| 1. |
- Ramdas, S., et al.
(author)
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A multi-layer functional genomic analysis to understand noncoding genetic variation in lipids
- 2022
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In: American Journal of Human Genetics. - : Elsevier BV. - 0002-9297 .- 1537-6605. ; 109:8, s. 1366-1387
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Journal article (peer-reviewed)abstract
- A major challenge of genome-wide association studies (GWASs) is to translate phenotypic associations into biological insights. Here, we integrate a large GWAS on blood lipids involving 1.6 million individuals from five ancestries with a wide array of functional genomic datasets to discover regulatory mechanisms underlying lipid associations. We first prioritize lipid-associated genes with expression quantitative trait locus (eQTL) colocalizations and then add chromatin interaction data to narrow the search for functional genes. Polygenic enrichment analysis across 697 annotations from a host of tissues and cell types confirms the central role of the liver in lipid levels and highlights the selective enrichment of adipose-specific chromatin marks in high-density lipoprotein cholesterol and triglycerides. Overlapping transcription factor (TF) binding sites with lipid-associated loci identifies TFs relevant in lipid biology. In addition, we present an integrative framework to prioritize causal variants at GWAS loci, producing a comprehensive list of candidate causal genes and variants with multiple layers of functional evidence. We highlight two of the prioritized genes, CREBRF and RRBP1, which show convergent evidence across functional datasets supporting their roles in lipid biology.
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| 2. |
- Kanoni, Stavroula, et al.
(author)
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Implicating genes, pleiotropy, and sexual dimorphism at blood lipid loci through multi-ancestry meta-analysis.
- 2022
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In: Genome biology. - : Springer Science and Business Media LLC. - 1474-760X .- 1465-6906 .- 1474-7596. ; 23:1
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Journal article (peer-reviewed)abstract
- Genetic variants within nearly 1000 loci are known to contribute to modulation of blood lipid levels. However, the biological pathways underlying these associations are frequently unknown, limiting understanding of these findings and hindering downstream translational efforts such as drug target discovery.To expand our understanding of the underlying biological pathways and mechanisms controlling blood lipid levels, we leverage a large multi-ancestry meta-analysis (N = 1,654,960) of blood lipids to prioritize putative causal genes for 2286 lipid associations using six gene prediction approaches. Using phenome-wide association (PheWAS) scans, we identify relationships of genetically predicted lipid levels to other diseases and conditions. We confirm known pleiotropic associations with cardiovascular phenotypes and determine novel associations, notably with cholelithiasis risk. We perform sex-stratified GWAS meta-analysis of lipid levels and show that 3-5% of autosomal lipid-associated loci demonstrate sex-biased effects. Finally, we report 21 novel lipid loci identified on the X chromosome. Many of the sex-biased autosomal and X chromosome lipid loci show pleiotropic associations with sex hormones, emphasizing the role of hormone regulation in lipid metabolism.Taken together, our findings provide insights into the biological mechanisms through which associated variants lead to altered lipid levels and potentially cardiovascular disease risk.
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| 3. |
- Wilman, H. R., et al.
(author)
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Genetic studies of abdominal MRI data identify genes regulating hepcidin as major determinants of liver iron concentration
- 2019
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In: Journal of Hepatology. - : Elsevier. - 0168-8278 .- 1600-0641. ; 71:3, s. 594-602
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Journal article (peer-reviewed)abstract
- Background & Aims: Excess liver iron content is common and is linked to the risk of hepatic and extrahepatic diseases. We aimed to identify genetic variants influencing liver iron content and use genetics to understand its link to other traits and diseases. Methods: First, we performed a genome-wide association study (GWAS) in 8,289 individuals from UK Biobank, whose liver iron level had been quantified by magnetic resonance imaging, before validating our findings in an independent cohort (n = 1,513 from IMI DIRECT). Second, we used Mendelian randomisation to test the causal effects of 25 predominantly metabolic traits on liver iron content. Third, we tested phenome-wide associations between liver iron variants and 770 traits and disease outcomes. Results: We identified 3 independent genetic variants (rs1800562 [C282Y] and rs1799945 [H63D] in HFE and rs855791 [V736A] in TMPRSS6) associated with liver iron content that reached the GWAS significance threshold (p <5 × 10−8). The 2 HFE variants account for ∼85% of all cases of hereditary haemochromatosis. Mendelian randomisation analysis provided evidence that higher central obesity plays a causal role in increased liver iron content. Phenome-wide association analysis demonstrated shared aetiopathogenic mechanisms for elevated liver iron, high blood pressure, cirrhosis, malignancies, neuropsychiatric and rheumatological conditions, while also highlighting inverse associations with anaemias, lipidaemias and ischaemic heart disease. Conclusion: Our study provides genetic evidence that mechanisms underlying higher liver iron content are likely systemic rather than organ specific, that higher central obesity is causally associated with higher liver iron, and that liver iron shares common aetiology with multiple metabolic and non-metabolic diseases. Lay summary: Excess liver iron content is common and is associated with liver diseases and metabolic diseases including diabetes, high blood pressure, and heart disease. We identified 3 genetic variants that are linked to an increased risk of developing higher liver iron content. We show that the same genetic variants are linked to higher risk of many diseases, but they may also be associated with some health advantages. Finally, we use genetic variants associated with waist-to-hip ratio as a tool to show that central obesity is causally associated with increased liver iron content.
