Abstract
Conventional genome-wide association studies (GWASs) of complex traits, such as Multiple Sclerosis (MS), are reliant on per-SNP p-values and are therefore heavily burdened by multiple testing correction. Thus, in order to detect more subtle alterations, ever increasing sample sizes are required, while ignoring potentially valuable information that is readily available in existing datasets. To overcome this, we used penalised regression incorporating elastic net with a stability selection method by iterative subsampling to detect the potential interaction of loci with MS risk. Through re-analysis of the ANZgene dataset (1617 cases and 1988 controls) and an IMSGC dataset as a replication cohort (1313 cases and 1458 controls), we identified new association signals for MS predisposition, including SNPs above and below conventional significance thresholds while targeting two natural killer receptor loci and the well-established HLA loci. For example, rs2844482 (98.1% iterations), otherwise ignored by conventional statistics (p = 0.673) in the same dataset, was independently strongly associated with MS in another GWAS that required more than 40 times the number of cases (~45 K). Further comparison of our hits to those present in a large-scale meta-analysis, confirmed that the majority of SNPs identified by the elastic net model reached conventional statistical GWAS thresholds (p < 5 × 10−8) in this much larger dataset. Moreover, we found that gene variants involved in oxidative stress, in addition to innate immunity, were associated with MS. Overall, this study highlights the benefit of using more advanced statistical methods to (re-)analyse subtle genetic variation among loci that have a biological basis for their contribution to disease risk.
Highlights
Multiple sclerosis (MS) is an autoimmune disease driven by a combination of genetic predisposition and environmental factors [1] including reduced levels of vitamin D [2], smoking [3] and viral infections, such as Epstein–Barr virus (EBV) and cytomegalovirus (CMV) [4]
A preliminary comparison of analytical approaches was performed using the ANZgene genome-wide association studies (GWASs) dataset as a discovery cohort with single nucleotide polymorphisms (SNPs) extracted from the natural killer (NK) receptor loci (NKC and Leukocyte receptor complex (LRC)) and human leukocyte antigen (HLA) region that were directly genotyped
We identified a robust set of ‘SNP hits’, validated across two independent cohorts and confirmed using summary statistics from a large-scale meta-analysis
Summary
Multiple sclerosis (MS) is an autoimmune disease driven by a combination of genetic predisposition and environmental factors [1] including reduced levels of vitamin D [2], smoking [3] and viral infections, such as Epstein–Barr virus (EBV) and cytomegalovirus (CMV) [4]. The HLA loci is involved in distinguishing ‘self’ from ’non-self’ through the expression of proteins involved in antigen processing and presentation, primarily interacting with CD4+ and CD8+ T cells [7]. Beyond binding antigens and interacting directly with CD8+ T lymphocytes, HLA class I proteins engage with natural killer (NK) cell receptors to either promote or inhibit their function [8]. NK receptors are encoded within the Natural Killer gene complex (NKC) [10] and Leukocyte receptor complex (LRC) [11], and act in combination with their respective HLA ligands on the target cell [12]. NK cells in autoimmune diseases are reported to have duplicitous roles [17,18]; activated NK cells have been shown to be able to kill autologous and heterologous oligodendrocytes in vitro [19], present in acute inflammatory lesions [20] and expansion and reduction of specific NK subsets (reviewed in Chanvillard et al [14]). Determining if there is a genetic predisposition for altered
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