Journal
SCIENTIFIC REPORTS
Volume 6, Issue -, Pages -Publisher
NATURE PUBLISHING GROUP
DOI: 10.1038/srep25014
Keywords
-
Categories
Funding
- Higher Education Funding Council for England (HEFCE)
- Cure Kids NZ
- innovative medicines initiative joint undertaking (IMI JU) [115142-2]
- Arthritis Research UK grant [17552]
- Manchester Biomedical Research Centre
- Arthritis Foundation
- US National Institutes Health [K08AR055688, 1R01AR062886-01]
- Wellcome Trust grant [076113/C/04/Z, 068545/Z/02]
- US National Institutes for Health research program grant [RP-PG-0310-1002]
- UK Medical Research Council grant [G0000934]
- US National Institutes of Health [RO1-AR-4-4422, NO1-AR-2-2263, NO1-AR1-2256, RO1 AI068759, RC2AR059092-01]
- Eileen Ludwig Greenland Center for Rheumatoid Arthritis
- MRC [G0000934] Funding Source: UKRI
- Medical Research Council [G0000934] Funding Source: researchfish
Ask authors/readers for more resources
Genotypic variability based genome-wide association studies (vGWASs) can identify potentially interacting loci without prior knowledge of the interacting factors. We report a two-stage approach to make vGWAS applicable to diseases: firstly using a mixed model approach to partition dichotomous phenotypes into additive risk and non-additive environmental residuals on the liability scale and secondly using the Levene's (Brown-Forsythe) test to assess equality of the residual variances across genotype groups per marker. We found widespread significant (P < 2.5e-05) vGWAS signals within the major histocompatibility complex (MHC) across all three study cohorts of rheumatoid arthritis. We further identified 10 epistatic interactions between the vGWAS signals independent of the MHC additive effects, each with a weak effect but jointly explained 1.9% of phenotypic variance. PTPN22 was also identified in the discovery cohort but replicated in only one independent cohort. Combining the three cohorts boosted power of vGWAS and additionally identified TYK2 and ANKRD55. Both PTPN22 and TYK2 had evidence of interactions reported elsewhere. We conclude that vGWAS can help discover interacting loci for complex diseases but require large samples to find additional signals.
Authors
I am an author on this paper
Click your name to claim this paper and add it to your profile.
Reviews
Recommended
No Data Available