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  1. ; ; ; ; ; ; ; ; ; ; et al ( , Alzheimer's Research & Therapy)
    Abstract Background

    Uncovering the functional relevance underlying verbal declarative memory (VDM) genome-wide association study (GWAS) results may facilitate the development of interventions to reduce age-related memory decline and dementia.

     Methods

    We performed multi-omics and pathway enrichment analyses of paragraph (PAR-dr) and word list (WL-dr) delayed recall GWAS from 29,076 older non-demented individuals of European descent. We assessed the relationship between single-variant associations and expression quantitative trait loci (eQTLs) in 44 tissues and methylation quantitative trait loci (meQTLs) in the hippocampus. We determined the relationship between gene associations and transcript levels in 53 tissues, annotation as immune genes, and regulation by transcription factors (TFs) and microRNAs. To identify significant pathways, gene set enrichment was tested in each cohort and meta-analyzed across cohorts. Analyses of differential expression in brain tissues were conducted for pathway component genes.

     Results

    The single-variant associations of VDM showed significant linkage disequilibrium (LD) with eQTLs across all tissues and meQTLs within the hippocampus. Stronger WL-dr gene associations correlated with reduced expression in four brain tissues, including the hippocampus. More robust PAR-dr and/or WL-dr gene associations were intricately linked with immunity and were influenced by 31 TFs and 2 microRNAs. Six pathways, including type I diabetes, exhibited significant associations with both PAR-dr and WL-dr. These pathways included fifteen MHC genes intricately linked to VDM performance, showing diverse expression patterns based on cognitive status in brain tissues.

     Conclusions

    VDM genetic associations influence expression regulation via eQTLs and meQTLs. The involvement of TFs, microRNAs, MHC genes, and immune-related pathways contributes to VDM performance in older individuals.

     
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  2. ; ; ; ; ; ; ; ; ; ; et al ( , Translational Psychiatry)
    Abstract

    Tourette Syndrome (TS) is a complex neurodevelopmental disorder characterized by vocal and motor tics lasting more than a year. It is highly polygenic in nature with both rare and common previously associated variants. Epidemiological studies have shown TS to be correlated with other phenotypes, but large-scale phenome wide analyses in biobank level data have not been performed to date. In this study, we used the summary statistics from the latest meta-analysis of TS to calculate the polygenic risk score (PRS) of individuals in the UK Biobank data and applied a Phenome Wide Association Study (PheWAS) approach to determine the association of disease risk with a wide range of phenotypes. A total of 57 traits were found to be significantly associated with TS polygenic risk, including multiple psychosocial factors and mental health conditions such as anxiety disorder and depression. Additional associations were observed with complex non-psychiatric disorders such as Type 2 diabetes, heart palpitations, and respiratory conditions. Cross-disorder comparisons of phenotypic associations with genetic risk for other childhood-onset disorders (e.g.: attention deficit hyperactivity disorder [ADHD], autism spectrum disorder [ASD], and obsessive-compulsive disorder [OCD]) indicated an overlap in associations between TS and these disorders. ADHD and ASD had a similar direction of effect with TS while OCD had an opposite direction of effect for all traits except mental health factors. Sex-specific PheWAS analysis identified differences in the associations with TS genetic risk between males and females. Type 2 diabetes and heart palpitations were significantly associated with TS risk in males but not in females, whereas diseases of the respiratory system were associated with TS risk in females but not in males. This analysis provides further evidence of shared genetic and phenotypic architecture of different complex disorders.

     
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  3. ; ; ; ; ; ; ; ; ; ; et al ( , Nature Communications)
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  4. ; ; ; ; ; ; ; ; ; ; et al ( , Epilepsia)
    Summary Objective

    Copy number variations (CNVs) represent a significant genetic risk for several neurodevelopmental disorders including epilepsy. As knowledge increases, reanalysis of existing data is essential. Reliable estimates of the contribution ofCNVs to epilepsies from sizeable populations are not available.

     Methods

    We assembled a cohort of 1255 patients with preexisting array comparative genomic hybridization or single nucleotide polymorphism array basedCNVdata. All patients had “epilepsy plus,” defined as epilepsy with comorbid features, including intellectual disability, psychiatric symptoms, and other neurological and nonneurological features.CNVclassification was conducted using a systematic filtering workflow adapted to epilepsy.

     Results

    Of 1097 patients remaining after genetic data quality control, 120 individuals (10.9%) carried at least one autosomalCNVclassified as pathogenic; 19 individuals (1.7%) carried at least one autosomalCNVclassified as possibly pathogenic. Eleven patients (1%) carried more than one (possibly) pathogenicCNV. We identifiedCNVs covering recently reported (HNRNPU)or emerging (RORB) epilepsy genes, and further delineated the phenotype associated with mutations of these genes. Additional novel epilepsy candidate genes emerge from our study. Comparing phenotypic features of pathogenicCNVcarriers to those of noncarriers of pathogenicCNVs, we show that patients with nonneurological comorbidities, especially dysmorphism, were more likely to carry pathogenicCNVs (odds ratio = 4.09, confidence interval = 2.51‐6.68;P = 2.34 × 10−9). Meta‐analysis including data from published control groups showed that the presence or absence of epilepsy did not affect the detected frequency ofCNVs.

     Significance

    The use of a specifically adapted workflow enabled identification of pathogenic autosomalCNVs in 10.9% of patients with epilepsy plus, which rose to 12.7% when we also considered possibly pathogenicCNVs. Our data indicate that epilepsy with comorbid features should be considered an indication for patients to be selected for a diagnostic algorithm includingCNVdetection. Collaborative large‐scaleCNVreanalysis leads to novel declaration of pathogenicity in unexplained cases and can promote discovery of promising candidate epilepsy genes.

     
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