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

Elucidating the Genetic Underpinnings of Human Musculoskeletal System Aging Through Genomic Structural Equation Modeling

The genetic architecture underlying traits related to Human Musculoskeletal System Aging (MSA) remains largely unexplored. In this study, we conducted a large-scale multivariate genome-wide association study (GWAS) of MSA utilizing Genomic Structural Equation Modeling (Genomic SEM). We estimated causal single nucleotide polymorphisms (SNPs) associated with independent variation and identified 14 genome-wide significant loci (mean.PP > 0.95). We employed multiple transcriptome-wide association methods to analyze tissue, cellular levels, and genomic elements, identifying loci with high relevance to MSA susceptibility, along with associated element information. Our research represents the first comprehensive delineation of the genetic architecture of Musculoskeletal System Aging through a GWAS of unmeasured phenotype.

Dissecting the Genetic Architecture of Intracranial Aneurysms

BACKGROUND

The genetic risk of intracranial aneurysm (IA) development has been ascribed to the genetic risk of smoking exposure and hypertension. The relationship of IA to other cardiovascular traits and the contribution of IA risk loci to aberrant gene programs within cerebrovascular cell types remains unclear.

METHODS

We performed a genome-wide association study in the Million Veteran Program and Finnish cohort study testing association of roughly 25 million DNA variants with unruptured IA (4694 cases and 877 091 controls) in individuals of European, African, and Hispanic ancestries. Meta-analysis with publicly available summary statistics generated a final cohort of 15 438 cases and 1 183 973 controls. We constructed a cerebrovascular single-nuclear RNA sequencing data set and integrated IA summary statistics to prioritize candidate causal cell types. We constructed a polygenic risk score to identify patients at risk of developing IA.

RESULTS

We identified 5 novel associations with IA, increasing the number of known susceptibility loci to 22. At these susceptibility loci, we prioritized 17 candidate causal genes. We found a significant positive genetic correlation of IA with coronary artery disease and abdominal aortic aneurysm. Integration of an IA gene set with cerebrovascular single-nuclear RNA sequencing data revealed a significant association with pericytes and smooth muscle cells. Finally, a polygenic risk score was significantly associated with IA across European (odds ratio, 1.87 [95% CI, 1.61-2.17]; P=8.8×10-17), African (odds ratio, 1.62 [95% CI, 1.19-2.15]; P=1.2×10-3), and Hispanic (odds ratio, 2.28 [95% CI, 1.47-3.38]; P=1.0×10-4) ancestries.

CONCLUSIONS

Here, we identify 5 novel loci associated with IA. Integration of summary statistics with cerebrovascular single-nuclear RNA sequencing reveals an association of cell types involved in matrix production. We validated a polygenic risk score that predicts IA, controlling for demographic variables including smoking status and blood pressure. Our findings suggest that a deficit in matrix production may drive IA pathogenesis independent of hypertension and smoking.

Chromatin Interaction and Histone Mark Signatures Associated With TBXT Expression in Metastatic Lung Cancer

BACKGROUND

TBXT, a member of the T-box transcription factor family, drives epithelial-to-mesenchymal transition in the metastasis of some cancers. However, the relationship between the epigenetic regulatory landscape and its expression in lung cancers remains elusive.

METHODS

Circularized chromosome capture combined with sequencing (4C-seq) was employed to analyze physical chromatin interactions at the TBXT loci in the lung cancer cell line H460, a high TBXT-expressing cell line, compared to H358 and A549, which do not express TBXT. To define the regulatory landscape, the targeted TBXT chromatin interactions were integrated with histone modification profiles from respective cells, followed with motif analysis.

RESULTS

Our analysis identified distinct patterns of potential cis-regulatory elements (pCREs) associated with the TBXT promoter, with increased near-cis pCRE enrichment in the TBXT-expressing cells. Integration of pCREs with epigenetic histone modification revealed two unique pCREs in TBXT-expressing H460 cells enriched with the active histone mark H3K27ac, harboring binding sites for transcription factors of the forkhead box, zinc finger, and musculoaponeurotic fibrosarcoma families that are linked to cancer metastasis.

