Publications
46 results found
Schilder B, Murphy A, Skene N, 2024, rworkflows: automating reproducible practices for the R community, Nature Communications, Vol: 15, ISSN: 2041-1723
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.
Murphy AE, Fancy N, Skene N, 2023, Avoiding false discoveries in single-cell RNA-seq by revisiting the first Alzheimer's disease dataset, eLife, Vol: 12, ISSN: 2050-084X
Mathys et al. conducted the first single-nucleus RNA-seq (snRNA-seq) study of Alzheimer's disease (AD) (Mathys et al., 2019). With bulk RNA-seq, changes in gene expression across cell types can be lost, potentially masking the differentially expressed genes (DEGs) across different cell types. Through the use of single-cell techniques, the authors benefitted from increased resolution with the potential to uncover cell type-specific DEGs in AD for the first time. However, there were limitations in both their data processing and quality control and their differential expression analysis. Here, we correct these issues and use best-practice approaches to snRNA-seq differential expression, resulting in 549 times fewer DEGs at a false discovery rate of 0.05. Thus, this study highlights the impact of quality control and differential analysis methods on the discovery of disease-associated genes and aims to refocus the AD research field away from spuriously identified genes.
Murphy A, Fancy N, Skene N, 2023, Avoiding false discoveries in single-cell RNA-seq by revisiting the first Alzheimer’s disease dataset, eLife, Vol: 12, ISSN: 2050-084X
Mathys et al. conducted the first single-nucleus RNA-seq (snRNA-seq) study of Alzheimer’s disease (AD) (Mathys et al., 2019). With bulk RNA-seq, changes in gene expression across cell types can be lost, potentially masking the differentially expressed genes (DEGs) across different cell types. Through the use of single-cell techniques, the authors benefitted from increased resolution with the potential to uncover cell type-specific DEGs in AD for the first time. However, there were limitations in both their data processing and quality control and their differential expression analysis. Here, we correct these issues and use best-practice approaches to snRNA-seq differential expression, resulting in 549 times fewer DEGs at a false discovery rate of 0.05. Thus, this study highlights the impact of quality control and differential analysis methods on the discovery of disease-associated genes and aims to refocus the AD research field away from spuriously identified genes.
Bettencourt C, Skene N, Bandres-Ciga S, et al., 2023, Artificial intelligence for dementia genetics and omics, ALZHEIMERS & DEMENTIA, ISSN: 1552-5260
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- Citations: 2
Choi S, Schilder BM, Abbasova L, et al., 2023, EpiCompare: R package for the comparison and quality control of epigenomic peak files, Bioinformatics Advances, Vol: 3, ISSN: 2635-0041
SUMMARY: EpiCompare combines a variety of downstream analysis tools to compare, quality control and benchmark different epigenomic datasets. The package requires minimal input from users, can be run with just one line of code and provides all results of the analysis in a single interactive HTML report. EpiCompare thus enables downstream analysis of multiple epigenomic datasets in a simple, effective and user-friendly manner. AVAILABILITY AND IMPLEMENTATION: EpiCompare is available on Bioconductor (≥ v3.15): https://bioconductor.org/packages/release/bioc/html/EpiCompare.html; all source code is publicly available via GitHub: https://github.com/neurogenomics/EpiCompare; documentation website https://neurogenomics.github.io/EpiCompare; and EpiCompare DockerHub repository: https://hub.docker.com/repository/docker/neurogenomicslab/epicompare.
