Next Generation Sequencing and Genome-Wide Association Studies to Identify Mitochondrial Genomic Features Associated with Diabetic Kidney Disease

Abstract

Diabetic kidney disease (DKD) affects ~40% of persons with diabetes and is the leading cause of chronic kidney disease and end-stage renal disease globally. Mitochondrial dysfunction is implicated in the pathophysiology of DKD. Previous research reported SNPs in nuclear genes, which influence mitochondrial function, are significantly associated with DKD. Furthermore, these genetic and functional data prompted further investigation of single nucleotide polymorphisms (SNPs) affecting mitochondrial function for association with DKD.

Targeted genome wide association analyses focusing on mitochondrial DNA (mtDNA) and nuclear genes involved with mitochondrial function were performed using DNA samples from the All Ireland / Warren 3 Genetics of Kidneys in Diabetes UK Collection. SNPs in NEMGs found to be suggestive of association were followed up in a larger collection which included up to 19,406 individual and updated imputation to approximately 49 million SNPs. The SNP that showed most evidence for association with decreased glomerular filtration rate after adjusting for covariates was MitoG11915A (P=0.0003) which is a synonymous variant found in the mitochondrial gene coding for the NADH-ubiquinone oxidoreductase chain 4 protein. In nuclear genes involved with mitochondrial function there were eight SNPs in four genes associated with DKD related phenotypes.

Next generation sequencing offers the ability to investigate genetic and epigenetic features in more detail than genetic association studies and these have been successful used for research into various disease including DKD. In Chapter 3 of this thesis Illumina sequencing technology is used to investigate mtDNA, gene expression and methylation in DKD with the aim of establishing a sequencing and analyses workflow for targeted analysis of mtDNA.

The work in this thesis has utilised genome wide association studies and next generation sequencing to investigate mitochondrial genetic variants which may be used to identify predisposition to DKD in individuals with type 1 diabetes. While substantial progress has been made in DKD research there is still a long way to go and genetics and epigenetics represent only a small piece of the puzzle. The widespread use of precision medicine will require seamless integration of clinical data with full “omics” profiles for each patient with large data repositories which can identify and generate treatment options at an individual patient level.

Date of Award	Jul 2020
Original language	English
Awarding Institution	Queen's University Belfast
Sponsors	Northern Ireland Department for the Economy
Supervisor	Peter Maxwell (Supervisor) & Amy Jayne McKnight (Supervisor)