In CRC cell lines, APC/{beta}-catenin/TCF signaling suppressed p38 phosphorylation and activity, whereas inhibition of APC/{beta}-catenin/TCF restored p38 signaling. In the absence of APC/{beta}-catenin/TCF signaling, inhibiting p38 repressed GUCY2C hormone expression. Inhibiting APC/{beta}-catenin/TCF signaling enriched phosphorylated p38 at the GUCA2A promoter. In that context, analysis of the CPTAC proteomic database revealed that phosphorylation of p38 and its upstream kinases MKK3 and MKK4 was reduced in human CRC. Finally, transcriptomic analysis revealed that p38 is a critical signaling node controlling the expression of a substantial subset of APC/{beta}-catenin/TCF-regulated genes in human CRC cells and patient tumors. Conclusions: APC/{beta}-catenin/TCF signaling inhibits p38 phosphorylation and transcriptional activity, suppressing GUCA2A and GUCA2B expression and functionally silencing the retained GUCY2C receptor. These findings identify p38 as a signaling intermediary integrating oncogenic APC/{beta}-catenin/TCF activity to extinction of the GUCY2C tumor-suppressor axis and reveal a broader p38-dependent component of the APC/{beta}-catenin/TCF-regulated transcriptome.

cancer biology↗

bioRxiv2026

Wavelet Transform improves time-frequency characterization of the Electroretinographic Oscillatory Potentials as compared to Short-Time Fourier Transform across experimental and clinical models

Purpose: To compare continuous wavelet transform (CWT) and short-time Fourier transform (STFT) analyses of the oscillatory potentials (OPs) of the dark-adapted full-field electroretinogram (FFERG). Methods: FFERG recordings from Royal College of Surgeons (RCS) and healthy Brown Norway (BN) rats, wild-type and Atoh7 enhancer-knockout (KO) mice, and patients with diabetic retinopathy and healthy controls were analyzed with STFT and with CWT employing the real Morlet wavelet, after 60 Hz high-pass and sharp 60 Hz notch filtering. Time- and frequency-localized OP features were quantified and the methods compared on detection of transient OP signals. Both were additionally validated using synthetic OP-like bursts of known frequency embedded in each cohort's recorded noise. Results: CWT revealed distinct time-frequency patterns and disease-specific OP alterations across all rat, mouse, and human cohorts, with improved temporal localization and frequency resolution, whereas STFT merged transient features and was more susceptible to noise. CWT-derived dominant-frequency differences reached significance in all three models; the corresponding STFT difference reached significance only in rats (separate tests; methods not directly compared). In synthetic bursts, both methods recovered injected frequencies to within 1-2.5 Hz, but only CWT resolved two bursts separated by 15 ms. Conclusions: CWT employing the real Morlet wavelet provides a robust, physiologically relevant evaluation of OPs, with improved temporal localization and reduced spectral dispersion relative to STFT in clinical and experimental settings. Translational Relevance: CWT analysis may improve the characterization of inner retinal dysfunction by preserving transient OP time-frequency structure, as illustrated in preclinical rodent models and diabetic retinopathy patients.

physiology↗

bioRxiv2026

Creating DNAm Algorithms Using the Illumina Methylation Screening Array (MSA)

Most established DNA methylation (DNAm) biomarkers were developed on legacy Illumina EPIC arrays. The Infinium Methylation Screening Array (MSA) offers a lower-cost, higher-throughput alternative with reduced probe content, but EPIC-trained algorithms cannot be assumed to transfer directly. Here we present a reproducibility-based framework for developing and transferring DNAm algorithms on the MSA. Using paired biological replicates profiled on EPICv1 and MSA (1,764 EPICv1-MSA sample pairs, plus within-array MSA replicates on the same and different beadchips), we quantified probe-level agreement using mean absolute error (MAE) and intraclass correlation coefficients (ICC). Of 140,150 CpG sites shared between EPICv1 and MSA, 40,786 (29.1%) met both stability criteria (MAE < 0.05 and ICC(2,k) > 0.6). This stable feature space supported two modelling streams. First, we trained 134 epigenetic biomarker proxies (EBPs) natively on MSA, with and without kernel principal component analysis (kPCA) for sample-level harmonisation. All 134 reached same-beadchip ICC(2,1) >= 0.80 (median 0.97) and 96.3% reached different-beadchip ICC(2,1) >= 0.60 (median 0.81), with a median Spearman correlation of 0.48 against observed values. Among the 72 kPCA-selected models with a comparable stable-probe baseline, 70 (97%) showed higher cross-beadchip ICC (median improvement +0.18). Second, we transferred three established clocks using model-specific strategies: OMICmAge and SystemsAge were retrained to estimate their EPICv1-derived values (held-out test-set rho = 0.944 and 0.912-0.949), whereas DunedinPACE required stable-probe normalisation and robust linear calibration, which raised cross-array ICC(2,1) from 0.784-0.810 to 0.891-0.925 and reduced MAE from 0.085-0.089 to 0.041-0.050 across three sample sets. Reduced probe content does not preclude reproducible DNAm biomarker measurement, and transfer strategy must be matched to model architecture.