Mucha M, Skrzypiec AE, Kolenchery JB, et al., 2023, miR-483-5p offsets functional and behavioural effects of stress in male mice through synapse-targeted repression of <i>Pgap2</i> in the basolateral amygdala, NATURE COMMUNICATIONS, Vol: 14
Wahedi A, Soondram C, Murphy AE, et al., 2023, Transcriptomic analyses reveal neuronal specificity of Leigh syndrome associated genes, JOURNAL OF INHERITED METABOLIC DISEASE, Vol: 46, Pages: 243-260, ISSN: 0141-8955
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- Citations: 2
Ranson JM, Bucholc M, Lyall D, et al., 2023, Harnessing the potential of machine learning and artificial intelligence for dementia research, Brain Informatics, Vol: 10, ISSN: 2198-4018
Progress in dementia research has been limited, with substantial gaps in our knowledge of targets for prevention, mechanisms for disease progression, and disease-modifying treatments. The growing availability of multimodal data sets opens possibilities for the application of machine learning and artificial intelligence (AI) to help answer key questions in the field. We provide an overview of the state of the science, highlighting current challenges and opportunities for utilisation of AI approaches to move the field forward in the areas of genetics, experimental medicine, drug discovery and trials optimisation, imaging, and prevention. Machine learning methods can enhance results of genetic studies, help determine biological effects and facilitate the identification of drug targets based on genetic and transcriptomic information. The use of unsupervised learning for understanding disease mechanisms for drug discovery is promising, while analysis of multimodal data sets to characterise and quantify disease severity and subtype are also beginning to contribute to optimisation of clinical trial recruitment. Data-driven experimental medicine is needed to analyse data across modalities and develop novel algorithms to translate insights from animal models to human disease biology. AI methods in neuroimaging outperform traditional approaches for diagnostic classification, and although challenges around validation and translation remain, there is optimism for their meaningful integration to clinical practice in the near future. AI-based models can also clarify our understanding of the causality and commonality of dementia risk factors, informing and improving risk prediction models along with the development of preventative interventions. The complexity and heterogeneity of dementia requires an alternative approach beyond traditional design and analytical approaches. Although not yet widely used in dementia research, machine learning and AI have the potential to unlock curr
Schilder B, Murphy A, Skene N, 2023, The rworkflows suite: automated continuous integration for quality checking, documentation website creation, and containerised deployment of R packages, Publisher: Research Square
Reproducibility is essential to the progress of research, yet achieving it remains elusive even in computational fields. Continuous Integration (CI) platforms offer a powerful way to launch automated workflows to check and document code, but often require considerable time, effort, and technical expertise to setup. We therefore developed the rworkflows suite to make robust CI workflows easy and freely accessible to all R package developers (https://github.com/neurogenomics/rworkflows). rworkflows consists of 1) a CRAN/Bioconductor-compatible R package template, 2) an R package to quickly implement a standardised workflow, and 3) a centrally maintained GitHub Action. Each time it is triggered by a push to a GitHub repository, it automatically creates virtual machines across multiple OS, installs all dependencies, runs code checks, builds/deploys a documentation website, and builds/deploys version-controlled containers with a built-in RStudio interface. Additional analyses demonstrate that >50% of all R packages are only available via GitHub, highlighting the need for accessible solutions. Thus, rworkflows greatly reduces the barriers to implementing robust and reproducible best practices.
Murphy AE, Skene NG, 2022, A balanced measure shows superior performance of pseudobulk methods in single-cell RNA-sequencing analysis, NATURE COMMUNICATIONS, Vol: 13
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- Citations: 5
Ranson JM, Khleifat AA, Lyall DM, et al., 2022, The Deep Dementia Phenotyping (DEMON) Network: A global platform for innovation using data science and artificial intelligence, Alzheimer's & Dementia, Vol: 18, ISSN: 1552-5260
