Genome-wide association studies in nephrology: using known associations for data checks

Abstract

Prior to conducting genome-wide association studies (GWAS) of renal traits and diseases, systematic checks to ensure data integrity and analytical work flow should be conducted. Using positive controls (ie, known associations between a single-nucleotide polymorphism [SNP] and a corresponding trait) allows for identifying errors that are not apparent solely from global evaluation of summary statistics. Strong genetic control associations of chronic kidney disease (CKD), as derived from GWAS, are lacking in the non-African ancestry CKD population; thus, in this perspective, we provide examples of and considerations for using positive controls among patients with CKD. Using data from individuals with CKD who participated in the CRIC (Chronic Renal Insufficiency Cohort) Study or PediGFR (Pediatric Investigation for Genetic Factors Linked to Renal Progression) Consortium, we evaluated 2 kinds of positive control traits: traits unrelated to kidney function (bilirubin level and body height) and those related to kidney function (cystatin C and urate levels). For the former, the proportion of variance in the control trait that is explained by the control SNP is the main determinant of the strength of the observable association, irrespective of adjustment for kidney function. For the latter, adjustment for kidney function can be effective in uncovering known associations among patients with CKD. For instance, in 1,092 participants in the PediGFR Consortium, the P value for the association of cystatin C concentrations and rs911119 in the CST3 gene decreased from 2.7×10(-3) to 2.4×10(-8) upon adjustment for serum creatinine-based estimated glomerular filtration rate. In this perspective, we give recommendations for the appropriate selection of control traits and SNPs that can be used for data checks prior to conducting GWAS among patients with CKD.

Keywords: Genome-wide association study (GWAS); chronic kidney disease (CKD); cystatin C; data checking; genetic marker; positive control; renal trait; single-nucleotide polymorphism (SNP); systematic error.