Artificial intelligence for dementia genetics and omics.

Genetics and omics studies of Alzheimer's disease and other dementia subtypes enhance our understanding of underlying mechanisms and pathways that can be targeted. We identified key remaining challenges: First, can we enhance genetic studies to address missing heritability? Can we identify reproducible omics signatures that differentiate between dementia subtypes? Can high-dimensional omics data identify improved biomarkers? How can genetics inform our understanding of causal status of dementia risk factors? And which biological processes are altered by dementia-related genetic variation? Artificial intelligence (AI) and machine learning approaches give us powerful new tools in helping us to tackle these challenges, and we review possible solutions and examples of best practice. However, their limitations also need to be considered, as well as the need for coordinated multidisciplinary research and diverse deeply phenotyped cohorts. Ultimately AI approaches improve our ability to interrogate genetics and omics data for precision dementia medicine. HIGHLIGHTS: We have identified five key challenges in dementia genetics and omics studies. AI can enable detection of undiscovered patterns in dementia genetics and omics data. Enhanced and more diverse genetics and omics datasets are still needed. Multidisciplinary collaborative efforts using AI can boost dementia research.

Correction: The genomic basis of mood instability: identification of 46 loci in 363,705 UK Biobank participants, genetic correlation with psychiatric disorders, and association with gene expression and function.

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

The genomic basis of mood instability: identification of 46 loci in 363,705 UK Biobank participants, genetic correlation with psychiatric disorders, and association with gene expression and function.

Genome-wide association studies (GWAS) of psychiatric phenotypes have tended to focus on categorical diagnoses, but to understand the biology of mental illness it may be more useful to study traits which cut across traditional boundaries. Here, we report the results of a GWAS of mood instability as a trait in a large population cohort (UK Biobank, n = 363,705). We also assess the clinical and biological relevance of the findings, including whether genetic associations show enrichment for nervous system pathways. Forty six unique loci associated with mood instability were identified with a SNP heritability estimate of 9%. Linkage Disequilibrium Score Regression (LDSR) analyses identified genetic correlations with Major Depressive Disorder (MDD), Bipolar Disorder (BD), Schizophrenia, anxiety, and Post Traumatic Stress Disorder (PTSD). Gene-level and gene set analyses identified 244 significant genes and 6 enriched gene sets. Tissue expression analysis of the SNP-level data found enrichment in multiple brain regions, and eQTL analyses highlighted an inversion on chromosome 17 plus two brain-specific eQTLs. In addition, we used a Phenotype Linkage Network (PLN) analysis and community analysis to assess for enrichment of nervous system gene sets using mouse orthologue databases. The PLN analysis found enrichment in nervous system PLNs for a community containing serotonin and melatonin receptors. In summary, this work has identified novel loci, tissues and gene sets contributing to mood instability. These findings may be relevant for the identification of novel trans-diagnostic drug targets and could help to inform future stratified medicine innovations in mental health.

The Parkinson's Disease Genome-Wide Association Study Locus Browser.

BACKGROUND: Parkinson's disease (PD) is a neurodegenerative disease with an often complex component identifiable by genome-wide association studies. The most recent large-scale PD genome-wide association studies have identified more than 90 independent risk variants for PD risk and progression across more than 80 genomic regions. One major challenge in current genomics is the identification of the causal gene(s) and variant(s) at each genome-wide association study locus. The objective of the current study was to create a tool that would display data for relevant PD risk loci and provide guidance with the prioritization of causal genes and potential mechanisms at each locus. METHODS: We included all significant genome-wide signals from multiple recent PD genome-wide association studies including themost recent PD risk genome-wide association study, age-at-onset genome-wide association study, progression genome-wide association study, and Asian population PD risk genome-wide association study. We gathered data for all genes 1 Mb up and downstream of each variant to allow users to assess which gene(s) are most associated with the variant of interest based on a set of self-ranked criteria. Multiple databases were queried for each gene to collect additional causal data. RESULTS: We created a PD genome-wide association study browser tool (https://pdgenetics.shinyapps.io/GWASBrowser/) to assist the PD research community with the prioritization of genes for follow-up functional studies to identify potential therapeutic targets. CONCLUSIONS: Our PD genome-wide association study browser tool provides users with a useful method of identifying potential causal genes at all known PD risk loci from large-scale PD genome-wide association studies. We plan to update this tool with new relevant data as sample sizes increase and new PD risk loci are discovered. © 2020 The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society. This article has been contributed to by US Government employees and their work is in the public domain in the USA.

