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1.
  • Fernandez-Rozadilla, Ceres, et al. (creator_code:aut_t)
  • Deciphering colorectal cancer genetics through multi-omic analysis of 100,204 cases and 154,587 controls of European and east Asian ancestries
  • 2023
  • record:In_t: Nature Genetics. - : Nature Publishing Group. - 1061-4036 .- 1546-1718. ; 55, s. 89-99
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • Colorectal cancer (CRC) is a leading cause of mortality worldwide. We conducted a genome-wide association study meta-analysis of 100,204 CRC cases and 154,587 controls of European and east Asian ancestry, identifying 205 independent risk associations, of which 50 were unreported. We performed integrative genomic, transcriptomic and methylomic analyses across large bowel mucosa and other tissues. Transcriptome- and methylome-wide association studies revealed an additional 53 risk associations. We identified 155 high-confidence effector genes functionally linked to CRC risk, many of which had no previously established role in CRC. These have multiple different functions and specifically indicate that variation in normal colorectal homeostasis, proliferation, cell adhesion, migration, immunity and microbial interactions determines CRC risk. Crosstissue analyses indicated that over a third of effector genes most probably act outside the colonic mucosa. Our findings provide insights into colorectal oncogenesis and highlight potential targets across tissues for new CRC treatment and chemoprevention strategies.
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2.
  • Law, Philip J., et al. (creator_code:aut_t)
  • Association analyses identify 31 new risk loci for colorectal cancer susceptibility
  • 2019
  • record:In_t: Nature Communications. - : Springer Science and Business Media LLC. - 2041-1723. ; 10
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • Colorectal cancer (CRC) is a leading cause of cancer-related death worldwide, and has a strong heritable basis. We report a genome-wide association analysis of 34,627 CRC cases and 71,379 controls of European ancestry that identifies SNPs at 31 new CRC risk loci. We also identify eight independent risk SNPs at the new and previously reported European CRC loci, and a further nine CRC SNPs at loci previously only identified in Asian populations. We use in situ promoter capture Hi-C (CHi-C), gene expression, and in silico annotation methods to identify likely target genes of CRC SNPs. Whilst these new SNP associations implicate target genes that are enriched for known CRC pathways such as Wnt and BMP, they also highlight novel pathways with no prior links to colorectal tumourigenesis. These findings provide further insight into CRC susceptibility and enhance the prospects of applying genetic risk scores to personalised screening and prevention.
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3.
  • Allesøe, Rosa Lundbye, et al. (creator_code:aut_t)
  • Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models
  • 2023
  • record:In_t: Nature Biotechnology. - : Springer Nature. - 1087-0156 .- 1546-1696. ; 41:3, s. 399-408
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • The application of multiple omics technologies in biomedical cohorts has the potential to reveal patient-level disease characteristics and individualized response to treatment. However, the scale and heterogeneous nature of multi-modal data makes integration and inference a non-trivial task. We developed a deep-learning-based framework, multi-omics variational autoencoders (MOVE), to integrate such data and applied it to a cohort of 789 people with newly diagnosed type 2 diabetes with deep multi-omics phenotyping from the DIRECT consortium. Using in silico perturbations, we identified drug–omics associations across the multi-modal datasets for the 20 most prevalent drugs given to people with type 2 diabetes with substantially higher sensitivity than univariate statistical tests. From these, we among others, identified novel associations between metformin and the gut microbiota as well as opposite molecular responses for the two statins, simvastatin and atorvastatin. We used the associations to quantify drug–drug similarities, assess the degree of polypharmacy and conclude that drug effects are distributed across the multi-omics modalities.
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4.
