Genome wide association study (GWAS) meta-analysis for the identification and characterisation of genetic variants associated with kidney function deterioration

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Chronic kidney disease (CKD) is a major public health issue affecting every ca. 10th person in the general population, with diabetes a key risk factor. Current preventive and therapeutic strategies, targeting cardiovascular risk factors, are not kidney specific and limited by side effects. To develop novel strategies, it is necessary to uncover unknown mechanisms in kidney function deterioration in the general population and in patients with risk factors such as diabetes.Genome wide association study (GWAS) is successful in unraveling mechanisms by systematic search for genetic loci associated with diseases or traits. GWAS meta-analysis (GWAMA), pooling data from several cohorts, increases statistical power, and was first facilitated by imputation of genetic variants using HapMap reference panels. In the first GWAMA of kidney function deterioration phenotypes, coordinated by the applicant in the international CKDGen consortium, several associated loci were identified, in spite of limitations in sample size and in HapMap imputed genetic data.Imputation with 1000 Genomes Project reference panels and Exome variant genotypes now provides improved density of common variants (minor allele frequency, MAF >5%) and more reliable genetic information on less frequent variants (MAF<5%), promising new insights into the genetics of complex diseases.The key aims of this proposal are a) to identify novel genetic variants associated with kidney function deterioration phenotypes in the general population and in high risk subgroups (i.e. individuals with diabetes and CKD), and b) to extend significantly the understanding of biological mechanisms related to these loci.We will perform GWAMAs of kidney function deterioration phenotypes in 47 CKDGen cohorts with 96.366 individuals in stage 1 discovery analysis and follow-up of lead variants in 12.200 individuals in stage 2 analysis, thus doubling the sample size of our previous work. We will use, for the first time, a) genetic variants imputed with 1000 Genomes Project reference panels in all cohorts and b) rare genetic variants from Exome Chip genotyping in 45.409 individuals of 16 cohorts. We will thus significantly extend genetic coverage and statistical power compared to our previous work performed exclusively with HapMap-data. Thus identified loci will be characterized with a battery of bioinformatics tools (fine-mapping with conditional association testing and SNP prioritization with Bayesian methods; functional genomic annotation including interrogation of ENCODE data and drug target analysis; pathway, genetic risk score and eQTL analyses; analysis of pleiotropy and phenotypic variance explained) to determine potentially causal variants, genes and biological pathways underlying the association signals. Thus, this work will provide important novel insights into the biology of kidney function deterioration which is key to the development of novel therapeutic and preventive strategies.