Identifying genetic variants that influence the abundance of cell states in single-cell data

Rumker, L.; Sakaue, S.; Reshef, Y.; Kang, J. B.; Yazar, S.; Alquicira-Hernandez, J.; Valencia, C.; Lagattuta, K. A.; Mah-Som, A.; Nathan, A.; Powell, J. E.; Loh, P.-R.; Raychaudhuri, S.

doi:10.1101/2023.11.13.566919

bioRxiv · 10.1101/2023.11.13.566919

Identifying genetic variants that influence the abundance of cell states in single-cell data

Rumker, L.·Sakaue, S.·Reshef, Y.·Kang, J. B.·Yazar, S.·Alquicira-Hernandez, J.·Valencia, C.·Lagattuta, K. A.·Mah-Som, A.·Nathan, A.·Powell, J. E.·Loh, P.-R.·Raychaudhuri, S.

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Abstract

Introductory ParagraphTo 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,1 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- 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.

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Rumker, L., Sakaue, S., Reshef, Y., Kang, J. B., Yazar, S., Alquicira-Hernandez, J., Valencia, C., Lagattuta, K. A., Mah-Som, A., Nathan, A., Powell, J. E., Loh, P.-R., Raychaudhuri, S.. 2023-11-15. Identifying genetic variants that influence the abundance of cell states in single-cell data. https://doi.org/10.1101/2023.11.13.566919

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