Modeling genetic susceptibility to multiple sclerosis

The main aim of this thesis was to investigate genetic and environmental factors and their role in the etiology of Multiple Sclerosis (MS) by using comprehensive registry data or novel computationally intense methods. To date, over 100 genes associated with MS have been identified, but how they interact in the risk for the disease is not yet fully understood. The presence of high prevalence clusters has led researchers to believe that there might be as yet unidentified rare variant involved in the disease etiology. In Paper I, we attempted to search for these rare variants by using a population based linkage approach, estimating haplotypes shared between individuals inherited by descent from some common ancestor. One significant hit was found on chromosome 19, but due to methodological problems the result should be interpreted with caution. MS is commonly attributed high familial risks, decreasing with relatedness, which indicates a large genetic component involved in the disease etiology. In Paper II, nationwide registry data was used to reinvestigate the familial risks and estimate the proportion of genetics and environment contributing to disease etiology. The relative risks estimated were lower than usually reported, with a sibling relative risk of 7.1 and no significant differences between the sexes. The heritability was estimated to be 64% and the environmental 36% with a non-significant shared environmental component of 1%. In Paper III, the women-to-men ratio for MS in Sweden was reinvestigated. MS is a disease more common in women than men, and an increase in the women-to-men ratio has been reported in several countries. However, a report from Sweden did not show this increase in women and Paper III extended this report using data from nationwide registers. An increase among women compared to men was identified, and when comparing against the previous study, an inclusion bias, presumably caused by a higher mortality rate among the oldest men, was identified. One framework used to model complex diseases such as MS is the sufficent cause model, also known as Rothman's pie model. This model hypotehsizes that a disease can be caused by several mechanisms, or pies, each consisting of a set of different factors and when all factors are present they will inevitably cause disease. Paper IV extends this model into a stochastic version and presents an algorithm that can estimate the probability that an a priori suggested mechanism has caused disease in a certain individual. The algorithm showed high classification accuracy on synthetic data; however it needs further investigation of its properties. In conclusion, this thesis revise the familial risks for MS to more moderate levels, with no differences between the sexes, and confirms the global trend of an increasing women-to-men ratio. No rare variants contributing to MS on population level were identified. We also present a probabilitic version of Rothman's pie model, showing promising results on synthetic data

Does persistence to methotrexate treatment in early rheumatoid arthritis have a familial component?

Funding Information: Open access funding provided by Karolinska Institute. HW received support for the project from Stiftelsen Anna och Emil Olssons fond, Reumatikerförbundet [grant no R-940868]; Stiftelsen Professor Nanna Svartz Fond [grant no 2020-00334]; Kung Gustav V:s 80 year foundation [grant no FAI-2020-0666]; Karolinska Institutet foundations [grant no 2020-02508 and 2020-02398]. TF and SS were supported by the Swedish Research Council [DNR 2016-01355 and DNR 2018-02803, respectively]. JA was supported by the Swedish Research Council; Nordforsk; Vinnova; Region Stockholm/Karolinska Institutet Funds (ALF) and the Swedish Heart Lung Foundation. Publisher Copyright: © 2022, The Author(s).Objectives: To assess whether persistence to treatment with methotrexate (MTX) in early rheumatoid arthritis (RA) is shared among first-degree relatives with RA and to estimate any underlying heritability. Methods: First-degree relative pairs diagnosed with RA 1999–2018 and starting MTX (in monotherapy) as their first disease-modifying anti-rheumatic drug (DMARD) treatment were identified by linking the Swedish Rheumatology Quality Register to national registers. Short- and long-term persistence to MTX was defined as remaining on treatment at 1 and 3 years, respectively, with no additional DMARDs added. We assessed familial aggregation through relative risks (RR) using log-binomial regression with robust standard errors and estimated heritability using tetrachoric correlations. We also explored the familial aggregation of EULAR treatment response after 3 and 6 months. To mimic the clinical setting, we also tested the association between having a family history of MTX persistence and persistence within the index patient. Results: Familial persistence was not associated with persistence at 1 (RR=1.02, 95% CI 0.87–1.20), only at 3 (RR=1.41, 95% CI 1.14–1.74) years. Heritability at 1 and 3 years was estimated to be 0.08 (95% CI 0–0.43) and 0.58 (95% CI 0.27–0.89), respectively. No significant associations were found between family history and EULAR response at 3 and 6 months, neither overall nor in the clinical setting analysis. Conclusions: Our findings imply a familial component, including a possible genetic element, within the long-term persistence to MTX following RA diagnosis. Whether this component is reflective of characteristics of the underlying RA disease or determinants for sustained response to MTX in itself will require further investigation.Peer reviewe

Age Related Multiple Sclerosis Severity Score: Disability ranked by age.

BACKGROUND: The Multiple Sclerosis Severity Score (MSSS) is obtained by normalising the Expanded Disability Status Scale (EDSS) score for disease duration and has been a valuable tool in cross-sectional studies. OBJECTIVE: To assess whether use of age rather than the inherently ambiguous disease duration was a feasible approach. METHOD: We pooled disability data from three population-based cohorts and developed an Age Related Multiple Sclerosis Severity (ARMSS) score by ranking EDSS scores based on the patient's age at the time of assessment. We established the power to detect a difference between groups afforded by the ARMSS score and assessed its relative consistency over time. RESULTS: The study population included 26058 patients from Sweden ( n = 11846), Canada ( n = 6179) and the United Kingdom ( n = 8033). There was a moderate correlation between EDSS and disease duration ( r = 0.46, 95% confidence interval (CI): 0.45-0.47) and between EDSS and age ( r = 0.44, 95% CI: 0.43-0.45). The ARMSS scores showed comparable power to detect disability differences between groups to the updated and original MSSS. CONCLUSION: Since age is typically unbiased and readily obtained, and the ARMSS and MSSS were comparable, the ARMSS may provide a more versatile tool and could minimise study biases and loss of statistical power caused by inaccurate or missing onset dates.Biogen, Neuro Sweden (Neuroförbundet), Cambridge NIHR Biomedical Research Centr

