Biology subjects

Gaunt, T.

Publications and source records attributed to Gaunt, T..

5 recordsLinked to original sources

bioRxiv2018

Appraising the causal relevance of DNA methylation for risk of lung cancer

DNA methylation changes in peripheral blood have been identified in relation to lung cancer risk. However, the causal nature of these associations remains to be fully elucidated. Meta-analysis of four epigenome-wide association studies (918 cases, 918 controls) revealed differential methylation at 16 CpG sites (FDR < 0.05) in relation to lung cancer risk. A two-sample Mendelian randomization analysis, using genetic instruments for methylation at 14 of the 16 CpG sites, and 29,863 cases and 55,586 controls from the TRICL-ILCCO lung cancer consortium, was performed to appraise the causal role of methylation at these sites on lung cancer. This approach provided little evidence that DNA methylation in peripheral blood at the 14 CpG sites play a causal role in lung cancer development, including for cg05575921 AHRR, where methylation is strongly associated with lung cancer risk. Further studies are needed to investigate the causal role played by DNA methylation in lung tissue.

epidemiology↗

bioRxiv2017

PhenoSpD: an atlas of phenotypic correlations and a multiple testing correction for the human phenome

BackgroundIdentifying phenotypic correlations between complex traits and diseases can provide useful etiological insights. Restricted access to individual-level phenotype data makes it difficult to estimate large-scale phenotypic correlation across the human phenome. State-of-the-art methods, metaCCA and LD score regression, provide an alternative approach to estimate phenotypic correlation using genome-wide association study (GWAS) summary statistics.\n\nResultsHere, we present an integrated R toolkit, PhenoSpD, to 1) apply metaCCA (or LD score regression) to estimate phenotypic correlations using GWAS summary statistics; and 2) to utilize the estimated phenotypic correlations to inform correction of multiple testing for complex human traits using the spectral decomposition of matrices (SpD). The simulations suggest it is possible to estimate phenotypic correlation using samples with only a partial overlap, but as overlap decreases correlations will attenuate towards zero and multiple testing correction will be more stringent than in perfectly overlapping samples. In a case study, PhenoSpD using GWAS results suggested 324.4 independent tests among 452 metabolites, which is close to the 296 independent tests estimated using true phenotypic correlation. We further applied PhenoSpD to estimated 7,503 pair-wise phenotypic correlations among 123 metabolites using GWAS summary statistics from Kettunen et al. and PhenoSpD suggested 44.9 number of independent tests for theses metabolites.\n\nConclusionPhenoSpD integrates existing methods and provides a simple and conservative way to reduce dimensionality for complex human traits using GWAS summary statistics, which is particularly valuable for post-GWAS analysis of complex molecular traits.\n\nAvailabilityR code and documentation for PhenoSpD V1.0.0 is available online (https://github.com/MRCIEU/PhenoSpD).

bioinformatics↗

bioRxiv2017

MELODI - Mining Enriched Literature Objects to Derive Intermediates

MotivationThe scientific literature contains a wealth of information from different fields on potential disease mechanisms. However, prioritising mechanisms for further analytical evaluation presents enormous challenges in terms of the quantity and diversity of published research. The application of data mining approaches to the literature offers the potential to identify and prioritise mechanisms for more focused and detailed analysis.\n\nResultsHere we present MELODI, a literature mining platform that can identify mechanistic pathways between any two biomedical concepts. Two case studies demonstrate the potential uses of MELODI and how it can generate hypotheses for further investigation. Firstly, an analysis of ERG and prostate cancer derives the intermediate transcription factor SP1, recently confirmed to be physically interacting with ERG. Secondly, examining the relationship between a new potential risk factor for pancreatic cancer identifies possible mechanistic insights which can be studied in vitro.\n\nAvailabilityMELODI has been implemented as a Python/Django web application, and is freely available to use at www.melodi.biocompute.org.uk\n\nContactmelodi@biocompute.org.uk

bioinformatics↗

bioRxiv2017

Sex-associated autosomal DNA methylation differences are wide-spread and stablethroughout childhood

Almost all species show sexual discordance in many traits and diseases. DNA methylation is known to contribute to these differences through well-established mechanisms including X-inactivation in females, imprinting and parent-of-origin effects. Here we investigate sex discordance in DNA methylation throughout childhood in a sample of 700 individuals from the Avon Longitudinal Study of Parents and Children. We show that autosomal sex-discordant methylation is widespread, affecting approximately 12,000 CpG sites at any given age, and stable; at least 8,500 sites are consistently different across all time points and a large proportion discordant in both the fetal and adult brain cortices. Just over 1,000 methylation differences change from birth to late adolescence, 90% of these between birth and around age seven. Sexually discordant CpG sites are enriched in genomic loci containing androgen but not estrogen targets and in genes involved in tissue development but not housekeeping functions. A methylation-derived sex score capturing the variance was calculated at each time point and found to be highly correlated between time points. This score is nominally associated with sex hormone levels in childhood as well as some phenotypes previously linked to sex hormone levels. These findings suggest that sex-discordant autosomal DNA methylation is widespread throughout the genome, likely due to the first androgen exposures in utero. It is then stably maintained from birth to late adolescence. Methylation variation at sex-discordant sites within the sexes, as summarized by the methylation sex score, likely reflects in utero androgen exposure which is relevant to human health.\n\nSignificance StatementAlthough we know that sex hormones are critical for establishing sexual discordance, less is known about how this discordance is achieved and maintained. Here we present evidence for widespread differences in DNA methylation between male and female children. We show that most of these differences are established prenatally, likely due to the first androgen exposures in utero, and then stably maintained throughout childhood, despite extreme fluctuations in the levels of these very same hormones. Our results support a role for DNA methylation as a means for recording and maintaining the effects of exposure to sex hormones and thus to better understand sexual variation and how it is driven by the prenatal environment.

epidemiology↗

bioRxiv2017

PHESANT: a tool for performing automated phenome scans in UK Biobank

MotivationEpidemiological cohorts typically contain a diverse set of phenotypes such that automation of phenome scans is non-trivial, because they require highly heterogeneous models. For this reason, phenome scans have to date tended to use a smaller homogeneous set of phenotypes that can be analysed in a consistent fashion. We present PHESANT (PHEnome Scan ANalysis Tool), a software package for performing comprehensive phenome scans in UK Biobank.\n\nGeneral featuresPHESANT tests the association of a specified trait with all continuous, integer and categorical variables in UK Biobank, or a specified subset. PHESANT uses a novel rule-based algorithm to determine how to appropriately test each trait, then performs the analyses and produces plots and summary tables.\n\nImplementationThe PHESANT phenome scan is implemented in R. PHESANT includes a novel Javascript D3.js visualization, and accompanying Java code that converts the phenome scan results to the required JavaScript Object Notation (JSON) format.\n\nAVAILABILITYPHESANT is available on GitHub at [https://github.com/MRCIEU/PHESANT]. Git tag v0.2 corresponds to the version presented here.

epidemiology↗