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| 4. |
- Allesøe, Rosa Lundbye, et al.
(author)
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Discovery of drug–omics associations in type 2 diabetes with generative deep-learning models
- 2023
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In: Nature Biotechnology. - : Springer Nature. - 1087-0156 .- 1546-1696. ; 41:3, s. 399-408
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Journal article (peer-reviewed)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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| 5. |
- Bar, N., et al.
(author)
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A reference map of potential determinants for the human serum metabolome
- 2020
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In: Nature. - : Nature Research. - 0028-0836 .- 1476-4687. ; 588:7836, s. 135-140
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Journal article (peer-reviewed)abstract
- The serum metabolome contains a plethora of biomarkers and causative agents of various diseases, some of which are endogenously produced and some that have been taken up from the environment1. The origins of specific compounds are known, including metabolites that are highly heritable2,3, or those that are influenced by the gut microbiome4, by lifestyle choices such as smoking5, or by diet6. However, the key determinants of most metabolites are still poorly understood. Here we measured the levels of 1,251 metabolites in serum samples from a unique and deeply phenotyped healthy human cohort of 491 individuals. We applied machine-learning algorithms to predict metabolite levels in held-out individuals on the basis of host genetics, gut microbiome, clinical parameters, diet, lifestyle and anthropometric measurements, and obtained statistically significant predictions for more than 76% of the profiled metabolites. Diet and microbiome had the strongest predictive power, and each explained hundreds of metabolites—in some cases, explaining more than 50% of the observed variance. We further validated microbiome-related predictions by showing a high replication rate in two geographically independent cohorts7,8 that were not available to us when we trained the algorithms. We used feature attribution analysis9 to reveal specific dietary and bacterial interactions. We further demonstrate that some of these interactions might be causal, as some metabolites that we predicted to be positively associated with bread were found to increase after a randomized clinical trial of bread intervention. Overall, our results reveal potential determinants of more than 800 metabolites, paving the way towards a mechanistic understanding of alterations in metabolites under different conditions and to designing interventions for manipulating the levels of circulating metabolites.
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| 6. |
- Brand, J. S., et al.
