Biology subjects

Corbin, L. J.

Publications and source records attributed to Corbin, L. J..

6 recordsLinked to original sources

bioRxiv2025

metaboprep v2: Broadening the application of the metaboprep beyond metabolomics

High-throughput multiplex assays for metabolomics and proteomics offer opportunities for biomarker discovery and disease stratification in epidemiological research. The complexity of these datasets requires robust, standardized and transparent preprocessing workflows to ensure reproducibility and comparability across studies. We present an updated and enhanced version of the metaboprep R package. Originally designed for metabolomics data, it has now been extended to support proteomics datasets from platforms such as Olink(R) and SomaScan(R). This release introduces a user-friendly, modular, object-oriented architecture using Rs S7 system, enabling improved input format flexibility, streamlined report generation and increased compatibility with other third-party tools. The updated pipeline is structured in three parts: data import, filtering and summary, and output generation. This structure provides a reproducible yet customizable framework for pre-analysis data preparation with utility across multiple omics platforms and particular value in supporting multi-cohort epidemiological research. Availability and implementationThe metaboprep package is implemented in R and freely available at: https://github.com/MRCIEU/metaboprep.

bioinformatics↗

bioRxiv2021

A multi-variant recall-by-genotype study of the metabolomic signature of body mass index

ObjectiveWe estimated the effect of body mass index (BMI) on circulating metabolites in young adults using a recall-by-genotype (RbG) study design. MethodsAn RbG study was implemented in the Avon Longitudinal Study of Parents and Children. Samples from 756 participants were selected for untargeted metabolomics analysis based on low/high genetic liability for higher BMI defined by a genetic score (GS). Regression analyses were performed to investigate the association between BMI GS groups and relative abundance of 973 metabolites. ResultsAfter correction for multiple testing, 29 metabolites were associated with BMI GS group. Bilirubin was amongst the most strongly associated metabolites with reduced levels measured in individuals with the highest BMI GS (beta=-0.32, 95% confidence interval (CI): -0.46, -0.18, Benjamini-Hochberg (BH) adjusted p=0.005). We observed associations between BMI GS group and levels of several potentially diet-related metabolites including hippurate which had lower mean abundance in individuals in the high BMI GS group (beta=-0.29, 95% CI: -0.44, -0.15, BH adjusted p=0.008). ConclusionsTogether with existing literature our results suggest a genetic predisposition to higher BMI captures differences in metabolism leading to adiposity gain. In the absence of prospective data, separating these effects from the downstream consequences of weight gain is challenging. Study Importance questionsO_ST_ABSWhat is already known about this subject?C_ST_ABSO_LIMetabolomics, defined as the measurement and study of circulating small molecules that are the substrates and products of cellular metabolism, is increasingly used by epidemiologists to provide a functional read-out of bulk cellular activity and a proxy to individual current health. This approach also provides insight into biological pathways linking exposures and disease. C_LIO_LIIn observational studies, elevated body mass index (BMI) has been associated with a wide range of circulating metabolites. Researchers are now looking to genetic epidemiological methods, such as Mendelian randomization, to offer insight into potential causal relationships. C_LI What are the new findings in your manuscript?O_LIWe identified 29 metabolites whose relative abundance varies with a genetic predisposition to higher BMI. C_LIO_LIBilirubin, a key component of the heme catabolic pathway and a potent antioxidant, showed the strongest association with BMI score group. C_LI How might your results change the direction of research or the focus of clinical practice?O_LIResults of both Mendelian randomization and recall-by-genotype studies need to be combined with alternative study designs to distinguish between biomarkers that are intermediates on the pathway to BMI from those reflective of metabolic changes that result from increased adiposity. C_LIO_LISeparating causal biomarkers from non-causative biomarkers of adiposity is important since only the former are relevant to treatment and prevention, whilst both could be informative with respect to prediction and the downstream consequences of high BMI. C_LI

genetics↗

bioRxiv2021

metaboprep: an R package for pre-analysis data description and processing.

MotivationMetabolomics is an increasingly common part of health research and there is need for pre-analytical data processing. Researchers typically need to characterize the data and to exclude errors within the context of the intended analysis. While some pre-processing steps are common, there is currently a lack of standardization and reporting transparency for these procedures. ResultsHere we introduce metaboprep, a standardized data processing workflow to extract and characterize high quality metabolomics data sets. The package extracts data from pre-formed worksheets, provides summary statistics and enables the user to select samples and metabolites for their analysis based on a set of quality metrics. A report summarizing quality metrics and the influence of available batch variables on the data is generated for the purpose of open disclosure. Where possible, we provide users flexibility in defining their own selection thresholds. Availability and implementationmetaboprep is an open-source R package available at https://github.com/MRCIEU/metaboprep Contactd.a.hughes@bristol.ac.uk or laura.corbin@bristol.ac.uk

bioinformatics↗

bioRxiv2020

Schizophrenia-associated variation at ZNF804A correlates with altered experience-dependent dynamics of sleep slow-waves and spindles in healthy young adults

