| 1. |
- Bruzelius, Maria, et al.
(författare)
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Influence of coronary artery disease-associated genetic variants on risk of venous thromboembolism
- 2014
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Ingår i: Thrombosis Research. - : Elsevier BV. - 0049-3848 .- 1879-2472. ; 134:2, s. 426-432
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Tidskriftsartikel (refereegranskat)abstract
- Introduction: We investigated whether genetic variations robustly associated with coronary artery disease are also associated with risk of venous thromboembolism in a well-defined, female case-control study (n = 2753) from Sweden. Materials and Methods: 39 single nucleotide polymorphisms in 32 loci associated with coronary artery disease in genome-wide association studies were identified in a literature search and genotyped in the ThromboEmbolism Hormone Study (TEHS). Association with venous thromboembolism was assessed by logistic regression. Results: Only rs579459 in the ABO locus demonstrated a significant association with VTE. A tentative association between ANRIL and VTE in the discovery analysis failed to replicate in a meta-analysis of 4 independent cohorts (total n = 7181). Conclusions: It appears that only the ABO locus is a shared risk factor for coronary artery disease and VTE.
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| 2. |
- Iglesias, Maria Jesus, et al.
(författare)
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Combined Chromatin and Expression Analysis Reveals Specific Regulatory Mechanisms within Cytokine Genes in the Macrophage Early Immune Response
- 2012
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 7:2, s. e32306-
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Tidskriftsartikel (refereegranskat)abstract
- Macrophages play a critical role in innate immunity, and the expression of early response genes orchestrate much of the initial response of the immune system. Macrophages undergo extensive transcriptional reprogramming in response to inflammatory stimuli such as Lipopolysaccharide (LPS). To identify gene transcription regulation patterns involved in early innate immune responses, we used two genome-wide approaches - gene expression profiling and chromatin immunoprecipitation-sequencing (ChIP-seq) analysis. We examined the effect of 2 hrs LPS stimulation on early gene expression and its relation to chromatin remodeling (H3 acetylation; H3Ac) and promoter binding of Sp1 and RNA polymerase II phosphorylated at serine 5 (S5P RNAPII), which is a marker for transcriptional initiation. Our results indicate novel and alternative gene regulatory mechanisms for certain proinflammatory genes. We identified two groups of upregulated inflammatory genes with respect to chromatin modification and promoter features. One group, including highly up-regulated genes such as tumor necrosis factor (TNF), was characterized by H3Ac, high CpG content and lack of TATA boxes. The second group, containing inflammatory mediators (interleukins and CCL chemokines), was up-regulated upon LPS stimulation despite lacking H3Ac in their annotated promoters, which were low in CpG content but did contain TATA boxes. Genome-wide analysis showed that few H3Ac peaks were unique to either +/-LPS condition. However, within these, an unpacking/expansion of already existing H3Ac peaks was observed upon LPS stimulation. In contrast, a significant proportion of S5P RNAPII peaks (approx 40%) was unique to either condition. Furthermore, data indicated a large portion of previously unannotated TSSs, particularly in LPS-stimulated macrophages, where only 28% of unique S5P RNAPII peaks overlap annotated promoters. The regulation of the inflammatory response appears to occur in a very specific manner at the chromatin level for specific genes and this study highlights the level of fine-tuning that occurs in the immune response.
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| 3. |
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| 4. |
- Bruzelius, Maria, et al.
(författare)
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F11 is associated with recurrent VTE in women A prospective cohort study
- 2016
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Ingår i: Thrombosis and Haemostasis. - : Schattauer Gmbh. - 0340-6245 .- 2567-689X. ; 115:2, s. 406-414
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Tidskriftsartikel (refereegranskat)abstract
- Genetic associations for the reoccurrence of venous thromboembolism (VTE) are not well described. Our aim was to investigate if common genetic variants, previously found to contribute to the prediction of first time thrombosis in women, were associated with risk of recurrence. The Thromboembolism Hormone Study (TEHS) is a Swedish nationwide case-control study (2002-2009). A cohort of 1,010 women with first time VTE was followed up until a recurrent event, death or November 2011. The genetic variants in F5 rs6025, F2 rs1799963, ABO rs514659, FGG rs2066865, F11 rs2289252, PROC rs1799810 and KNG1 rs710446 were assessed together with clinical variables. Recurrence rate was calculated as the number of events over the accumulated patient-time. Cumulative recurrence was calculated by Kaplan-Meier curve. Cox proportional-hazard model was used to estimate hazard ratios (HR) and 95 % confidence intervals (95 % CI) between groups. A total of 101 recurrent events occurred during a mean follow-up time of five years. The overall recurrence rate was 20 per 1,000 person-years (95 % CI; 16-24). The recurrence rate was highest in women with unprovoked first event and obesity. Carriers of the risk alleles of F5 rs6025 (HR=1.7 (95 % CI; 1.1-2.6)) and F11 rs2289252 (HR=1.8 (95 % CI; 1.1-3.0)) had significantly higher rates of recurrence compared to non-carriers. The cumulative recurrence was 2.5-fold larger in carriers of both F5 rs6025 and F11 rs2289252 than in non-carriers at five years follow-up. In conclusion, F5 rs6025 and F11 rs2289252 contributed to the risk of recurrent VTE and the combination is of potential clinical relevance for risk prediction.
