Gene-gene (GxG) interactions play an important role in human genetics, potentially explaining part of the missing heritability of polygenic traits and the variable expressivity of monogenic traits. Many GxG interactions have been identified in model organisms through experimental breeding studies, but they have been difficult to identify in human populations. To address this challenge, we applied two complementary variance QTL (vQTL)-based approaches to identify GxG interactions that contribute to human blood traits and blood-related disease risk. First, we used the previously validated genome-wide scale test for each trait in ~450,000 people in the UK Biobank and identified 4 vQTLs. Genome-wide GxG interaction testing of these vQTLs enabled discovery of novel interactions between (1) CCL24 and CCL26 for eosinophil count and plasma CCL24 and CCL26 protein levels and (2) HLA-DQA1 and HLA-DQB1 for lymphocyte count and risk of celiac disease, both of which replicated in ~140,000 NIH All of Us and ~70,000 Vanderbilt BioVU participants. Second, we used a biologically informed approach to search for vQTL in disease-relevant genes. This approach identified (1) a known interaction for hemoglobin between two pathogenic variants in HFE which cause hereditary hemochromatosis and alters risk of cirrhosis and (2) a novel interaction between the JAK2 46/1 haplotype and a variant on chromosome 14 which modifies platelet count, JAK2 V617F clonal hematopoiesis, and risk of polycythemia vera. This work identifies novel disease-relevant GxG interactions and demonstrates the utility of vQTL-based approaches in identifying GxG interactions relevant to human health at scale.

The authors have declared no competing interest.

This work was supported by NIH grant DP5 OD029586, a Burroughs Wellcome Fund Career Award for Medical Scientists, an E.P. Evans Foundation grant, a RUNX1 Research Program grant, a Pew Charitable Trusts and Alexander and Margaret Stewart Trust Pew-Stewart Scholar for Cancer Research award, a Vanderbilt University Medical Center Brock Family Endowment grant, and a Young Ambassador Award (A.G.B.), NIH grant T32 GM007347 (Y.P.), NIH grant K12AR084232 (J.N.H), and Arthritis National Research Foundation grant 128808 (R.W.C.). We would also like to acknowledge DNANexus for providing cloud compute credits.

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UK Biobank data is available at https://ukbiobank.dnanexus.com. NIH AllofUs data is available at https://workbench.researchallofus.org. BioVU data is available to investigators at Vanderbilt University Medical Center.

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