bioinformatics↗

bioRxiv2025

An Updated Polygenic Index Repository: Expanded Phenotypes, New Cohorts, and Improved Causal Inference

Polygenic indexes (PGIs) -- DNA-based predictors of individual phenotypes -- have become essential tools across biomedical and social sciences. We introduce Version 2 of the Polygenic Index Repository, which expands phenotype coverage from 47 to 61, increases the number of participating datasets from 11 to 20, and adopts a more consistent and improved methodology for PGI construction. For 16 phenotypes, we leverage summary statistics from an updated GWAS meta-analysis with greater statistical power compared to the original release, thereby improving the PGIs predictive power. To improve power for family-based analyses, we provide imputed parental PGIs in all datasets with first-degree relatives and offer a framework for interpreting results from analyses that control for parental PGIs. We illustrate the utility of parental PGIs using two applications: (1) comparing PGI associations with and without parental PGI controls for all phenotypes in two Repository datasets with family data, and (2) for BMI and diastolic blood pressure, exploring the contribution of causal versus non-causal components of PGI associations to the imperfect portability of PGIs across subgroups within a genetic ancestry. Collectively, the updates enhance predictive performance, broaden the Repositorys scope, and introduce novel resources that reduce confounding bias and improve interpretability.

genomics↗

bioRxiv2024

An estimate of the longitudinal pace of aging from a single brain scan predicts dementia conversion, morbidity, and mortality

To understand how aging affects functional decline and increases disease risk, it is necessary to develop accurate and reliable measures of how fast a person is aging. Epigenetic clocks measure aging but require DNA methylation data, which many studies lack. Using data from the Dunedin Study, we introduce an accurate and reliable measure for the rate of longitudinal aging derived from cross-sectional brain MRI: the Dunedin Pace of Aging Calculated from NeuroImaging or DunedinPACNI. Exporting this measure to the Alzheimers Disease Neuroimaging Initiative and UK Biobank datasets revealed that faster DunedinPACNI predicted participants cognitive impairment, accelerated brain atrophy, and conversion to diagnosed dementia. Underscoring close links between longitudinal aging of the body and brain, faster DunedinPACNI also predicted physical frailty, poor health, future chronic diseases, and mortality in older adults. Furthermore, DunedinPACNI followed an established socioeconomic health gradient with people of lower socioeconomic status showing faster DunedinPACNI. Associations between DunedinPACNI and cognitive impairment were replicated in BrainLat, a sample of Latin American patients with dementia. When compared to brain age gap, an existing MRI aging biomarker, DunedinPACNI was similarly or more strongly related to clinical outcomes. DunedinPACNI is a "next generation" MRI measure that will be made publicly available to the research community to help accelerate aging research and evaluate the effectiveness of dementia prevention and anti-aging strategies.

