Common variants in antibody deficiencies
type: CreativeWork
description: The inborn errors of immunity (IEIs) comprise a group of almost 500 diseases charac- terised by immune dysfunction of genetic origin. Many of these disorders represent canoni- cal examples of Mendelian disease and study of their genetic aetiology is largely conducted within a rare-variant, monogenic paradigm. However, most cases of the two most common IEIs, selective IgA deficiency (SIgAD) and common variable immunodeficiency (CVID), have no known Mendelian origin. In this work I investigate the common-variant architec- ture of SIgAD and CVID through genome-wide association studies (GWAS) and use of the pleiotropy-informed conditional false discovery rate (cFDR), which allows one to leverage information from highly-powered studies of related traits. Application of the cFDR requires identification of ‘auxiliary’ traits which are genetically related with the primary trait. Estimation of the genetic correlation is unsuitable for this task in the small-sample regime due to a lack of power. I improve an existing nonparametric test of genetic similarity, the genome-wide pairwise-association signal sharing (GPS) test, through identification of a more suitable null distribution to achieve uniformity of p-values under the null. I devise a permutation testing procedure to estimate the parameters of this distribution and implement a performant algorithm for fast bivariate ecdf evaluation. I demonstrate an advantage for the GPS test in the small-sample setting over tests of non- zero genetic correlation and the nonparametric comparator Hoeffding’s test using simulated case-control GWAS and a panel of disease traits from the UK Biobank. To conventionally maximise the power of GWAS-based discovery, I conduct the largest meta-analysis to date of SIgAD and CVID, compiling publicly available summary statistics with novel GWAS using data from the UK Biobank and the ‘Intrepid’ Study’s IEI cohort. I report four new associations for SIgAD and one for CVID, buttressing the case for a pol…
abstract: The inborn errors of immunity (IEIs) comprise a group of almost 500 diseases charac- terised by immune dysfunction of genetic origin. Many of these disorders represent canoni- cal examples of Mendelian disease and study of their genetic aetiology is largely conducted within a rare-variant, monogenic paradigm. However, most cases of the two most common IEIs, selective IgA deficiency (SIgAD) and common variable immunodeficiency (CVID), have no known Mendelian origin. In this work I investigate the common-variant architec- ture of SIgAD and CVID through genome-wide association studies (GWAS) and use of the pleiotropy-informed conditional false discovery rate (cFDR), which allows one to leverage information from highly-powered studies of related traits. Application of the cFDR requires identification of ‘auxiliary’ traits which are genetically related with the primary trait. Estimation of the genetic correlation is unsuitable for this task in the small-sample regime due to a lack of power. I improve an existing nonparametric test of genetic similarity, the genome-wide pairwise-association signal sharing (GPS) test, through identification of a more suitable null distribution to achieve uniformity of p-values under the null. I devise a permutation testing procedure to estimate the parameters of this distribution and implement a performant algorithm for fast bivariate ecdf evaluation. I demonstrate an advantage for the GPS test in the small-sample setting over tests of non- zero genetic correlation and the nonparametric comparator Hoeffding’s test using simulated case-control GWAS and a panel of disease traits from the UK Biobank. To conventionally maximise the power of GWAS-based discovery, I conduct the largest meta-analysis to date of SIgAD and CVID, compiling publicly available summary statistics with novel GWAS using data from the UK Biobank and the ‘Intrepid’ Study’s IEI cohort. I report four new associations for SIgAD and one for CVID, buttressing the case for a pol…
author: Willis, Thomas
language: eng
keywords: biostatistics, GWAS, pleiotropy, statistical genetics
license: https://apollo8-f-pro.lib.cam.ac.uk/bitstreams/7d8146b3-f131-45a6-878c-01650a985eac/download
license: https://creativecommons.org/licenses/by/4.0/
date published: 2024-09-16
date modified: 2026-07-22T22:24:32Z
provider: Cambridge University
publisher: Networked Digital Library of Theses and Dissertations
type: WebPage
source organization: University of Cambridge

PropertyValue
property ID: DOI
value: 10.17863/CAM.115427