#795 Genetics of serum electrolytes: a genome-wide association approach in two European cohorts
Authors: Treccani Mirko , van der Burgh Anna Cornelia , Ghanbari Mohsen , Malerba Giovanni , Ferraro Pietro Manuel , Gambaro Giovanni 
Published: May 2024

Background and Aims

Chloride, potassium and sodium are electrolytes that play a key role in several physiological processes. To date, the genetic factors underlying their differences in the general population are not fully understood and need to be further investigated. In this study, we aimed at dissecting the genetics of the levels of these serum electrolytes in the European population by genomic approaches.

Method

We genotyped and analysed data from 3057 North-Eastern Italian individuals participating in the INCIPE (Initiative on Nephropathy, of relevance to public health, which is Chronic, possibly in its Initial stages, and carries a Potential risk of major clinical End-points) cohort. Genomic data were processed for quality controls (using plink and bcftools), following these exclusion criteria: sample missingness &gt;0.15, genotype missingness &gt;0.02, relatedness up to 2nd degree, non-European ethnicity. Next, the dataset underwent genotype imputation on the Michigan Imputation Server, using the Human Reference Consortium panel and filtering for imputation confidence (R2 &gt;0.3). Typed and imputed data were then analysed on genome-wide association studies (GWAS, using rvtest) for each of the three serum electrolytes. The same analytical procedure was applied to 14926 individuals from the Rotterdam Study (RS) cohort (a prospective cohort study ongoing since 1990 in Rotterdam). Thus, summary statistics from all the phenotypes and cohorts were jointly analysed in a genome-wide meta-analysis setting (using METAL). Meta-analyses results underwent functional annotation on the FUMAGWAS platform, to obtain biological and functional insights and to allow variants and genes prioritisation.

Results

Quality controls and genotype imputation resulted in a total of 857 (INCIPE1) and 1343 (INCIPE2) samples and 8 million variants for the INCIPE cohort; 2056 (RS1), 2042 (RS2) and 2958 (RS3) samples and 7.7 million variants for the Rotterdam Study cohort. GWAS showed genome-wide signals (p &lt; 5e-8) for chloride and sodium and suggestive signals (p &lt; 5e-5) for potassium. Meta-analyses partially replicated results obtained on the INCIPE cohort, showing signals for all the three electrolytes over the suggestive threshold only. Finally, functional annotation gave insights on associated genes and markers. Chloride showed association with genes reported in nephrotic syndrome (NUP205, p = 9e-7), voltage-gated channels (CACNA1E, p = 1e-6) and chloride channels (CLDN4, p = 4e-6; GABRA2, p = 1e-5), comprising a total of 47 candidate risk loci. Potassium showed mostly association with a gene involved in ion transport and polycystic kidney disease (PKD2L2, p = 1e-7), and a total of 28 candidate risk loci. Sodium showed association with genes implicated in several diseases with kidney involvement such as nephropathy (IMMP2L, P = 1e-7), Bardet-Biedl syndrome (BBSP, p = 1-e6) and ARCS1 (SH3BP2, p = 2e-6), and acting in cell polarity (PARD6G, p = 1e-6) and ion transport (ANO3, p = 3e-6), for a total of 76 candidate risk loci. In addition to these genes, other genes were already reported to be associated with cardiomyopathies, such as ABTB2 in chloride and sodium and MYOT and PRKAG2 in potassium.

Conclusion

This study showed novel candidate genes that may explain interindividual differences in serum electrolytes. Genes already reported to be associated with kidney abnormalities offer hints of their actual involvement in electrolyte imbalances and related disease. Moreover, associations pointing at cardiac diseases strengthen the involvement of these genes in renal electrolyte abnormalities, being one of the secondary causes of cardiomyopathies. Finally, the identification of risk loci across the three phenotypes opens the opportunity for the development of polygenic risk scores, which may benefit of an increasing number of tested samples to reach genome-wide significant signals and additional cohorts to validate the computed risk model.