Biology subjects

Theodoratou, E.

Publications and source records attributed to Theodoratou, E..

6 recordsLinked to original sources

bioRxiv2022

Exploring the association between Body Mass Index, Sex and Gene Expression in human colorectal epithelium

IntroductionColorectal cancer (CRC) is the second most common cause of cancer death globally. Genome-wide association studies have established that cancer risk mediated through common genetic variants can be linked to variation in gene expression. Since obesity and male sex impart substantially elevated CRC risk, we studied transcriptional profiles of normal colorectal mucosa using RNA sequencing to better understand the relationship of these risk factors with gene expression levels. MethodsNormal colorectal mucosa was sampled from 365 participants (208 males, 157 females) either during surgery (n=103) or through endoscopic biopsy (n=262) from cancer patients and patients with other unrelated conditions. In total, 238 samples were used for our discovery dataset and 380 samples were obtained for the validation of our findings. The transcription analysis was done using paired-end total RNA sequencing. Data processing and gene filtering followed the Genotype-Tissue Expression (GTEx) Project pipeline v8. Differential Expression Analysis (DEA) was performed on normalised counts to evaluate effects of sex and body mass index on the total gene expression, as well as possible confounding effects of cancer presence on the gene expression in normal colorectal tissue. ResultsFollowing filtering, there were 15,465 genes available for analysis. DEA identified two genes that were significantly associated with sex and five associated with body mass index. However, whilst these nominal signals are of interest, none of the genes associated with sex remained significant in a replication dataset. Due to the missing BMI information, replication of DEA by BMI was not possible. ConclusionWe found no systematic differences in gene expression in normal colorectal epithelium between males and females, nor did we find a strong association between gene expression and BMI. Although sample size may limit our analysis, the results suggest no or limited confounding effects of BMI and sex on gene expression in normal colorectal mucosa samples.

genomics↗

bioRxiv2022

BioNE: Integration of network embeddings for supervised learning

A network embedding approach reduces the analysis complexity of large biological networks by converting them to lowdimensional vector representations (features/embeddings). These lower-dimensional vectors can then be used in machine learning prediction tasks with a wide range of applications in computational biology and bioinformatics. Several network embedding approaches have been proposed with different methods of generating vector representations. These network embedding approaches can be quite diverse in terms of data representation and implementation. Moreover, most were not originally developed for biological networks. Therefore comparing and assessing the performance of these diverse models in practice, in biological contexts, can be challenging. To facilitate such comparisons, we have developed the BioNE framework for integration of different embedding methods in prediction tasks. Using this framework one can easily assess, for instance, whether combined vector representations from multiple embedding methods offer complementary information with regards to the network features and thus better performance on prediction tasks. In this paper, we present the BioNE software suite for embedding integration, which applies network embedding methods following standardised network preparation steps, and integrates the vector representations achieved by these methods using three different techniques. BioNE enables selection of prediction models, oversampling methods, feature selection methods, cross-validation type and cross-validation parameters. Availability and implementationBioNE pipeline and detailed explanation of implementation is freely available on GitHub, at https://github.com/pooryaparvizi/BioNE

bioinformatics↗

bioRxiv2019

Physical activity reduces colorectal cancer risk independent of BMI - A two-sample Mendelian randomisation study

BackgroundEvidence from observational studies suggests a protective role for physical activity (PA) against colorectal cancer (CRC) risk. However, it has yet to be established a causal relationship. We conducted a two-sample Mendelian randomisation (MR) study to examine causality between physical activity and CRC risk.\n\nMethodsWe used common genetic variants associated with self-reported and accelerometer-based physical activity as instrumental variables (IVs) in this MR study. The IVs were derived from the largest available genome-wide association study (GWAS) of physical activity, namely UK Biobank. We analysed the effect of the IVs for physical activity in a large CRC GWAS that included 31 197 cases and 61 770 controls. We applied inverse variance weighted (IVW) method as the main analysis method.\n\nResultsOur results demonstrate a protective effect between accelerometer-based physical activity and CRC risk (the outlier-adjusted ORIVW was 0.92 per one standard deviation (SD) increase of accelerometer-base physical activity [95% CI: 0.87-0.98, P: 0.01]). The effect between self-reported physical activity and CRC risk was not statistically significant but was also supportive of an inverse association (the outlier-adjusted ORIVW was 0.61 per 1 SD increase of moderate-to-vigorous physical activity [95%CI: 0.36-1.06, P: 0.08]).\n\nConclusionsThe findings of this large MR study show for the first time that objectively measured physical activity is causally implicated in reducing CRC risk. The limitations of the study are that it is based on only two genetic instruments and that it has limited power, despite the study size. Nonetheless, at a population level, these findings provide strong reinforcing evidence to support public health policy measures that encourage exercise, even in obese individuals.

genetics↗

bioRxiv2019

A Phenome-wide Mendelian Randomisation study on genetically determined serum urate levels in UK Biobank cohort

