Sign in
GenomicsbioRxiv

Evaluating intra- and inter-individual variation in the human placental transcriptome

David A Hughes, Martin Kircher, Zhisong HeiD, Song Guo, Genevieve L Fairbrother, Carlos S Moreno, Philipp KhaitovichiD, Mark StonekingiD

Abstract

BackgroundGene expression variation is a phenotypic trait of particular interest as it represents the initial link between genotype and other phenotypes. Analyzing how such variation apportions among and within groups allows for the evaluation of how genetic and environmental factors influence such traits. It also provides opportunities to identify genes and pathways that may have been influenced by non-neutral processes. Here we use a population genetics framework and next generation sequencing to evaluate how gene expression variation is apportioned among four human groups in a natural biological tissue, the placenta.\n\nResultsWe estimate that on average, 33.2%, 58.9% and 7.8% of the placental transcriptome is explained by variation within individuals, among individuals and among human groups, respectively. Additionally, when technical and biological traits are included in models of gene expression they account for roughly 2% of total gene expression variation. Notably, the variation that is significantly different among groups is enriched in biological pathways associated with immune response, cell signaling and metabolism. Many biological traits demonstrated correlated changes in expression in numerous pathways of potential interest to clinicians and evolutionary biologists. Finally, we estimate that the majority of the human placental transcriptome (65% of expressed genes) exhibits expression profiles consistent with neutrality; the remainder are consistent with stabilizing selection (26%), directional selection (4.9%), or diversifying selection (4.8%).\n\nConclusionWe apportion placental gene expression variation into individual, population and biological trait factors and identify how each influence the transcriptome. Additionally, we advance methods to associate expression profiles with different forms of selection.

02 /Specification & Classification

Click any tag to browse matching papers

Reproducibility ledger

Nobody has reported trying to reproduce this paper yet. Did you replicate an experiment or run the code? Your report saves the next lab a week.

Sign in to file a reproducibility report.

Similarity Map
SelectedSame fieldCross-field
THISDelahaye '18Josephs '20Roberts '22Armstrong '17Olney '22Reynolds '23
59% semantic matchGenomics
Fabien Delahaye, Catherine Do, Yu Kong+5 more·PLoS Genetics·Nov 19, 2018· 74 citations