CONCLUSION

Our findings shed light on active chromatin interactions with TBXT expression in lung cancers, pointing to specific DNA elements and regulatory proteins that may be involved. This knowledge paves the way for understanding TBXT expression dynamics at the onset and progression of metastatic cancers.

Fine-mapping genomic loci refines bipolar disorder risk genes

Bipolar disorder (BD) is a heritable mental illness with complex etiology. While the largest published genome-wide association study identified 64 BD risk loci, the causal SNPs and genes within these loci remain unknown. We applied a suite of statistical and functional fine-mapping methods to these loci, and prioritized 17 likely causal SNPs for BD. We mapped these SNPs to genes, and investigated their likely functional consequences by integrating variant annotations, brain cell-type epigenomic annotations, brain quantitative trait loci, and results from rare variant exome sequencing in BD. Convergent lines of evidence supported the roles of genes involved in neurotransmission and neurodevelopment including SCN2A, TRANK1, DCLK3, INSYN2B, SYNE1, THSD7A, CACNA1B, TUBBP5, PLCB3, PRDX5, KCNK4, CRTC3, AP001453.3, TRPT1, FKBP2, DNAJC4, RASGRP1, FURIN, FES, DPH1, GSDMB, MED24 and THRA in BD. These represent promising candidates for functional experiments to understand biological mechanisms and therapeutic potential. Additionally, we demonstrated that fine-mapping effect sizes can improve performance of BD polygenic risk scores across diverse populations, and present a high-throughput fine-mapping pipeline (https://github.com/mkoromina/SAFFARI).

The goldmine of GWAS summary statistics: a systematic review of methods and tools

Genome-wide association studies (GWAS) have revolutionized our understanding of the genetic architecture of complex traits and diseases. GWAS summary statistics have become essential tools for various genetic analyses, including meta-analysis, fine-mapping, and risk prediction. However, the increasing number of GWAS summary statistics and the diversity of software tools available for their analysis can make it challenging for researchers to select the most appropriate tools for their specific needs. This systematic review aims to provide a comprehensive overview of the currently available software tools and databases for GWAS summary statistics analysis. We conducted a comprehensive literature search to identify relevant software tools and databases. We categorized the tools and databases by their functionality, including data management, quality control, single-trait analysis, and multiple-trait analysis. We also compared the tools and databases based on their features, limitations, and user-friendliness. Our review identified a total of 305 functioning software tools and databases dedicated to GWAS summary statistics, each with unique strengths and limitations. We provide descriptions of the key features of each tool and database, including their input/output formats, data types, and computational requirements. We also discuss the overall usability and applicability of each tool for different research scenarios. This comprehensive review will serve as a valuable resource for researchers who are interested in using GWAS summary statistics to investigate the genetic basis of complex traits and diseases. By providing a detailed overview of the available tools and databases, we aim to facilitate informed tool selection and maximize the effectiveness of GWAS summary statistics analysis.

Genome-wide association study implicates lipid pathway dysfunction in antipsychotic-induced weight gain: multi-ancestry validation

