The role of circadian rhythms and sleep in the aetiology of autism spectrum disorder and attention-deficit/hyperactivity disorder: New evidence from bidirectional two-sample Mendelian randomization analysis.

LAY ABSTRACT: Research shows that people with autism spectrum disorder and attention-deficit/hyperactivity disorder often have sleep issues and problems with the body's natural daily rhythms, known as circadian rhythms. By exploring the genetic variants associated with these rhythms and the conditions, this study reveals that these rhythm changes and sleep patterns are directly linked to autism spectrum disorder and attention-deficit/hyperactivity disorder. It found that the timing of one's most active hours can increase the likelihood of having both autism spectrum disorder and attention-deficit/hyperactivity disorder. Importantly, it also shows that good sleep quality might protect against autism spectrum disorder, while disturbed sleep in people with attention-deficit/hyperactivity disorder seems to be a result rather than the cause of the condition. This understanding can help doctors and researchers develop better treatment approaches that focus on the specific ways sleep and body rhythms affect those with autism spectrum disorder and attention-deficit/hyperactivity disorder, considering their unique associations with circadian rhythms and sleep patterns. Understanding these unique links can lead to more effective, personalized care for those affected by these conditions.

Gene Association Study of the Urokinase Plasminogen Activator and Its Receptor Gene in Alzheimer's Disease.

Background: The role of the innate immune system has long been associated with Alzheimer's disease (AD). There is now accumulating evidence that the soluble Urokinase Plasminogen Activator Receptor pathway, and its genes, PLAU and PLAUR may be important in AD, and yet there have been few genetic association studies to explore this. Objective: This study utilizes the DNA bank of the Brains for Dementia Research cohort to investigate the genetic association of common polymorphisms across the PLAU and PLAUR genes with AD. Methods: TaqMan genotyping assays were used with standard procedures followed by association analysis in PLINK. Results: No association was observed between the PLAU gene and AD; however, two SNPs located in the PLAUR gene were indicative of a trend towards association but did not surpass multiple testing significance thresholds. Conclusions: Further genotyping studies and exploration of the consequences of these SNPs on gene expression and alternative splicing are warranted to fully uncover the role this system may have in AD.

Dementias Platform UK: Bringing genetics into life.

INTRODUCTION: The Dementias Platform UK (DPUK) Data Portal is a data repository bringing together a wide range of cohorts. Neurodegenerative dementias are a group of diseases with highly heterogeneous pathology and an overlapping genetic component that is poorly understood. The DPUK collection of independent cohorts can facilitate research in neurodegeneration by combining their genetic and phenotypic data. METHODS: For genetic data processing, pipelines were generated to perform quality control analysis, genetic imputation, and polygenic risk score (PRS) derivation with six genome-wide association studies of neurodegenerative diseases. Pipelines were applied to five cohorts. DISCUSSION: The data processing pipelines, research-ready imputed genetic data, and PRS scores are now available on the DPUK platform and can be accessed upon request though the DPUK application process. Harmonizing genome-wide data for multiple datasets increases scientific opportunity and allows the wider research community to access and process data at scale and pace.

Dementia with Lewy Bodies: Genomics, Transcriptomics, and Its Future with Data Science.

Dementia with Lewy bodies (DLB) is a significant public health issue. It is the second most common neurodegenerative dementia and presents with severe neuropsychiatric symptoms. Genomic and transcriptomic analyses have provided some insight into disease pathology. Variants within SNCA, GBA, APOE, SNCB, and MAPT have been shown to be associated with DLB in repeated genomic studies. Transcriptomic analysis, conducted predominantly on candidate genes, has identified signatures of synuclein aggregation, protein degradation, amyloid deposition, neuroinflammation, mitochondrial dysfunction, and the upregulation of heat-shock proteins in DLB. Yet, the understanding of DLB molecular pathology is incomplete. This precipitates the current clinical position whereby there are no available disease-modifying treatments or blood-based diagnostic biomarkers. Data science methods have the potential to improve disease understanding, optimising therapeutic intervention and drug development, to reduce disease burden. Genomic prediction will facilitate the early identification of cases and the timely application of future disease-modifying treatments. Transcript-level analyses across the entire transcriptome and machine learning analysis of multi-omic data will uncover novel signatures that may provide clues to DLB pathology and improve drug development. This review will discuss the current genomic and transcriptomic understanding of DLB, highlight gaps in the literature, and describe data science methods that may advance the field.

