Association of whole-person eigen-polygenic risk scores with Alzheimer's disease.

Late-Onset Alzheimer's Disease (LOAD) is a heterogeneous neurodegenerative disorder with complex etiology and high heritability. Its multifactorial risk profile and large portions of unexplained heritability suggest the involvement of yet unidentified genetic risk factors. Here we describe the "whole person" genetic risk landscape of polygenic risk scores for 2218 traits in 2044 elderly individuals and test if novel eigen-PRSs derived from clustered subnetworks of single-trait PRSs can improve the prediction of LOAD diagnosis, rates of cognitive decline, and canonical LOAD neuropathology. Network analyses revealed distinct clusters of PRSs with clinical and biological interpretability. Novel eigen-PRSs (ePRS) from these clusters significantly improved LOAD-related phenotypes prediction over current state-of-the-art LOAD PRS models. Notably, an ePRS representing clusters of traits related to cholesterol levels was able to improve variance explained in a model of the brain-wide beta-amyloid burden by 1.7% (likelihood ratio test P = 9.02 × 10-7). All associations of ePRS with LOAD phenotypes were eliminated by the removal of APOE-proximal loci. However, our association analysis identified modules characterized by PRSs of high cholesterol and LOAD. We believe this is due to the influence of the APOE region from both PRSs. We found significantly higher mean SNP effects for LOAD in the intersecting APOE region SNPs. Combining genetic risk factors for vascular traits and dementia could improve current single-trait PRS models of LOAD, enhancing the use of PRS in risk stratification. Our results are catalogued for the scientific community, to aid in generating new hypotheses based on our maps of clustered PRSs and associations with LOAD-related phenotypes.

Person-specific differences in ubiquitin-proteasome mediated proteostasis in human neurons.

BACKGROUND: Impairment of the ubiquitin-proteasome system (UPS) has been implicated in abnormal protein accumulation in Alzheimer's disease. It remains unclear if genetic variation affects the intrinsic properties of neurons that render some individuals more vulnerable to UPS impairment. METHODS: Induced pluripotent stem cell (iPSC)-derived neurons were generated from over 50 genetically variant and highly characterized participants of cohorts of aging. Proteomic profiling, proteasome activity assays, and Western blotting were employed to examine neurons at baseline and in response to UPS perturbation. RESULTS: Neurons with lower basal UPS activity were more vulnerable to tau accumulation following mild UPS inhibition. Chronic reduction in proteasome activity in human neurons induced compensatory elevation of regulatory proteins involved in proteostasis and several proteasome subunits. DISCUSSION: These findings reveal that genetic variation influences basal UPS activity in human neurons and differentially sensitizes them to external factors perturbing the UPS, leading to the accumulation of aggregation-prone proteins such as tau. HIGHLIGHTS: Polygenic risk score for AD is associated with the ubiquitin-proteasome system (UPS) in neurons. Basal proteasome activity correlates with aggregation-prone protein levels in neurons. Genetic variation affects the response to proteasome inhibition in neurons. Neuronal proteasome perturbation induces an elevation in specific proteins involved in proteostasis. Low basal proteasome activity leads to enhanced tau accumulation with UPS challenge.

CHRNA5 links chandelier cells to severity of amyloid pathology in aging and Alzheimer's disease.

Changes in high-affinity nicotinic acetylcholine receptors are intricately connected to neuropathology in Alzheimer's Disease (AD). Protective and cognitive-enhancing roles for the nicotinic α5 subunit have been identified, but this gene has not been closely examined in the context of human aging and dementia. Therefore, we investigate the nicotinic α5 gene CHRNA5 and the impact of relevant single nucleotide polymorphisms (SNPs) in prefrontal cortex from 922 individuals with matched genotypic and post-mortem RNA sequencing in the Religious Orders Study and Memory and Aging Project (ROS/MAP). We find that a genotype robustly linked to increased expression of CHRNA5 (rs1979905A2) predicts significantly reduced cortical ß-amyloid load. Intriguingly, co-expression analysis suggests CHRNA5 has a distinct cellular expression profile compared to other nicotinic receptor genes. Consistent with this prediction, single nucleus RNA sequencing from 22 individuals reveals CHRNA5 expression is disproportionately elevated in chandelier neurons, a distinct subtype of inhibitory neuron known for its role in excitatory/inhibitory (E/I) balance. We show that chandelier neurons are enriched in amyloid-binding proteins compared to basket cells, the other major subtype of PVALB-positive interneurons. Consistent with the hypothesis that nicotinic receptors in chandelier cells normally protect against ß-amyloid, cell-type proportion analysis from 549 individuals reveals these neurons show amyloid-associated vulnerability only in individuals with impaired function/trafficking of nicotinic α5-containing receptors due to homozygosity of the missense CHRNA5 SNP (rs16969968A2). Taken together, these findings suggest that CHRNA5 and its nicotinic α5 subunit exert a neuroprotective role in aging and Alzheimer's disease centered on chandelier interneurons.