Antipsychotic-induced weight gain (AIWG) is a common side effect of antipsychotic medication and may contribute to diabetes and coronary heart disease. To expand the unclear genetic mechanism underlying AIWG, we conducted a two-stage genome-wide association study in Han Chinese patients with schizophrenia. The study included a discovery cohort of 1936 patients and a validation cohort of 534 patients, with an additional 630 multi-ancestry patients from the CATIE study for external validation. We applied Mendelian randomization (MR) analysis to investigate the relationship between AIWG and antipsychotic-induced lipid changes. Our results identified two novel genome-wide significant loci associated with AIWG: rs10422861 in PEPD (P = 1.373 × 10-9) and rs3824417 in PTPRD (P = 3.348 × 10-9) in Chinese Han samples. The association of rs10422861 was validated in the European samples. Fine-mapping and functional annotation revealed that PEPD and PTPRD are potentially causal genes for AIWG, with their proteins being prospective therapeutic targets. Colocalization analysis suggested that AIWG and type 2 diabetes (T2D) shared a causal variant in PEPD. Polygenic risk scores (PRSs) for AIWG and T2D significantly predicted AIWG in multi-ancestry samples. Furthermore, MR revealed a risky causal effect of genetically predicted changes in low-density lipoprotein cholesterol (P = 7.58 × 10-4) and triglycerides (P = 2.06 × 10-3) caused by acute-phase of antipsychotic treatment on AIWG, which had not been previously reported. Our model, incorporating antipsychotic-induced lipid changes, PRSs, and clinical predictors, significantly predicted BMI percentage change after 6-month antipsychotic treatment (AUC = 0.79, R2 = 0.332). Our results highlight that the mechanism of AIWG involves lipid pathway dysfunction and may share a genetic basis with T2D through PEPD. Overall, this study provides new insights into the pathogenesis of AIWG and contributes to personalized treatment of schizophrenia.

A machine learning approach for gene prioritization in Parkinson's disease

This scientific commentary refers to ‘Machine learning nominates the inositol pathway and novel genes in Parkinson’s disease’ by Yu et al. (https://doi.org/10.1093/brain/awad345).

rworkflows: automating reproducible practices for the R community

Despite calls to improve reproducibility in research, achieving this goal remains elusive even within computational fields. Currently, >50% of R packages are distributed exclusively through GitHub. While the trend towards sharing open-source software has been revolutionary, GitHub does not have any default built-in checks for minimal coding standards or software usability. This makes it difficult to assess the current quality R packages, or to consistently use them over time and across platforms. While GitHub-native solutions are technically possible, they require considerable time and expertise for each developer to write, implement, and maintain. To address this, we develop rworkflows; a suite of tools to make robust continuous integration and deployment ( https://github.com/neurogenomics/rworkflows ). rworkflows can be implemented by developers of all skill levels using a one-time R function call which has both sensible defaults and extensive options for customisation. Once implemented, any updates to the GitHub repository automatically trigger parallel workflows that install all software dependencies, run code checks, generate a dedicated documentation website, and deploy a publicly accessible containerised environment. By making the rworkflows suite free, automated, and simple to use, we aim to promote widespread adoption of reproducible practices across a continually growing R community.

Genome-wide analysis of a model-derived binge eating disorder phenotype identifies risk loci and implicates iron metabolism

Binge eating disorder (BED) is the most common eating disorder, yet its genetic architecture remains largely unknown. Studying BED is challenging because it is often comorbid with obesity, a common and highly polygenic trait, and it is underdiagnosed in biobank data sets. To address this limitation, we apply a supervised machine-learning approach (using 822 cases of individuals diagnosed with BED) to estimate the probability of each individual having BED based on electronic medical records from the Million Veteran Program. We perform a genome-wide association study of individuals of African (n = 77,574) and European (n = 285,138) ancestry while controlling for body mass index to identify three independent loci near the HFE, MCHR2 and LRP11 genes and suggest APOE as a risk gene for BED. We identify shared heritability between BED and several neuropsychiatric traits, and implicate iron metabolism in the pathophysiology of BED. Overall, our findings provide insights into the genetics underlying BED and suggest directions for future translational research.

