Biology subjects

Lougheed, D.

Publications and source records attributed to Lougheed, D..

3 recordsLinked to original sources

bioRxiv2025

Long-read sequencing reveals telomere inheritance patterns from human trios

Telomeres are essential for maintaining genomic integrity and are associated with cellular aging and disease, yet the factors influencing their inheritance across generations remain poorly understood. Leveraging PacBio HiFi long-read sequencing and 75 parent-offspring trios (n = 225) from the Genomic Answers for Kids program, we analyzed individual telomeres across chromosomes and their inheritance. Telomere length (TL) varied between chromosome arms in a way that was consistent in parents and offsprings, with average values ranging from 5000 to 8000 base pairs. Maternal and paternal TL together were a strong predictor of child TL (R2 = 0.59). Notably, using telomeric variant repeats, we developed a tool that enabled allelic tracing for 53.3% of maternally and 49.9% of paternally inherited telomeres. In the child, paternally transmitted alleles were significantly longer than age-matched maternal ones ({Delta}mean = 409 bp, p = 2.6e-05), particularly when from older parents ({Delta}mean = 698 bp, p = 8.9e-05) and at chromosome arms with shorter average TL ({Delta}mean = 752 bp, p = 1.6e-06). These findings reveal parent-of-origin effects and heritable influences on TL, providing novel insights into telomere dynamics and their potential implications in age-related disease susceptibility.

genetics↗

bioRxiv2022

Epigenetic variation impacts ancestry-associated differences in the transcriptional response to influenza infection

Humans display remarkable inter-individual variation in immune response when exposed to identical immune challenges. Yet, our understanding of the genetic and epigenetic factors contributing to such variation remains limited. Here we carried out in-depth genetic, epigenetic, and transcriptional profiling on primary macrophages derived from a panel of European and African-ancestry individuals before and after infection with influenza A virus (IAV). We show that baseline epigenetic profiles are strongly predictive of the transcriptional response to IAV across individuals, and that ancestry-associated differences in gene expression are tightly coupled with variation in enhancer activity. Quantitative trait locus (QTL) mapping revealed highly coordinated genetic effects on gene regulation with many cis-acting genetic variants impacting concomitantly gene expression and multiple epigenetic marks. These data reveal that ancestry-associated differences in the epigenetic landscape are genetically controlled, even more so than variation in gene expression. Lastly, we show that among QTL variants that colocalized with immune-disease loci, only 7% were gene expression QTL, the remaining corresponding to genetic variants that impact one or more epigenetic marks, which stresses the importance of considering molecular phenotypes beyond gene expression in disease-focused studies.

genetics↗

bioRxiv2018

baRcodeR with PyTrackDat: Open-source labelling and tracking of biological samples for repeatable science

Repeatable experiments with accurate data collection and reproducible analyses are fundamental to the scientific method but may be difficult to achieve in practice. Several flexible, open-source tools developed for the R and Python coding environments aid the reproducibility of data wrangling and analysis in scientific research. In contrast, analogous tools are generally lacking for earlier stages, such as systematic labelling and processing of field samples with hierarchical structure (e.g. time points of individuals from multiple lines or populations) or curating heterogenous data collected by different researchers over several years. Such tools are critical for modern research given trends toward globally distributed collaborators using higher-throughput technologies. As a step toward improving repeatability of methods for the collection of biological samples, and curation of biological data, we introduce the R package baRcodeR and the PyTrackDat pipeline in Python. The baRcodeR package provides tools for generating biologically informative, hierarchical labels with digitally encoded 2D barcodes that can be printed and scanned using low-cost commercial hardware. The PyTrackDat pipeline integrates with baRcodeR output to build a web interface for sample management and tracking along with data collection and curation. We briefly describe the application of principles from baRcodeR and PyTrackDat in three large research projects, which demonstrate their value to (i) help document sampling methods, (ii) facilitate collaboration and (iii) reduce opportunities for human errors and omissions that could otherwise propagate through downstream data analysis to compromise biological inference.

bioinformatics↗