Evaluating the Classification Accuracy of Expression Quantitative Trait Loci Calculated Polygenic Risk Scores in Alzheimer's Disease.

Polygenic risk scores (PRS) hold promise for the early identification of those at risk from neurodegenerative disorders such as Alzheimer's Disease (AD), allowing for intervention to occur prior to neuronal damage. The current selection of informative single nucleotide polymorphisms (SNPs) to generate the risk scores is based on the modelling of large genome-wide association data using significance thresholds. However, the biological relevance of these SNPs is largely unknown. This study, in contrast, aims to identify SNPs with biological relevance to AD and then assess them for their ability to accurately classify cases and controls. Samples selected from the Brains for Dementia Research (BDR) were used to produce gene expression data to identify potential expression quantitative trait loci (eQTLs) relevant to AD. These SNPs were then incorporated into a PRS model to classify AD and controls in the full BDR cohort. Models derived from these eQTLs demonstrate modest classification potential with an accuracy between 61% and 67%. Although the model accuracy is not as high as some values in the literature based on significance thresholds from genome-wide association studies, these models may reflect a more biologically relevant model, which may provide novel targets for therapeutic intervention.

Age-related changes in tau and autophagy in human brain in the absence of neurodegeneration.

Tau becomes abnormally hyper-phosphorylated and aggregated in tauopathies like Alzheimers disease (AD). As age is the greatest risk factor for developing AD, it is important to understand how tau protein itself, and the pathways implicated in its turnover, change during aging. We investigated age-related changes in total and phosphorylated tau in brain samples from two cohorts of cognitively normal individuals spanning 19-74 years, without overt neurodegeneration. One cohort utilised resected tissue and the other used post-mortem tissue. Total soluble tau levels declined with age in both cohorts. Phosphorylated tau was undetectable in the post-mortem tissue but was clearly evident in the resected tissue and did not undergo significant age-related change. To ascertain if the decline in soluble tau was correlated with age-related changes in autophagy, three markers of autophagy were tested but only two appeared to increase with age and the third was unchanged. This implies that in individuals who do not develop neurodegeneration, there is an age-related reduction in soluble tau which could potentially be due to age-related changes in autophagy. Thus, to explore how an age-related increase in autophagy might influence tau-mediated dysfunctions in vivo, autophagy was enhanced in a Drosophila model and all age-related tau phenotypes were significantly ameliorated. These data shed light on age-related physiological changes in proteins implicated in AD and highlights the need to study pathways that may be responsible for these changes. It also demonstrates the therapeutic potential of interventions that upregulate turnover of aggregate-prone proteins during aging.

DNA methylation signatures of Alzheimer's disease neuropathology in the cortex are primarily driven by variation in non-neuronal cell-types.

Alzheimer's disease (AD) is a chronic neurodegenerative disease characterized by the progressive accumulation of amyloid-beta and neurofibrillary tangles of tau in the neocortex. We profiled DNA methylation in two regions of the cortex from 631 donors, performing an epigenome-wide association study of multiple measures of AD neuropathology. We meta-analyzed our results with those from previous studies of DNA methylation in AD cortex (total n = 2013 donors), identifying 334 cortical differentially methylated positions (DMPs) associated with AD pathology including methylomic variation at loci not previously implicated in dementia. We subsequently profiled DNA methylation in NeuN+ (neuronal-enriched), SOX10+ (oligodendrocyte-enriched) and NeuN-/SOX10- (microglia- and astrocyte-enriched) nuclei, finding that the majority of DMPs identified in 'bulk' cortex tissue reflect DNA methylation differences occurring in non-neuronal cells. Our study highlights the power of utilizing multiple measures of neuropathology to identify epigenetic signatures of AD and the importance of characterizing disease-associated variation in purified cell-types.

A genetic link between risk for Alzheimer's disease and severe COVID-19 outcomes via the OAS1 gene.