neuroscience↗

bioRxiv2024

Cross-Tissue Comparison of Epigenetic Aging Clocks in Humans

Epigenetic clocks are a common group of tools used to measure biological aging - the progressive deterioration of cells, tissues and organs. Epigenetic clocks have been trained almost exclusively using blood-based tissues but there is growing interest in estimating epigenetic age using less-invasive oral-based tissues (i.e., buccal or saliva) in both research and commercial settings. However, differentiated cell types across body tissues exhibit unique DNA methylation landscapes and age-related alterations to the DNA methylome. Applying epigenetic clocks derived from blood-based tissues to estimate epigenetic age of oral-based tissues may introduce biases. We tested the within-person comparability of common epigenetic clocks across five tissue types: buccal epithelial, saliva, dry blood spots, buffy coat (i.e., leukocytes), and peripheral blood mononuclear cells. We tested 284 distinct tissue samples from 83 individuals aged 9-70 years. Overall, there were significant within-person differences in epigenetic clock estimates from oral-based versus blood-based tissues, with average differences of almost 30 years observed in some age clocks. In addition, most epigenetic clock estimates of blood-based tissues exhibited low correlation with estimates from oral-based tissues despite controlling for cellular proportions and other technical factors. Our findings indicate that application of blood-derived epigenetic clocks in oral-based tissues may not yield comparable estimates of epigenetic age, highlighting the need for careful consideration of tissue type when estimating epigenetic age.

genomics↗

bioRxiv2023

Functional Topography of the Neocortex Predicts Covariation in Complex Cognitive and Basic Motor Abilities

Although higher-order cognitive and lower-order sensorimotor abilities are generally regarded as distinct and studied separately, there is evidence that they not only covary but also that this covariation increases across the lifespan. This pattern has been leveraged in clinical settings where a simple assessment of sensory or motor ability (e.g., hearing, gait speed) can forecast age-related cognitive decline and risk for dementia. However, the brain mechanisms underlying cognitive, sensory, and motor covariation are largely unknown. Here, we examined whether such covariation in midlife reflects variability in common versus distinct neocortical networks using individualized maps of functional topography derived from BOLD fMRI data collected in 769 45-year old members of a population-representative cohort. Analyses revealed that variability in basic motor but not hearing ability reflected individual differences in the functional topography of neocortical networks typically supporting cognitive ability. These patterns suggest that covariation in motor and cognitive abilities in midlife reflects convergence of function in higher-order neocortical networks and that gait speed may not be simply a measure of physical function but rather an integrative index of nervous system health.

neuroscience↗

bioRxiv2022

A comparison of feature selection methodologies and learning algorithms in the development of a DNA methylation-based telomere length estimator

The field of epigenomics holds great promise in understanding and treating disease with advances in machine learning (ML) and artificial intelligence being vitally important in this pursuit. Increasingly, research now utilises DNA methylation measures at cytosine-guanine dinucleotides (CpG) to detect disease and estimate biological traits such as aging. Given the high dimensionality of DNA methylation data, feature-selection techniques are commonly employed to reduce dimensionality and identify the most important subset of features. In this study, we test and compare a range of feature-selection methods and ML algorithms in the development of a novel DNA methylation-based telomere length (TL) estimator. We found that principal component analysis in advance of elastic net regression led to the overall best performing estimator when evaluated using a nested cross-validation analysis and two independent test cohorts. In contrast, the baseline model of elastic net regression with no prior feature reduction stage performed worst - suggesting a prior feature-selection stage may have important utility. The variance in performance across tested approaches shows that estimators are sensitive to data set heterogeneity and the development of an optimal DNA methylation-based estimator should benefit from the robust methodological approach used in this study. Additionally, we observed that different DNA methylation-based TL estimators, which have few common CpGs, are associated with many of the same biological entities. Moreover, our methodology which utilises a range of feature-selection approaches and ML algorithms could be applied to other biological markers and disease phenotypes, to examine their relationship with DNA methylation and predictive value.

bioinformatics↗

bioRxiv2021

Resource Profile and User Guide of the Polygenic Index Repository

Polygenic indexes (PGIs) are DNA-based predictors. Their value for research in many scientific disciplines is rapidly growing. As a resource for researchers, we used a consistent methodology to construct PGIs for 47 phenotypes in 11 datasets. To maximize the PGIs prediction accuracies, we constructed them using genome-wide association studies--some of which are novel--from multiple data sources, including 23andMe and UK Biobank. We present a theoretical framework to help interpret analyses involving PGIs. A key insight is that a PGI can be understood as an unbiased but noisy measure of a latent variable we call the "additive SNP factor." Regressions in which the true regressor is the additive SNP factor but the PGI is used as its proxy therefore suffer from errors-in-variables bias. We derive an estimator that corrects for the bias, illustrate the correction, and make a Python tool for implementing it publicly available.