  • Atabaki-Pasdar, Naeimeh, et al. (creator_code:aut_t)
  • Inferring causal pathways between metabolic processes and liver fat accumulation: an IMI DIRECT study
  • 2021
  • swepub:Mat_publicationother_t (swepub:level_scientificother_t)abstract
    • Type 2 diabetes (T2D) and non-alcoholic fatty liver disease (NAFLD) often co-occur. Defining causal pathways underlying this relationship may help optimize the prevention and treatment of both diseases. Thus, we assessed the strength and magnitude of the putative causal pathways linking dysglycemia and fatty liver, using a combination of causal inference methods.Measures of glycemia, insulin dynamics, magnetic resonance imaging (MRI)-derived abdominal and liver fat content, serological biomarkers, lifestyle, and anthropometry were obtained in participants from the IMI DIRECT cohorts (n=795 with new onset T2D and 2234 individuals free from diabetes). UK Biobank (n=3641) was used for modelling and replication purposes. Bayesian networks were employed to infer causal pathways, with causal validation using two-sample Mendelian randomization.Bayesian networks fitted to IMI DIRECT data identified higher basal insulin secretion rate (BasalISR) and MRI-derived excess visceral fat (VAT) accumulation as the features of dysmetabolism most likely to cause liver fat accumulation; the unconditional probability of fatty liver (>5%) increased significantly when conditioning on high levels of BasalISR and VAT (by 23%, 32% respectively; 40% for both). Analyses in UK Biobank yielded comparable results. MR confirmed most causal pathways predicted by the Bayesian networks.Here, BasalISR had the highest causal effect on fatty liver predisposition, providing mechanistic evidence underpinning the established association of NAFLD and T2D. BasalISR may represent a pragmatic biomarker for NAFLD prediction in clinical practice.Competing Interest StatementHR is an employee and shareholder of Sanofi. MIM: The views expressed in this article are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health. MIM has served on advisory panels for Pfizer, NovoNordisk and Zoe Global, has received honoraria from Merck, Pfizer, Novo Nordisk and Eli Lilly, and research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier, and Takeda. As of June 2019, MIM is an employee of Genentech, and a holder of Roche stock. AM is a consultant for Lilly and has received research grants from several diabetes drug companies. PWF has received research grants from numerous diabetes drug companies and fess as consultant from Novo Nordisk, Lilly, and Zoe Global Ltd. He is currently the Scientific Director in Patient Care at the Novo Nordisk Foundation. Other authors declare non competing interests.Funding StatementThe work leading to this publication has received support from the Innovative Medicines Initiative Joint Undertaking under grant agreement 115317 (DIRECT) resources of which are composed of financial contribution from the European Union Seventh Framework Programme (FP7/2007-2013) and EFPIA companies in kind contribution. NAP is supported in part by Henning och Johan Throne-Holsts Foundation, Hans Werthen Foundation, an IRC award from the Swedish Foundation for Strategic Research and a European Research Council award ERC-2015-CoG - 681742_NASCENT. HPM is supported by an IRC award from the Swedish Foundation for Strategic Research and a European Research Council award ERC-2015-CoG - 681742_NASCENT. AGJ is supported by an NIHR Clinician Scientist award (17/0005624). RK is funded by the Novo Nordisk Foundation (NNF18OC0031650) as part of a postdoctoral fellowship, an IRC award from the Swedish Foundation for Strategic Research and a European Research Council award ERC-2015-CoG - 681742_NASCENT. AK, PM, HF, JF and GNG are supported by an IRC award from the Swedish Foundation for Strategic Research and a European Research Council award ERC-2015-CoG - 681742_NASCENT. TJM is funded by an NIHR clinical senior lecturer fellowship. S.Bru acknowledges support from the Novo Nordisk Foundation (grants NNF17OC0027594 and NNF14CC0001). ATH is a Wellcome Trust Senior Investigator and is also supported by the NIHR Exeter Clinical Research Facility. JMS acknowledges support from Science for Life Laboratory (Plasma Profiling Facility), Knut and Alice Wallenberg Foundation (Human Protein Atlas) and Erling-Persson Foundation (KTH Centre for Precision Medicine). MIM is supported by the following grants; Wellcome (090532, 098381, 106130, 203141, 212259); NIH (U01-DK105535). PWF is supported by an IRC award from the Swedish Foundation for Strategic Research and a European Research Council award ERC-2015-CoG - 681742_NASCENT. The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.Author DeclarationsI confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.YesThe details of the IRB/oversight body that provided approval or exemption for the research described are given below:Approval for the study protocol was obtained from each of the regional research ethics review boards separately (Lund, Sweden: 20130312105459927, Copenhagen, Denmark: H-1-2012-166 and H-1-2012-100, Amsterdam, Netherlands: NL40099.029.12, Newcastle, Dundee and Exeter, UK: 12/NE/0132), and all participants provided written informed consent at enrolment. The research conformed to the ethical principles for medical research involving human participants outlined in the Declaration of Helsinki.All necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived.YesI understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).YesI have followed all appropriate research reporting guidelines and uploaded the relevant EQUATOR Network research reporting checklist(s) and other pertinent material as supplementary files, if applicable.YesAuthors agree to make data and materials supporting the results or analyses presented in their paper available upon reasonable request
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5.
