| 1. |
- Carland, Corinne, et al.
(författare)
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Proteomic analysis of 92 circulating proteins and their effects in cardiometabolic diseases
- 2023
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Ingår i: Clinical Proteomics. - : BMC. - 1542-6416 .- 1559-0275. ; 20:1
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Tidskriftsartikel (refereegranskat)abstract
- Background: Human plasma contains a wide variety of circulating proteins. These proteins can be important clinical biomarkers in disease and also possible drug targets. Large scale genomics studies of circulating proteins can identify genetic variants that lead to relative protein abundance.Methods: We conducted a meta-analysis on genome-wide association studies of autosomal chromosomes in 22,997 individuals of primarily European ancestry across 12 cohorts to identify protein quantitative trait loci (pQTL) for 92 cardiometabolic associated plasma proteins.Results: We identified 503 (337 cis and 166 trans) conditionally independent pQTLs, including several novel variants not reported in the literature. We conducted a sex-stratified analysis and found that 118 (23.5%) of pQTLs demonstrated heterogeneity between sexes. The direction of effect was preserved but there were differences in effect size and significance. Additionally, we annotate trans-pQTLs with nearest genes and report plausible biological relationships. Using Mendelian randomization, we identified causal associations for 18 proteins across 19 phenotypes, of which 10 have additional genetic colocalization evidence. We highlight proteins associated with a constellation of cardiometabolic traits including angiopoietin-related protein 7 (ANGPTL7) and Semaphorin 3F (SEMA3F).Conclusion: Through large-scale analysis of protein quantitative trait loci, we provide a comprehensive overview of common variants associated with plasma proteins. We highlight possible biological relationships which may serve as a basis for further investigation into possible causal roles in cardiometabolic diseases.
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| 2. |
- Fukaya, Eri, et al.
(författare)
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Clinical and Genetic Determinants of Varicose Veins Prospective, Community-Based Study of approximate to 500 000 Individuals
- 2018
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Ingår i: Circulation. - 0009-7322 .- 1524-4539. ; 138:25, s. 2869-2880
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Varicose veins are a common problem with no approved medical therapies. Although it is believed that varicose vein pathogenesis is multifactorial, there is limited understanding of the genetic and environmental factors that contribute to their formation. Large-scale studies of risk factors for varicose veins may highlight important aspects of pathophysiology and identify groups at increased risk for disease. METHODS: We applied machine learning to agnostically search for risk factors of varicose veins in 493 519 individuals in the UK Biobank. Predictors were further studied with univariable and multivariable Cox regression analyses (2441 incident events). A genome-wide association study of varicose veins was also performed among 337 536 unrelated individuals (9577 cases) of white British descent, followed by expression quantitative loci and pathway analyses. Because height emerged as a new candidate risk factor, we performed mendelian randomization analyses to assess a potential causal role for height in varicose vein development. RESULTS: Machine learning confirmed several known (age, sex, obesity, pregnancy, history of deep vein thrombosis) and identified several new risk factors for varicose vein disease, including height. After adjustment for traditional risk factors in Cox regression, greater height remained independently associated with varicose veins (hazard ratio for upper versus lower quartile, 1.74; 95% Cl, 1.51-2.01; P<0.0001). A genomewide association study identified 30 new genome-wide significant loci, identifying pathways involved in vascular development and skeletal/ limb biology. Mendelian randomization analysis provided evidence that increased height is causally related to varicose veins (inverse -variance weighted: odds ratio, 1.26; P=2.07x10(-16)). CONCLUSIONS: Using data from nearly a half -million individuals, we present a comprehensive genetic and epidemiological study of varicose veins. We identified novel clinical and genetic risk factors that provide pathophysiological insights and could help future improvements of treatment of varicose vein disease.
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| 3. |
- Harati, Hadi, et al.
