Biology subjects

Villicana, S.

Publications and source records attributed to Villicana, S..

4 recordsLinked to original sources

bioRxiv2025

Genetic impacts on within-pair DNA methylation variance in monozygotic twins capture gene-environment interactions and cell-type effects

BackgroundGenetic variants that are associated with phenotypic variability, or variance quantitative trait loci (vQTLs), have been detected for multiple human traits. Gene-environment interactions can lead to differential phenotypic variability across genotype groups, therefore, genetic variants that interact with environmental exposures can manifest as vQTLs. Although changes in DNA methylation variability have been observed in several diseases, vQTLs for methylation levels (vmeQTL) have not yet been explored in depth. ResultsWe optimize the value of monozygotic (MZ) twin studies to identify and replicate vmeQTLs for blood DNA methylation variance at 358 CpGs in 988 adult MZ twin pairs from two European twin registries. Over a third of vmeQTLs captured identical vmeQTL-environmental factor interactions in both datasets, and the majority of interactions were observed with blood cell counts. Correspondingly, over 60% of CpGs affected by genotype-monocyte and genotype-T cell interactions replicated as CpGs affected by genetic effects in the relevant cell type in an independent dataset. Most vmeQTLs also replicated in 1,348 UK non-twin adults and showed longitudinal stability in a sample subset. Integrating gene expression and phenotype association results identified multiple vmeQTLs that capture GxE effects relevant to human health. Examples include vmeQTLs interacting with blood cell type to influence DNA methylation in FAM65A, NAPRT, and CSGALNACT1 underlying immune disease susceptibility and progression. ConclusionOur findings identify novel genetic effects on human DNA methylation variability within a unique MZ twin study design. The results show the potential of vmeQTLs to identify gene-environment interactions and provide novel insights into complex traits.

genetics↗

bioRxiv2025

Genetic regulation of fatty acid content in adipose tissue

Fatty acids are important as structural components, energy sources, and signaling mediators. While studies have extensively explored genetic regulation of fatty acids in serum and other bodily fluids, their regulation within adipose tissue, a crucial regulator of cardiovascular and metabolic health remains unclear. Here, we investigated the genetic regulation of 18 fatty acids in subcutaneous adipose tissue from 569 female twins from TwinsUK. Using twin models, the heritability of fatty acids ranged from 5% to 59%, indicating a substantial genetic regulation of fatty acid levels within adipose tissue, which was also tissue-specific in many cases. Genome-wide association studies identified ten significant loci, in SCD, ADAMTSL1, ZBTB41, SNTB1, EXOC6B, ACSL3, LINC02055, MKRN2/TSEN2, FADS1 and HAPLN across 13 fatty acids or fatty acid product-to-precursor ratios. Using adipose gene expression and methylation, which were concurrently measured in these samples, we detected five fatty acid-associated signals that colocalized with eQTL and meQTL signals, highlighting fatty acids that are regulated by molecular processes within adipose tissue. We identified strong associations of adipose fatty acids-associated loci with type 2 diabetes, body fat percentage, and cardiovascular disease. We explored links between polygenic scores of common metabolic traits and adipose fatty acid levels, and identified associations between polygenic scores of BMI, body-fat distribution and triglycerides and several fatty acids, indicating these risk scores impact local adipose tissue content. Overall, our results identified local genetic regulation of fatty acids within adipose tissue and highlighted their links with renal and cardio-metabolic health.

genetics↗

bioRxiv2024

DNA methylation and gene expression trajectories of human postprandial metabolism

Human postprandial metabolism is characterised by a highly individualised response to food that is predictive of cardiometabolic health and underexplored at the molecular level. We profiled blood DNA methylation (DNAm) and gene expression trajectories before and after a test meal in 225 European participants. We identify DNAm changes at fasting, 30 minutes and 4 hours after meal challenge, including in metabolically relevant genes INPP4A, GHRL, ASIP and ABCG1, with changes observed as early as 30mins postprandially. Gene expression trajectories also changed postprandially predominantly at 4 hours, with replication of lipid metabolism (CPT1A) and circadian rhythm (PER1) genes. Genetic variants affect postprandial molecular trajectories at genes linked to obesity (PDE9A) and glucose response (GPT2). Multiple signals associated with postprandial glucose and triglyceride levels, with replication of CPT1A methylation. The postprandial DNAm and expression trajectories target metabolically relevant genes, giving insights towards mechanisms underlying inter-individual response to food and cardiometabolic disease risk.

genomics↗

bioRxiv2023

Genetic impacts on DNA methylation help elucidate regulatory genomic processes

Pinpointing genetic impacts on DNA methylation can improve our understanding of pathways that underlie gene regulation and disease risk. We report heritability and methylation quantitative trait locus (meQTL) analysis at 724,499 CpGs profiled with the Illumina Infinium MethylationEPIC array in 2,358 blood samples from three UK cohorts, with replication. Methylation levels at 34.2% of CpGs were affected by SNPs, and 98% of effects were cis-acting or within 1 Mbp of the tested CpG. Our results are consistent with meQTL analyses based on the former Illumina Infinium HumanMethylation450 array. Both meQTL SNPs and CpGs with meQTLs were overrepresented in enhancers, which have improved coverage on this platform compared to previous approaches. Co-localisation analyses across genetic effects on DNA methylation and 56 human traits identified 1,520 co-localisations across 1,325 unique CpGs and 34 phenotypes, including in disease-relevant genes, such ICOSLG (inflammatory bowel disease), and USP1 and DOCK7 (total cholesterol levels). Enrichment analysis of meQTLs and integration with expression QTLs gave insights into mechanisms underlying cis-meQTLs, for example through disruption of transcription factor binding sites for CTCF and SMC3, and trans-meQTLs, for example through regulating the expression of ACD and SENP7 which can modulate DNA methylation at distal sites. Our findings improve the characterisation of the mechanisms underlying DNA methylation variability and are informative for prioritisation of GWAS variants for functional follow-ups. A results database and viewer are available online.

genomics↗