Genetic meta-analysis of levodopa induced dyskinesia in Parkinson's disease

The genetic basis of levodopa-induced-dyskinesia (LiD) is poorly understood, and there have been few well-powered genome-wide studies. We performed a genome-wide survival meta-analyses to study the effect of genetic variation on the development of LiD in five separate longitudinal cohorts, and meta-analysed the results. We included 2784 PD patients, of whom 14.6% developed LiD. We found female sex (HR = 1.35, SE = 0.11, P = 0.007) and younger age at onset (HR = 1.8, SE = 0.14, P = 2 × 10-5) increased the probability of developing LiD. We identified three genetic loci significantly associated with time-to-LiD onset. rs72673189 on chromosome 1 (HR = 2.77, SE = 0.18, P = 1.53 × 10-8) located at the LRP8 locus, rs189093213 on chromosome 4 (HR = 3.06, SE = 0.19, P = 2.81 × 10-9) in the non-coding RNA LINC02353 locus, and rs180924818 on chromosome 16 (HR = 3.13, SE = 0.20, P = 6.27 × 10-9) in the XYLT1 locus. Based on a functional annotation analysis on chromosome 1, we determined that changes in DNAJB4 gene expression, close to LRP8, are an additional potential cause of increased susceptibility to LiD. Baseline anxiety status was significantly associated with LiD (OR = 1.14, SE = 0.03, P = 7.4 × 10-5). Finally, we performed a candidate variant analysis of previously reported loci, and found that genetic variability in ANKK1 (rs1800497, HR = 1.27, SE = 0.09, P = 8.89 × 10-3) and BDNF (rs6265, HR = 1.21, SE = 0.10, P = 4.95 × 10-2) loci were significantly associated with time to LiD in our large meta-analysis.

Multivariate genome-wide analysis of aging-related traits identifies novel loci and new drug targets for healthy aging

The concept of aging is complex, including many related phenotypes such as healthspan, lifespan, extreme longevity, frailty and epigenetic aging, suggesting shared biological underpinnings; however, aging-related endpoints have been primarily assessed individually. Using data from these traits and multivariate genome-wide association study methods, we modeled their underlying genetic factor ('mvAge'). mvAge (effective n = ~1.9 million participants of European ancestry) identified 52 independent variants in 38 genomic loci. Twenty variants were novel (not reported in input genome-wide association studies). Transcriptomic imputation identified age-relevant genes, including VEGFA and PHB1. Drug-target Mendelian randomization with metformin target genes showed a beneficial impact on mvAge (P value = 8.41 × 10-5). Similarly, genetically proxied thiazolidinediones (P value = 3.50 × 10-10), proprotein convertase subtilisin/kexin 9 inhibition (P value = 1.62 × 10-6), angiopoietin-like protein 4, beta blockers and calcium channel blockers also had beneficial Mendelian randomization estimates. Extending the drug-target Mendelian randomization framework to 3,947 protein-coding genes prioritized 122 targets. Together, these findings will inform future studies aimed at improving healthy aging.

Not every estimate counts - evaluation of cell composition estimation approaches in brain bulk tissue data

BACKGROUND

Variation in cell composition can dramatically impact analyses in bulk tissue samples. A commonly employed approach to mitigate this issue is to adjust statistical models using estimates of cell abundance derived directly from omics data. While an arsenal of estimation methods exists, the applicability of these methods to brain tissue data and whether or not cell estimates can sufficiently account for confounding cellular composition has not been adequately assessed.

METHODS

We assessed the correspondence between different estimation methods based on transcriptomic (RNA sequencing, RNA-seq) and epigenomic (DNA methylation and histone acetylation) data from brain tissue samples of 49 individuals. We further evaluated the impact of different estimation approaches on the analysis of H3K27 acetylation chromatin immunoprecipitation sequencing (ChIP-seq) data from entorhinal cortex of individuals with Alzheimer's disease and controls.

RESULTS

We show that even closely adjacent tissue samples from the same Brodmann area vary greatly in their cell composition. Comparison across different estimation methods indicates that while different estimation methods applied to the same data produce highly similar outcomes, there is a surprisingly low concordance between estimates based on different omics data modalities. Alarmingly, we show that cell type estimates may not always sufficiently account for confounding variation in cell composition.

CONCLUSIONS

Our work indicates that cell composition estimation or direct quantification in one tissue sample should not be used as a proxy to the cellular composition of another tissue sample from the same brain region of an individual-even if the samples are directly adjacent. The highly similar outcomes observed among vastly different estimation methods, highlight the need for brain benchmark datasets and better validation approaches. Finally, unless validated through complementary experiments, the interpretation of analyses outcomes based on data confounded by cell composition should be done with great caution, and ideally avoided all together.