<jats:title>Abstract</jats:title><jats:sec><jats:title>Background</jats:title><jats:p>The increasing availability of large high‐dimensional data from experimental medicine, population‐based and clinical cohorts, clinical trials, and electronic health records has the potential to transform dementia research. Our ability to make best use of this rich data will depend on utilisation of advanced machine learning and artificial intelligence (AI) techniques and collaboration across disciplinary and geographic boundaries.</jats:p></jats:sec><jats:sec><jats:title>Method</jats:title><jats:p>The Deep Dementia Phenotyping (DEMON) Network launched in 2019<jats:sup>1</jats:sup> to support the growing interest in machine learning and AI. Led by Director Prof David Llewellyn and Deputy Director Dr Janice Ranson, the leadership team additionally includes 5 Theme Leads and 14 Working Group Leads, supported by an international Steering Committee of world‐leading academics. Core funding is provided by Alzheimer’s Research UK, the Alan Turing Institute and the University of Exeter, with additional support from strategic partners including the UK Dementia Research Institute and the Alzheimer’s Society. Grand Challenges were established at a National Strategy Workshop in June 2020. Multidisciplinary Working Groups were formed to coordinate practical activities in seven key areas: Genetics and omics, experimental medicine, drug discovery and trials optimisation, biomarkers, imaging, dementia prevention, and applied models and digital health. Additional Special Interest Groups coordinate topic specific collaborations.</jats:p></jats:sec><jats:sec><jats:title>Result</jats:title><jats:p>Membership on 4th February 2022 comprised 1,321 individuals from 61 countries across 6 continents (see Figure). Areas of expertise include dementia research (904; 68%), data scie
Ranson JM, Khleifat AA, Lyall DM, et al., 2022, The deep dementia phenotyping (DEMON) network: a global platform for innovation using data science and artificial intelligence., Alzheimer's and Dementia, Vol: 18, Pages: 1-2, ISSN: 1552-5260
BACKGROUND: The increasing availability of large high-dimensional data from experimental medicine, population-based and clinical cohorts, clinical trials, and electronic health records has the potential to transform dementia research. Our ability to make best use of this rich data will depend on utilisation of advanced machine learning and artificial intelligence (AI) techniques and collaboration across disciplinary and geographic boundaries. METHOD: The Deep Dementia Phenotyping (DEMON) Network launched in 20191 to support the growing interest in machine learning and AI. Led by Director Prof David Llewellyn and Deputy Director Dr Janice Ranson, the leadership team additionally includes 5 Theme Leads and 14 Working Group Leads, supported by an international Steering Committee of world-leading academics. Core funding is provided by Alzheimer's Research UK, the Alan Turing Institute and the University of Exeter, with additional support from strategic partners including the UK Dementia Research Institute and the Alzheimer's Society. Grand Challenges were established at a National Strategy Workshop in June 2020. Multidisciplinary Working Groups were formed to coordinate practical activities in seven key areas: Genetics and omics, experimental medicine, drug discovery and trials optimisation, biomarkers, imaging, dementia prevention, and applied models and digital health. Additional Special Interest Groups coordinate topic specific collaborations. RESULT: Membership on 4th February 2022 comprised 1,321 individuals from 61 countries across 6 continents (see Figure). Areas of expertise include dementia research (904; 68%), data science (692; 52%), clinical practice (244; 18%), industry (162; 12%), and regulation (26; 2%). Individual membership is free, and regular knowledge transfer events are provided including a monthly seminar series, talks and workshops, training, networking, and early career development. Each Working Group meets monthly, with multiple grants, reviews
McIntosh A, Lu Y, Skene N, 2022, CELL- AND TISSUE-TYPE ENRICHMENT TESTING BASED ON GENETIC ASSOCIATION STUDIES, World Congress of Psychiatric Genetics (WCPG), Publisher: ELSEVIER, Pages: E35-E35, ISSN: 0924-977X
Choi S, Schilder B, Abbasova L, et al., 2022, EpiCompare: R package for the comparison and quality control of epigenomic peak files, Publisher: Cold Spring Harbor Laboratory
Summary EpiCompare combines a variety of downstream analysis tools to compare, quality control and benchmark different epigenomic datasets. The package requires minimal input from users, can be run with just one line of code and provides all results of the analysis in a single interactive HTML report. EpiCompare thus enables downstream analysis of multiple epigenomic datasets in a simple, effective and user-friendly manner.Availability and Implementation EpiCompare is available on Bioconductor (≥ v3.15):https://bioconductor.org/packages/release/bioc/html/EpiCompare.htmlAll source code is publically available via GitHub:https://github.com/neurogenomics/EpiCompareDocumentation websitehttps://neurogenomics.github.io/EpiCompareEpiCompare DockerHub repository:https://hub.docker.com/repository/docker/neurogenomicslab/epicompareCompeting Interest StatementThe authors have declared no competing interest.