(författare)
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No evidence of a causal association of type 2 diabetes and glucose metabolism with atrial fibrillation
- 2019
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Ingår i: Diabetologia. - : SPRINGER. - 0012-186X .- 1432-0428. ; 62:5, s. 800-804
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Tidskriftsartikel (refereegranskat)abstract
- Aims/hypothesisSeveral epidemiological studies have shown an increased risk of atrial fibrillation in individuals with type 2 diabetes or milder forms of dysglycaemia. We aimed to assess whether this relation is causal using a Mendelian randomisation approach.MethodsTwo-sample Mendelian randomisation was used to obtain estimates of the influence of type 2 diabetes, fasting blood glucose (FBG), and HbA(1c) on the risk of atrial fibrillation. Instrumental variables were constructed using available summary statistics from meta-analyses of genome-wide association studies (GWAS) for type 2 diabetes and associated phenotypes. Pleiotropic SNPs were excluded from the analyses. The most recent GWAS meta-analysis summary statistics for atrial fibrillation, which included over 1 million individuals (approximately 60,000 individuals with atrial fibrillation) was used for outcome analysis.ResultsNeither type 2 diabetes (OR 1.01 [95% CI 0.98, 1.03]; p=0.37), nor FBG (OR 0.95 [95% CI 0.82, 1.09] per mmol/l; p=0.49) or HbA(1c) (OR 1.01 [95% CI, 0.85, 1.17] per mmol/mol [%]; p=0.88) were associated with atrial fibrillation in Mendelian randomisation analyses. We had >80% statistical power to detect ORs of 1.08, 1.06 and 1.09 or larger for type 2 diabetes, FBG and HbA(1c), respectively, for associations with atrial fibrillation.Conclusions/interpretationThis Mendelian randomisation analysis does not support a causal role of clinical significance between genetically programmed type 2 diabetes, FBG or HbA(1c) and development of atrial fibrillation. These data suggest that drug treatment to reduce dysglycaemia is unlikely to be an effective strategy for atrial fibrillation prevention.
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| 4. |
- Klaric, Lucija, et al.
(författare)
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Mendelian randomisation identifies alternative splicing of the FAS death receptor as a mediator of severe COVID-19.
- 2021
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Ingår i: medRxiv : the preprint server for health sciences. - : Cold Spring Harbor Laboratory. ; , s. 1-28
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Annan publikation (övrigt vetenskapligt/konstnärligt)abstract
- Severe COVID-19 is characterised by immunopathology and epithelial injury. Proteomic studies have identified circulating proteins that are biomarkers of severe COVID-19, but cannot distinguish correlation from causation. To address this, we performed Mendelian randomisation (MR) to identify proteins that mediate severe COVID-19. Using protein quantitative trait loci (pQTL) data from the SCALLOP consortium, involving meta-analysis of up to 26,494 individuals, and COVID-19 genome-wide association data from the Host Genetics Initiative, we performed MR for 157 COVID-19 severity protein biomarkers. We identified significant MR results for five proteins: FAS, TNFRSF10A, CCL2, EPHB4 and LGALS9. Further evaluation of these candidates using sensitivity analyses and colocalization testing provided strong evidence to implicate the apoptosis-associated cytokine receptor FAS as a causal mediator of severe COVID-19. This effect was specific to severe disease. Using RNA-seq data from 4,778 individuals, we demonstrate that the pQTL at the FAS locus results from genetically influenced alternate splicing causing skipping of exon 6. We show that the risk allele for very severe COVID-19 increases the proportion of transcripts lacking exon 6, and thereby increases soluble FAS. Soluble FAS acts as a decoy receptor for FAS-ligand, inhibiting apoptosis induced through membrane-bound FAS. In summary, we demonstrate a novel genetic mechanism that contributes to risk of severe of COVID-19, highlighting a pathway that may be a promising therapeutic target.
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| 5. |
- Lind, Lars, et al.
(författare)
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Commonly used clinical chemistry tests as mortality predictors : Results from two large cohort studies
- 2020
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Ingår i: PLOS ONE. - : Public Library of Science (PLoS). - 1932-6203. ; 15:11
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: The normal ranges for clinical chemistry tests are usually defined by cut-offs given by the distribution in healthy individuals. This approach does however not indicate if individuals outside the normal range are more prone to disease.METHODS: We studied the associations and risk prediction of 11 plasma and serum biomarkers with all-cause mortality in two population-based cohorts: a Swedish cohort (X69) initiated in 1969, and the UK Biobank (UKB) initiated in 2006-2010, with up to 48- and 9-years follow-up, respectively.RESULTS: In X69 and in UKB, 18,529 and 425,264 individuals were investigated, respectively. During the follow-up time, 14,475 deaths occurred in X69 and 17,116 in UKB. All evaluated tests were associated with mortality in X69 (P<0.0001, except bilirubin P<0.005). For calcium, blood urea nitrogen, bilirubin, hematocrit, uric acid, and iron, U-shaped associations were seen (P<0.0001). For leukocyte count, gamma-glutamyl transferase, alkaline phosphatases and lactate dehydrogenase, linear positive associations were seen, while for albumin the association was negative. Similar associations were seen in UKB. Addition of all biomarkers to a model with classical risk factors improved mortality prediction (delta C-statistics: +0.009 in X69 and +0.023 in UKB, P<0.00001 in both cohorts).CONCLUSIONS: Commonly used clinical chemistry tests were associated with all-cause mortality both in the medium- and long-term perspective, and improved mortality prediction beyond classical risk factors. Since both linear and U-shaped relationships were found, we propose to define the normal range of a clinical chemistry test based on its association with mortality, rather than from the distribution.
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| 6. |
- Lind, Lars, et al.