(author)
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Diabetes and Onset of Natural Menopause : Results From the European Prospective Investigation Into Cancer and Nutrition EDITORIAL COMMENT
- 2015
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In: Obstetrical and Gynecological Survey. - 0029-7828 .- 1533-9866. ; 70:8, s. 507-508
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Journal article (other academic/artistic)abstract
- The age at natural menopause (ANM) in the Western world ranges from 40 to 60 years, with an average onset of 51 years. The exact mechanisms underlying the timing of ANM are not completely understood. Both genetic and environmental factors are involved. The best-established environmental factor affecting ANM is smoking; menopause occurs 1 to 2 years earlier in smokers. In addition to genetic and environmental factors, chronic metabolic diseases may influence ANM. Some evidence suggests that diabetes may accelerate menopausal onset. With more women of childbearing age receiving a diagnosis of diabetes, it is important to examine the impact of diabetes on reproductive health. This study was designed to determine whether ANM occurs at an earlier age among women who have diabetes before menopause than in women without diabetes. Data were obtained from the European Prospective Investigation into Cancer and Nutrition (EPIC) study, a large multicenter prospective cohort study investigating the relationship between diet, lifestyle, and genetic factors and the incidence of cancer and other chronic diseases. A cohort of 519,978 men and women, mostly aged 27 to 70 years, were recruited primarily from the general population between 1992 and 2000. A total of 367,331 women participated in the EPIC study. After exclusions, 258,898 of these women met study inclusion criteria. Diabetes status at baseline and menopausal age were based on self-report and were obtained through questionnaires. Participants were asked if they had ever been diagnosed with diabetes and if so at what age. Associations of diabetes and age at diabetes diagnosis with ANM were estimated using time-dependent Cox regression analyses, with stratification by center and adjustments for age, smoking, reproductive, and known diabetes risk factors including smoking and with age from birth to menopause or censoring as the underlying time scale. Overall, there was no statistically significant lower risk of becoming menopausal among women with diabetes than women with no diabetes; the hazard ratio (HR) was 0.94, with a 95% confidence interval (CI) of 0.89 to 1.01. However, compared with women with no diabetes, women with diabetes before the age of 20 years had an earlier menopause (10-20 years [HR, 1.43; 95% CI, 1.02-2.01] and <10 years [HR, 1.59; 95% CI, 1.03-2.43]), whereas women with diabetes at age 50 years or older had a later menopause (HR, 0.81; 95% CI, 0.70-0.95). No association with ANM was found for diabetes onset between the ages 20 and 50 years. Strengths of the study include its large sample size and the measurement of a broad set of potential confounders. However, there were several limitations. First, results may have been underestimated because of survival bias. Second, the sequence of menopause and diabetes in women with a late age at diabetes is uncertain, as both events occur in a short period, and both diabetes and menopause status were based on self-report, not verified by medical records. Third, no distinction was made between types 1 and 2 diabetes. Although there is no overall association between diabetes and age at menopause, the data suggest that early-onset diabetes may accelerate menopause. The delaying effect of late-onset diabetes on ANM is not in agreement with other studies suggesting the opposite association.
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| 7. |
- Brown, A.A., et al.
(author)
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Genetic analysis of blood molecular phenotypes reveals common properties in the regulatory networks affecting complex traits
- 2023
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In: Nature Communications. - : Springer Nature. - 2041-1723. ; 14
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Journal article (peer-reviewed)abstract
- We evaluate the shared genetic regulation of mRNA molecules, proteins and metabolites derived from whole blood from 3029 human donors. We find abundant allelic heterogeneity, where multiple variants regulate a particular molecular phenotype, and pleiotropy, where a single variant associates with multiple molecular phenotypes over multiple genomic regions. The highest proportion of share genetic regulation is detected between gene expression and proteins (66.6%), with a further median shared genetic associations across 49 different tissues of 78.3% and 62.4% between plasma proteins and gene expression. We represent the genetic and molecular associations in networks including 2828 known GWAS variants, showing that GWAS variants are more often connected to gene expression in trans than other molecular phenotypes in the network. Our work provides a roadmap to understanding molecular networks and deriving the underlying mechanism of action of GWAS variants using different molecular phenotypes in an accessible tissue. © 2023, Springer Nature Limited.
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| 8. |
- Franks, P. W., et al.
(author)
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Causal inference in obesity research
- 2017
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In: Journal of Internal Medicine. - : Wiley. - 0954-6820 .- 1365-2796. ; 281:3, s. 222-232
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Research review (peer-reviewed)abstract
- Obesity is a risk factor for a plethora of severe morbidities and premature death. Most supporting evidence comes from observational studies that are prone to chance, bias and confounding. Even data on the protective effects of weight loss from randomized controlled trials will be susceptible to confounding and bias if treatment assignment cannot be masked, which is usually the case with lifestyle and surgical interventions. Thus, whilst obesity is widely considered the major modifiable risk factor for many chronic diseases, its causes and consequences are often difficult to determine. Addressing this is important, as the prevention and treatment of any disease requires that interventions focus on causal risk factors. Disease prediction, although not dependent on knowing the causes, is nevertheless enhanced by such knowledge. Here, we provide an overview of some of the barriers to causal inference in obesity research and discuss analytical approaches, such as Mendelian randomization, that can help to overcome these obstacles. In a systematic review of the literature in this field, we found: (i) probable causal relationships between adiposity and bone health/disease, cancers (colorectal, lung and kidney cancers), cardiometabolic traits (blood pressure, fasting insulin, inflammatory markers and lipids), uric acid concentrations, coronary heart disease and venous thrombosis (in the presence of pulmonary embolism), (ii) possible causal relationships between adiposity and gray matter volume, depression and common mental disorders, oesophageal cancer, macroalbuminuria, end-stage renal disease, diabetic kidney disease, nuclear cataract and gall stone disease, and (iii) no evidence for causal relationships between adiposity and Alzheimer's disease, pancreatic cancer, venous thrombosis (in the absence of pulmonary embolism), liver function and periodontitis.