genomics↗

bioRxiv2020

Assessing the co-variability of DNA methylation across peripheral cells and tissues: implications for the interpretation of findings in epigenetic epidemiology

Summary/AbstractO_ST_ABSBackgroundC_ST_ABSMost epigenome-wide association studies (EWAS) quantify DNA methylation (DNAm) in peripheral tissues such as whole blood to identify positions in the genome where variation is statistically associated with a trait or exposure. As whole blood comprises a mix of cell types, it is unclear whether trait-associated variation is specific to an individual cellular population. MethodsWe collected three peripheral tissues (whole blood, buccal and nasal epithelial cells) from thirty individuals. Whole blood samples were subsequently processed using fluorescence-activated cell sorting (FACS) to purify five constituent cell-types (monocytes, granulocytes, CD4+ T cells, CD8+ T cells, and B cells). DNAm was profiled in all eight sample-types from each individual using the Illumina EPIC array. ResultsWe identified significant differences in both the level and variability of DNAm between different tissues and cell types, and DNAm data-derived estimates of age and smoking were found to differ dramatically across sample types from the same individual. We found that for the majority of loci variation in DNAm in individual blood cell types was only weakly predictive of variance in DNAm measured in whole blood, however, the proportion of variance explained was greater than that explained by either buccal or nasal tissues. Instead we observe that DNAm variation in whole blood is additively influenced by a combination of the major blood cell types. For a subset of sites variable DNAm detected in whole blood can be attributed to variation in a single blood cell type providing potential mechanistic insight. ConclusionsWe identified major differences in DNAm between blood cell types and peripheral tissues, with each sample type being characterized by a unique DNAm signature across multiple loci. Our results suggest that associations between whole blood DNAm and traits or exposures reflect differences in multiple cell types and provide important insights for the interpretation of EWAS performed in whole blood. Key MessagesO_LIWe identified major differences in DNA methylation between blood cell types and peripheral tissues, with each sample type being characterized by a unique DNA methylation signature across multiple loci. C_LIO_LIEstimates of DNAmAge and tobacco smoking from DNA methylation data can be highly variable across different sample types collected from the same individual at the same time. C_LIO_LIWhile individual blood cell types did predict more of the variation in whole blood compared to buccal epithelial and nasal epithelial cells, the percentage of variance explained was still small. C_LIO_LIInstead our data indicate that at the majority of sites, variation in multiple blood cell types additively combines to drive variation in DNA methylation in whole blood. C_LIO_LIThere are subset of sites where variable DNA methylation detected in whole blood can be attributed to variation in a single blood cell type. C_LI

genomics↗

bioRxiv2020

Quantification of the pace of biological aging in humans through a blood test: a DNA methylation algorithm

Biological aging is the gradual, progressive decline in system integrity that occurs with advancing chronological age, causing morbidity and disability. Measurements of the pace of aging are needed to serve as surrogate endpoints in trials of therapies designed to prevent disease by slowing biological aging. We report a blood DNA-methylation measure that is sensitive to variation in the pace of biological aging among individuals born the same year. We first modeled longitudinal change in 18 biomarkers tracking organ-system integrity across 12 years of follow-up in the Dunedin birth cohort. Rates of change in each biomarker were composited to form a measure of aging-related decline, termed Pace of Aging. Elastic-net regression was used to develop a DNA-methylation predictor of Pace of Aging, called DunedinPoAm for Dunedin (P)ace (o)f (A)ging (m)ethylation. Validation analyses showed DunedinPoAm was associated with functional decline in the Dunedin Study and more advanced biological age in the Understanding Society Study, predicted chronic disease and mortality in the Normative Aging Study, was accelerated by early-life adversity in the E-risk Study, and DunedinPoAm prediction was disrupted by caloric restriction in the CALERIE trial. DunedinPoAm generally outperformed epigenetic clocks. Findings provide proof-of-principle for DunedinPoAm as a single-time-point measure of a persons pace of biological aging.

genomics↗

bioRxiv2020

Investigating the Genetic Architecture of Non-Cognitive Skills Using GWAS-by-Subtraction