Recently, we reported oligoadenylate synthetase 1 (OAS1) contributed to the risk of Alzheimer's disease, by its enrichment in transcriptional networks expressed by microglia. However, the function of OAS1 within microglia was not known. Using genotyping from 1313 individuals with sporadic Alzheimer's disease and 1234 control individuals, we confirm the OAS1 variant, rs1131454, is associated with increased risk for Alzheimer's disease. The same OAS1 locus has been recently associated with severe coronavirus disease 2019 (COVID-19) outcomes, linking risk for both diseases. The single nucleotide polymorphisms rs1131454(A) and rs4766676(T) are associated with Alzheimer's disease, and rs10735079(A) and rs6489867(T) are associated with severe COVID-19, where the risk alleles are linked with decreased OAS1 expression. Analysing single-cell RNA-sequencing data of myeloid cells from Alzheimer's disease and COVID-19 patients, we identify co-expression networks containing interferon (IFN)-responsive genes, including OAS1, which are significantly upregulated with age and both diseases. In human induced pluripotent stem cell-derived microglia with lowered OAS1 expression, we show exaggerated production of TNF-α with IFN-γ stimulation, indicating OAS1 is required to limit the pro-inflammatory response of myeloid cells. Collectively, our data support a link between genetic risk for Alzheimer's disease and susceptibility to critical illness with COVID-19 centred on OAS1, a finding with potential implications for future treatments of Alzheimer's disease and COVID-19, and development of biomarkers to track disease progression.

Genome-wide association findings from the brains for dementia research cohort.

The Brains for Dementia Research (BDR) cohort (~3200) is a longitudinal clinicopathological programme, complimented with genetic analysis for the purposes of aetiological investigation into dementia. Here the data from genetic association analyses are presented from the initial collection of DNA from the BDR cohort. The aim of this study was to investigate the preliminary association signals for pathologically confirmed Alzheimer's disease samples compared to controls with no other pathology (n = 520). Genome-wide genotyping was carried out using the NeuroChip platform. Analysis utilised the standard PLINK software for association studies. Genome-wide Bonferroni significant association were observed on chr19 around the APOE/TOMM40 locus across 2 distinct linkage disequilibrium blocks. Eleven of the top 35 association signals have been identified in previous studies, in addition to an intriguing SNP association within the FPR1 gene locus. This study suggests the BDR is genetically comparable to other Alzheimer's disease cohorts and offers an independent resource to verify findings, and additional genetic data for meta-analyses.

Genetic variants in glutamate-, Aß-, and tau-related pathways determine polygenic risk for Alzheimer's disease.

Synapse loss is an early event in late-onset Alzheimer's disease (LOAD). In this study, we have assessed the capacity of a polygenic risk score (PRS) restricted to synapse-encoding loci to predict LOAD. We used summary statistics from the International Genetics of Alzheimer's Project genome-wide association meta-analysis of 74,046 patients for model construction and tested the "synaptic PRS" in 2 independent data sets of controls and pathologically confirmed LOAD. The mean synaptic PRS was 2.3-fold higher in LOAD than that in controls (p < 0.0001) with a predictive accuracy of 72% in the target data set (n = 439) and 73% in the validation data set (n = 136), a 5%-6% improvement compared with the APOE locus (p < 0.00001). The model comprises 8 variants from 4 previously identified (BIN1, PTK2B, PICALM, APOE) and 2 novel (DLG2, MINK1) LOAD loci involved in glutamate signaling (p = 0.01) or APP catabolism or tau binding (p = 0.005). As the simplest PRS model with good predictive accuracy to predict LOAD, we conclude that synapse-encoding genes are enriched for LOAD risk-modifying loci. The synaptic PRS could be used to identify individuals at risk of LOAD before symptom onset.

Genetic risk for Alzheimer's disease influences neuropathology via multiple biological pathways.

