Oral Presentation GENEMAPPERS 2024

Variance quantitative trait loci for urinary secretion phenotypes reveal potential genetic interactions (#26)

Danielle M Adams 1 2 , William R Reay 3 , Murray J Cairns 1 2
  1. School of Biomedical Science and Pharmacy, University of Newcastle, Callaghan, New South Wales, Australia
  2. Precision Medicine Research Program, Hunter Medical Research Institute, Newcastle, New South Wales, Australia
  3. Menzies Institute for Medical Research, University of Tasmania, Hobart, Tasmania, Australia

Irregular levels of substances secreted in urine may indicate the presence of kidney damage or disease1. While research has been performed to investigate additive risk factors for complex traits such as these, fewer studies have estimated the potential effects that interactions may have on these phenotypes. Variance-quantitative trait loci (vQTL) are genetic variants which are associated with changes in the variance of a phenotype and represent variants which are more likely than mean-quantitative trait loci (muQTL) to reflect genetic interactions2. The presence of a vQTL indicates that changes in phenotype level for a particular allele are dependent on an unknown factor potentially indicating an interaction, which could be environmental or genetic. We performed genome wide mean- and variance-quantitative trait analysis using the Plink generalised linear model3 and the OmicS-data-based Complex trait Analysis Levene’s test4 respectively for four urinary secretion phenotypes (urinary: microalbumin, potassium, creatinine and sodium) in the UK Biobank. Across the four traits, we identified 70 genome wide significant and independent muQTLs and 14 vQTLs, of which 4 were also muQTLs. Genes through which vQTLs may act were prioritised systematically based on proximity, fine-mapping, expression quantitative trait loci, and data from open targets genetics (V2G, RDB and CADD)5,6. One of these genes encodes the protein carbamoyl phosphate synthetase I (CPS1) which is an enzyme that catalyses the beginning of the urea cycle and was prioritised from a vQTL and muQTL for urinary creatinine (rs1047891)7. Further, a linear regression interaction model was applied to the identified vQTLs which found 26 significant interaction models associated with urinary secretion level after Bonferroni correction (P < 9.48x10-5). These loci represent genetic variants which may either directly or indirectly exert an interaction effect on the urinary secretion of sodium, potassium, creatinine and microalbumin and through which indicate deteriorating kidney function.

  1. Thompson, L.E., and Joy, M.S. (2022). Endogenous markers of kidney function and renal drug clearance processes of filtration, secretion, and reabsorption. Curr Opin Toxicol 31. 10.1016/j.cotox.2022.03.005.
  2. Westerman, K.E., Majarian, T.D., Giulianini, F., Jang, D.-K., Miao, J., Florez, J.C., Chen, H., Chasman, D.I., Udler, M.S., Manning, A.K., and Cole, J.B. (2022). Variance-quantitative trait loci enable systematic discovery of gene-environment interactions for cardiometabolic serum biomarkers. Nature Communications 13, 3993. 10.1038/s41467-022-31625-5.
  3. Purcell, S., Neale, B., Todd-Brown, K., Thomas, L., Ferreira, M.A., Bender, D., Maller, J., Sklar, P., de Bakker, P.I., Daly, M.J., and Sham, P.C. (2007). PLINK: a tool set for whole-genome association and population-based linkage analyses. Am J Hum Genet 81, 559-575. 10.1086/519795.
  4. Wang, H., Zhang, F., Zeng, J., Wu, Y., Kemper, K.E., Xue, A., Zhang, M., Powell, J.E., Goddard, M.E., Wray, N.R., et al. (2019). Genotype-by-environment interactions inferred from genetic effects on phenotypic variability in the UK Biobank. Science Advances 5, eaaw3538. doi:10.1126/sciadv.aaw3538.
  5. Ghoussaini, M., Mountjoy, E., Carmona, M., Peat, G., Schmidt, E.M., Hercules, A., Fumis, L., Miranda, A., Carvalho-Silva, D., Buniello, A., et al. (2021). Open Targets Genetics: systematic identification of trait-associated genes using large-scale genetics and functional genomics. Nucleic Acids Res 49, D1311-d1320. 10.1093/nar/gkaa840.
  6. Mountjoy, E., Schmidt, E.M., Carmona, M., Schwartzentruber, J., Peat, G., Miranda, A., Fumis, L., Hayhurst, J., Buniello, A., Karim, M.A., et al. (2021). An open approach to systematically prioritize causal variants and genes at all published human GWAS trait-associated loci. Nat Genet 53, 1527-1533. 10.1038/s41588-021-00945-5.
  7. Nitzahn, M., and Lipshutz, G.S. (2020). CPS1: Looking at an ancient enzyme in a modern light. Mol Genet Metab 131, 289-298. 10.1016/j.ymgme.2020.10.003.