Educational attainment (EA) is influenced by cognitive abilities and by other characteristics and traits. However little is known about the genetic architecture of these "non-cognitive" contributions to EA. Here, we use Genomic Structural Equation Modelling and results of prior genome-wide association studies (GWASs) of EA (N = 1,131,881) and cognitive test performance (N = 257,841) to estimate SNP associations with variation in EA that is independent of cognitive ability. We identified 157 genome-wide significant loci and a polygenic architecture accounting for 57% of genetic variance in EA. Phenotypic and biological annotation revealed that (1) both cognitive and non-cognitive contributions to EA were genetically correlated with socioeconomic success and longevity; and (2) non-cognitive contributions to EA were related to personality, decision making, risk-behavior, and increased risk for psychiatric disorders; (3) non-cognitive and cognitive contributions to EA were enriched in the same tissues and cell types, but (4) showed different associations with gray-matter neuroimaging phenotypes.

genetics↗

bioRxiv2019

A pervasively thinner neocortex is a transdiagnostic feature of general psychopathology

ObjectiveNeuroimaging research has revealed that structural brain alterations are common across broad diagnostic families of disorders rather than specific to a single psychiatric disorder. Such overlap in the structural brain correlates of mental disorders mirrors already well-documented phenotypic comorbidity of psychiatric symptoms and diagnoses, which can be indexed by a general psychopathology or p factor. We hypothesized that if general psychopathology drives the convergence of structural alterations common across disorders then 1) there should be few associations unique to any one diagnostic family of disorders, and 2) associations with the p factor should overlap with those for the broader diagnostic families. MethodsAnalyses were conducted on structural MRI and psychopathology data collected from 861 members of the population representative Dunedin Study at age 45. ResultsStudy members with high scores across three broad diagnostic families of disorders (Externalizing, Internalizing, Thought Disorder) exhibited highly overlapping patterns of reduced global and widely distributed parcel-wise neocortical thickness. Study members with high p factor scores exhibited patterns of reduced global and parcel-wise neocortical thickness nearly identical to those associated with the three broad diagnostic families. ConclusionsA pattern of pervasively reduced neocortical thickness appears common across all forms of mental disorders and may represent a transdiagnostic feature of general psychopathology. As has been documented with regard to symptoms and diagnoses, the underlying brain structural correlates of mental disorders may not exhibit specificity, the continued pursuit of which may limit progress toward more effective strategies for etiological understanding, prevention, and intervention.

neuroscience↗

bioRxiv2019

Brain-age in midlife is associated with accelerated biological aging and cognitive decline in a longitudinal birth-cohort

An individuals brain-age is the difference between chronological age and age predicted from machine-learning models of brain-imaging data. Brain-age has been proposed as a biomarker of age-related deterioration of the brain. Having an older brain-age has been linked to Alzheimers, dementia and mortality. However, these findings are largely based on cross-sectional associations which can confuse age differences with cohort differences. To illuminate the validity of brain-age a biomarker of accelerated brain aging, a study is needed of a large cohort all born the same year who nevertheless vary on brain-age. In a population-representative 1972-73 birth cohort we measured brain-age at age 45, as well as the pace of biological aging and cognitive decline in longitudinal data from childhood to midlife (N=869). In this cohort, all chronological age 45 years, brain-age was measured reliably (ICC=.81) and ranged from 24 to 72 years. Those with older midlife brain-ages tended to have poorer cognitive function in both adulthood and childhood, as well as impaired brain health at age 3. Furthermore, those with older brain-ages had an accelerated pace of biological aging, older facial appearance and early signs of cognitive decline from childhood to midlife. These findings help to validate brain-age as a potential surrogate biomarker for midlife intervention studies that seek to measure treatment response to dementia-prevention efforts in midlife. However, the findings also caution against the assumption that brain-age scores represent only age-related deterioration of the brain as they may also index central nervous system variation present since childhood.

neuroscience↗

bioRxiv2019

White matter hyperintensities are common in midlife and already associated with cognitive decline