(författare)
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Large-scale plasma protein profiling of incident myocardial infarction, ischemic stroke, and heart failure
- 2021
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Ingår i: Journal of the American Heart Association. - : American Heart Association Inc.. - 2047-9980. ; 10:23
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: We recently reported a link between plasma levels of 2 of 84 cardiovascular disease (CVD)– related proteins and the 3 major CVDs, myocardial infarction, ischemic stroke, and heart failure. The present study investigated whether measurement of almost 10 times the number of proteins could lead to discovery of additional risk markers for CVD. METHODS AND RESULTS: We measured 742 proteins using the proximity extension assay in 826 male participants of ULSAM (Uppsala Longitudinal Study of Adult Men) who were free from CVD at the age of 70 years. Cox proportional hazards models were adjusted for age only, as well as all traditional risk factors. During a 12.5-year median follow-up (maximal, 22.0 years), 283 incident CVDs occurred. Forty-one proteins were significantly (false discovery rate <0.05) related to the combined end point of incident CVD, with N-terminal pro– brain natriuretic peptide as the top finding, while 53 proteins were related to incident myocardial infarction. A total of 13 and 16 proteins were significantly related to incident ischemic stroke and heart failure, respectively. Growth differentiation factor 15, 4-disulfide core domain protein 2, and kidney injury molecule were related to all of the 3 major CVD outcomes. A lasso selection of 11 proteins improved discrimination of incident CVD by 5.0% (P=0.0038). CONCLUSIONS: Large-scale proteomics seem useful for the discovery of new risk markers for CVD and to improve risk prediction in an elderly population of men. Further studies are needed to replicate the findings in independent samples of both men and women of different ages. © 2021 The Authors.
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| 7. |
- Lind, Lars, et al.
(författare)
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Plasma Protein Profiling of Incident Cardiovascular Diseases : A Multisample Evaluation
- 2023
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Ingår i: CIRCULATION-GENOMIC AND PRECISION MEDICINE. - : Wolters Kluwer. - 2574-8300. ; 16:6
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Tidskriftsartikel (refereegranskat)abstract
- BACKGROUND: Proteomic profiling could potentially disclose new pathophysiological pathways for cardiovascular diseases (CVD) and improve prediction at the individual level. We therefore aimed to study the plasma protein profile associated with the incidence of different CVDs.METHODS: Plasma levels of 245 proteins suspected to be linked to CVD or metabolism were measured in 4 Swedish prospective population-based cohorts (SIMPLER [Swedish Infrastructure for Medical Population-Based Life-Course and Environmental Research], ULSAM (Uppsala Longitudinal Study of Adult Men), EpiHealth, and POEM [Prospective Investigation of Obesity, Energy Production, and Metabolism]) comprising 11 869 individuals, free of CVD diagnoses at baseline. Our primary CVD outcome was defined by a combined end point that included either incident myocardial infarction, stroke, or heart failure.RESULTS: Using a discovery/validation approach, 42 proteins were associated with our primary composite end point occurring in 1163 subjects. In separate meta-analyses for each of the 3 CVD outcomes, 49 proteins were related to myocardial infarction, 34 to ischemic stroke, and 109 to heart failure. Thirteen proteins were related to all 3 outcomes. Of those, urokinase plasminogen activator surface receptor, adrenomedullin, and KIM-1 (kidney injury molecule 1) were also related to several markers of subclinical CVD in Prospective Investigation of Obesity, Energy production and Metabolism, reflecting myocardial or arterial pathologies. In prediction analysis, a lasso selection of 11 proteins in ULSAM improved the discrimination of CVD by 3.3% (P<0.0001) in SIMPLER when added to traditional risk factors.CONCLUSIONS: Protein profiling in multiple samples disclosed several new proteins to be associated with subsequent myocardial infarction, stroke, and heart failure, suggesting common pathophysiological pathways for these diseases. KIM-1, urokinase plasminogen activator surface receptor, and adrenomedullin were novel early markers of CVD. A selection of 11 proteins improved the discrimination of CVD.
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| 8. |
- Petrov, Dmitry, et al.
(författare)
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Machine Learning for Large-Scale Quality Control of 3D Shape Models in Neuroimaging
- 2017
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Ingår i: Machine learning in medical imaging. MLMI (Workshop). - Cham : Springer International Publishing. ; 10541, s. 371-378
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Tidskriftsartikel (refereegranskat)abstract
- As very large studies of complex neuroimaging phenotypes become more common, human quality assessment of MRI-derived data remains one of the last major bottlenecks. Few attempts have so far been made to address this issue with machine learning. In this work, we optimize predictive models of quality for meshes representing deep brain structure shapes. We use standard vertex-wise and global shape features computed homologously across 19 cohorts and over 7500 human-rated subjects, training kernelized Support Vector Machine and Gradient Boosted Decision Trees classifiers to detect meshes of failing quality. Our models generalize across datasets and diseases, reducing human workload by 30-70%, or equivalently hundreds of human rater hours for datasets of comparable size, with recall rates approaching inter-rater reliability.
