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Differential expression analysis

Definition
AI-generated

Differential expression analysis statistically compares gene expression (or transcript counts) between conditions or groups—case/control, treatment, genotype classes, or cell types—while modeling variability, library size, and confounders, classically yielding per-gene effect sizes and significance.

Topics
Sequencing Data Analysis Bioinformatics
Related
Expression Quantitative Trait Locus (eQTL) Gene expression Pseudobulk

Why it matters in GWAS

DE results contextualize loci in relevant phenotypes and tissues, prioritize candidate pathways, and complement eQTL evidence when the same variants or pathways are tested in orthogonal expression cohorts.

Example usage

"We performed differential expression analysis comparing carriers and non-carriers of the risk allele in iPSC-derived neurons."

Expression Quantitative Trait Locus (eQTL) Gene expression Pseudobulk RNA Sequencing (RNA-seq) Transcript quantification
Related-term network

Click any node to open that term page.

Node order. The current term is first. Next come every page linked from Related terms, in the same top-to-bottom order as that list. Then new terms appear in two-hop layers: for each of those direct neighbors (in that same order), their related terms are appended if not already listed. A third pass does the same for two-hop nodes. The walk stops when a fixed maximum number of nodes is reached.

References

  • Love MI, Huber W, Anders S. (2014). Moderated estimation of fold change and dispersion for RNA-seq data with DESeq2. Genome Biol.
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