Transcriptomic profiling of purified patient-derived dopamine neurons identifies convergent perturbations and therapeutics for Parkinson's disease.

While induced pluripotent stem cell (iPSC) technologies enable the study of inaccessible patient cell types, cellular heterogeneity can confound the comparison of gene expression profiles between iPSC-derived cell lines. Here, we purified iPSC-derived human dopaminergic neurons (DaNs) using the intracellular marker, tyrosine hydroxylase. Once purified, the transcriptomic profiles of iPSC-derived DaNs appear remarkably similar to profiles obtained from mature post-mortem DaNs. Comparison of the profiles of purified iPSC-derived DaNs derived from Parkinson's disease (PD) patients carrying LRRK2 G2019S variants to controls identified significant functional convergence amongst differentially-expressed (DE) genes. The PD LRRK2-G2019S associated profile was positively matched with expression changes induced by the Parkinsonian neurotoxin rotenone and opposed by those induced by clioquinol, a compound with demonstrated therapeutic efficacy in multiple PD models. No functional convergence amongst DE genes was observed following a similar comparison using non-purified iPSC-derived DaN-containing populations, with cellular heterogeneity appearing a greater confound than genotypic background.

Diverse type 2 diabetes genetic risk factors functionally converge in a phenotype-focused gene network.

Type 2 Diabetes (T2D) constitutes a global health burden. Efforts to uncover predisposing genetic variation have been considerable, yet detailed knowledge of the underlying pathogenesis remains poor. Here, we constructed a T2D phenotypic-linkage network (T2D-PLN), by integrating diverse gene functional information that highlight genes, which when disrupted in mice, elicit similar T2D-relevant phenotypes. Sensitising the network to T2D-relevant phenotypes enabled significant functional convergence to be detected between genes implicated in monogenic or syndromic diabetes and genes lying within genomic regions associated with T2D common risk. We extended these analyses to a recent multiethnic T2D case-control exome of 12,940 individuals that found no evidence of T2D risk association for rare frequency variants outside of previously known T2D risk loci. Examining associations involving protein-truncating variants (PTV), most at low population frequencies, the T2D-PLN was able to identify a convergent set of biological pathways that were perturbed within four of five independent T2D case/control ethnic sets of 2000 to 5000 exomes each. These same pathways were found to be over-represented among both known monogenic or syndromic diabetes genes and genes within T2D-associated common risk loci. Our study demonstrates convergent biology amongst variants representing different classes of T2D genetic risk. Although convergence was observed at the pathway level, few of the contributing genes were found in common between different cohorts or variant classes, most notably between the exome variant sets which suggests that future rare variant studies may be better focusing their power onto a single population of recent common ancestry.

Use of a multiethnic approach to identify rheumatoid- arthritis-susceptibility loci, 1p36 and 17q12.

We have previously shown that rheumatoid arthritis (RA) risk alleles overlap between different ethnic groups. Here, we utilize a multiethnic approach to show that we can effectively discover RA risk alleles. Thirteen putatively associated SNPs that had not yet exceeded genome-wide significance (p < 5 × 10(-8)) in our previous RA genome-wide association study (GWAS) were analyzed in independent sample sets consisting of 4,366 cases and 17,765 controls of European, African American, and East Asian ancestry. Additionally, we conducted an overall association test across all 65,833 samples (a GWAS meta-analysis plus the replication samples). Of the 13 SNPs investigated, four were significantly below the study-wide Bonferroni corrected p value threshold (p < 0.0038) in the replication samples. Two SNPs (rs3890745 at the 1p36 locus [p = 2.3 × 10(-12)] and rs2872507 at the 17q12 locus [p = 1.7 × 10(-9)]) surpassed genome-wide significance in all 16,659 RA cases and 49,174 controls combined. We used available GWAS data to fine map these two loci in Europeans and East Asians, and we found that the same allele conferred risk in both ethnic groups. A series of bioinformatic analyses identified TNFRSF14-MMEL1 at the 1p36 locus and IKZF3-ORMDL3-GSDMB at the 17q12 locus as the genes most likely associated with RA. These findings demonstrate empirically that a multiethnic approach is an effective strategy for discovering RA risk loci, and they suggest that combining GWASs across ethnic groups represents an efficient strategy for gaining statistical power.