Interactions between genetic risk for 21 neurodevelopmental and psychiatric disorders and sport activity on youth mental health.

Childhood is a sensitive period where behavioral disturbances, determined by genetics and environmental factors including sport activity, may emerge and impact risk of mental illness in adulthood. We aimed to determine if participation in sports can mitigate genetic risk for neurodevelopmental and psychiatric disorders in youth. We analyzed 4975 unrelated European youth (ages 9-10) from the Adolescent Brain Cognitive Development Study. Our outcomes were eight Child Behavior Checklist (CBCL) scores, measured annually. Polygenic risk scores (PRSs) were calculated for 21 disorders, and sport frequency and type were summarized. PRSs and sport variables were tested for main effects and interactions against CBCL outcomes using linear models. Cross-sectionally, PRSs for attention-deficit/hyperactivity disorder and major depressive disorder were associated with increases in multiple CBCL outcomes. Participation in non-contact or team sports, as well as more frequent sport participation reduced all cross-sectional CBCL outcomes, whereas involvement in contact sports increased attention problems and rule-breaking behavior. Interactions revealed that more frequent exercise was significantly associated with less rule breaking behavior in individuals with high genetic risk for obsessive compulsive disorder. Associations with longitudinal CBCL outcomes demonstrated weaker effects. We highlight the importance of genetic context when considering sports as an intervention for early life behavioural problems.

Unbiased classification of the elderly human brain proteome resolves distinct clinical and pathophysiological subtypes of cognitive impairment.

Cognitive impairment in the elderly features complex molecular pathophysiology extending beyond the hallmark pathologies of traditional disease classification. Molecular subtyping using large-scale -omic strategies can help resolve this biological heterogeneity. Using quantitative mass spectrometry, we measured ∼8000 proteins across >600 dorsolateral prefrontal cortex tissues with clinical diagnoses of no cognitive impairment (NCI), mild cognitive impairment (MCI), and Alzheimer's disease (AD) dementia. Unbiased classification of MCI and AD cases based on individual proteomic profiles resolved three classes with expression differences across numerous cell types and biological ontologies. Two classes displayed molecular signatures atypical of AD neurodegeneration, such as elevated synaptic and decreased inflammatory markers. In one class, these atypical proteomic features were associated with clinical and pathological hallmarks of cognitive resilience. We were able to replicate these classes and their clinicopathological phenotypes across two additional tissue cohorts. These results promise to better define the molecular heterogeneity of cognitive impairment and meaningfully impact its diagnostic and therapeutic precision.

Testing a Polygenic Risk Score for Morphological Microglial Activation in Alzheimer's Disease and Aging.

BACKGROUND: Neuroinflammation and the activation of microglial cells are among the earliest events in Alzheimer's disease (AD). However, direct observation of microglia in living people is not currently possible. Here, we indexed the heritable propensity for neuroinflammation with polygenic risk scores (PRS), using results from a recent genome-wide analysis of a validated post-mortem measure of morphological microglial activation. OBJECTIVE: We sought to determine whether a PRS for microglial activation (PRSmic) could augment the predictive performance of existing AD PRSs for late-life cognitive impairment. METHODS: First, PRSmic were calculated and optimized in a calibration cohort (Alzheimer's Disease Neuroimaging Initiative (ADNI), n = 450), with resampling. Second, predictive performance of optimal PRSmic was assessed in two independent, population-based cohorts (total n = 212,237). Finally, we explored associations of PRSmic with a comprehensive set of imaging and fluid AD biomarkers in ADNI. RESULTS: Our PRSmic showed no significant improvement in predictive power for either AD diagnosis or cognitive performance in either external cohort. Some nominal associations were found in ADNI, but with inconsistent effect directions. CONCLUSION: While genetic scores capable of indexing risk for neuroinflammatory processes in aging are highly desirable, more well-powered genome-wide studies of microglial activation are required. Further, biobank-scale studies would benefit from phenotyping of proximal neuroinflammatory processes to improve the PRS development phase.

