Fine-mapping of Parkinson's disease susceptibility loci identifies putative causal variants

LRRK2 G2019S variant is associated with transcriptional changes in Parkinson's disease human myeloid cells under proinflammatory environment

The G2019S mutation in the leucine-rich repeat kinase 2 (LRRK2) gene is a major risk factor for the development of Parkinson's disease (PD). LRRK2, although ubiquitously expressed, is highly abundant in cells of the innate immune system. Given the importance of central and peripheral immune cells in the development of PD, we sought to investigate the consequences of the G2019S mutation on microglial and monocyte transcriptome and function. We have generated large-scale transcriptomic profiles of isogenic human induced microglial cells (iMGLs) and patient derived monocytes carrying the G2019S mutation under baseline culture conditions and following exposure to the proinflammatory factors IFNγ and LPS. We demonstrate that the G2019S mutation exerts a profound impact on the transcriptomic profile of these myeloid cells, and describe corresponding functional differences in iMGLs. The G2019S mutation led to an upregulation in lipid metabolism and phagolysosomal pathway genes in untreated and LPS/IFNγ stimulated iMGLs, which was accompanied by an increased phagocytic capacity of myelin debris. We also identified dysregulation of cell cycle genes, with a downregulation of the E2F4 regulon. Transcriptomic characterization of human-derived monocytes carrying the G2019S mutation confirmed alteration in lipid metabolism associated genes. Altogether, these findings reveal the influence of G2019S on the dysregulation of the myeloid cell transcriptome under proinflammatory conditions.

Machine learning nominates the inositol pathway and novel genes in Parkinson's disease

There are 78 loci associated with Parkinson's disease in the most recent genome-wide association study (GWAS), yet the specific genes driving these associations are mostly unknown. Herein, we aimed to nominate the top candidate gene from each Parkinson's disease locus and identify variants and pathways potentially involved in Parkinson's disease. We trained a machine learning model to predict Parkinson's disease-associated genes from GWAS loci using genomic, transcriptomic and epigenomic data from brain tissues and dopaminergic neurons. We nominated candidate genes in each locus and identified novel pathways potentially involved in Parkinson's disease, such as the inositol phosphate biosynthetic pathway (INPP5F, IP6K2, ITPKB and PPIP5K2). Specific common coding variants in SPNS1 and MLX may be involved in Parkinson's disease, and burden tests of rare variants further support that CNIP3, LSM7, NUCKS1 and the polyol/inositol phosphate biosynthetic pathway are associated with the disease. Functional studies are needed to further analyse the involvements of these genes and pathways in Parkinson's disease.

The Genetic Drivers of Juvenile, Young, and Early-Onset Parkinson's Disease in India

BACKGROUND

Recent studies have advanced our understanding of the genetic drivers of Parkinson's disease (PD). Rare variants in more than 20 genes are considered causal for PD, and the latest PD genome-wide association study (GWAS) identified 90 independent risk loci. However, there remains a gap in our understanding of PD genetics outside of the European populations in which the vast majority of these studies were focused.

OBJECTIVE

The aim was to identify genetic risk factors for PD in a South Asian population.

METHODS

A total of 674 PD subjects predominantly with age of onset (AoO) ≤50 years (encompassing juvenile, young, or early-onset PD) were recruited from 10 specialty movement disorder centers across India over a 2-year period; 1376 control subjects were selected from the reference population GenomeAsia, Phase 2. We performed various case-only and case-control genetic analyses for PD diagnosis and AoO.

RESULTS

A genome-wide significant signal for PD diagnosis was identified in the SNCA region, strongly colocalizing with SNCA region signal from European PD GWAS. PD cases with pathogenic mutations in PD genes exhibited, on average, lower PD polygenic risk scores than PD cases lacking any PD gene mutations. Gene burden studies of rare, predicted deleterious variants identified BSN, encoding the presynaptic protein Bassoon that has been previously associated with neurodegenerative disease.

CONCLUSIONS

This study constitutes the largest genetic investigation of PD in a South Asian population to date. Future work should seek to expand sample numbers in this population to enable improved statistical power to detect PD genes in this understudied group. © 2023 Denali Therapeutics and The Authors. Movement Disorders published by Wiley Periodicals LLC on behalf of International Parkinson and Movement Disorder Society.

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.