Genetic variants in REC8, RNF212, and PRDM9 influence male recombination in cattle.

We use >250,000 cross-over events identified in >10,000 bovine sperm cells to perform an extensive characterization of meiotic recombination in male cattle. We map Quantitative Trait Loci (QTL) influencing genome-wide recombination rate, genome-wide hotspot usage, and locus-specific recombination rate. We fine-map three QTL and present strong evidence that genetic variants in REC8 and RNF212 influence genome-wide recombination rate, while genetic variants in PRDM9 influence genome-wide hotspot usage.

Digital outcome measures from smartwatch data relate to non-motor features of Parkinson's disease.

Monitoring of Parkinson's disease (PD) has seen substantial improvement over recent years as digital sensors enable a passive and continuous collection of information in the home environment. However, the primary focus of this work has been motor symptoms, with little focus on the non-motor aspects of the disease. To address this, we combined longitudinal clinical non-motor assessment data and digital multi-sensor data from the Verily Study Watch for 149 participants from the Parkinson's Progression Monitoring Initiative (PPMI) cohort with a diagnosis of PD. We show that digitally collected physical activity and sleep measures significantly relate to clinical non-motor assessments of cognitive, autonomic, and daily living impairment. However, the poor predictive performance we observed, highlights the need for better targeted digital outcome measures to enable monitoring of non-motor symptoms.

Macro- and micro-structural insights into primary dystonia: a UK Biobank study.

BACKGROUND: Dystonia is a hyperkinetic movement disorder with key motor network dysfunction implicated in pathophysiology. The UK Biobank encompasses > 500,000 participants, of whom 42,565 underwent brain MRI scanning. This study applied an optimized pre-processing pipeline, aimed at better accounting for artifact and improving data reliability, to assess for grey and white matter structural MRI changes between individuals diagnosed with primary dystonia and an unaffected control cohort. METHODS: Individuals with dystonia (n = 76) were identified from the UK Biobank using published algorithms, alongside an age- and sex-matched unaffected control cohort (n = 311). Grey matter morphometric and diffusion measures were assessed, together with white matter diffusion tensor and diffusion kurtosis metrics using tractography and tractometry. Post-hoc Neurite Orientation and Density Distribution Imaging (NODDI) was also undertaken for tracts in which significant differences were observed. RESULTS: Grey matter tremor-specific striatal differences were observed, with higher radial kurtosis. Tractography identified no white matter differences, however segmental tractometry identified localised differences, particularly in the superior cerebellar peduncles and anterior thalamic radiations, including higher fractional anisotropy and lower orientation distribution index in dystonia, compared to controls. Additional tremor-specific changes included lower neurite density index in the anterior thalamic radiations. CONCLUSIONS: Analysis of imaging data from one of the largest dystonia cohorts to date demonstrates microstructural differences in cerebellar and thalamic white matter connections, with architectural differences such as less orientation dispersion potentially being a component of the morphological structural changes implicated in dystonia. Distinct tremor-related imaging features are also implicated in both grey and white matter.

Wearable movement-tracking data identify Parkinson's disease years before clinical diagnosis.