  • Atabaki Pasdar, Naeimeh, et al. (creator_code:aut_t)
  • Predicting and elucidating the etiology of fatty liver disease: A machine learning modeling and validation study in the IMI DIRECT cohorts
  • 2020
  • record:In_t: PLoS Medicine. - San Francisco : Public Library of Science (PLoS). - 1549-1676 .- 1549-1277. ; 17:6, s. 1003149-1003149
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • BACKGROUND: Non-alcoholic fatty liver disease (NAFLD) is highly prevalent and causes serious health complications in individuals with and without type 2 diabetes (T2D). Early diagnosis of NAFLD is important, as this can help prevent irreversible damage to the liver and, ultimately, hepatocellular carcinomas. We sought to expand etiological understanding and develop a diagnostic tool for NAFLD using machine learning. METHODS AND FINDINGS: We utilized the baseline data from IMI DIRECT, a multicenter prospective cohort study of 3,029 European-ancestry adults recently diagnosed with T2D (n = 795) or at high risk of developing the disease (n = 2,234). Multi-omics (genetic, transcriptomic, proteomic, and metabolomic) and clinical (liver enzymes and other serological biomarkers, anthropometry, measures of beta-cell function, insulin sensitivity, and lifestyle) data comprised the key input variables. The models were trained on MRI-image-derived liver fat content (
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6.
  • Behravesh, Masoud, et al. (creator_code:aut_t)
  • A prospective study of the relationships between movement and glycemic control during day and night in pregnancy
  • 2021
  • record:In_t: Scientific Reports. - : Springer Science and Business Media LLC. - 2045-2322. ; 11:1
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • Both disturbed sleep and lack of exercise can disrupt metabolism in pregnancy. Accelerometery was used to objectively assess movement during waking (physical activity) and movement during sleeping (sleep disturbance) periods and evaluated relationships with continuous blood glucose variation during pregnancy. Data was analysed prospectively. 15-women without pre-existing diabetes mellitus wore continuous glucose monitors and triaxial accelerometers from February through June 2018 in Sweden. The relationships between physical activity and sleep disturbance with blood glucose rate of change were assessed. An interaction term was fitted to determine difference in the relationship between movement and glucose variation, conditional on waking/sleeping. Total movement was inversely related to glucose rate of change (p < 0.001, 95% CI (− 0.037, − 0.026)). Stratified analyses showed total physical activity was inversely related to glucose rate of change (p < 0.001, 95% CI (− 0.040, − 0.028)), whereas sleep disturbance was not related to glucose rate of change (p = 0.07, 95% CI (< − 0.001, 0.013)). The interaction term was positively related to glucose rate of change (p < 0.001, 95% CI (0.029, 0.047)). This study provides temporal evidence of a relationship between total movement and glycemic control in pregnancy, which is conditional on time of day. Movement is beneficially related with glycemic control while awake, but not during sleep.
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7.