Importance of Human Leukocyte Antigen (HLA) Class I and II Alleles on the Risk of Multiple Sclerosis

Multiple sclerosis (MS) is a complex disease of the central nervous system of unknown etiology. The human leukocyte antigen (HLA) locus on chromosome 6 confers a considerable part of the susceptibility to MS, and the most important factor is the class II allele HLA-DRB1*15:01. In addition, we and others have previously established a protective effect of HLA-A*02. Here, we genotyped 1,784 patients and 1,660 healthy controls from Scandinavia for the HLA-A, HLA-B, HLA-C and HLA-DRB1 genes and investigated their effects on MS risk by logistic regression. Several allele groups were found to exert effects independently of DRB1*15 and A*02, in particular DRB1*01 (OR = 0.82, p = 0.034) and B*12 (including B*44/45, OR = 0.76, p = 0.0028), confirming previous reports. Furthermore, we observed interaction between allele groups: DRB1*15 and DRB1*01 (multiplicative: OR = 0.54, p = 0.0041; additive: AP = 0.47, p = 4×10−06), DRB1*15 and C*12 (multiplicative: OR = 0.37, p = 0.00035; additive: AP = 0.58, p = 2.6×10−05), indicating that the effect size of these allele groups varies when taking DRB1*15 into account. Analysis of inferred haplotypes showed that almost all DRB1*15 bearing haplotypes were risk haplotypes, and that all A*02 bearing haplotypes were protective as long as they did not carry DRB1*15. In contrast, we found one class I haplotype, carrying A*02-C*05-B*12, which abolished the risk of DRB1*15. In conclusion, these results confirms a complex role of HLA class I and II genes that goes beyond DRB1*15 and A*02, in particular by including all three classical HLA class I genes as well as functional interactions between DRB1*15 and several alleles of DRB1 and class I genes

Measures of Additive Interactionand Effect Direction

Measures for additive interaction are defined using risk ratios. These ratios need to be modeled so that all combinations of the exposures are harmful, as the scale between protective and harmful factors differs. This remodeling is referred to as recoding. Previously, recoding has been thought of as random. In this paper, we will examine and discuss the impact of recoding in studies with small effect sizes, such as genome wide association studies, and the impact recoding has on significance testing.QC 20190930</p

Measures of Additive Interactionand Effect Direction

Measures for additive interaction are defined using risk ratios. These ratios need to be modeled so that all combinations of the exposures are harmful, as the scale between protective and harmful factors differs. This remodeling is referred to as recoding. Previously, recoding has been thought of as random. In this paper, we will examine and discuss the impact of recoding in studies with small effect sizes, such as genome wide association studies, and the impact recoding has on significance testing.QC 20190930</p

Measures of Additive Interactionand Effect Direction

Measures for additive interaction are defined using risk ratios. These ratios need to be modeled so that all combinations of the exposures are harmful, as the scale between protective and harmful factors differs. This remodeling is referred to as recoding. Previously, recoding has been thought of as random. In this paper, we will examine and discuss the impact of recoding in studies with small effect sizes, such as genome wide association studies, and the impact recoding has on significance testing.QC 20190930</p

On the Existence of Suitable Models for Additive Interaction with Continuous Exposures

Additive interaction can be of importance for public health interventions and it is commonly defined using binary exposures. There has been expansions of the models to also include continuous exposures, which could lead to better and more precise estimations of the effect of interventions. In this paper we define the intervention for a continuous exposure as a monotonic function. Based on this function for the interventions we prove that there is no model for estimating additive interactions with continuous exposures for which it holds that; (i) both exposures have marginal effects and no additive interaction on the exposure level for both exposures, (ii) neither exposure has marginal effect and there is additive interaction between the exposures. We also show that a logistic regression model for continuous exposures will always produce additive interaction if both exposures have marginal effects.QC 20190925</p

On the Existence of Suitable Models for Additive Interaction with Continuous Exposures

Additive interaction can be of importance for public health interventions and it is commonly defined using binary exposures. There has been expansions of the models to also include continuous exposures, which could lead to better and more precise estimations of the effect of interventions. In this paper we define the intervention for a continuous exposure as a monotonic function. Based on this function for the interventions we prove that there is no model for estimating additive interactions with continuous exposures for which it holds that; (i) both exposures have marginal effects and no additive interaction on the exposure level for both exposures, (ii) neither exposure has marginal effect and there is additive interaction between the exposures. We also show that a logistic regression model for continuous exposures will always produce additive interaction if both exposures have marginal effects.QC 20190925</p

On the Existence of Suitable Models for Additive Interaction with Continuous Exposures

Additive interaction can be of importance for public health interventions and it is commonly defined using binary exposures. There has been expansions of the models to also include continuous exposures, which could lead to better and more precise estimations of the effect of interventions. In this paper we define the intervention for a continuous exposure as a monotonic function. Based on this function for the interventions we prove that there is no model for estimating additive interactions with continuous exposures for which it holds that; (i) both exposures have marginal effects and no additive interaction on the exposure level for both exposures, (ii) neither exposure has marginal effect and there is additive interaction between the exposures. We also show that a logistic regression model for continuous exposures will always produce additive interaction if both exposures have marginal effects.QC 20190925</p