Long-read RNA-seq atlas of novel microglia isoforms elucidates disease-associated genetic regulation of splicing

Microglia, the innate immune cells of the central nervous system, have been genetically implicated in multiple neurodegenerative diseases. We previously mapped the genetic regulation of gene expression and mRNA splicing in human microglia, identifying several loci where common genetic variants in microglia-specific regulatory elements explain disease risk loci identified by GWAS. However, identifying genetic effects on splicing has been challenging due to the use of short sequencing reads to identify causal isoforms. Here we present the isoform-centric microglia genomic atlas (isoMiGA) which leverages the power of long-read RNA-seq to identify 35,879 novel microglia isoforms. We show that the novel microglia isoforms are involved in stimulation response and brain region specificity. We then quantified the expression of both known and novel isoforms in a multi-ethnic meta-analysis of 555 human microglia short-read RNA-seq samples from 391 donors, the largest to date, and found associations with genetic risk loci in Alzheimer's disease and Parkinson's disease. We nominate several loci that may act through complex changes in isoform and splice site usage.

White matter changes in Parkinson's disease

Parkinson's disease (PD) is the second most common neurodegenerative disease after Alzheimer's disease (AD). It is characterized by a progressive loss of dopaminergic neurons in the substantia nigra pars compacta (SNc) and the formation of Lewy bodies (LBs). Although PD is primarily considered a gray matter (GM) disease, alterations in white matter (WM) have gained increasing attention in PD research recently. Here we review evidence collected by magnetic resonance imaging (MRI) techniques which indicate WM abnormalities in PD, and discuss the correlations between WM changes and specific PD symptoms. Then we summarize transcriptome and genome studies showing the changes of oligodendrocyte (OLs)/myelin in PD. We conclude that WM abnormalities caused by the changes of myelin/OLs might be important for PD pathology, which could be potential targets for PD treatment.

Computational analysis of five neurodegenerative diseases reveals shared and specific genetic loci

Neurodegenerative diseases (ND) are heterogeneous disorders of the central nervous system that share a chronic and selective process of neuronal cell death. A computational approach to investigate shared genetic and specific loci was applied to 5 different ND: Amyotrophic lateral sclerosis (ALS), Alzheimer's disease (AD), Parkinson's disease (PD), Multiple sclerosis (MS), and Lewy body dementia (LBD). The datasets were analyzed separately, and then we compared the obtained results. For this purpose, we applied a genetic correlation analysis to genome-wide association datasets and revealed different genetic correlations with several human traits and diseases. In addition, a clumping analysis was carried out to identify SNPs genetically associated with each disease. We found 27 SNPs in AD, 6 SNPs in ALS, 10 SNPs in PD, 17 SNPs in MS, and 3 SNPs in LBD. Most of them are located in non-coding regions, with the exception of 5 SNPs on which a protein structure and stability prediction was performed to verify their impact on disease. Furthermore, an analysis of the differentially expressed miRNAs of the 5 examined pathologies was performed to reveal regulatory mechanisms that could involve genes associated with selected SNPs. In conclusion, the results obtained constitute an important step toward the discovery of diagnostic biomarkers and a better understanding of the diseases.

The systemic immune response in Parkinson's disease: focus on the peripheral immune component

During Parkinson's disease (PD), both the central nervous system (CNS) and peripheral nervous system (PNS) are affected. In parallel, innate immune cells respond early to neuronal changes and alpha-synuclein (α-syn) pathology. Moreover, some of the affected neuronal groups innervate organs with a relevant role in immunity. Consequently, not only microglia, but also peripheral immune cells are altered, resulting in a systemic immune response. Innate and adaptive immune cells may participate in the neurodegenerative process by acting peripherally, infiltrating the brain, or releasing mediators that can protect or harm neurons. However, the sequence of the changes and the significance of each immune compartment in the disease remain to be clarified. In this review, we describe current understanding of the peripheral immune response in PD and discuss the road ahead.

Genetic insights into immune mechanisms of Alzheimer's and Parkinson's disease

Microglia, the macrophages of the brain, are vital for brain homeostasis and have been implicated in a broad range of brain disorders. Neuroinflammation has gained traction as a possible therapeutic target for neurodegeneration, however, the precise function of microglia in specific neurodegenerative disorders is an ongoing area of research. Genetic studies offer valuable insights into understanding causality, rather than merely observing a correlation. Genome-wide association studies (GWAS) have identified many genetic loci that are linked to susceptibility to neurodegenerative disorders. (Post)-GWAS studies have determined that microglia likely play an important role in the development of Alzheimer's disease (AD) and Parkinson's disease (PD). The process of understanding how individual GWAS risk loci affect microglia function and mediate susceptibility is complex. A rapidly growing number of publications with genomic datasets and computational tools have formulated new hypotheses that guide the biological interpretation of AD and PD genetic risk. In this review, we discuss the key concepts and challenges in the post-GWAS interpretation of AD and PD GWAS risk alleles. Post-GWAS challenges include the identification of target cell (sub)type(s), causal variants, and target genes. Crucially, the prediction of GWAS-identified disease-risk cell types, variants and genes require validation and functional testing to understand the biological consequences within the pathology of the disorders. Many AD and PD risk genes are highly pleiotropic and perform multiple important functions that might not be equally relevant for the mechanisms by which GWAS risk alleles exert their effect(s). Ultimately, many GWAS risk alleles exert their effect by changing microglia function, thereby altering the pathophysiology of these disorders, and hence, we believe that modelling this context is crucial for a deepened understanding of these disorders.