Genetic meta-analysis of levodopa induced dyskinesia in Parkinson's disease

Importance

Forty percent of Parkinson's disease patients develop levodopa-induced-dyskinesia (LiD) within 4 years of starting levodopa. The genetic basis of LiD remains poorly understood, and there have been few well powered studies.

Objective

To discover common genetic variants in the PD population that increase the probability of developing LiD.

Design setting and Participants

We performed survival analyses to study the development of LiD in 5 separate longitudinal cohorts. We performed a meta-analysis to combine the results of genetic association from each study based on a fixed effects model weighting the effect sizes by the inverse of their standard error. The selection criteria was specific to each cohort. We studied individuals that were genotyped from each cohort and that passed our analysis specific inclusion criteria.

Main Outcomes and Measures

We measured the time for PD patients on levodopa treatment to develop LiD as defined by reaching a score higher or equal than 2 from the MDS-UPDRS part IV, item 1, which is equivalent to a range of 26%-50% of the waking time with dyskinesia. We carried out a genome-wide analysis of the hazard ratio and the association of genome-wide SNPs with the probability of developing LiD using cox proportional hazard models (CPH).

Results

This study included 2,784 PD patients of European ancestry, of whom 14.6% developed LiD. Consistent with previous studies, we found female gender (HR = 1.35, SE = 0.11, P = 0.007) and younger age at onset (HR = 1.8, SE = 0.14, P = 2 × 10 -5 ) to increase the probability of developing LiD. We identified three loci significantly associated with time-to-LiD onset. rs72673189 on chromosome 1 (HR = 2.77, SE = 0.18, P = 1.53 × 10 -8 ) located in the LRP8 locus, rs189093213 on chromosome 4 (HR = 3.06,, SE = 0.19, P = 2.81 × 10 -9 ) in the non-coding RNA LINC02353 locus, and rs180924818 on chromosome 16 (HR = 3.13, SE = 0.20, P = 6.27 × 10 -9 ) in the XYLT1 locus. Subsequent colocalization analyses on chromosome 1 identified DNAJB4 as a candidate gene associated with LiD through a change in gene expression. We computed a PRS based on our GWAS meta-analysis and found high accuracy to stratify between PD-LID and PD (AUC 83.9). We also performed a stepwise regression analysis for baseline features selection associated with LiD status. We found baseline anxiety status to be significantly associated with LiD (OR = 1.14, SE = 0.03, P = 7.4 × 10 -5 ). Finally, we performed a candidate variant analysis and found that genetic variability in ANKK1 ( rs1800497 , Beta = 0.24, SE = 0.09, P = 8.89 × 10 -3 ) and BDNF ( rs6265 , Beta = 0.19, SE = 0.10, P = 4.95 × 10 -2 ) loci were significantly associated with time to LiD in our large meta-analysis.

Conclusion

In this association study, we have found three novel genetic variants associated with LiD, as well as confirming reports that variability in ANKK1 and BDNF loci were significantly associated with LiD probability. A PRS nominated from our time-to-LiD meta-analysis significantly differentiated between PD-LiD and PD. In addition, we have found female gender, young PD onset and anxiety to be significantly associated with LiD.

GeneticsMakie.jl: a versatile and scalable toolkit for visualizing locus-level genetic and genomic data

SUMMARY

With the continued deluge of results from genome-wide association and functional genomic studies, it has become increasingly imperative to quickly combine and visualize different layers of genetic and genomic data within a given locus to facilitate exploratory and integrative data analyses. While several tools have been developed to visualize locus-level genetic results, the limited speed, scalability and flexibility of current approaches remain a significant bottleneck. Here, we present a Julia package for high-performance genetics and genomics-related data visualization that enables fast, simultaneous plotting of hundreds of association results along with multiple relevant genomic annotations. Leveraging the powerful plotting and layout utilities from Makie.jl facilitates the customization and extensibility of every component of a plot, enabling generation of publication-ready figures.