Diagnostic classification of childhood cancer using multiscale transcriptomics.

The causes of pediatric cancers' distinctiveness compared to adult-onset tumors of the same type are not completely clear and not fully explained by their genomes. In this study, we used an optimized multilevel RNA clustering approach to derive molecular definitions for most childhood cancers. Applying this method to 13,313 transcriptomes, we constructed a pediatric cancer atlas to explore age-associated changes. Tumor entities were sometimes unexpectedly grouped due to common lineages, drivers or stemness profiles. Some established entities were divided into subgroups that predicted outcome better than current diagnostic approaches. These definitions account for inter-tumoral and intra-tumoral heterogeneity and have the potential of enabling reproducible, quantifiable diagnostics. As a whole, childhood tumors had more transcriptional diversity than adult tumors, maintaining greater expression flexibility. To apply these insights, we designed an ensemble convolutional neural network classifier. We show that this tool was able to match or clarify the diagnosis for 85% of childhood tumors in a prospective cohort. If further validated, this framework could be extended to derive molecular definitions for all cancer types.

Testing a polygenic risk score for morphological microglial activation in Alzheimer's disease and aging.

Neuroinflammation and the activation of microglial cells are among the earliest events in Alzheimer's disease (AD). However, direct observation of microglia in living people is not currently possible. Here, we indexed the heritable propensity for neuroinflammation with polygenic risk scores (PRS), using results from a recent genome-wide analysis of a validated post-mortem measure of morphological microglial activation. We sought to determine whether a PRS for microglial activation (PRS mic ) could augment the predictive performance of existing AD PRSs for late-life cognitive impairment. First, PRS mic were calculated and optimized in a calibration cohort (Alzheimer's Disease Neuroimaging Initiative (ADNI), n=450), with resampling. Second, predictive performance of optimal PRS mic was assessed in two independent, population-based cohorts (total n=212,237). Our PRS mic showed no significant improvement in predictive power for either AD diagnosis or cognitive performance. Finally, we explored associations of PRS mic with a comprehensive set of imaging and fluid AD biomarkers in ADNI. This revealed some nominal associations, but with inconsistent effect directions. While genetic scores capable of indexing risk for neuroinflammatory processes in aging are highly desirable, more well-powered genome-wide studies of microglial activation are required. Further, biobank-scale studies would benefit from phenotyping of proximal neuroinflammatory processes to improve the PRS development phase.

Evidence for the biopsychosocial model of suicide: a review of whole person modeling studies using machine learning.

Background: Traditional approaches to modeling suicide-related thoughts and behaviors focus on few data types from often-siloed disciplines. While psychosocial aspects of risk for these phenotypes are frequently studied, there is a lack of research assessing their impact in the context of biological factors, which are important in determining an individual's fulsome risk profile. To directly test this biopsychosocial model of suicide and identify the relative importance of predictive measures when considered together, a transdisciplinary, multivariate approach is needed. Here, we systematically review the emerging literature on large-scale studies using machine learning to integrate measures of psychological, social, and biological factors simultaneously in the study of suicide. Methods: We conducted a systematic review of studies that used machine learning to model suicide-related outcomes in human populations including at least one predictor from each of biological, psychological, and sociological data domains. Electronic databases MEDLINE, EMBASE, PsychINFO, PubMed, and Web of Science were searched for reports published between August 2013 and August 30, 2023. We evaluated populations studied, features emerging most consistently as risk or resilience factors, methods used, and strength of evidence for or against the biopsychosocial model of suicide. Results: Out of 518 full-text articles screened, we identified a total of 20 studies meeting our inclusion criteria, including eight studies conducted in general population samples and 12 in clinical populations. Common important features identified included depressive and anxious symptoms, comorbid psychiatric disorders, social behaviors, lifestyle factors such as exercise, alcohol intake, smoking exposure, and marital and vocational status, and biological factors such as hypothalamic-pituitary-thyroid axis activity markers, sleep-related measures, and selected genetic markers. A minority of studies conducted iterative modeling testing each data type for contribution to model performance, instead of reporting basic measures of relative feature importance. Conclusion: Studies combining biopsychosocial measures to predict suicide-related phenotypes are beginning to proliferate. This literature provides some early empirical evidence for the biopsychosocial model of suicide, though it is marred by harmonization challenges. For future studies, more specific definitions of suicide-related outcomes, inclusion of a greater breadth of biological data, and more diversity in study populations will be needed.