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Article ; Online: rworkflows: automating reproducible practices for the R community.

Schilder, Brian M / Murphy, Alan E / Skene, Nathan G

2024 Volume 15, Issue 1, Page(s) 149

Abstract: 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 ...

Abstract	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.
Language	English
Publishing date	2024-01-02
Publishing country	England
Document type	Journal Article
ZDB-ID	2553671-0
ISSN	2041-1723 ; 2041-1723
ISSN (online)	2041-1723
ISSN	2041-1723
DOI	10.1038/s41467-023-44484-5
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Fine-mapping of Parkinson's disease susceptibility loci identifies putative causal variants.

Schilder, Brian M / Raj, Towfique

Human molecular genetics

2021 Volume 31, Issue 6, Page(s) 888–900

Abstract: 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- ... ...

Abstract	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.
MeSH term(s)	Chromosome Mapping ; Genetic Predisposition to Disease ; Genome-Wide Association Study/methods ; Humans ; Parkinson Disease/genetics ; Polymorphism, Single Nucleotide/genetics
Language	English
Publishing date	2021-10-07
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1108742-0
ISSN	1460-2083 ; 0964-6906
ISSN (online)	1460-2083
ISSN	0964-6906
DOI	10.1093/hmg/ddab294
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Article ; Online: EpiCompare: R package for the comparison and quality control of epigenomic peak files.

Choi, Sera / Schilder, Brian M / Abbasova, Leyla / Murphy, Alan E / Skene, Nathan G

Bioinformatics advances

2023 Volume 3, Issue 1, Page(s) vbad049

Abstract: 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 ...

Abstract	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.
Language	English
Publishing date	2023-04-13
Publishing country	England
Document type	Journal Article
ISSN	2635-0041
ISSN (online)	2635-0041
DOI	10.1093/bioadv/vbad049
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Article ; Online: MungeSumstats: a Bioconductor package for the standardization and quality control of many GWAS summary statistics.

Murphy, Alan E / Schilder, Brian M / Skene, Nathan G

Bioinformatics (Oxford, England)

2021 Volume 37, Issue 23, Page(s) 4593–4596

Abstract: Motivation: Genome-wide association studies (GWAS) summary statistics have popularized and accelerated genetic research. However, a lack of standardization of the file formats used has proven problematic when running secondary analysis tools or ... ...

Abstract	Motivation: Genome-wide association studies (GWAS) summary statistics have popularized and accelerated genetic research. However, a lack of standardization 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 standardization and quality control of GWAS summary statistics. MungeSumstats can handle the most common summary statistic formats, including variant call format (VCF) producing a reformatted, standardized, tabular summary statistic file, VCF or R native data object. Availability and implementation: MungeSumstats is available on Bioconductor (v 3.13) and can also be found on Github at: https://neurogenomics.github.io/MungeSumstats. Supplementary information: Supplementary data are available at Bioinformatics online.
MeSH term(s)	Genome-Wide Association Study ; Quality Control ; Reference Standards ; Software
Language	English
Publishing date	2021-10-01
Publishing country	England
Document type	Journal Article ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btab665
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Article ; Online: echolocatoR: an automated end-to-end statistical and functional genomic fine-mapping pipeline.

Schilder, Brian M / Humphrey, Jack / Raj, Towfique

Bioinformatics (Oxford, England)

2021 Volume 38, Issue 2, Page(s) 536–539

Abstract: 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 ... ...

Abstract	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.
MeSH term(s)	Software ; Genomics ; Chromosome Mapping ; Epigenomics ; Quantitative Trait Loci
Language	English
Publishing date	2021-09-14
Publishing country	England
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't
ZDB-ID	1422668-6
ISSN	1367-4811 ; 1367-4803
ISSN (online)	1367-4811
ISSN	1367-4803
DOI	10.1093/bioinformatics/btab658
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Article ; Online: Multi-omic insights into Parkinson's Disease: From genetic associations to functional mechanisms.

Schilder, Brian M / Navarro, Elisa / Raj, Towfique

Neurobiology of disease

2021 Volume 163, Page(s) 105580

Abstract: 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, ... ...