BackgroundThe rs1344706 polymorphism in ZNF804A is robustly associated with schizophrenia (SZ), yet brain and behavioral phenotypes related to this variant have not been extensively characterized. In turn, SZ is associated with abnormal non-rapid eye movement (NREM) sleep neurophysiology. To examine whether rs1344706 is associated with intermediate neurophysiological traits in the absence of disease, we assessed the relationship between genotype, sleep neurophysiology, and sleep-dependent memory consolidation in healthy participants. MethodsWe recruited healthy adult males, with no history of psychiatric disorder, from the Avon Longitudinal Study of Parents and Children (ALSPAC) birth cohort. Participants were homozygous for either the SZ-associated A allele (N=25) or the alternative C allele (N=22) at rs1344706. Actigraphy, polysomnography (PSG) and a motor sequencing task (MST) were used to characterize daily activity patterns, sleep neurophysiology and sleep-dependent memory consolidation. ResultsAverage MST learning and sleep-dependent performance improvements were similar across genotype groups, but with increased variability in the AA group. CC participants showed increased slow-wave and spindle amplitudes, plus augmented coupling of slow-wave activity across recording electrodes after learning. Slow-waves and spindles in those with the AA genotype were insensitive to learning, whilst slow-wave coherence decreased following MST training. ConclusionWe describe evidence that rs1344706 polymorphism in ZNF804A is associated with changes in experience- and sleep-dependent, local and distributed neural network activity that supports offline information processing during sleep in a healthy population. These findings highlight the utility of sleep neurophysiology in mapping the impacts of SZ-associated variants on neural circuit oscillations and function.

neuroscience↗

bioRxiv2018

Epigenetic regulation of PAR4-related platelet activation: mechanistic links between environmental exposure and cardiovascular disease

Protease-activated receptor 4 (PAR4) is a potent thrombin receptor. Epigenetic control of the F2RL3 locus (which encodes for PAR4) via DNA methylation is associated with both smoking and cardiovascular disease. We examined the association between DNA hypomethylation at F2RL3 and risk of cardiovascular disease, focusing on acute myocardial infarction (AMI) (n=853 cases / 2,352 controls). We used in vitro cell models to dissect the role of DNA methylation in regulating expression of F2RL3. We investigated the interplay between F2RL3 DNA methylation and platelet function in human (n=41). Lastly, we used Mendelian randomization to unify observational and functional work by assessing evidence for causal relationships using data from UK Biobank (n=407,141) and CARDIoGRAMplusC4D (n=184,305). Observationally, one standard deviation (SD) decrease in DNA methylation at F2RL3 was associated with a 25% increase in the odds of AMI. In vitro, short-term exposure of cells to cigarette smoke reduced F2RL3 DNA methylation and increased gene expression. Transcriptional assays flagged a role for a CEBP recognition sequence in modulating the enhancer activity of F2RL3 exon 2. Lower DNA methylation at F2RL3 was associated with increased platelet reactivity in human. The estimated casual odds ratio of ischaemic heart disease was 1.03 (95% CI: 1.00, 1.07) per 1 SD decrease in F2RL3 DNA. In conclusion, we show that DNA methylation-dependent platelet activation is part of a complex system of features contributing to cardiovascular health. Tailoring therapeutic intervention to new knowledge of F2RL3/PAR4 function should be explored to ameliorate the detrimental effects of this risk factor on cardiovascular health.\n\nOne sentence summaryDNA methylation-dependent platelet activation is a likely causal contributor to cardiovascular health.

epidemiology↗

bioRxiv2017

Causal Analyses, Statistical Efficiency And Phenotypic Precision Through Recall-By-Genotype Study Design

Genome-wide association studies have been useful in identifying common genetic variants related to a variety of complex traits and diseases; however, they are often limited in their ability to inform about underlying biology. Whilst bioinformatics analyses, studies of cells, animal models and applied genetic epidemiology have provided some understanding of genetic associations or causal pathways, there is a need for new genetic studies that elucidate causal relationships and mechanisms in a cost-effective, precise and statistically efficient fashion. We discuss the motivation for and the characteristics of the Recall-by-Genotype (RbG) study design, an approach that enables genotype-directed deep-phenotyping and improvement in drawing causal inferences. Specifically, we present RbG designs using single and multiple variants and discuss the inferential properties, analytical approaches and applications of both. We consider the efficiency of the RbG approach, the likely value of RbG studies for the causal investigation of disease aetiology and the practicalities of incorporating genotypic data into population studies in the context of the RbG study design. Finally, we provide a catalogue of the UK-based resources for such studies, an online tool to aid the design of new RbG studies and discuss future developments of this approach.

genetics↗