Functional characterization of human genomic variation linked to polygenic diseases

The burden of human disease lies predominantly in polygenic diseases. Since the early 2000s, genome-wide association studies (GWAS) have identified genetic variants and loci associated with complex traits. These have ranged from variants in coding sequences to mutations in regulatory regions, such as promoters and enhancers, as well as mutations affecting mediators of mRNA stability and other downstream regulators, such as 5' and 3'-untranslated regions (UTRs), long noncoding RNA (lncRNA), and miRNA. Recent research advances in genetics have utilized a combination of computational techniques, high-throughput in vitro and in vivo screening modalities, and precise genome editing to impute the function of diverse classes of genetic variants identified through GWAS. In this review, we highlight the vastness of genomic variants associated with polygenic disease risk and address recent advances in how genetic tools can be used to functionally characterize them.

A proteogenomic view of Parkinson's disease causality and heterogeneity

The pathogenesis and clinical heterogeneity of Parkinson's disease (PD) have been evaluated from molecular, pathophysiological, and clinical perspectives. High-throughput proteomic analysis of cerebrospinal fluid (CSF) opened new opportunities for scrutinizing this heterogeneity. To date, this is the most comprehensive CSF-based proteomics profiling study in PD with 569 patients (350 idiopathic patients, 65 GBA + mutation carriers and 154 LRRK2 + mutation carriers), 534 controls, and 4135 proteins analyzed. Combining CSF aptamer-based proteomics with genetics we determined protein quantitative trait loci (pQTLs). Analyses of pQTLs together with summary statistics from the largest PD genome wide association study (GWAS) identified 68 potential causal proteins by Mendelian randomization. The top causal protein, GPNMB, was previously reported to be upregulated in the substantia nigra of PD patients. We also compared the CSF proteomes of patients and controls. Proteome differences between GBA + patients and unaffected GBA + controls suggest degeneration of dopaminergic neurons, altered dopamine metabolism and increased brain inflammation. In the LRRK2 + subcohort we found dysregulated lysosomal degradation, altered alpha-synuclein processing, and neurotransmission. Proteome differences between idiopathic patients and controls suggest increased neuroinflammation, mitochondrial dysfunction/oxidative stress, altered iron metabolism and potential neuroprotection mediated by vasoactive substances. Finally, we used proteomic data to stratify idiopathic patients into "endotypes". The identified endotypes show differences in cognitive and motor disease progression based on previously reported protein-based risk scores.Our findings not only contribute to the identification of new therapeutic targets but also to shape personalized medicine in CNS neurodegeneration.

Deep learning approaches for noncoding variant prioritization in neurodegenerative diseases

Determining how noncoding genetic variants contribute to neurodegenerative dementias is fundamental to understanding disease pathogenesis, improving patient prognostication, and developing new clinical treatments. Next generation sequencing technologies have produced vast amounts of genomic data on cell type-specific transcription factor binding, gene expression, and three-dimensional chromatin interactions, with the promise of providing key insights into the biological mechanisms underlying disease. However, this data is highly complex, making it challenging for researchers to interpret, assimilate, and dissect. To this end, deep learning has emerged as a powerful tool for genome analysis that can capture the intricate patterns and dependencies within these large datasets. In this review, we organize and discuss the many unique model architectures, development philosophies, and interpretation methods that have emerged in the last few years with a focus on using deep learning to predict the impact of genetic variants on disease pathogenesis. We highlight both broadly-applicable genomic deep learning methods that can be fine-tuned to disease-specific contexts as well as existing neurodegenerative disease research, with an emphasis on Alzheimer's-specific literature. We conclude with an overview of the future of the field at the intersection of neurodegeneration, genomics, and deep learning.