Parkinson's disease is a progressive neurodegenerative movement disorder with a long latent phase and currently no disease-modifying treatments. Reliable predictive biomarkers that could transform efforts to develop neuroprotective treatments remain to be identified. Using UK Biobank, we investigated the predictive value of accelerometry in identifying prodromal Parkinson's disease in the general population and compared this digital biomarker with models based on genetics, lifestyle, blood biochemistry or prodromal symptoms data. Machine learning models trained using accelerometry data achieved better test performance in distinguishing both clinically diagnosed Parkinson's disease (n = 153) (area under precision recall curve (AUPRC) 0.14 ± 0.04) and prodromal Parkinson's disease (n = 113) up to 7 years pre-diagnosis (AUPRC 0.07 ± 0.03) from the general population (n = 33,009) compared with all other modalities tested (genetics: AUPRC = 0.01 ± 0.00, P = 2.2 × 10-3; lifestyle: AUPRC = 0.03 ± 0.04, P = 2.5 × 10-3; blood biochemistry: AUPRC = 0.01 ± 0.00, P = 4.1 × 10-3; prodromal signs: AUPRC = 0.01 ± 0.00, P = 3.6 × 10-3). Accelerometry is a potentially important, low-cost screening tool for determining people at risk of developing Parkinson's disease and identifying participants for clinical trials of neuroprotective treatments.

New cases of dementia are rising in elderly populations in Wales, UK.

Dementia is one of the most common diseases in elderly populations, and older populations are one of the fastest growing groups globally. Consequently, the number of people developing and living with dementia is likely to grow. Using longitudinal medical records from Wales, UK between 1999 and 2018, diagnoses of overall dementia and common subtypes were combined with demographic data to assess numbers of new and existing cases per year. Data extraction resulted in 161,186 diagnoses from 116,645 individuals. Mean age at diagnosis of dementia increased over this period, resulting in fewer younger people with the disease. New cases of dementia have risen, as has the number of people living with dementia. Individuals with dementia are also living longer, even accounting for their older age. This may present a challenge for healthcare systems as the number of elderly people living with dementia is expected to continue to grow.

Deep phenotyping for precision medicine in Parkinson's disease.

A major challenge in medical genomics is to understand why individuals with the same disorder have different clinical symptoms and why those who carry the same mutation may be affected by different disorders. In every complex disorder, identifying the contribution of different genetic and non-genetic risk factors is a key obstacle to understanding disease mechanisms. Genetic studies rely on precise phenotypes and are unable to uncover the genetic contributions to a disorder when phenotypes are imprecise. To address this challenge, deeply phenotyped cohorts have been developed for which detailed, fine-grained data have been collected. These cohorts help us to investigate the underlying biological pathways and risk factors to identify treatment targets, and thus to advance precision medicine. The neurodegenerative disorder Parkinson's disease has a diverse phenotypical presentation and modest heritability, and its underlying disease mechanisms are still being debated. As such, considerable efforts have been made to develop deeply phenotyped cohorts for this disorder. Here, we focus on Parkinson's disease and explore how deep phenotyping can help address the challenges raised by genetic and phenotypic heterogeneity. We also discuss recent methods for data collection and computation, as well as methodological challenges that have to be overcome.

Clinical and genotypic analysis in determining dystonia non-motor phenotypic heterogeneity: a UK Biobank study.

The spectrum of non-motor symptoms in dystonia remains unclear. Using UK Biobank data, we analysed clinical phenotypic and genetic information in the largest dystonia cohort reported to date. Case-control comparison of dystonia and matched control cohort was undertaken to identify domains (psychiatric, pain, sleep and cognition) of increased symptom burden in dystonia. Whole exome data were used to determine the rate and likely pathogenicity of variants in Mendelian inherited dystonia causing genes and linked to clinical data. Within the dystonia cohort, phenotypic and genetic single-nucleotide polymorphism (SNP) data were combined in a mixed model analysis to derive genetically informed phenotypic axes. A total of 1572 individuals with dystonia were identified, including cervical dystonia (n = 775), blepharospasm (n = 131), tremor (n = 488) and dystonia, unspecified (n = 154) groups. Phenotypic patterns highlighted a predominance of psychiatric symptoms (anxiety and depression), excess pain and sleep disturbance. Cognitive impairment was limited to prospective memory and fluid intelligence. Whole exome sequencing identified 798 loss of function variants in dystonia-linked genes, 67 missense variants (MPC > 3) and 305 other forms of non-synonymous variants (including inframe deletion, inframe insertion, stop loss and start loss variants). A single loss of function variant (ANO3) was identified in the dystonia cohort. Combined SNP and clinical data identified multiple genetically informed phenotypic axes with predominance of psychiatric, pain and sleep non-motor domains. An excess of psychiatric, pain and sleep symptoms were evident across all forms of dystonia. Combination with genetic data highlights phenotypic subgroups consistent with the heterogeneity observed in clinical practice.

