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- 2019
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Tidskriftsartikel (refereegranskat)
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- Kato, Norihiro, et al.
(författare)
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Trans-ancestry genome-wide association study identifies 12 genetic loci influencing blood pressure and implicates a role for DNA methylation
- 2015
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Ingår i: Nature Genetics. - : Nature Publishing Group. - 1061-4036 .- 1546-1718. ; 47:11, s. 1282-1293
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Tidskriftsartikel (refereegranskat)abstract
- We carried out a trans-ancestry genome-wide association and replication study of blood pressure phenotypes among up to 320,251 individuals of East Asian, European and South Asian ancestry. We find genetic variants at 12 new loci to be associated with blood pressure (P = 3.9 × 10−11 to 5.0 × 10−21). The sentinel blood pressure SNPs are enriched for association with DNA methylation at multiple nearby CpG sites, suggesting that, at some of the loci identified, DNA methylation may lie on the regulatory pathway linking sequence variation to blood pressure. The sentinel SNPs at the 12 new loci point to genes involved in vascular smooth muscle (IGFBP3, KCNK3, PDE3A and PRDM6) and renal (ARHGAP24, OSR1, SLC22A7 and TBX2) function. The new and known genetic variants predict increased left ventricular mass, circulating levels of NT-proBNP, and cardiovascular and all-cause mortality (P = 0.04 to 8.6 × 10−6). Our results provide new evidence for the role of DNA methylation in blood pressure regulation.
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- Momozawa, Y, et al.
(författare)
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IBD risk loci are enriched in multigenic regulatory modules encompassing putative causative genes
- 2018
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Ingår i: Nature communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 9:1, s. 2427-
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Tidskriftsartikel (refereegranskat)abstract
- GWAS have identified >200 risk loci for Inflammatory Bowel Disease (IBD). The majority of disease associations are known to be driven by regulatory variants. To identify the putative causative genes that are perturbed by these variants, we generate a large transcriptome data set (nine disease-relevant cell types) and identify 23,650 cis-eQTL. We show that these are determined by ∼9720 regulatory modules, of which ∼3000 operate in multiple tissues and ∼970 on multiple genes. We identify regulatory modules that drive the disease association for 63 of the 200 risk loci, and show that these are enriched in multigenic modules. Based on these analyses, we resequence 45 of the corresponding 100 candidate genes in 6600 Crohn disease (CD) cases and 5500 controls, and show with burden tests that they include likely causative genes. Our analyses indicate that ≥10-fold larger sample sizes will be required to demonstrate the causality of individual genes using this approach.
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- Mansouri, Kamel, et al.
(författare)
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CERAPP : Collaborative Estrogen Receptor Activity Prediction Project
- 2016
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Ingår i: Journal of Environmental Health Perspectives. - : Environmental Health Perspectives. - 0091-6765 .- 1552-9924. ; 124:7, s. 1023-1033
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Humans are exposed to thousands of man-made chemicals in the environment. Some chemicals mimic natural endocrine hormones and, thus, have the potential to be endocrine disruptors. Most of these chemicals have never been tested for their ability to interact with the estrogen receptor (ER). Risk assessors need tools to prioritize chemicals for evaluation in costly in vivo tests, for instance, within the U.S. EPA Endocrine Disruptor Screening Program. OBJECTIVES: We describe a large-scale modeling project called CERAPP (Collaborative Estrogen Receptor Activity Prediction Project) and demonstrate the efficacy of using predictive computational models trained on high-throughput screening data to evaluate thousands of chemicals for ER-related activity and prioritize them for further testing. METHODS: CERAPP combined multiple models developed in collaboration with 17 groups in the United States and Europe to predict ER activity of a common set of 32,464 chemical structures. Quantitative structure-activity relationship models and docking approaches were employed, mostly using a common training set of 1,677 chemical structures provided by the U.S. EPA, to build a total of 40 categorical and 8 continuous models for binding, agonist, and antagonist ER activity. All predictions were evaluated on a set of 7,522 chemicals curated from the literature. To overcome the limitations of single models, a consensus was built by weighting models on scores based on their evaluated accuracies. RESULTS: Individual model scores ranged from 0.69 to 0.85, showing high prediction reliabilities. Out of the 32,464 chemicals, the consensus model predicted 4,001 chemicals (12.3%) as high priority actives and 6,742 potential actives (20.8%) to be considered for further testing.CONCLUSION: This project demonstrated the possibility to screen large libraries of chemicals using a consensus of different in silico approaches. This concept will be applied in future projects related to other end points.
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