The genetics of autoimmune and proteinuric disease

Thesis

The genetics of complex common diseases are not fully understood, but rare variants with large phenotypic effects contribute to heritability. The objective of this thesis is to identify rare variants of relevance to autoimmune and renal disease, by developing ways of analysing whole genome sequencing (WGS) data and exploring the variants identified. Forward genetic experimental approaches are used, both in mutagenised mice, and in humans with extreme trait forms of the steroid resistant nephrotic syndrome (SRNS) and systemic lupus erythematosus (SLE). This work demonstrates that N–ethyl–N–nitrosourea (ENU) mutations can be distinguished within WGS data, including a hypomorphic mutation in Lamb2 in a strain with the nephrotic syndrome, a murine model for the milder spectrum of human Pierson syndrome. In a B–cell deficient ENU strain the causative mutation in Lyn was isolated by sequencing multiple affected mice and applying an implementation of the Lander–Green algorithm to search for identity by descent. This method for the first time overcomes a rate-limiting step in ENU programmes and offers the potential to accelerate gene discovery, eliminating the need for out–crossing and conventional linkage analysis. Knowledge of the ENU genomic intervals allowed calculation of a mutation frequency, 1.5 mutations per mega base, and modelling of an efficient ENU strategy. Short–lists of candidate variants from 14 unrelated patients with steroid resistant nephrotic syndrome or systemic lupus erythematosus provide a substrate for future experiments, for example a candidate in the Wiskott–Aldrich syndrome gene was excluded using DNA from family members. In conclusion, WGS coupled with identity by descent analysis offers a powerful tool to improve the efficiency of ENU programmes. Rare variant discovery in humans without obvious Mendelian inheritance is more challenging and will require strategies to prioritise variants that combine bioinformatic filters and experimental verification in a high throughput way.

Authors: Bull, K

• DOI: 10.5287/ora-aeqmxwpx6
• Type of award: DPhil
• Level of award: Doctoral
• Awarding institution: University of Oxford