Inflammatory Animal Models of Parkinson’s Disease

Accumulating evidence suggests that microglia and peripheral immune cells may play determinant roles in the pathogenesis of Parkinson’s disease (PD). Consequently, there is a need to take advantage of immune-related models of PD to study the potential contribution of microglia and peripheral immune cells to the degeneration of the nigrostriatal system and help develop potential therapies for PD. In this review, we have summarised the main PD immune models. From a historical perspective, we highlight first the main features of intranigral injections of different pro-inflammogens, including lipopolysaccharide (LPS), thrombin, neuromelanin, etc. The use of adenoviral vectors to promote microglia-specific overexpression of different molecules in the ventral mesencephalon, including α-synuclein, IL-1β, and TNF, are also presented and briefly discussed. Finally, we summarise different models associated with peripheral inflammation whose contribution to the pathogenesis of neurodegenerative diseases is now an outstanding question. Illustrative examples included systemic LPS administration and dextran sulfate sodium-induced colitis in rodents.

Association of a common genetic variant with Parkinson's disease is mediated by microglia

Studies of multiple neurodegenerative disorders have identified many genetic variants that are associated with risk of disease throughout a lifetime. For example, Parkinson's disease (PD) risk is attributed in part to both coding mutations in the leucine-rich repeat kinase 2 (LRRK2) gene and to a common noncoding variation in the 5' region of the LRRK2 locus, as identified by genome-wide association studies (GWAS). However, the mechanisms linking GWAS variants to pathogenicity are largely unknown. Here, we found that the influence of PD-associated noncoding variation on LRRK2 expression is specifically propagated through microglia and not by other cell types that express LRRK2 in the human brain. We find microglia-specific regulatory chromatin regions that modulate the LRRK2 expression in human frontal cortex and substantia nigra and confirm these results in a human-induced pluripotent stem cell-derived microglia model. We showed, using a large-scale clustered regularly interspaced short palindromic repeats interference (CRISPRi) screen, that a regulatory DNA element containing the single-nucleotide variant rs6581593 influences the LRRK2 expression in microglia. Our study demonstrates that cell type should be considered when evaluating the role of noncoding variation in disease pathogenesis and sheds light on the mechanism underlying the association of the 5' region of LRRK2 with PD risk.

What have we learned from genome-wide association studies (GWAS) in Parkinson's disease?

After fifteen years of genome-wide association studies (GWAS) in Parkinson's disease (PD), what have we learned? Addressing this question will help catalogue the progress made towards elucidating disease mechanisms, improving the clinical utility of the identified loci, and envisioning how we can harness the strides to develop translational GWAS strategies. Here we review the advances of PD GWAS made to date while critically addressing the challenges and opportunities for next-generation GWAS. Thus, deciphering the missing heritability in underrepresented populations is currently at the reach of hand for a truly comprehensive understanding of the genetics of PD across the different ethnicities. Moreover, state-of-the-art GWAS designs hold a true potential for enhancing the clinical applicability of genetic findings, for instance, by improving disease prediction (PD risk and progression). Lastly, advanced PD GWAS findings, alone or in combination with clinical and environmental parameters, are expected to have the capacity for defining patient enriched cohorts stratified by genetic risk profiles and readily available for neuroprotective clinical trials. Overall, envisioning future strategies for advanced GWAS is currently timely and can be instrumental in providing novel genetic readouts essential for a true clinical translatability of PD genetic findings.

echolocatoR: an automated end-to-end statistical and functional genomic fine-mapping pipeline

SUMMARY

echolocatoR integrates a diverse suite of statistical and functional fine-mapping tools to identify, test enrichment in, and visualize high-confidence causal consensus variants in any phenotype. It requires minimal input from users (a summary statistics file), can be run in a single R function, and provides extensive access to relevant datasets (e.g. reference linkage disequilibrium panels, quantitative trait loci, genome-wide annotations, cell-type-specific epigenomics), thereby enabling rapid, robust and scalable end-to-end fine-mapping investigations.

AVAILABILITY AND IMPLEMENTATION

echolocatoR is an open-source R package available through GitHub under the GNU General Public License (Version 3) license: https://github.com/RajLabMSSM/echolocatoR.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Multi-omic insights into Parkinson's Disease: From genetic associations to functional mechanisms

Genome-Wide Association Studies (GWAS) have elucidated the genetic components of Parkinson's Disease (PD). However, because the vast majority of GWAS association signals fall within non-coding regions, translating these results into an interpretable, mechanistic understanding of the disease etiology remains a major challenge in the field. In this review, we provide an overview of the approaches to prioritize putative causal variants and genes as well as summarise the primary findings of previous studies. We then discuss recent efforts to integrate multi-omics data to identify likely pathogenic cell types and biological pathways implicated in PD pathogenesis. We have compiled full summary statistics of cell-type, tissue, and phentoype enrichment analyses from multiple studies of PD GWAS and provided them in a standardized format as a resource for the research community (https://github.com/RajLabMSSM/PD_omics_review). Finally, we discuss the experimental, computational, and conceptual advances that will be necessary to fully elucidate the effects of functional variants and genes on cellular dysregulation and disease risk.