Alzheimer's disease is a highly heritable, common neurodegenerative disease characterized neuropathologically by the accumulation of ß-amyloid plaques and tau-containing neurofibrillary tangles. In addition to the well-established risk associated with the APOE locus, there has been considerable success in identifying additional genetic variants associated with Alzheimer's disease. Major challenges in understanding how genetic risk influences the development of Alzheimer's disease are clinical and neuropathological heterogeneity, and the high level of accompanying comorbidities. We report a multimodal analysis integrating longitudinal clinical and cognitive assessment with neuropathological data collected as part of the Brains for Dementia Research study to understand how genetic risk factors for Alzheimer's disease influence the development of neuropathology and clinical performance. Six hundred and ninety-three donors in the Brains for Dementia Research cohort with genetic data, semi-quantitative neuropathology measurements, cognitive assessments and established diagnostic criteria were included in this study. We tested the association of APOE genotype and Alzheimer's disease polygenic risk score-a quantitative measure of genetic burden-with survival, four common neuropathological features in Alzheimer's disease brains (neurofibrillary tangles, ß-amyloid plaques, Lewy bodies and transactive response DNA-binding protein 43 proteinopathy), clinical status (clinical dementia rating) and cognitive performance (Mini-Mental State Exam, Montreal Cognitive Assessment). The APOE ε4 allele was significantly associated with younger age of death in the Brains for Dementia Research cohort. Our analyses of neuropathology highlighted two independent pathways from APOE ε4, one where ß-amyloid accumulation co-occurs with the development of tauopathy, and a second characterized by direct effects on tauopathy independent of ß-amyloidosis. Although we also detected association between APOE ε4 and dementia status and cognitive performance, these were all mediated by tauopathy, highlighting that they are a consequence of the neuropathological changes. Analyses of polygenic risk score identified associations with tauopathy and ß-amyloidosis, which appeared to have both shared and unique contributions, suggesting that different genetic variants associated with Alzheimer's disease affect different features of neuropathology to different degrees. Taken together, our results provide insight into how genetic risk for Alzheimer's disease influences both the clinical and pathological features of dementia, increasing our understanding about the interplay between APOE genotype and other genetic risk factors.

Recalibrating the epigenetic clock: implications for assessing biological age in the human cortex.

Human DNA methylation data have been used to develop biomarkers of ageing, referred to as 'epigenetic clocks', which have been widely used to identify differences between chronological age and biological age in health and disease including neurodegeneration, dementia and other brain phenotypes. Existing DNA methylation clocks have been shown to be highly accurate in blood but are less precise when used in older samples or in tissue types not included in training the model, including brain. We aimed to develop a novel epigenetic clock that performs optimally in human cortex tissue and has the potential to identify phenotypes associated with biological ageing in the brain. We generated an extensive dataset of human cortex DNA methylation data spanning the life course (n = 1397, ages = 1 to 108 years). This dataset was split into 'training' and 'testing' samples (training: n = 1047; testing: n = 350). DNA methylation age estimators were derived using a transformed version of chronological age on DNA methylation at specific sites using elastic net regression, a supervised machine learning method. The cortical clock was subsequently validated in a novel independent human cortex dataset (n = 1221, ages = 41 to 104 years) and tested for specificity in a large whole blood dataset (n = 1175, ages = 28 to 98 years). We identified a set of 347 DNA methylation sites that, in combination, optimally predict age in the human cortex. The sum of DNA methylation levels at these sites weighted by their regression coefficients provide the cortical DNA methylation clock age estimate. The novel clock dramatically outperformed previously reported clocks in additional cortical datasets. Our findings suggest that previous associations between predicted DNA methylation age and neurodegenerative phenotypes might represent false positives resulting from clocks not robustly calibrated to the tissue being tested and for phenotypes that become manifest in older ages. The age distribution and tissue type of samples included in training datasets need to be considered when building and applying epigenetic clock algorithms to human epidemiological or disease cohorts.

Psychological stress, cognitive decline and the development of dementia in amnestic mild cognitive impairment.

To determine the relationship between psychological stress with cognitive outcomes in a multi-centre longitudinal study of people with amnestic mild cognitive impairment (aMCI) we assessed three parameters of psychological stress (Recent Life Changes Questionnaire (RLCQ); the Perceived Stress Scale (PSS) and salivary cortisol) and their relationship with rates of cognitive decline over an 18 month follow up period and conversion to dementia over a 5.5 year period. In 133 aMCI and 68 cognitively intact participants the PSS score was higher in the aMCI compared with control group but neither the RLCQ scores nor salivary cortisol measures were different between groups. In the aMCI group the RLCQ and the PSS showed no significant association with cognitive function at baseline, cognitive decline or with conversion rates to dementia but high salivary cortisol levels were associated with RLCQ scores and poorer cognitive function at baseline and lower rates of cognitive decline. No relationship was found between salivary cortisol levels and conversion rate to dementia. We conclude that psychological stress as measured by the RLCQ or PSS was not associated with adverse cognitive outcomes in an aMCI population and hypothesise that this may reflect diminished cortisol production to psychological stress as the disease progresses.

