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Identifying genetic variants that influence the abundance of cell states in single-cell data
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Identifying genetic variants that influence the abundance of cell states in single-cell data
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Identifying genetic variants that influence the abundance of cell states in single-cell data
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Journal Article
Identifying genetic variants that influence the abundance of cell states in single-cell data
2023
Overview
To understand genetic mechanisms driving disease, it is essential but difficult to map how risk alleles affect the composition of cells present in the body. Single-cell profiling quantifies granular information about tissues, but variant-associated cell states may reflect diverse combinations of the profiled cell features that are challenging to predefine. We introduce GeNA (Genotype-Neighborhood Associations), a statistical tool to identify cell state abundance quantitative trait loci (csaQTLs) in high-dimensional single-cell datasets. Instead of testing associations to predefined cell states, GeNA flexibly identifies the cell states whose abundance is most associated with genetic variants. In a genome-wide survey of scRNA-seq peripheral blood profiling from 969 individuals, GeNA identifies five independent loci associated with shifts in the relative abundance of immune cell states. For example, rs3003-T (p=1.96x10-11) associates with increased abundance of NK cells expressing TNF-α response programs. This csaQTL colocalizes with increased risk for psoriasis, an autoimmune disease that responds to anti-TNF treatments. Flexibly characterizing csaQTLs for granular cell states may help illuminate how genetic background alters cellular composition to confer disease risk.Competing Interest StatementThe authors have declared no competing interest.
Publisher
Cold Spring Harbor Laboratory Press,Cold Spring Harbor Laboratory
