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.

An alternative method of SNP inclusion to develop a generalized polygenic risk score analysis across Alzheimer's disease cohorts.

Introduction: Polygenic risk scores (PRSs) have great clinical potential for detecting late-onset diseases such as Alzheimer's disease (AD), allowing the identification of those most at risk years before the symptoms present. Although many studies use various and complicated machine learning algorithms to determine the best discriminatory values for PRSs, few studies look at the commonality of the Single Nucleotide Polymorphisms (SNPs) utilized in these models. Methods: This investigation focussed on identifying SNPs that tag blocks of linkage disequilibrium across the genome, allowing for a generalized PRS model across cohorts and genotyping panels. PRS modeling was conducted on five AD development cohorts, with the best discriminatory models exploring for a commonality of linkage disequilibrium clumps. Clumps that contributed to the discrimination of cases from controls that occurred in multiple cohorts were used to create a generalized model of PRS, which was then tested in the five development cohorts and three further AD cohorts. Results: The model developed provided a discriminability accuracy average of over 70% in multiple AD cohorts and included variants of several well-known AD risk genes. Discussion: A key element of devising a polygenic risk score that can be used in the clinical setting is one that has consistency in the SNPs that are used to calculate the score; this study demonstrates that using a model based on commonality of association findings rather than meta-analyses may prove useful.