Risk of Chronic Kidney Disease Associated with GSTM1 Deletions

点击翻译按钮获取中文摘要

Chronic kidney disease (CKD) affects ~10% of the U.S. adults and has few effective treatments. Studies of genetic susceptibility can provide insight into the underlying causal pathways and may lead to novel treatment targets. One promising candidate gene for CKD is the glutathione S- transferase mu 1 (GSTM1), which catalyzes the conjugation of glutathione with a range of electrophiles to facilitate the degradation or excretion of the electrophiles. Loss of the GSTM1 gene has been associated with two-fold higher risk for CKD progression in the African American Study of Kidney Disease and Hypertension (AASK). The GSTM1 homozygous deletion (0 copy) is a common variant (27% in African Americans and 53% in European Americans) and cannot be tagged reliably using single nucleotide polymorphisms (SNPs). Large scale association studies of the GSTM1 copy number variation (CNV) with kidney outcome have not been conducted. Recent availability of large scale exome sequencing data provides an unprecedented opportunity for determining exonic CNVs, which are likely to change protein function. The Atherosclerosis Risk in Communities (ARIC) study have existing exome sequencing data in ~7800 European Americans and ~3000 African Americans, carefully measured CKD risk factors and validated CKD outcomes. Using the rich data resources in the ARIC study, we aim to 1) determine GSTM1 CNV using exome sequencing reads and validate the CNV calls using an enhanced quantitative polymerase chain reaction (qPCR) method in a subsample (N=224); 2) characterize the association between the GSTM1 CNV with incident CKD and its progression; 3) Identify metabolites in the glutathione pathway that mediate CKD risk associated with GSTM1 CNV. Exome sequencing reads provides a novel and cost effective resources for determining exonic CNVs. Few studies have investigated the association between exonic CNVs and CKD with validation on the CNV calls. Leveraging the long-running ARIC study with exome sequencing and metabolomic data, we can conduct large-scale association studies of the GSTM1 CNV. This proposal will result in substantial enhancements to the CNV calling algorithms, which can be applied to other known exonic CNVs in the ARIC study and other large cohort studies in which we may validate associations. If GSTM1 deletion is found to be underlying CKD risk, GSTM1 and its metabolic pathways can be a potential target for the prevention and treatment of CKD.