Universal clinical Parkinson's disease axes identify a major influence of neuroinflammation.

BACKGROUND: There is large individual variation in both clinical presentation and progression between Parkinson's disease patients. Generation of deeply and longitudinally phenotyped patient cohorts has enormous potential to identify disease subtypes for prognosis and therapeutic targeting. METHODS: Replicating across three large Parkinson's cohorts (Oxford Discovery cohort (n = 842)/Tracking UK Parkinson's study (n = 1807) and Parkinson's Progression Markers Initiative (n = 472)) with clinical observational measures collected longitudinally over 5-10 years, we developed a Bayesian multiple phenotypes mixed model incorporating genetic relationships between individuals able to explain many diverse clinical measurements as a smaller number of continuous underlying factors ("phenotypic axes"). RESULTS: When applied to disease severity at diagnosis, the most influential of three phenotypic axes "Axis 1" was characterised by severe non-tremor motor phenotype, anxiety and depression at diagnosis, accompanied by faster progression in cognitive function measures. Axis 1 was associated with increased genetic risk of Alzheimer's disease and reduced CSF Aß1-42 levels. As observed previously for Alzheimer's disease genetic risk, and in contrast to Parkinson's disease genetic risk, the loci influencing Axis 1 were associated with microglia-expressed genes implicating neuroinflammation. When applied to measures of disease progression for each individual, integration of Alzheimer's disease genetic loci haplotypes improved the accuracy of progression modelling, while integrating Parkinson's disease genetics did not. CONCLUSIONS: We identify universal axes of Parkinson's disease phenotypic variation which reveal that Parkinson's patients with high concomitant genetic risk for Alzheimer's disease are more likely to present with severe motor and non-motor features at baseline and progress more rapidly to early dementia.

Non-motor phenotypic subgroups in adult-onset idiopathic, isolated, focal cervical dystonia.

BACKGROUND: Non-motor symptoms are well established phenotypic components of adult-onset idiopathic, isolated, focal cervical dystonia (AOIFCD). However, improved understanding of their clinical heterogeneity is needed to better target therapeutic intervention. Here, we examine non-motor phenotypic features to identify possible AOIFCD subgroups. METHODS: Participants diagnosed with AOIFCD were recruited via specialist neurology clinics (dystonia wales: n = 114, dystonia coalition: n = 183). Non-motor assessment included psychiatric symptoms, pain, sleep disturbance, and quality of life, assessed using self-completed questionnaires or face-to-face assessment. Both cohorts were analyzed independently using Cluster, and Bayesian multiple mixed model phenotype analyses to investigate the relationship between non-motor symptoms and determine evidence of phenotypic subgroups. RESULTS: Independent cluster analysis of the two cohorts suggests two predominant phenotypic subgroups, one consisting of approximately a third of participants in both cohorts, experiencing increased levels of depression, anxiety, sleep impairment, and pain catastrophizing, as well as, decreased quality of life. The Bayesian approach reinforced this with the primary axis, which explained the majority of the variance, in each cohort being associated with psychiatric symptomology, and also sleep impairment and pain catastrophizing in the Dystonia Wales cohort. CONCLUSIONS: Non-motor symptoms accompanying AOIFCD parse into two predominant phenotypic sub-groups, with differences in psychiatric symptoms, pain catastrophizing, sleep quality, and quality of life. Improved understanding of these symptom groups will enable better targeted pathophysiological investigation and future therapeutic intervention.