Abstract	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.
MeSH term(s)	Genetic Predisposition to Disease ; Genome-Wide Association Study ; Genomics ; Humans ; Parkinson Disease/genetics ; Polymorphism, Single Nucleotide ; Quantitative Trait Loci
Language	English
Publishing date	2021-12-04
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, Non-U.S. Gov't ; Review
ZDB-ID	1211786-9
ISSN	1095-953X ; 0969-9961
ISSN (online)	1095-953X
ISSN	0969-9961
DOI	10.1016/j.nbd.2021.105580
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Article ; Online: Phenome-wide and expression quantitative trait locus associations of coronavirus disease 2019 genetic risk loci.

Moon, Chang Yoon / Schilder, Brian M / Raj, Towfique / Huang, Kuan-Lin

iScience

2021 Volume 24, Issue 6, Page(s) 102550

Abstract: While several genes and clinical traits have been associated with higher risk of severe coronavirus disease 2019 (COVID-19), how host genetic variants may interact with these parameters and contribute to severe disease is still unclear. Herein, we ... ...

Abstract	While several genes and clinical traits have been associated with higher risk of severe coronavirus disease 2019 (COVID-19), how host genetic variants may interact with these parameters and contribute to severe disease is still unclear. Herein, we performed phenome-wide association study, tissue and immune-cell-specific expression quantitative trait locus (eQTL)/splicing quantitative trait locus, and colocalization analyses for genetic risk loci suggestively associated with severe COVID-19 with respiratory failure. Thirteen phenotypes/traits were associated with the severe COVID-19-associated loci at the genome-wide significance threshold, including monocyte counts, fat metabolism traits, and fibrotic idiopathic interstitial pneumonia. In addition, we identified tissue and immune subtype-specific eQTL associations affecting 48 genes, including several ones that may directly impact host immune responses, colocalized with the severe COVID-19 genome-wide association study associations, and showed altered expression in single-cell transcriptomes. Collectively, our work demonstrates that host genetic variations associated with multiple genes and traits show genetic pleiotropy with severe COVID-19 and may inform disease etiology.
Language	English
Publishing date	2021-05-18
Publishing country	United States
Document type	Journal Article
ISSN	2589-0042
ISSN (online)	2589-0042
DOI	10.1016/j.isci.2021.102550
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Article ; Online: Evolutionary shifts dramatically reorganized the human hippocampal complex.

Schilder, Brian M / Petry, Heywood M / Hof, Patrick R

The Journal of comparative neurology

2019 Volume 528, Issue 17, Page(s) 3143–3170

Abstract: The hippocampal complex (HC) is central to long-term memory storage and retrieval as well as spatial navigation across many species. Notably, humans appear to have greatly enhanced and possibly unique HC-mediated capacities such as constructive episodic ... ...

Abstract	The hippocampal complex (HC) is central to long-term memory storage and retrieval as well as spatial navigation across many species. Notably, humans appear to have greatly enhanced and possibly unique HC-mediated capacities such as constructive episodic simulation. Key studies have shown that the human HC is disproportionately large amongst hominoids, but much remains unknown at the levels of substructural evolutionary reorganization and ecological selection. Here, we calculated relative sizes of 12 HC subregions in a diverse sample of 44 primate species. We then used a Bayesian phylogenetic method, selective regime analysis, to identify 27 separate evolutionary shifts in HC organization across 65 million years of primate evolution. Additionally, a series of multivariate phylogenetic regressions using HC-related ecological variables as predictors (Diet Breadth, Population Density, Group Size, Home Range Size, and Residual Home Range) revealed that relative fascia dentata and CA1 size were both significantly predicted by species' home range size (after correcting for body size). However, perhaps the most notable finding of this study was that the shifts in HC size and subregional organization in the human lineage were the largest seen in all of primate evolution, rendering modern humans with a HC that is a clear outlier amongst all nonhuman primates investigated here. Given the extensive literature confirming the relationship between HC organization and function, these selective shifts are likely to have played a significant role in the emergence of human-specific capacities, such as constructive episodic simulation.
MeSH term(s)	Animals ; Biological Evolution ; Hippocampus/cytology ; Hippocampus/physiology ; Humans ; Phylogeny
Language	English
Publishing date	2019-12-02
Publishing country	United States
Document type	Journal Article
ZDB-ID	3086-7
ISSN	1096-9861 ; 0021-9967 ; 0092-7317
ISSN (online)	1096-9861
ISSN	0021-9967 ; 0092-7317
DOI	10.1002/cne.24822
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Article ; Online: Artificial Intelligence for Dementia Research Methods Optimization.