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| 5. |
- Bruzelius, Maria, et al.
(författare)
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PDGFB, a new candidate plasma biomarker for venous thromboembolism : results from the VEREMA affinity proteomics study
- 2016
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Ingår i: Blood. - : American Society of Hematology. - 0006-4971 .- 1528-0020. ; 128:23, s. E59-E66
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Tidskriftsartikel (refereegranskat)abstract
- There is a clear clinical need for high-specificity plasma biomarkers for predicting risk of venous thromboembolism (VTE), but thus far, such markers have remained elusive. Utilizing affinity reagents from the Human Protein Atlas project and multiplexed immuoassays, we extensively analyzed plasma samples from 2 individual studies to identify candidate protein markers associated with VTE risk. We screened plasma samples from 88 VTE cases and 85 matched controls, collected as part of the Swedish Venous Thromboembolism Biomarker Study, using suspension bead arrays composed of 755 antibodies targeting 408 candidate proteins. We identified significant associations between VTE occurrence and plasma levels of human immunodeficiency virus type I enhancer binding protein 1 (HIVEP1), von Willebrand factor (VWF), glutathione peroxidase 3 (GPX3), and platelet-derived growth factor beta (PDGFB). For replication, we profiled plasma samples of 580 cases and 589 controls from the French FARIVE study. These results confirmed the association of VWF and PDGFB with VTE after correction for multiple testing, whereas only weak trends were observed for HIVEP1 and GPX3. Although plasma levels of VWF and PDGFB correlated modestly (rho similar to 0.30) with each other, they were independently associated with VTE risk in a joint model in FARIVE (VWF P < .001; PDGFB P = .002). PDGF. was verified as the target of the capture antibody by immunocapture mass spectrometry and sandwich enzyme-linked immunosorbent assay. In conclusion, we demonstrate that high-throughput affinity plasma proteomic profiling is a valuable research strategy to identify potential candidate biomarkers for thrombosis-related disorders, and our study suggests a novel association of PDGFB plasma levels with VTE.
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| 6. |
- Edsgärd, Daniel, et al.
(författare)
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GeneiASE : Detection of condition-dependent and static allele-specific expression from RNA-seq data without haplotype information
- 2016
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Ingår i: Scientific Reports. - : Nature Publishing Group. - 2045-2322. ; 6
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Tidskriftsartikel (refereegranskat)abstract
- Allele-specific expression (ASE) is the imbalance in transcription between maternal and paternal alleles at a locus and can be probed in single individuals using massively parallel DNA sequencing technology. Assessing ASE within a single sample provides a static picture of the ASE, but the magnitude of ASE for a given transcript may vary between different biological conditions in an individual. Such condition-dependent ASE could indicate a genetic variation with a functional role in the phenotypic difference. We investigated ASE through RNA-sequencing of primary white blood cells from eight human individuals before and after the controlled induction of an inflammatory response, and detected condition-dependent and static ASE at 211 and 13021 variants, respectively. We developed a method, GeneiASE, to detect genes exhibiting static or condition-dependent ASE in single individuals. GeneiASE performed consistently over a range of read depths and ASE effect sizes, and did not require phasing of variants to estimate haplotypes. We observed condition-dependent ASE related to the inflammatory response in 19 genes, and static ASE in 1389 genes. Allele-specific expression was confirmed by validation of variants through real-time quantitative RT-PCR, with RNA-seq and RT-PCR ASE effect-size correlations r = 0.67 and r = 0.94 for static and condition-dependent ASE, respectively.
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| 7. |
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| 8. |
- Frånberg, Mattias, 1985-, et al.
(författare)
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Discovering Genetic Interactions in Large-Scale Association Studies by Stage-wise Likelihood Ratio Tests
- 2015
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Ingår i: PLOS Genetics. - : Public Library of Science (PLoS). - 1553-7390 .- 1553-7404. ; 11:9
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Tidskriftsartikel (refereegranskat)abstract
- Despite the success of genome-wide association studies in medical genetics, the underlying genetics of many complex diseases remains enigmatic. One plausible reason for this could be the failure to account for the presence of genetic interactions in current analyses. Exhaustive investigations of interactions are typically infeasible because the vast number of possible interactions impose hard statistical and computational challenges. There is, therefore, a need for computationally efficient methods that build on models appropriately capturing interaction. We introduce a new methodology where we augment the interaction hypothesis with a set of simpler hypotheses that are tested, in order of their complexity, against a saturated alternative hypothesis representing interaction. This sequential testing provides an efficient way to reduce the number of non-interacting variant pairs before the final interaction test. We devise two different methods, one that relies on a priori estimated numbers of marginally associated variants to correct for multiple tests, and a second that does this adaptively. We show that our methodology in general has an improved statistical power in comparison to seven other methods, and, using the idea of closed testing, that it controls the family-wise error rate. We apply our methodology to genetic data from the PRO-CARDIS coronary artery disease case/control cohort and discover three distinct interactions. While analyses on simulated data suggest that the statistical power may suffice for an exhaustive search of all variant pairs in ideal cases, we explore strategies for a priori selecting subsets of variant pairs to test. Our new methodology facilitates identification of new disease-relevant interactions from existing and future genome-wide association data, which may involve genes with previously unknown association to the disease. Moreover, it enables construction of interaction networks that provide a systems biology view of complex diseases, serving as a basis for more comprehensive understanding of disease pathophysiology and its clinical consequences.
