A multi-phenotype approach implicates SH2B3 in the genetics of chronic kidney disease

Chronic kidney disease (CKD) is a complex condition with diverse underlying causes that lead to a progressive decline in kidney function. Genome-wide association studies (GWASs) have identified numerous genetic loci associated with CKD, yet much of the genetic basis remains unexplained. Part of the reason is that most GWASs have only assessed kidney function via single biomarkers such as estimated glomerular filtration rate (eGFR). This study employs a novel multi-phenotype approach, combinatorial Principal Component Analysis (cPCA), to better understand the genetic architecture of CKD. Utilizing a discovery cohort of white British individuals from the UK Biobank (n=337,112), we analyzed 21 CKD-related phenotypes using cPCA to generate over 2 million composite phenotypes (CPs). More than 46,000 CPs demonstrated superior performance in classifying clinical CKD compared to any single biomarker, and those CPs were most frequently comprised of eGFR, cystatin C, HbA1c, microalbuminuria, albumin, and LDL. GWASs of the top 1,000 CPs revealed seven novel genetic loci, with CST3 and SH2B3 successfully replicated in an independent Irish cohort (n=11,106). Notably, the index SNP of the SH2B3 gene, which encodes a regulator in immune responses and cytokine signaling, is a loss-of-function variant with a combined beta of -0.046 and a p-value of 3.1E-56. These results highlight the effectiveness of a multi-phenotype approach in GWASs and implicate a novel functional variant in SH2B3 in CKD phenotypes. TRANSLATIONAL STATEMENTThe application of combinatorial Principal Component Analysis (cPCA) in our study has identified SH2B3 as a novel genetic locus associated with chronic kidney disease (CKD). This discovery advances our understanding of CKDs genetic architecture beyond single biomarker analyses, potentially leading to more precise diagnostic tools and personalized treatment strategies. Future research should focus on validating these findings in diverse populations and integrating cPCA-derived biomarkers into clinical practice to enhance CKD prediction and management, ultimately improving patient outcomes.