Hu D, Abbasova L, Schilder B, et al., 2022, CUT&Tag recovers up to half of ENCODE ChIP-seq peaks, Publisher: Cold Spring Harbor Laboratory
Techniques for genome-wide epigenetic profiling have been undergoing rapid development toward recovery of high quality data from bulk and single cell samples. DNA-protein interactions have traditionally been profiled via chromatin immunoprecipitation followed by next generation sequencing (ChIP-seq), which has become the gold standard for studying histone modifications or transcription factor binding. Cleavage Under Targets & Tagmentation (CUT&Tag) is a promising new technique, which enables profiling of such interactions in situ at high sensitivity and is adaptable to single cell applications. However thorough evaluation and benchmarking against established ChIP-seq datasets are still lacking. Here we comprehensively benchmarked CUT&Tag for H3K27ac and H3K27me3 against published ChIP-seq profiles from ENCODE in K562 cells. Across a total of 30 new and 6 published CUT&Tag datasets we found that no experiment recovers more than 50% of known ENCODE peaks, regardless of the histone mark. We tested peak callers MACS2 and SEACR, identifying optimal peak calling parameters. Balancing both precision and recall of known ENCODE peaks, SEACR without retention of duplicates showed the best performance. We found that reducing PCR cycles during library preparation lowered duplication rates at the expense of ENCODE peak recovery. Despite the moderate ENCODE peak recovery, peaks identified by CUT&Tag represent the strongest ENCODE peaks and show the same functional and biological enrichments as ChIP-seq peaks identified by ENCODE. Our workflow systematically evaluates the merits of methodological adjustments and will facilitate future efforts to apply CUT&Tag in human tissues and single cells.
Murphy AE, Skene NG, 2022, A balanced measure shows superior performance of pseudobulk methods over mixed models and pseudoreplication approaches in single-cell RNA-sequencing analysis
<jats:title>Summary</jats:title><jats:p>Recently, Zimmerman<jats:italic>et al</jats:italic>.,<jats:sup>1</jats:sup>highlighted the importance of accounting for the dependence between cells from the same individual when conducting differential expression analysis on single-cell RNA-sequencing data. Their work proved the inadequacy of pseudoreplication approaches for such analysis – This was an important step forward that was conclusively proven by them. A hierarchical single-cell expression simulation approach (<jats:underline>hierarchicell</jats:underline>) was developed by Zimmerman<jats:italic>et al</jats:italic>.,<jats:sup>1</jats:sup>to generate non-differentially expressed genes upon which performance was evaluated using the type 1 error rate; the proportion of non-differentially expressed genes indicated as differentially expressed by a model. However, evaluating such models on their type 1 or type 2 error rate in isolation is insufficient to determine their true performance – for example, a method with low type 1 error may have a high type 2 error rate. Moreover, because no seed was set for the pseudo-random number generator used in hierarchicell, the different methods evaluated by Zimmerman<jats:italic>et al</jats:italic>. were done so on different simulated datasets. Here, we corrected these issues, reran the author’s analysis and found pseudobulk methods outperformed mixed models.</jats:p><jats:sec><jats:title>Contact</jats:title><jats:p>Alan Murphy:<jats:email>a.murphy@imperial.ac.uk</jats:email>, Nathan Skene:<jats:email>n.skene@imperial.ac.uk</jats:email></jats:p></jats:sec><jats:sec><jats:title>Code availability</jats:title><jats:p>The modified version of hierarchicell which returns all error metrics, uses the same simulated data across approaches and has ch
Andrews B, Murphy AE, Stofella M, et al., 2022, Multidimensional Dynamics of the Proteome in the Neurodegenerative and Aging Mammalian Brain, MOLECULAR & CELLULAR PROTEOMICS, Vol: 21
Ranson JM, Bucholc M, Lyall D, et al., 2022, The Emerging Role of AI in Dementia Research and Healthcare, Artificial Intelligence in Healthcare, Publisher: Springer Nature Singapore, Pages: 95-106, ISBN: 9789811952715
Murphy A, Schilder BM, Skene N, 2021, MungeSumstats: a Bioconductor package for the standardisation and quality control of many GWAS summary statistics, Bioinformatics, Vol: 37, Pages: 4593-4596, ISSN: 1367-4803
Motivation:Genome-wide association studies (GWAS) summary statistics have popularised and accelerated genetic research. However, a lack of standardisation of the file formats used has proven problematic when running secondary analysis tools or performing meta-analysis studies.Results:To address this issue, we have developed MungeSumstats, a Bioconductor R package for the standardisation and quality control of GWAS summary statistics. MungeSumstats can handle the most common summary statistic formats, including variant call format (VCF) producing a reformatted, standardised, tabular summary statistic file, VCF or R native data object.Availability:MungeSumstats is available on Bioconductor (v 3.13) and can also be found on Github at: https://neurogenomics.github.io/MungeSumstats