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| 9. |
- Wang, Zhaoming, et al.
(författare)
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Imputation and subset-based association analysis across different cancer types identifies multiple independent risk loci in the TERT-CLPTM1L region on chromosome 5p15.33
- 2014
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Ingår i: Human Molecular Genetics. - : Oxford University Press (OUP). - 0964-6906 .- 1460-2083. ; 23:24, s. 6616-6633
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Tidskriftsartikel (refereegranskat)abstract
- Genome-wide association studies (GWAS) have mapped risk alleles for at least 10 distinct cancers to a small region of 63 000 bp on chromosome 5p15.33. This region harbors the TERT and CLPTM1L genes; the former encodes the catalytic subunit of telomerase reverse transcriptase and the latter may play a role in apoptosis. To investigate further the genetic architecture of common susceptibility alleles in this region, we conducted an agnostic subset-based meta-analysis (association analysis based on subsets) across six distinct cancers in 34 248 cases and 45 036 controls. Based on sequential conditional analysis, we identified as many as six independent risk loci marked by common single-nucleotide polymorphisms: five in the TERT gene (Region 1: rs7726159, P = 2.10 × 10(-39); Region 3: rs2853677, P = 3.30 × 10(-36) and PConditional = 2.36 × 10(-8); Region 4: rs2736098, P = 3.87 × 10(-12) and PConditional = 5.19 × 10(-6), Region 5: rs13172201, P = 0.041 and PConditional = 2.04 × 10(-6); and Region 6: rs10069690, P = 7.49 × 10(-15) and PConditional = 5.35 × 10(-7)) and one in the neighboring CLPTM1L gene (Region 2: rs451360; P = 1.90 × 10(-18) and PConditional = 7.06 × 10(-16)). Between three and five cancers mapped to each independent locus with both risk-enhancing and protective effects. Allele-specific effects on DNA methylation were seen for a subset of risk loci, indicating that methylation and subsequent effects on gene expression may contribute to the biology of risk variants on 5p15.33. Our results provide strong support for extensive pleiotropy across this region of 5p15.33, to an extent not previously observed in other cancer susceptibility loci.
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| 10. |
- Yang, Zhijian, et al.
(författare)
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Genetic Landscape of the ACE2 Coronavirus Receptor
- 2022
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Ingår i: Circulation. - : Ovid Technologies (Wolters Kluwer Health). - 0009-7322 .- 1524-4539. ; 30:SUPPL 1, s. 36-36
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Tidskriftsartikel (refereegranskat)abstract
- Background: SARS-CoV-2, the causal agent of COVID-19, enters human cells using the ACE2 (angiotensin-converting enzyme 2) protein as a receptor. ACE2 is thus key to the infection and treatment of the coronavirus. ACE2 is highly expressed in the heart and respiratory and gastrointestinal tracts, playing important regulatory roles in the cardiovascular and other biological systems. However, the genetic basis of the ACE2 protein levels is not well understood.Methods: We have conducted the largest genome-wide association meta-analysis of plasma ACE2 levels in >28 000 individuals of the SCALLOP Consortium (Systematic and Combined Analysis of Olink Proteins). We summarize the cross-sectional epidemiological correlates of circulating ACE2. Using the summary statistics-based high-definition likelihood method, we estimate relevant genetic correlations with cardiometabolic phenotypes, COVID-19, and other human complex traits and diseases. We perform causal inference of soluble ACE2 on vascular disease outcomes and COVID-19 severity using mendelian randomization. We also perform in silico functional analysis by integrating with other types of omics data.Results: We identified 10 loci, including 8 novel, capturing 30% of the heritability of the protein. We detected that plasma ACE2 was genetically correlated with vascular diseases, severe COVID-19, and a wide range of human complex diseases and medications. An X-chromosome cis-protein quantitative trait loci-based mendelian randomization analysis suggested a causal effect of elevated ACE2 levels on COVID-19 severity (odds ratio, 1.63 [95% CI, 1.10-2.42]; P=0.01), hospitalization (odds ratio, 1.52 [95% CI, 1.05-2.21]; P=0.03), and infection (odds ratio, 1.60 [95% CI, 1.08-2.37]; P=0.02). Tissue- and cell type-specific transcriptomic and epigenomic analysis revealed that the ACE2 regulatory variants were enriched for DNA methylation sites in blood immune cells.Conclusions: Human plasma ACE2 shares a genetic basis with cardiovascular disease, COVID-19, and other related diseases. The genetic architecture of the ACE2 protein is mapped, providing a useful resource for further biological and clinical studies on this coronavirus receptor.
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