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- Diaz-Gallo, LM, et al.
(författare)
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Systematic approach demonstrates enrichment of multiple interactions between non-HLA risk variants and HLA-DRB1 risk alleles in rheumatoid arthritis
- 2018
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Ingår i: Annals of the rheumatic diseases. - : BMJ. - 1468-2060 .- 0003-4967. ; 77:10, s. 1454-1462
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Tidskriftsartikel (refereegranskat)abstract
- In anti-citrullinated protein antibody positive rheumatoid arthritis (ACPA-positive RA), a particular subset of HLA-DRB1 alleles, called shared epitope (SE) alleles, is a highly influential genetic risk factor. Here, we investigated whether non-HLA single nucleotide polymorphisms (SNP), conferring low disease risk on their own, interact with SE alleles more frequently than expected by chance and if such genetic interactions influence the HLA-DRB1 SE effect concerning risk to ACPA-positive RA.MethodsWe computed the attributable proportion (AP) due to additive interaction at genome-wide level for two independent ACPA-positive RA cohorts: the Swedish epidemiological investigation of rheumatoid arthritis (EIRA) and the North American rheumatoid arthritis consortium (NARAC). Then, we tested for differences in the AP p value distributions observed for two groups of SNPs, non-associated and associated with disease. We also evaluated whether the SNPs in interaction with HLA-DRB1 were cis-eQTLs in the SE alleles context in peripheral blood mononuclear cells from patients with ACPA-positive RA (SE-eQTLs).ResultsWe found a strong enrichment of significant interactions (AP p<0.05) between the HLA-DRB1 SE alleles and the group of SNPs associated with ACPA-positive RA in both cohorts (Kolmogorov-Smirnov test D=0.35 for EIRA and D=0.25 for NARAC, p<2.2e-16 for both). Interestingly, 564 out of 1492 SNPs in consistent interaction for both cohorts were significant SE-eQTLs. Finally, we observed that the effect size of HLA-DRB1 SE alleles for disease decreases from 5.2 to 2.5 after removal of the risk alleles of the two top interacting SNPs (rs2476601 and rs10739581).ConclusionOur data demonstrate that there are massive genetic interactions between the HLA-DRB1 SE alleles and non-HLA genetic variants in ACPA-positive RA.
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| 13. |
- Diaz-Gallo, LM, et al.
(författare)
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Understanding interactions between risk factors, and assessing the utility of the additive and multiplicative models through simulations
- 2021
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Ingår i: PloS one. - : Public Library of Science (PLoS). - 1932-6203. ; 16:4, s. e0250282-
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Tidskriftsartikel (refereegranskat)abstract
- Understanding the genetic background of complex diseases requires the expansion of studies beyond univariate associations. Therefore, it is important to use interaction assessments of risk factors in order to discover whether, and how genetic risk variants act together on disease development. The principle of interaction analysis is to explore the magnitude of the combined effect of risk factors on disease causation. In this study, we use simulations to investigate different scenarios of causation to show how the magnitude of the effect of two risk factors interact. We mainly focus on the two most commonly used interaction models, the additive and multiplicative risk scales, since there is often confusion regarding their use and interpretation. Our results show that the combined effect is multiplicative when two risk factors are involved in the same chain of events, an interaction called synergism. Synergism is often described as a deviation from additivity, which is a broader term. Our results also confirm that it is often relevant to estimate additive effect relationships, because they correspond to independent risk factors at low disease prevalence. Importantly, we evaluate the threshold of more than two required risk factors for disease causation, called the multifactorial threshold model. We found a simple mathematical relationship (square root) between the threshold and an additive-to-multiplicative linear effect scale (AMLES), where 0 corresponds to an additive effect and 1 to a multiplicative. We propose AMLES as a metric that could be used to test different effects relationships at the same time, given that it can simultaneously reveal additive, multiplicative and intermediate risk effects relationships. Finally, the utility of our simulation study was demonstrated using real data by analyzing and interpreting gene-gene interaction odds ratios from a rheumatoid arthritis case-control cohort.
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