Khozoie C, Fancy N, Marjaneh MM, et al., 2021, scFlow: A Scalable and Reproducible Analysis Pipeline for Single-Cell RNA Sequencing Data
<jats:title>Abstract</jats:title><jats:p>Advances in single-cell RNA-sequencing technology over the last decade have enabled exponential increases in throughput: datasets with over a million cells are becoming commonplace. The burgeoning scale of data generation, combined with the proliferation of alternative analysis methods, led us to develop the scFlow toolkit and the nf-core/scflow pipeline for reproducible, efficient, and scalable analyses of single-cell and single-nuclei RNA-sequencing data. The scFlow toolkit provides a higher level of abstraction on top of popular single-cell packages within an R ecosystem, while the nf-core/scflow Nextflow pipeline is built within the nf-core framework to enable compute infrastructure-independent deployment across all institutions and research facilities. Here we present our flexible pipeline, which leverages the advantages of containerization and the potential of Cloud computing for easy orchestration and scaling of the analysis of large case/control datasets by even non-expert users. We demonstrate the functionality of the analysis pipeline from sparse-matrix quality control through to insight discovery with examples of analysis of four recently published public datasets and describe the extensibility of scFlow as a modular, open-source tool for single-cell and single nuclei bioinformatic analyses.</jats:p>
Khozoie C, Fancy N, Moradi Marjaneh M, et al., 2021, scFlow: A Scalable and Reproducible Analysis Pipeline for Single-Cell RNA Sequencing Data
Byrne EM, Zhu Z, Qi T, et al., 2021, Conditional GWAS analysis to identify disorder-specific SNPs for psychiatric disorders, MOLECULAR PSYCHIATRY, Vol: 26, Pages: 2070-2081, ISSN: 1359-4184
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- Citations: 34
Månberg A, Skene N, Sanders F, et al., 2021, Altered perivascular fibroblast activity precedes ALS disease onset, Nature Medicine, Vol: 27, Pages: 640-646, ISSN: 1078-8956
Apart from well-defined factors in neuronal cells1, only a few reports consider that the variability of sporadic amyotrophic lateral sclerosis (ALS) progression can depend on less-defined contributions from glia2,3 and blood vessels4. In this study we use an expression-weighted cell-type enrichment method to infer cell activity in spinal cord samples from patients with sporadic ALS and mouse models of this disease. Here we report that patients with sporadic ALS present cell activity patterns consistent with two mouse models in which enrichments of vascular cell genes preceded microglial response. Notably, during the presymptomatic stage, perivascular fibroblast cells showed the strongest gene enrichments, and their marker proteins SPP1 and COL6A1 accumulated in enlarged perivascular spaces in patients with sporadic ALS. Moreover, in plasma of 574 patients with ALS from four independent cohorts, increased levels of SPP1 at disease diagnosis repeatedly predicted shorter survival with stronger effect than the established risk factors of bulbar onset or neurofilament levels in cerebrospinal fluid. We propose that the activity of the recently discovered perivascular fibroblast can predict survival of patients with ALS and provide a new conceptual framework to re-evaluate definitions of ALS etiology.
Thrupp N, Frigerio CS, Wolfs L, et al., 2020, Single-nucleus RNA-seq is not suitable for detection of microglial activation genes in humans, Cell Reports, Vol: 32, Pages: 1-13, ISSN: 2211-1247
Single-nucleus RNA sequencing (snRNA-seq) is used as an alternative to single-cell RNA-seq, as it allows transcriptomic profiling of frozen tissue. However, it is unclear whether snRNA-seq is able to detect cellular state in human tissue. Indeed, snRNA-seq analyses of human brain samples have failed to detect a consistent microglial activation signature in Alzheimer’s disease. Our comparison of microglia from single cells and single nuclei of four human subjects reveals that, although most genes show similar relative abundances in cells and nuclei, a small population of genes (∼1%) is depleted in nuclei compared to whole cells. This population is enriched for genes previously implicated in microglial activation, including APOE, CST3, SPP1, and CD74, comprising 18% of previously identified microglial-disease-associated genes. Given the low sensitivity of snRNA-seq to detect many activation genes, we conclude that snRNA-seq is not suited for detecting cellular activation in microglia in human disease.