AVAILABILITY AND IMPLEMENTATION

The GeneticsMakie.jl package is open source and distributed under the MIT license via GitHub (https://github.com/mmkim1210/GeneticsMakie.jl). The GitHub repository contains installation instructions as well as examples and documentation for built-in functions.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Brain Catalog: a comprehensive resource for the genetic landscape of brain-related traits

A broad range of complex phenotypes are related to dysfunctions in brain (hereafter referred to as brain-related traits), including various mental and behavioral disorders and diseases of the nervous system. These traits in general share overlapping symptoms, pathogenesis, and genetic components. Here, we present Brain Catalog (https://ngdc.cncb.ac.cn/braincatalog), a comprehensive database aiming to delineate the genetic components of more than 500 GWAS summary statistics datasets for brain-related traits from multiple aspects. First, Brain Catalog provides results of candidate causal variants, causal genes, and functional tissues and cell types for each trait identified by multiple methods using comprehensive annotation datasets (58 QTL datasets spanning 6 types of QTLs). Second, Brain Catalog estimates the SNP-based heritability, the partitioning heritability based on functional annotations, and genetic correlations among traits. Finally, through bidirectional Mendelian randomization analyses, Brain Catalog presents inference of risk factors that are likely causal to each trait. In conclusion, Brain Catalog presents a one-stop shop for the genetic components of brain-related traits, potentially serving as a valuable resource for worldwide researchers to advance the understanding of how GWAS signals may contribute to the biological etiology of brain-related traits.

Genome-wide association study identifies Sjögren's risk loci with functional implications in immune and glandular cells

Sjögren's disease is a complex autoimmune disease with twelve established susceptibility loci. This genome-wide association study (GWAS) identifies ten novel genome-wide significant (GWS) regions in Sjögren's cases of European ancestry: CD247, NAB1, PTTG1-MIR146A, PRDM1-ATG5, TNFAIP3, XKR6, MAPT-CRHR1, RPTOR-CHMP6-BAIAP6, TYK2, SYNGR1. Polygenic risk scores yield predictability (AUROC = 0.71) and relative risk of 12.08. Interrogation of bioinformatics databases refine the associations, define local regulatory networks of GWS SNPs from the 95% credible set, and expand the implicated gene list to >40. Many GWS SNPs are eQTLs for genes within topologically associated domains in immune cells and/or eQTLs in the main target tissue, salivary glands.

Flashfm-ivis: interactive visualization for fine-mapping of multiple quantitative traits

SUMMARY

flashfm-ivis provides a suite of interactive visualization plots to view potential causal genetic variants that underlie associations that are shared or distinct between multiple quantitative traits and compares results between single- and multi-trait fine-mapping. Unique features include network diagrams that show joint effects between variants for each trait and regional association plots that integrate fine-mapping results, all with user-controlled zoom features for an interactive exploration of potential causal variants across traits.

AVAILABILITY AND IMPLEMENTATION

flashfm-ivis is an open-source software under the MIT license. It is available as an interactive web-based tool (http://shiny.mrc-bsu.cam.ac.uk/apps/flashfm-ivis/) and as an R package. Code and documentation are available at https://github.com/fz-cambridge/flashfm-ivis and https://zenodo.org/record/6376244#.YjnarC-l2X0. Additional features can be downloaded as standalone R libraries to encourage reuse.

SUPPLEMENTARY INFORMATION

Supplementary information are available at Bioinformatics online.

Genome-wide association analysis and replication in 810,625 individuals with varicose veins

Varicose veins affect one-third of Western society, with a significant subset of patients developing venous ulceration, costing $14.9 billion annually in the USA. Current management consists of either compression stockings, or surgical ablation for more advanced disease. Most varicose veins patients report a positive family history, and heritability is ~17%. We describe the largest two-stage genome-wide association study of varicose veins in 401,656 individuals from UK Biobank, and replication in 408,969 individuals from 23andMe (total 135,514 cases and 675,111 controls). Forty-nine signals at 46 susceptibility loci were discovered. We map 237 genes to these loci, several of which are biologically plausible and tractable to therapeutic targeting. Pathway analysis identified enrichment in extracellular matrix biology, inflammation, (lymph)angiogenesis, vascular smooth muscle cell migration, and apoptosis. Using a polygenic risk score (PRS) derived in an independent cohort, we demonstrate its predictive utility and correlation with varicose veins surgery.

