Biology subjects

Westerman, K.

Publications and source records attributed to Westerman, K..

3 recordsLinked to original sources

bioRxiv2026

Genetic Impacts on Variability of Body Fat Distribution Uncover Gene-Environment and Gene-Gene Interactions

Body fat distribution is a strong predictor of cardiometabolic disease risk. Gene-environment and gene-gene interactions can affect body fat distribution, resulting in differential phenotypic variance across genotype groups that can be detected through variance quantitative trait loci (vQTLs). Using UK Biobank MRI data in conjunction with genetic data, we explored evidence for vQTLs for body fat distribution phenotypes aiming to uncover potential genetic interactions. We identified three vQTLs for liver fat distribution, including rs738408 (PNPLA3), rs4293458 (APOE), and rs58542926 (TM6SF2), and one vQTL region (FTO) for abdominal subcutaneous adipose tissue. To dissect putative gene-environment and gene-gene interactions underlying these signals, we identified multiple vQTL-environment interactions and one epistatic effect (rs58542926*rs429358) for liver fat. The vQTLs and interaction results were validated in multiple UK Biobank and external replication cohort datasets (Framingham Heart Study, All of Us, and TwinsUK), showing replication of the three liver vQTLs with the greatest reproducibility for vQTL rs738408. Our findings uncover vQTLs and underlying interaction effects on body fat distribution, especially liver fat, that may be useful for the development of precision medicine approaches.

genetics↗

bioRxiv2020

PhyteByte: Identification of foods containing compounds with specific pharmacological properties

BackgroundPhytochemicals and other molecules in foods elicit positive health benefits, often by poorly established or unknown mechanisms. While there is a wealth of data on the biological and biophysical properties of drugs and therapeutic compounds, there is a notable lack of similar data for compounds commonly present in food. Computational methods for high-throughput identification of food compounds with specific biological effects, especially when accompanied by relevant food composition data, could enable more effective and more personalized dietary planning. We have created a machine learning-based tool (PhyteByte) to leverage existing pharmacological data to predict bioactivity across a comprehensive molecular database of foods and food compounds. ResultsPhyteByte uses a cheminformatic approach to structure-based activity prediction and applies it to uncover the putative bioactivity of food compounds. The tool takes an input protein target and develops a random forest classifier to predict the effect of an input molecule based on its molecular fingerprint, using structure and activity data available from the ChEMBL database. It then predicts the relevant bioactivity of a library of food compounds with known molecular structures from the FooDB database. The output is a list of food compounds with high confidence of eliciting relevant biological effects, along with their source foods and associated quantities in those foods, where available. Applying PhyteByte to the PPARG gene, we identified irigenin, sesamin, fargesin, and delta-sanshool as putative agonists of PPARG, along with previously identified agonists of this important metabolic regulator. ConclusionsPhyteByte identifies food-based compounds that are predicted to interact with specific protein targets. The identified relationships can be used to prioritize food compounds for experimental or epidemiological follow-up and can contribute to the rapid development of precision approaches to new nutraceuticals as well as personalized dietary planning.

bioinformatics↗

bioRxiv2018

DNA methylation modules associate with incident cardiovascular disease and cumulative risk factor exposure

1 AbstractO_ST_ABSBackgroundC_ST_ABSEpigenome-wide association studies using DNA methylation have the potential to uncover novel biomarkers and mechanisms of cardiovascular disease (CVD) risk. However, the direction of causation for these associations is not always clear, and investigations to-date have generally failed to replicate at the level of individual loci. Here, we undertook module- and region-based DNA methylation analyses of incident CVD in the Womens Health Initiative (WHI) and Framingham Heart Study Offspring Cohort (FHS) in order to find more robust epigenetic biomarkers for cardiovascular risk.\n\nMethods and FindingsWe applied weighted gene correlation network analysis (WGCNA) and the Comb-p algorithm to find methylation modules and regions associated with incident CVD in the WHI dataset. We discovered two modules whose activation correlated with CVD risk and replicated across cohorts. One of these modules was enriched for development-related processes and overlaps strongly with epigenetic aging sites. For the other, we showed preliminary evidence for monocyte-specific effects and statistical links to cumulative exposure to traditional cardiovascular risk factors. Additionally, we found three regions (associated with the genes SLC9A1, SLC1A5, and TNRC6C) whose methylation associates with CVD risk.\n\nConclusionsIn sum, we present several epigenetic associations with incident CVD that reveal disease mechanisms related to development and monocyte biology. Furthermore, we show that epigenetic modules may act as a molecular readout of cumulative cardiovascular risk factor exposure, with implications for the improvement of clinical risk prediction.

genomics↗