Computational genomics approaches for kidney diseases in Africa

Abstract

End stage renal disease (ESRD), a more severe form of kidney disease, is considered to be a complex trait that may involve multiple processes which work together on a background of a significant genetic susceptibility. Black Africans have been shown to bear an unequal burden of this disease compared to white Europeans, Americans and Caucasians. Despite this, most of the genetic and epidemiological advances made in understanding the aetiology of kidney diseases have been done in other populations outside of sub-Saharan Africa (SSA). Very little research has been undertaken to investigate key genetic factors that drive ESRD in Africans compared to patients from rest of world populations. Therefore, the primary aim of this Bioinformatics thesis was twofold: firstly, to develop and apply a whole exome sequencing (WES) analysis pipeline and use it to understand a genetic mechanism underlying ESRD in a South African population of mixed ancestry. As I hypothesized that the pipeline would enable the discovery of highly penetrate rare variants with large effect size, which are expected to explain an important fraction of the genetic aetiology and pathogenesis of ESRD in these African patients. Secondly, the aim was to develop and set up a multicenter clinical database that would capture a plethora of clinical data for patients with Lupus, one of the risk factors of ESRD. From WES of six family members (five cases and one control); a total of 23 196 SNVs, 1445 insertions and 1340 deletions, overlapped amongst all affected family members. The variants were consistent with an autosomal dominant inheritance pattern inferred in this family. Of these, only 1550 SNVs, 67 insertions and 112 deletions were present in all affected family members but absent in the unaffected family member. Following detailed evaluation of evidence for variant implication and pathogenicity, only 3 very rare heterozygous missense variants in 3 genes COL4A1 [p.R476W], ICAM1 [p.P352L], COL16A1 [p.T116M] were considered potentially disease causing. Computational relatedness analysis revealed approximate amount of DNA shared by family members and confirmed reported relatedness. Genotyping for the Y chromosome was additionally performed to assist in sample identity. The clinical database has been designed and is being piloted at Groote Schuur medical Hospital at the University of Cape Town. Currently, about 290 patients have already been entered in the registry. The resources and methodologies developed in this thesis have the potential to contribute not only to the understanding of ESRD and its risk factors, but to the successful application of WES in clinical practice. Importantly, it contributes significant information on the genetics of ESRD based on an African family and will also improve scientific infrastructure on the African continent. Clinical databasing will go a long way to enable clinicians to collect and store standardised clinical data for their patients.