IntroductionThe role of serum urate level has been extensively investigated in observational studies. However, the extent of any causal effect remains unclear, making it difficult to evaluate its clinical relevance.\n\nObjectivesTo explore any causal or pleiotropic association between serum urate level and a broad spectrum of disease outcomes.\n\nMethodsPhenome-wide association study (PheWAS) together with a Bayesian analysis of tree-structured phenotypic models (TreeWAS) was performed to examine disease outcomes related to genetically determined serum urate levels in 339,256 UK Biobank participants. Mendelian Randomisation (MR) analyses were performed to replicate significant findings using various GWAS consortia data. Sensitivity analyses were conducted to examine possible pleiotropic effects on metabolic traits of the genetic variants used as instruments for serum urate.\n\nResultsPheWAS analysis, examining the association with 1,431 disease outcomes, identified a multitude of disease outcomes including gout, hypertension, hypercholesterolemia, and heart diseases (e.g., coronary atherosclerosis, myocardial infarction, and ischaemic heart disease) that were associated (p<3.35e-04) with genetically determined serum urate levels. TreeWAS analysis, examining 10,750 ICD-10 diagnostic terms, identified more sub-phenotypes of cardiovascular and cerebrovascular diseases (e.g., angina pectoris, heart failure, cerebral infarction). MR analysis successfully replicated the association with gout, hypertension, heart diseases and blood lipid levels, but indicated the existence of genetic pleiotropy. Sensitivity analyses support an inference that pleiotropic effects of genetic variants on urate and metabolic traits contribute to the observational associations with cardiovascular diseases.\n\nConclusionsHigh serum urate levels are associated with different types of cardiac events. The finding of genetic pleiotropy indicates the existence of common upstream pathological elements influencing both urate and metabolic traits, and this may suggest new opportunities and challenges for developing drugs targeting a more distal mediator that would be beneficial for both the treatment of gout and the prevention of cardiovascular comorbidities.

epidemiology↗

bioRxiv2018

Genetic variants related to antihypertensive targets inform drug efficacy and side effects

BackgroundDrug effects can be investigated through natural variation in the genes for their protein targets. We aimed to use this approach to explore the potential side effects and repurposing potential of antihypertensive drugs, which are amongst the most commonly used medications worldwide.\n\nMethodsWe identified genetic instruments for antihypertensive drug classes as variants in the gene for the corresponding target that associated with systolic blood pressure at genome-wide significance. To validate the instruments, we compared Mendelian randomisation (MR) estimates for drug effects on coronary heart disease (CHD) and stroke risk to randomised controlled trial (RCT) results. Phenome-wide association study (PheWAS) in the UK Biobank was performed to identify potential side effects and repurposing opportunities, with findings investigated in the Vanderbilt University Biobank (BioVU) and in observational analysis of the UK Biobank.\n\nFindingsWe identified suitable genetic instruments for beta-blockers (BBs) and calcium channel blockers (CCBs). MR estimates for their effect on CHD and stroke risk respectively were comparable to results from RCTs against placebo. PheWAS in the UK Biobank identified an association of the CCB genetic risk score (scaled to drug effect) with increased risk of diverticulosis (odds ratio [OR] 1.23, 95%CI 1.10-1.38), with a consistent estimate found in BioVU (OR 1.16, 95%CI 0.94-1.44). Association with diverticulosis was further supported in observational analysis of CCB use in the UK Biobank (OR 1.08, 95%CI 1.02-1.15).\n\nInterpretationWe identified valid genetic instruments for BBs and CCBs. Using genetic and observational approaches, we highlighted a previously unreported potential detrimental effect of CCBs on risk of diverticulosis. This work serves as a proof of concept that investigation of genetic variants can offer a complementary approach to exploring the efficacy and side effects of anti-hypertensive medications.\n\nFundingWellcome Trust.

genetics↗

bioRxiv2018

Developing and Evaluating Mappings of ICD-10 and ICD-10-CM codes to Phecodes

BackgroundThe PheCode system was built upon the International Classification of Diseases, Ninth Revision, Clinical Modification (ICD-9-CM) for phenome-wide association studies (PheWAS) in the electronic health record (EHR).\n\nObjectiveHere, we present our work on the development and evaluation of maps from ICD-10 and ICD-10-CM codes to PheCodes.\n\nMethodsWe mapped ICD-10 and ICD-10-CM codes to PheCodes using a number of methods and resources, such as concept relationships and explicit mappings from the Unified Medical Language System (UMLS), Observational Health Data Sciences and Informatics (OHDSI), Systematized Nomenclature of Medicine - Clinical Terms (SNOMED CT), and National Library of Medicine (NLM). We assessed the coverage of the maps in two databases: Vanderbilt University Medical Center (VUMC) using ICD-10-CM and the UK Biobank (UKBB) using ICD-10. We assessed the fidelity of the ICD-10-CM map in comparison to the gold-standard ICD-9-CM[->]PheCode map by investigating phenotype reproducibility and conducting a PheWAS.\n\nResultsWe mapped >75% of ICD-10-CM and ICD-10 codes to PheCodes. Of the unique codes observed in the VUMC (ICD-10-CM) and UKBB (ICD-10) cohorts, >90% were mapped to PheCodes. We observed 70-75% reproducibility for chronic diseases and <10% for an acute disease. A PheWAS with a lipoprotein(a) (LPA) genetic variant, rs10455872, using the ICD-9-CM and ICD-10-CM maps replicated two genotype-phenotype associations with similar effect sizes: coronary atherosclerosis (ICD-9-CM: P < .001, OR = 1.60 vs. ICD-10-CM: P < .001, OR = 1.60) and with chronic ischemic heart disease (ICD-9-CM: P < .001, OR = 1.5 vs. ICD-10-CM: P < .001, OR = 1.47).\n\nConclusionsThis study introduces the initial \"beta\" versions of ICD-10 and ICD-10-CM to PheCode maps that will enable researchers to leverage accumulated ICD-10 and ICD-10-CM data for high-throughput PheWAS in the EHR.

bioinformatics↗