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| 9. |
- Frånberg, Mattias, 1985-, et al.
(författare)
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Fast and general tests of genetic interaction for genome-wide association studies
- 2017
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Ingår i: PloS Computational Biology. - : PUBLIC LIBRARY SCIENCE. - 1553-734X .- 1553-7358. ; 13:6
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Tidskriftsartikel (refereegranskat)abstract
- A complex disease has, by definition, multiple genetic causes. In theory, these causes could be identified individually, but their identification will likely benefit from informed use of anticipated interactions between causes. In addition, characterizing and understanding interactions must be considered key to revealing the etiology of any complex disease. Large-scale collaborative efforts are now paving the way for comprehensive studies of interaction. As a consequence, there is a need for methods with a computational efficiency sufficient for modern data sets as well as for improvements of statistical accuracy and power. Another issue is that, currently, the relation between different methods for interaction inference is in many cases not transparent, complicating the comparison and interpretation of results between different interaction studies. In this paper we present computationally efficient tests of interaction for the complete family of generalized linear models (GLMs). The tests can be applied for inference of single or multiple interaction parameters, but we show, by simulation, that jointly testing the full set of interaction parameters yields superior power and control of false positive rate. Based on these tests we also describe how to combine results from multiple independent studies of interaction in a meta-analysis. We investigate the impact of several assumptions commonly made when modeling interactions. We also show that, across the important class of models with a full set of interaction parameters, jointly testing the interaction parameters yields identical results. Further, we apply our method to genetic data for cardiovascular disease. This allowed us to identify a putative interaction involved in Lp(a) plasma levels between two 'tag' variants in the LPA locus (p = 2.42 . 10(-09)) as well as replicate the interaction (p = 6.97 . 10(-07)). Finally, our meta-analysis method is used in a small (N = 16,181) study of interactions in myocardial infarction.
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| 10. |
- Frånberg, Mattias, 1985-
(författare)
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Statistical methods for detecting gene-gene and gene-environment interactions in genome-wide association studies
- 2019
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Doktorsavhandling (övrigt vetenskapligt/konstnärligt)abstract
- Despite considerable effort to elucidate the genetic architecture of multi-factorial traits and diseases, there remains a gap between the estimated heritability (e.g., from twin studies) and the heritability explained by discovered genetic variants. The existence of interactions between different genes, and between genes and the environment, has frequently been hypothesized as a likely cause of this discrepancy. However, the statistical inference of interactions is plagued by limited sample sizes, high computational requirements, and incomplete knowledge of how the measurement scale and parameterization affect the analysis.This thesis addresses the major statistical, computational, and modeling issues that hamper large-scale interaction studies today. Furthermore, it investigates whether gene-gene and gene-environment interactions are significantly involved in the development of diseases linked to atherosclerosis. Firstly, I develop two statistical methods that can be used to study of gene-gene interactions: the first is tailored for limited sample size situations, and the second enables multiple analyses to be combined into large meta-analyses. I perform comprehensive simulation studies to determine that these methods have higher or equal statistical power than contemporary methods, scale-invariance is required to guard against false positives, and that saturated parameterizations perform well in terms of statistical power. In two studies, I apply the two proposed methods to case/control data from myocardial infarction and associated phenotypes. In both studies, we identify putative interactions for myocardial infarction but are unable to replicate the interactions in a separate cohort. In the second study, however, we identify and replicate a putative interaction involved in Lp(a) plasma levels between two variants rs3103353 and rs9458157. Secondly, I develop a multivariate statistical method that simultaneously estimates the effects of genetic variants, environmental variables, and their interactions. I show by extensive simulations that this method achieves statistical power close to the optimal oracle method. We use this method to study the involvement of gene-environment interactions in intima-media thickness, a phenotype relevant for coronary artery disease. We identify a putative interaction between a genetic variant in the KCTD8 gene and alcohol use, thus suggesting an influence on intima-media thickness. The methods developed to support the analyses in this thesis as well as a selection of other prominent methods in the field is implemented in a software package called besiq.In conclusion, this thesis presents statistical methods, and the associated software, that allows large-scale studies of gene-gene and gene-environment interactions to be effortlessly undertaken.
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