Bucholc, Magda / James, Charlotte / Al Khleifat, Ahmad / Badhwar, AmanPreet / Clarke, Natasha / Dehsarvi, Amir / Madan, Christopher R / Marzi, Sarah J / Shand, Cameron / Schilder, Brian M / Tamburin, Stefano / Tantiangco, Hanz M / Lourida, Ilianna / Llewellyn, David J / Ranson, Janice M

ArXiv

2023

Abstract: Introduction: Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater.: Methods: We summarize and critically ... ...

Abstract	Introduction: Machine learning (ML) has been extremely successful in identifying key features from high-dimensional datasets and executing complicated tasks with human expert levels of accuracy or greater. Methods: We summarize and critically evaluate current applications of ML in dementia research and highlight directions for future research. Results: We present an overview of ML algorithms most frequently used in dementia research and highlight future opportunities for the use of ML in clinical practice, experimental medicine, and clinical trials. We discuss issues of reproducibility, replicability and interpretability and how these impact the clinical applicability of dementia research. Finally, we give examples of how state-of-the-art methods, such as transfer learning, multi-task learning, and reinforcement learning, may be applied to overcome these issues and aid the translation of research to clinical practice in the future. Discussion: ML-based models hold great promise to advance our understanding of the underlying causes and pathological mechanisms of dementia.
Language	English
Publishing date	2023-03-02
Publishing country	United States
Document type	Preprint
ISSN	2331-8422
ISSN (online)	2331-8422
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Article ; Online: Artificial intelligence for dementia research methods optimization.

Bucholc, Magda / James, Charlotte / Khleifat, Ahmad Al / Badhwar, AmanPreet / Clarke, Natasha / Dehsarvi, Amir / Madan, Christopher R / Marzi, Sarah J / Shand, Cameron / Schilder, Brian M / Tamburin, Stefano / Tantiangco, Hanz M / Lourida, Ilianna / Llewellyn, David J / Ranson, Janice M

Alzheimer's & dementia : the journal of the Alzheimer's Association

2023 Volume 19, Issue 12, Page(s) 5934–5951

Abstract: Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to ... ...

Abstract	Artificial intelligence (AI) and machine learning (ML) approaches are increasingly being used in dementia research. However, several methodological challenges exist that may limit the insights we can obtain from high-dimensional data and our ability to translate these findings into improved patient outcomes. To improve reproducibility and replicability, researchers should make their well-documented code and modeling pipelines openly available. Data should also be shared where appropriate. To enhance the acceptability of models and AI-enabled systems to users, researchers should prioritize interpretable methods that provide insights into how decisions are generated. Models should be developed using multiple, diverse datasets to improve robustness, generalizability, and reduce potentially harmful bias. To improve clarity and reproducibility, researchers should adhere to reporting guidelines that are co-produced with multiple stakeholders. If these methodological challenges are overcome, AI and ML hold enormous promise for changing the landscape of dementia research and care. HIGHLIGHTS: Machine learning (ML) can improve diagnosis, prevention, and management of dementia. Inadequate reporting of ML procedures affects reproduction/replication of results. ML models built on unrepresentative datasets do not generalize to new datasets. Obligatory metrics for certain model structures and use cases have not been defined. Interpretability and trust in ML predictions are barriers to clinical translation.
MeSH term(s)	Humans ; Artificial Intelligence ; Reproducibility of Results ; Machine Learning ; Research Design ; Dementia/diagnosis
Language	English
Publishing date	2023-08-28
Publishing country	United States
Document type	Journal Article ; Review
ZDB-ID	2211627-8
ISSN	1552-5279 ; 1552-5260
ISSN (online)	1552-5279
ISSN	1552-5260
DOI	10.1002/alz.13441
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