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Biology subjects

Moffitt, T. E.

Publications and source records attributed to Moffitt, T. E..

17 recordsLinked to original sources

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↗

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↗

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↗

Associations between thinner retinal neuronal layers and suboptimal brain structural integrity: Are the eyes a window to the brain?

We investigated the extent to which measures of retinal neuronal thickness capture variability in the structural integrity of the brain in a large population-based cohort followed from birth to midlife. Using data from the Dunedin Multidisciplinary Health and Development Study (n=1037; analytic n=828, aged 45 years), we specifically tested for associations between optical coherence tomography-measured retinal neuronal layers and MRI-measured structural brain integrity. We found that Study members who had thinner retinal neuronal layers had thinner average cortex, smaller total cortical surface area, smaller subcortical grey matter volumes, larger volume of white matter hyperintensities as well as older looking brains. This suggests that retinal neuronal thickness reflects differences in midlife structural brain integrity consistent with accelerated cognitive decline and increased risk for later dementia, further supporting the proposition that the retina may be a biomarker of brain aging as early as midlife.

neuroscience↗

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↗

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↗

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↗

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↗

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↗

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↗

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↗

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↗

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↗

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↗

Genetics & the Geography of Health, Behavior, and Attainment

Peoples life chances can be predicted by their neighborhoods. This observation is driving efforts to improve lives by changing neighborhoods. Some neighborhood effects may be causal, supporting neighborhood-level interventions. Other neighborhood effects may reflect selection of families with different characteristics into different neighborhoods, supporting interventions that target families/individuals directly. To test how selection affects different neighborhood-linked problems, we linked neighborhood data with genetic, health, and social-outcome data for >7,000 European-descent UK and US young people in the E-Risk and Add Health Studies. We tested selection/concentration of genetic risks for obesity, schizophrenia, teen-pregnancy, and poor educational outcomes in high-risk neighborhoods, including genetic analysis of neighborhood mobility. Findings argue against genetic selection/concentration as an explanation for neighborhood gradients in obesity and mental-health problems, suggesting neighborhoods may be causal. In contrast, modest genetic selection/concentration was evident for teen-pregnancy and poor educational outcomes, suggesting neighborhood effects for these outcomes should be interpreted with care.

genetics↗

General Functional Connectivity: shared features of resting-state and task fMRI drive reliable individual differences in functional brain networks

Intrinsic connectivity, measured using resting-state fMRI, has emerged as a fundamental tool in the study of the human brain. However, due to practical limitations, many studies do not collect enough resting-state data to generate reliable measures of intrinsic connectivity necessary for studying individual differences. Here we present general functional connectivity (GFC) as a method for leveraging shared features across resting-state and task fMRI and demonstrate in the Human Connectome Project and the Dunedin Study that GFC offers better test-retest reliability than intrinsic connectivity estimated from the same amount of resting-state data alone. Furthermore, at equivalent scan lengths, GFC displays higher heritability on average than resting-state functional connectivity. We also show that predictions of cognitive ability from GFC generalize across datasets, performing as well or better than resting-state or task data alone. Collectively, our work suggests that GFC can improve the reliability of intrinsic connectivity estimates in existing datasets and, subsequently, the opportunity to identify meaningful correlates of individual differences in behavior. Given that task and resting-state data are often collected together, many researchers can immediately derive more reliable measures of intrinsic connectivity through the adoption of GFC rather than solely using resting-state data. Moreover, by better capturing heritable variation in intrinsic connectivity, GFC represents a novel endophenotype with broad applications in clinical neuroscience and biomarker discovery.

neuroscience↗

A polygenic p factor for major psychiatric disorders

It has recently been proposed that a single dimension, called the p factor, can capture a persons liability to mental disorder. Relevant to the p hypothesis, recent genetic research has found surprisingly high genetic correlations between pairs of psychiatric disorders. Here, for the first time we compare genetic correlations from different methods and examine their support for a genetic p factor. We tested the hypothesis of a genetic p factor by using principal component analysis on matrices of genetic correlations between major psychiatric disorders estimated by three methods - family study, Genome-wide Complex Trait Analysis, and Linkage-Disequilibrium Score Regression - and on a matrix of polygenic score correlations constructed for each individual in a UK-representative sample of 7,026 unrelated individuals. All disorders loaded on a first unrotated principal component, which accounted for 57%, 43%, 34% and 19% of the variance respectively for each method. Our results showed that all four methods provided strong support for a genetic p factor that represents the pinnacle of the hierarchical genetic architecture of psychopathology.

genomics↗