Bryois J, Skene NG, Hansen TF, et al., 2020, Genetic identification of cell types underlying brain complex traits yields insights into the etiology of Parkinson's disease, Nature Genetics, Vol: 52, Pages: 482-493, ISSN: 1061-4036
Genome-wide association studies have discovered hundreds of loci associated with complex brain disorders, but it remains unclear in which cell types these loci are active. Here we integrate genome-wide association study results with single-cell transcriptomic data from the entire mouse nervous system to systematically identify cell types underlying brain complex traits. We show that psychiatric disorders are predominantly associated with projecting excitatory and inhibitory neurons. Neurological diseases were associated with different cell types, which is consistent with other lines of evidence. Notably, Parkinson's disease was genetically associated not only with cholinergic and monoaminergic neurons (which include dopaminergic neurons) but also with enteric neurons and oligodendrocytes. Using post-mortem brain transcriptomic data, we confirmed alterations in these cells, even at the earliest stages of disease progression. Our study provides an important framework for understanding the cellular basis of complex brain maladies, and reveals an unexpected role of oligodendrocytes in Parkinson's disease.
Thrupp N, Frigerio CS, Wolfs L, et al., 2020, Single nucleus sequencing fails to detect microglial activation in human tissue
<jats:title>Abstract</jats:title><jats:p>Single nucleus RNA-Seq (snRNA-Seq) methods are used as an alternative to single cell RNA-Seq methods, as they allow transcriptomic profiling of frozen tissue. However, it is unclear whether snRNA-Seq is able to detect cellular state in human tissue. Indeed, snRNA-Seq analyses of human brain samples have failed to detect a consistent microglial activation signature in Alzheimer’s Disease. A comparison of microglia from single cells and single nuclei of four human subjects reveals that ~1% of genes is depleted in nuclei compared to whole cells. This small population contains 18% of genes previously implicated in microglial activation, including <jats:italic>APOE, CST3, FTL, SPP1</jats:italic>, and <jats:italic>CD74</jats:italic>. We confirm our findings across multiple previous single nucleus and single cell studies. Given the low sensitivity of snRNA-Seq to this population of activation genes, we conclude that snRNA-Seq is not suited to detecting cellular activation in microglia in human disease.</jats:p>
Qian X, Harris KD, Hauling T, et al., 2020, Probabilistic cell typing enables fine mapping of closely related cell types in situ, Nature Methods, Vol: 17, Pages: 101-106, ISSN: 1548-7091
Understanding the function of a tissue requires knowing the spatial organization of its constituent cell types. In the cerebral cortex, single-cell RNA sequencing (scRNA-seq) has revealed the genome-wide expression patterns that define its many, closely related neuronal types, but cannot reveal their spatial arrangement. Here we introduce probabilistic cell typing by in situ sequencing (pciSeq), an approach that leverages previous scRNA-seq classification to identify cell types using multiplexed in situ RNA detection. We applied this method by mapping the inhibitory neurons of mouse hippocampal area CA1, for which ground truth is available from extensive previous work identifying their laminar organization. Our method identified these neuronal classes in a spatial arrangement matching ground truth, and further identified multiple classes of isocortical pyramidal cell in a pattern matching their known organization. This method will allow identifying the spatial organization of closely related cell types across the brain and other tissues.
Hill WD, Davies NM, Ritchie SJ, et al., 2019, Genome-wide analysis identifies molecular systems and 149 genetic loci associated with income, NATURE COMMUNICATIONS, Vol: 10
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- Citations: 71
Jansen IE, Savage JE, Watanabe K, et al., 2019, Genome-wide meta-analysis identifies new loci and functional pathways influencing Alzheimer's disease risk, Nature Genetics, Vol: 51, Pages: 404-413, ISSN: 1061-4036
Alzheimer’s disease (AD) is highly heritable and recent studies have identified over 20 disease-associated genomic loci. Yet these only explain a small proportion of the genetic variance, indicating that undiscovered loci remain. Here, we performed a large genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 cases, 383,378 controls). AD-by-proxy, based on parental diagnoses, showed strong genetic correlation with AD (rg = 0.81). Meta-analysis identified 29 risk loci, implicating 215 potential causative genes. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver, and microglia). Gene-set analyses indicate biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomization results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD.
Jansen PR, Watanabe K, Stringer S, et al., 2019, Genome-wide analysis of insomnia in 1,331,010 individuals identifies new risk loci and functional pathways, NATURE GENETICS, Vol: 51, Pages: 394-+, ISSN: 1061-4036
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- Citations: 340
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