| Autor: |
Burnard SM; School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, NSW 2308, Australia.; Centre for Brain and Mental Health (CBMHR), Hunter Medical Research Institute (HMRI), New Lambton Heights, NSW 2305, Australia., Lea RA; School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, NSW 2308, Australia.; Centre for Brain and Mental Health (CBMHR), Hunter Medical Research Institute (HMRI), New Lambton Heights, NSW 2305, Australia.; Centre of Genomics and Personalised Health, School of Biomedical Sciences, Queensland University of Technology, Kelvin Grove, QLD 4059, Australia., Benton M; Human Genomics, Kenepuru Science Centre, Institute of Environmental Science and Research, Wellington 5240, New Zealand., Eccles D; Malaghan Institute of Medical Research, Wellington 6242, New Zealand., Kennedy DW; Australian Centre of Excellence for Mathematical and Statistical frontiers, Queensland University of technology, Brisbane, QLD 4000, Australia., Lechner-Scott J; Centre for Brain and Mental Health (CBMHR), Hunter Medical Research Institute (HMRI), New Lambton Heights, NSW 2305, Australia.; School of Medicine and Public Health, University of Newcastle, Callaghan, NSW 2308, Australia.; Department of Neurology, John Hunter Hospital, New Lambton Heights, NSW 2305, Australia., Scott RJ; School of Biomedical Sciences and Pharmacy, University of Newcastle, Callaghan, NSW 2308, Australia.; Division of Molecular Medicine, NSW Health Pathology-North, John Hunter Hospital, New Lambton Heights, NSW 2305, Australia.; Hunter Cancer Research Alliance (HCRA), Hunter Medical Research Institute (HMRI), New Lambton Heights, NSW 2305, Australia. |
| Abstrakt: |
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. |