White matter hyperintensities (WMHs) proliferate as the brain ages and are associated with increased risk for cognitive decline as well as Alzheimers disease and related dementias. As such, WMHs have been targeted as a surrogate biomarker in intervention trials with older adults. However, it is unclear at what stage of aging WMHs begin to relate to cognition and if they may be a viable target for early prevention. In a population-representative birth cohort of 843 45-year-olds we measured WMHs using T2-weighted MRI, and we assessed cognitive decline from childhood to midlife. We found that WMHs were common at age 45 and that WMH volume was modestly associated with both lower childhood ({beta}=-0.08, p=0.013) and adult IQ ({beta}=-0.15, p<0.001). Moreover, WMH volume was associated with greater cognitive decline from childhood to midlife ({beta}=-0.09, p<0.001). Our results demonstrate that a link between WMHs and early signs of cognitive decline is detectable decades before clinical symptoms of dementia emerge. Thus, WMHs may be a useful surrogate biomarker for identifying individuals in midlife at risk for future accelerated cognitive decline and selecting participants for dementia prevention trials.

neuroscience↗

bioRxiv2019

Poor test-retest reliability of task-fMRI: New empirical evidence and a meta-analysis

Identifying brain biomarkers of disease risk is a growing priority in neuroscience. The ability to identify meaningful biomarkers is limited by measurement reliability; unreliable measures are unsuitable for predicting clinical outcomes. Measuring brain activity using task-fMRI is a major focus of biomarker development; however, the reliability of task-fMRI has not been systematically evaluated. We present converging evidence demonstrating poor reliability of task-fMRI measures. First, a meta-analysis of 90 experiments (N=1,008) revealed poor overall reliability (mean ICC=.397). Second, the test-retest reliabilities of activity in a priori regions of interest across 11 common fMRI tasks collected in the context of the Human Connectome Project (N=45) and the Dunedin Study (N=20) were poor (ICCs=.067-.485). Collectively, these findings demonstrate that common task-fMRI measures are not currently suitable for brain biomarker discovery or individual differences research. We review how this state of affairs came to be and highlight avenues for improving task-fMRI reliability.

neuroscience↗

bioRxiv2019

Reproducibility of Structural Brain Alterations Associated with Transdiagnostic Risk for Mental Illness: Evidence from a Population-Representative Birth Cohort

Transdiagnostic research has identified a general psychopathology factor - often called the p factor - that accounts for shared variation across internalizing, externalizing, and thought disorders in diverse samples. It has been argued that the p factor may reflect dysfunctional thinking present in serious mental illness. In support of this, we previously used a theory-free, data-driven multimodal neuroimaging approach to find that higher p factor scores are associated with structural alterations within a cerebello-thalamo-cortical circuit (CTCC) and visual association cortex, both of which are important for monitoring and coordinating information processing in the service of executive control. Here we attempt to replicate these associations by conducting region-of-interest analyses of CTCC and visual association cortex using data from 875 members of the Dunedin Longitudinal Study, a five-decade study of a population-representative birth cohort now 45 years old. We further sought to replicate a more recent report that p factor scores can be predicted by patterns of distributed cerebellar morphology as estimated through independent component analysis. We successfully replicated associations between higher p factor scores and both reduced grey matter volume of the visual association cortex and fractional anisotropy of pontine white matter pathways within the CTCC. In contrast, we failed to replicate prior associations between cerebellar structure and p factor scores. Collectively, our findings encourage further focus on the CTCC and visual association cortex as core neural substrates and potential biomarkers of general psychopathology.

neuroscience↗

bioRxiv2018

Using DNA from mothers and children to study parental investment in children’s educational attainment

This study tested implications of new genetic discoveries for understanding the association between parental investment and childrens educational attainment. A novel design matched genetic data from 860 British mothers and their children with home-visit measures of parenting: the E-Risk Study. Three findings emerged. First, both mothers and childrens education-associated genetics, summarized in a genome-wide polygenic score, predicted parenting -- a gene-environment correlation. Second, accounting for genetic influences slightly reduced associations between parenting and childrens attainment -- indicating some genetic confounding. Third, mothers genetics influenced childrens attainment over and above genetic mother-to-child transmission, via cognitively-stimulating parenting -- an environmentally-mediated effect. Findings imply that, when interpreting parents effects on children, environmentalists must consider genetic transmission, but geneticists must also consider environmental transmission.

genetics↗