Correction: Alzheimer's disease polygenic risk score as a predictor of conversion from mild-cognitive impairment.

An amendment to this paper has been published and can be accessed via a link at the top of the paper.

Alzheimer's disease polygenic risk score as a predictor of conversion from mild-cognitive impairment.

Mild-cognitive impairment (MCI) occurs in up to one-fifth of individuals over the age of 65, with approximately a third of MCI individuals converting to dementia in later life. There is a growing necessity for early identification for those at risk of dementia as pathological processes begin decades before onset of symptoms. A cohort of 122 individuals diagnosed with MCI and followed up for a 36-month period for conversion to late-onset Alzheimer's disease (LOAD) were genotyped on the NeuroChip array along with pathologically confirmed cases of LOAD and cognitively normal controls. Polygenic risk scores (PRS) for each individual were generated using PRSice-2, derived from summary statistics produced from the International Genomics of Alzheimer's Disease Project (IGAP) genome-wide association study. Predictability models for LOAD were developed incorporating the PRS with APOE SNPs (rs7412 and rs429358), age and gender. This model was subsequently applied to the MCI cohort to determine whether it could be used to predict conversion from MCI to LOAD. The PRS model for LOAD using area under the precision-recall curve (AUPRC) calculated a predictability for LOAD of 82.5%. When applied to the MCI cohort predictability for conversion from MCI to LOAD was 61.0%. Increases in average PRS scores across diagnosis group were observed with one-way ANOVA suggesting significant differences in PRS between the groups (p < 0.0001). This analysis suggests that the PRS model for LOAD can be used to identify individuals with MCI at risk of conversion to LOAD.

The Epistasis Project: A Multi-Cohort Study of the Effects of BDNF, DBH, and SORT1 Epistasis on Alzheimer's Disease Risk.

Pre-synaptic secretion of brain-derived neurotrophic factor (BDNF) from noradrenergic neurons may protect the Alzheimer's disease (AD) brain from amyloid pathology. While the BDNF polymorphism (rs6265) is associated with faster cognitive decline and increased hippocampal atrophy, a replicable genetic association of BDNF with AD risk has yet to be demonstrated. This could be due to masking by underlying epistatic interactions between BDNF and other loci that encode proteins involved in moderating BDNF secretion (DBH and Sortilin). We performed a multi-cohort case-control association study of the BDNF, DBH, and SORT1 loci comprising 5,682 controls and 2,454 AD patients from Northern Europe (87% of samples) and Spain (13%). The BDNF locus was associated with increased AD risk (odds ratios; OR = 1.1-1.2, p = 0.005-0.3), an effect size that was consistent in the Northern European (OR = 1.1-1.2, p = 0.002-0.8) but not the smaller Spanish (OR = 0.8-1.6, p = 0.4-1.0) subset. A synergistic interaction between BDNF and sex (synergy factor; SF = 1.3-1.5 p = 0.002-0.02) translated to a greater risk of AD associated with BDNF in women (OR = 1.2-1.3, p = 0.007-0.00008) than men (OR = 0.9-1.0, p = 0.3-0.6). While the DBH polymorphism (rs1611115) was also associated with increased AD risk (OR = 1.1, p = 0.04) the synergistic interaction (SF = 2.2, p = 0.007) between BDNF (rs6265) and DBH (rs1611115) contributed greater AD risk than either gene alone, an effect that was greater in women (SF = 2.4, p = 0.04) than men (SF = 2.0, p = 0.2). These data support a complex genetic interaction at loci encoding proteins implicated in the DBH-BDNF inflammatory pathway that modifies AD risk, particularly in women.

Genetic variability in response to amyloid beta deposition influences Alzheimer's disease risk.