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| 9. |
- Franks, P. W.
(author)
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Genetic risk scores ascertained in early adulthood and the prediction of type 2 diabetes later in life
- 2012
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In: Diabetologia. - New York : Springer-Verlag New York. - 0012-186X .- 1432-0428. ; 55:10, s. 2555-2558
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Journal article (other academic/artistic)abstract
- It is hoped that information garnered from studies on population genetics will one day be translated into a form in which it meaningfully improves the prediction, prevention or treatment of type 2 diabetes. Type 2 diabetes genetics researchers have made extraordinary progress in identifying common genetic variants that are associated with type 2 diabetes, which has shed light on the biological pathways in which molecular defects that cause the disease likely reside. However, the expectation that genetic discoveries will aid the prevention or treatment of type 2 diabetes has not, so far, been fulfilled. In a paper published in this edition of the journal, Vassy and colleagues (DOI: 10.1007/s00125-012-2637-7) test the hypothesis that the predictive accuracy of established genetic risk markers for type 2 diabetes varies by age, with the predictive accuracy being greatest in younger cohorts. The authors found no substantive support for this hypothesis. However, a number of interesting questions are raised by their study concerning why risk alleles for a given genotype may differ in younger and older cohorts and why prospective cohort studies may yield results that are inconsistent with those derived from cross-sectional studies; this commentary discusses these points.
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| 10. |
- Franks, P. W., et al.
(author)
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Genomic variants at the PINK1 locus are associated with transcript abundance and plasma nonesterified fatty acid concentrations in European whites
- 2008
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In: The FASEB Journal. - : Wiley. - 0892-6638 .- 1530-6860. ; 22:9, s. 3135-3145
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Journal article (peer-reviewed)abstract
- The purpose of this study was to characterize associations between PINK1 genotypes, PINK1 transcript levels, and metabolic phenotypes in healthy adults and those with type 2 diabetes (T2D). We measured PINK1 skeletal muscle transcript levels and 8 independent PINK1 single nucleotide polymorphisms (SNPs) in a cohort of 208 Danish whites and in a cohort of 1701 British whites (SNPs and metabolic phenotypes only). Furthermore, we assessed the effects of PINK1 transcript ablation in primary adipocytes using RNA interference (RNAi). Six PINK1 SNPs were associated with PINK1 transcript levels (P < 0.04 to P < 0.0001). Obesity modified the association between PINK1 transcript levels and T2D risk (interaction P=0.005); transcript levels were inversely related with T2D in obese (n=105) [odds ratio (OR) per SD increase in expression levels=0.44; 95% confidence interval (CI): 0.23, 0.84; P=0.013] but not in nonobese (n=103) (OR=1.20; 95% CI: 0.82, 1.76; P=0.34) individuals. In the British cohort, several PINK1 SNPs were associated with plasma nonesterified fatty acid concentrations. Nominal genotype associations were also observed for fasting glucose, 2-h glucose, and maximal oxygen consumption, although these were not statistically significant after correcting for multiple testing. In primary adipocytes, Pink1 knockdown affected fatty acid binding protein 4 (Fabp4) expression, indicating that PINK1 may influence substrate metabolism. We demonstrate that PINK1 polymorphisms are associated with PINK1 transcript levels and measures of fatty acid metabolism in a concordant manner, whereas our RNAi data imply that PINK1 may indirectly influence lipid metabolism.
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