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

Recent genome-wide association studies have identified 78 loci associated with Parkinson's disease susceptibility but the underlying mechanisms remain largely unclear. To identify likely causal variants for disease risk, we fine-mapped these Parkinson's-associated loci using four different fine-mapping methods. We then integrated multi-assay cell type-specific epigenomic profiles to pinpoint the likely mechanism of action of each variant, allowing us to identify Consensus single nucleotide polymorphism (SNPs) that disrupt LRRK2 and FCGR2A regulatory elements in microglia, an MBNL2 enhancer in oligodendrocytes, and a DYRK1A enhancer in neurons. This genome-wide functional fine-mapping investigation of Parkinson's disease substantially advances our understanding of the causal mechanisms underlying this complex disease while avoiding focus on spurious, non-causal mechanisms. Together, these results provide a robust, comprehensive list of the likely causal variants, genes and cell-types underlying Parkinson's disease risk as demonstrated by consistently greater enrichment of our fine-mapped SNPs relative to lead GWAS SNPs across independent functional impact annotations. In addition, our approach prioritized an average of 3/85 variants per locus as putatively causal, making downstream experimental studies both more tractable and more likely to yield disease-relevant, actionable results. Large-scale studies comparing individuals with Parkinson's disease to age-matched controls have identified many regions of the genome associated with the disease. However, there is widespread correlation between different parts of the genome, making it difficult to tell which genetic variants cause Parkinson's and which are simply co-inherited with causal variants. We therefore applied a suite of statistical models to identify the most likely causal genetic variants (i.e. fine-mapping). We then linked these genetic variants with epigenomic and gene expression signatures across a wide variety of tissues and cell types to identify how these variants cause disease. Therefore, this study provides a comprehensive and robust list of cellular and molecular mechanisms that may serve as targets in the development of more effective Parkinson's therapeutics.

Genetic analysis of the human microglial transcriptome across brain regions, aging and disease pathologies

Microglia have emerged as important players in brain aging and pathology. To understand how genetic risk for neurological and psychiatric disorders is related to microglial function, large transcriptome studies are essential. Here we describe the transcriptome analysis of 255 primary human microglial samples isolated at autopsy from multiple brain regions of 100 individuals. We performed systematic analyses to investigate various aspects of microglial heterogeneities, including brain region and aging. We mapped expression and splicing quantitative trait loci and showed that many neurological disease susceptibility loci are mediated through gene expression or splicing in microglia. Fine-mapping of these loci nominated candidate causal variants that are within microglia-specific enhancers, finding associations with microglial expression of USP6NL for Alzheimer's disease and P2RY12 for Parkinson's disease. We have built the most comprehensive catalog to date of genetic effects on the microglial transcriptome and propose candidate functional variants in neurological and psychiatric disorders.

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.

Dysregulation of mitochondrial and proteolysosomal genes in Parkinson's disease myeloid cells

An increasing number of identified Parkinson's disease (PD) risk loci contain genes highly expressed in innate immune cells, yet their role in pathology is not understood. We hypothesize that PD susceptibility genes modulate disease risk by influencing gene expression within immune cells. To address this, we have generated transcriptomic profiles of monocytes from 230 individuals with sporadic PD and healthy subjects. We observed a dysregulation of mitochondrial and proteasomal pathways. We also generated transcriptomic profiles of primary microglia from brains of 55 subjects and observed discordant transcriptomic signatures of mitochondrial genes in PD monocytes and microglia. We further identified 17 PD susceptibility genes whose expression, relative to each risk allele, is altered in monocytes. These findings reveal widespread transcriptomic alterations in PD monocytes, with some being distinct from microglia, and facilitate efforts to understand the roles of myeloid cells in PD as well as the development of biomarkers.