Genome-wide association studies of late-onset Alzheimer's disease risk have previously identified genes primarily expressed in microglia that form a transcriptional network. Using transgenic mouse models of amyloid deposition, we previously showed that many of the mouse orthologues of these risk genes are co-expressed and associated with amyloid pathology. In this new study, we generate an improved RNA-seq-derived network that is expressed in amyloid-responsive mouse microglia and we statistically compare this with gene-level variation in previous human Alzheimer's disease genome-wide association studies to predict at least four new risk genes for the disease (OAS1, LAPTM5, ITGAM/CD11b and LILRB4). Of the mouse orthologues of these genes Oas1a is likely to respond directly to amyloid at the transcriptional level, similarly to established risk gene Trem2, because the increase in Oas1a and Trem2 transcripts in response to amyloid deposition in transgenic mice is significantly higher than both the increase of the average microglial transcript and the increase in microglial number. In contrast, the mouse orthologues of LAPTM5, ITGAM/CD11b and LILRB4 (Laptm5, Itgam/CD11b and Lilra5) show increased transcripts in the presence of amyloid plaques similar in magnitude to the increase of the average microglial transcript and the increase in microglia number, except that Laptm5 and Lilra5 transcripts increase significantly quicker than the average microglial transcript as the plaque load becomes dense. This work suggests that genetic variability in the microglial response to amyloid deposition is a major determinant for Alzheimer's disease risk, and identification of these genes may help to predict the risk of developing Alzheimer's disease. These findings also provide further insights into the mechanisms underlying Alzheimer's disease for potential drug discovery.

Observations of extensive gene expression differences in the cerebellum and potential relevance to Alzheimer's disease.

OBJECTIVES: In order to determine how gene expression is altered in disease it is of fundamental importance that the global distribution of gene expression levels across the disease-free brain are understood and how differences between tissue types might inform tissue choice for investigation of altered expression in disease state. The aim of this pilot project was to use RNA-sequencing to investigate gene expression differences between five general areas of post-mortem human brain (frontal, temporal, occipital, parietal and cerebellum), and in particular changes in gene expression in the cerebellum compared to cortex regions for genes relevant to Alzheimer's disease, as the cerebellum is largely preserved from disease pathology and could be an area of interest for neuroprotective pathways. RESULTS: General gene expression profiles were found to be similar between cortical regions of the brain, however the cerebellum presented a distinct expression profile. Focused exploration of gene expression for genes associated with Alzheimer's disease suggest that those involved in the immunity pathway show little expression in the brain. Furthermore some Alzheimer's disease associated genes display significantly different expression in the cerebellum compared with other brain regions, which might indicate potential neuroprotective measures.

Genotyping of the Alzheimer's Disease Genome-Wide Association Study Index Single Nucleotide Polymorphisms in the Brains for Dementia Research Cohort.

The Brains for Dementia Research project is a recently established longitudinal cohort which aims to provide brain tissue for research purposes from neuropathologically defined samples. Here we present the findings from our analysis on the 19 established GWAS index SNPs for Alzheimer's disease, in order to demonstrate if the BDR sample also displays association to these variants. A highly significant association of the APOEɛ4 allele was identified (p = 3.99×10-12). Association tests for the 19 GWAS SNPs found that although no SNPs survive multiple testing, nominal significant findings were detected and concordance with the Lambert et al. GWAS meta-analysis was observed.

Complement receptor 1 gene (CR1) intragenic duplication and risk of Alzheimer's disease.

Single nucleotide variants (SNVs) within and surrounding the complement receptor 1 (CR1) gene show some of the strongest genome-wide association signals with late-onset Alzheimer's disease. Some studies have suggested that this association signal is due to a duplication allele (CR1-B) of a low copy repeat (LCR) within the CR1 gene, which increases the number of complement C3b/C4b-binding sites in the mature receptor. In this study, we develop a triplex paralogue ratio test assay for CR1 LCR copy number allowing large numbers of samples to be typed with a limited amount of DNA. We also develop a CR1-B allele-specific PCR based on the junction generated by an historical non-allelic homologous recombination event between CR1 LCRs. We use these methods to genotype CR1 and measure CR1-B allele frequency in both late-onset and early-onset cases and unaffected controls from the United Kingdom. Our data support an association of late-onset Alzheimer's disease with the CR1-B allele, and confirm that this allele occurs most frequently on the risk haplotype defined by SNV alleles. Furthermore, regression models incorporating CR1-B genotype provide a better fit to our data compared to incorporating the SNV-defined risk haplotype, supporting the CR1-B allele as the variant underlying the increased risk of late-onset Alzheimer's disease.