  • Coral, Daniel (creator_code:aut_t)
  • Characterisation of the genetic discordance between body mass index and type 2 diabetes: a phenome-wide analysis : No 111
  • 2020
  • record:In_t: Diabetologia. - : Springer Science and Business Media LLC. - 1432-0428 .- 0012-186X. ; 63
  • swepub:Mat_conferencepaper_t (swepub:level_refereed_t)abstract
    • Background and aims: Obesity is on the rise globally, and is a leading risk factor for T2D. However, it is very heterogeneous, with varying degrees of T2D risk within the same levels of BMI. Better classification may lead to improve outcomes of current preventive and therapeutic strategies. Moreover, by elucidating the mechanisms uncoupling obesity from T2D risk, new possible therapeutic targets may emerge. Leveraging the vast amount of genetic data produced to date may contribute to reach these goals while overcoming the obstacles imposed by common assumptions, biases and confounders present in observational studies. Our aim is to compare the phenome-wide association patterns of BMI-increasing genetic profiles that either concordantly increase or discordantly decrease T2D risk. Materials and methods: Highly concordant and highly discordant SNPs between BMI and T2D were obtained from the latest GWAS for both conditions. Their standardized effect sizes (SES) across multiple traits in the phenome, metabolome, proteinome and transcriptome were retrieved from the online genomic repositories. After alignment to the BMI-increasing allele, these effects were organized into a SNP x Trait matrix. A hierarchical clustering technique, combining PCA and Random Forest algorithms was applied, retrieving the optimal number of clusters of traits, organized in order of importance, useful to distinguish a discordant from a concordant SNP. Posterior probabilities of colocalization with T2D were calculated for each gene using transcriptome results. Tissue, biological process, molecular mechanism and cellular component enrichments were evaluated. The predictive potential of GRSs informed by these findings were assessed in the UK Biobank dataset. Results: 121 SNPs were found to be significantly associated with BMI and T2D. 18 were discordant and 104 concordant. A total of 1372 variables were included in the analyses (Phenome = 546, Metabolome = 233, Proteinome = 593). The most important difference between discordant and concordant SNPs in the phenome matrix was found in a cluster of traits led by hypertension (Mean discordant SES = -1.59, Mean concordant SES = 2.56), highly correlated with two clusters led by coronary heart disease and overall health status, respectively. The second most important cluster was led by physical activity-adjusted WHR (Mean discordant SES = -2.69, Mean concordant SES = 0.24). The model obtained from the phenome matrix had the highest classification performance (Matthews Correlation Coefficient, MCC = 0.79). Metabolome results showed differences in polyunsaturated fatty acids and lipid contents in VLDL, but with lower performance (MCC = 0.67). The model from the proteinome matrix was unable to correctly classify SNPs (MCC = -0.03). Two genes (CCDC92 and DNAH10) showed the strongest association within the discordant set in adipose tissue, both involved in cilia formation. A GRS of these 121 SNPs with weights derived from the clusters with high classification performance was highly associated with T2D in both the general and obese populations in UK Biobank (p < 1x1016). Conclusion: The main difference between BMI-increasing genetic profiles that either discordantly decrease or concordantly increase T2D risk is found in hypertension risk and physical activity-adjusted WHR. These traits can be used to inform GRSs to better classify T2D risk in obesity. Molecular mechanisms behind the discordant profile appear to involve cilia formation in the adipose tissue.
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8.
  • Coral, Daniel E, et al. (creator_code:aut_t)
  • A phenome-wide comparative analysis of genetic discordance between obesity and type 2 diabetes
  • 2023
  • record:In_t: Nature Metabolism. - : Springer Science and Business Media LLC. - 2522-5812. ; 5:2, s. 237-247
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • Obesity and type 2 diabetes are causally related, yet there is considerable heterogeneity in the consequences of both conditions and the mechanisms of action are poorly defined. Here we show a genetic-driven approach defining two obesity profiles that convey highly concordant and discordant diabetogenic effects. We annotate and then compare association signals for these profiles across clinical and molecular phenotypic layers. Key differences are identified in a wide range of traits, including cardiovascular mortality, fat distribution, liver metabolism, blood pressure, specific lipid fractions and blood levels of proteins involved in extracellular matrix remodelling. We find marginal differences in abundance of Bacteroidetes and Firmicutes bacteria in the gut. Instrumental analyses reveal prominent causal roles for waist-to-hip ratio, blood pressure and cholesterol content of high-density lipoprotein particles in the development of diabetes in obesity. We prioritize 17 genes from the discordant signature that convey protection against type 2 diabetes in obesity, which may represent logical targets for precision medicine approaches.
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9.
  • Flannick, Jason, et al. (creator_code:aut_t)
  • Data Descriptor : Sequence data and association statistics from 12,940 type 2 diabetes cases and controls
  • 2017
  • record:In_t: Scientific Data. - : Springer Science and Business Media LLC. - 2052-4463. ; 4
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1-5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (> 80% of low-frequency coding variants in similar to ~82 K Europeans via the exome chip, and similar to ~90% of low-frequency non-coding variants in similar to ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.
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10.
  • Fuchsberger, Christian, et al. (creator_code:aut_t)
  • The genetic architecture of type 2 diabetes
  • 2016
  • record:In_t: Nature. - : Springer Science and Business Media LLC. - 0028-0836 .- 1476-4687. ; 536:7614, s. 41-47
  • swepub:Mat_article_t (swepub:level_refereed_t)abstract
    • The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.
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