Novel Crohn disease locus identified by genome-wide association maps to a gene desert on 5p13.1 and modulates expression of PTGER4.

To identify novel susceptibility loci for Crohn disease (CD), we undertook a genome-wide association study with more than 300,000 SNPs characterized in 547 patients and 928 controls. We found three chromosome regions that provided evidence of disease association with p-values between 10(-6) and 10(-9). Two of these (IL23R on Chromosome 1 and CARD15 on Chromosome 16) correspond to genes previously reported to be associated with CD. In addition, a 250-kb region of Chromosome 5p13.1 was found to contain multiple markers with strongly suggestive evidence of disease association (including four markers with p < 10(-7)). We replicated the results for 5p13.1 by studying 1,266 additional CD patients, 559 additional controls, and 428 trios. Significant evidence of association (p < 4 x 10(-4)) was found in case/control comparisons with the replication data, while associated alleles were over-transmitted to affected offspring (p < 0.05), thus confirming that the 5p13.1 locus contributes to CD susceptibility. The CD-associated 250-kb region was saturated with 111 SNP markers. Haplotype analysis supports a complex locus architecture with multiple variants contributing to disease susceptibility. The novel 5p13.1 CD locus is contained within a 1.25-Mb gene desert. We present evidence that disease-associated alleles correlate with quantitative expression levels of the prostaglandin receptor EP4, PTGER4, the gene that resides closest to the associated region. Our results identify a major new susceptibility locus for CD, and suggest that genetic variants associated with disease risk at this locus could modulate cis-acting regulatory elements of PTGER4.

A single-cell atlas of the human substantia nigra reveals cell-specific pathways associated with neurological disorders.

We describe a human single-nuclei transcriptomic atlas for the substantia nigra (SN), generated by sequencing approximately 17,000 nuclei from matched cortical and SN samples. We show that the common genetic risk for Parkinson's disease (PD) is associated with dopaminergic neuron (DaN)-specific gene expression, including mitochondrial functioning, protein folding and ubiquitination pathways. We identify a distinct cell type association between PD risk and oligodendrocyte-specific gene expression. Unlike Alzheimer's disease (AD), we find no association between PD risk and microglia or astrocytes, suggesting that neuroinflammation plays a less causal role in PD than AD. Beyond PD, we find associations between SN DaNs and GABAergic neuron gene expression and multiple neuropsychiatric disorders. Conditional analysis reveals that distinct neuropsychiatric disorders associate with distinct sets of neuron-specific genes but converge onto shared loci within oligodendrocytes and oligodendrocyte precursors. This atlas guides our aetiological understanding by associating SN cell type expression profiles with specific disease risk.

Deep phenotyping of peripheral tissue facilitates mechanistic disease stratification in sporadic Parkinson's disease.

Mechanistic disease stratification will be crucial to develop a precision medicine approach for future disease modifying therapy in sporadic Parkinson's disease (sPD). Mitochondrial and lysosomal dysfunction are key mechanisms in the pathogenesis of sPD and therefore promising targets for therapeutic intervention. We investigated mitochondrial and lysosomal function in skin fibroblasts of 100 sPD patients and 50 age-matched controls. A combination of cellular assays, RNA-seq based pathway analysis and genotyping was applied. Distinct subgroups with mitochondrial (mito-sPD) or lysosomal (lyso-sPD) dysfunction were identified. Mitochondrial dysfunction correlated with reduction in complex I and IV protein levels. RNA-seq based pathway analysis revealed marked activation of the lysosomal pathway with enrichment for lysosomal disease gene variants in lyso-sPD. Conversion of fibroblasts to induced neuronal progenitor cells and subsequent differentiation into tyrosine hydroxylase positive neurons confirmed and further enhanced both mitochondrial and lysosomal abnormalities. Treatment with ursodeoxycholic acid improved mitochondrial membrane potential and intracellular ATP levels even in sPD patient fibroblast lines with comparatively mild mitochondrial dysfunction. The results of our study suggest that in-depth phenotyping and focussed assessment of putative neuroprotective compounds in peripheral tissue are a promising approach towards disease stratification and precision medicine in sPD.