Imperial College London

DrDraganaVuckovic

Faculty of MedicineSchool of Public Health

Lecturer in Computational Epidemiology and Biostatistics
 
 
 
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d.vuckovic

 
 
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Sir Alexander Fleming BuildingSouth Kensington Campus

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Summary

 

Publications

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65 results found

Wada R, Peng F-J, Lin C-A, Vermeulen R, Iglesias-Gonzales A, Palazzi P, Bodinier B, Streel S, Guillaume M, Vuckovic D, Dagnino S, Chiquet J, Appenzeller B, Chadeau Met al., 2024, Hair-derived exposome exploration of cardiometabolic health: piloting a Bayesian multi-trait variable selection approach, Environmental Science and Technology (Washington), ISSN: 0013-936X

Journal article

Neufcourt L, Castagné R, Wilsgaard T, Grimsgaard S, Chadeau-Hyam M, Vuckovic D, Ugarteche-Perez A, Farbu EH, Sandanger TM, Delpierre C, Kelly-Irving Met al., 2024, Educational patterning in biological health seven years apart: findings from the Tromsø Study, Psychoneuroendocrinology, Vol: 160, ISSN: 0306-4530

BACKGROUND: Social-to-biological processes is one set of mechanisms underlying the relationship between social position and health. However, very few studies have focused on the relationship between social factors and biology at multiple time points. This work investigates the relationship between education and the dynamic changes in a composite Biological Health Score (BHS) using two time points seven years apart in a Norwegian adult population. METHODS: We used data from individuals aged 30 years and above who participated in Tromsø6 (2007-2008) and Tromsø7 (2015-2016) (n = 8117). BHS was defined using ten biomarkers measured from blood samples and representing three physiological systems (cardiovascular, metabolic, inflammatory). The higher the BHS, the poorer the health status. FINDINGS: Linear regression models carried out on BHS revealed a strong educational gradient at two distinct time points but also over time. People with lower educational attainment were at higher risk of poor biological health at a given time point (βlow education Tromsø6=0.30 [95 %-CI=0.18-0.43] and βlow education Tromsø7=0.30 [95 %-CI=0.17-0.42]). They also presented higher longitudinal BHS compared to people with higher education (βlow education = 0.89 [95 %-CI=0.56-1.23]). Certain biomarkers related to the cardiovascular system and the metabolic system were strongly socially distributed, even after adjustment for sex, age, health behaviours and body mass index. CONCLUSION: This longitudinal analysis highlights that participants with lower education had their biological health deteriorated to a greater extent over time compared to people with higher education. Our findings provide added evidence of the biological embodiment of social position, particularly with respect to dynamic aspects for which little evidence exists.

Journal article

Guo J, Walter K, Quiros PM, Gu M, Baxter EJ, Danesh J, Di Angelantonio E, Roberts D, Guglielmelli P, Harrison CN, Godfrey AL, Green AR, Vassiliou GS, Vuckovic D, Nangalia J, Soranzo Net al., 2024, Inherited polygenic effects on common hematological traits influence clonal selection on JAK2V617F and the development of myeloproliferative neoplasms., Nat Genet, Vol: 56, Pages: 273-280

Myeloproliferative neoplasms (MPNs) are chronic cancers characterized by overproduction of mature blood cells. Their causative somatic mutations, for example, JAK2V617F, are common in the population, yet only a minority of carriers develop MPN. Here we show that the inherited polygenic loci that underlie common hematological traits influence JAK2V617F clonal expansion. We identify polygenic risk scores (PGSs) for monocyte count and plateletcrit as new risk factors for JAK2V617F positivity. PGSs for several hematological traits influenced the risk of different MPN subtypes, with low PGSs for two platelet traits also showing protective effects in JAK2V617F carriers, making them two to three times less likely to have essential thrombocythemia than carriers with high PGSs. We observed that extreme hematological PGSs may contribute to an MPN diagnosis in the absence of somatic driver mutations. Our study showcases how polygenic backgrounds underlying common hematological traits influence both clonal selection on somatic mutations and the subsequent phenotype of cancer.

Journal article

Peng F-J, Lin C-A, Wada R, Bodinier B, Iglesias-González A, Palazzi P, Streel S, Guillaume M, Vuckovic D, Chadeau-Hyam M, Appenzeller BMR, NESCAV project groupet al., 2024, Association of hair polychlorinated biphenyls and multiclass pesticides with obesity, diabetes, hypertension and dyslipidemia in NESCAV study., J Hazard Mater, Vol: 461

Obesity, diabetes, hypertension and dyslipidemia are well-established risk factors for cardiovascular diseases (CVDs), and have been associated with exposure to persistent organic pollutants. However, studies have been lacking as regards effects of non-persistent pesticides on CVD risk factors. Here, we investigated whether background chronic exposure to polychlorinated biphenyls (PCBs) and multiclass pesticides were associated with the prevalence of these CVD risk factors in 502 Belgian and 487 Luxembourgish adults aged 18-69 years from the Nutrition, environment and cardiovascular health (NESCAV) study 2007-2013. We used hair analysis to evaluate the chronic internal exposure to three PCBs, seven organochlorine pesticides (OCs) and 18 non-persistent pesticides. We found positive associations of obesity with hexachlorobenzene (HCB), β-hexachlorocyclohexane (β-HCH) and chlorpyrifos, diabetes with pentachlorophenol (PCP), fipronil and fipronil sulfone, hypertension with PCB180 and chlorpyrifos, and dyslipidemia with diflufenican and oxadiazon, among others. However, we also found some inverse associations, such as obesity with PCP, diabetes with γ-HCH, hypertension with diflufenican, and dyslipidemia with chlorpyrifos. These results add to the existing evidence that OC exposure may contribute to the development of CVDs. Additionally, the present study revealed associations between CVD risk factors and chronic environmental exposure to currently used pesticides such as organophosphorus and pyrethroid pesticides.

Journal article

Asamoah K, Chung K, Bodinier B, Dahlen S-E, Djukanovic R, Bhavsar P, Adcock I, Vuckovic D, Chadeau Met al., 2024, Proteomic signatures of eosinophilic and neutrophilic asthma from serum and sputum, EBioMedicine, Vol: 99, ISSN: 2352-3964

BackgroundEosinophilic and neutrophilic asthma defined by high levels of blood and sputum eosinophils and neutrophils exemplifies the inflammatory heterogeneity of asthma, particularly severe asthma. We analysed the serum and sputum proteome to identify biomarkers jointly associated with these different phenotypes.MethodsProteomic profiles (N = 1129 proteins) were assayed in sputum (n = 182) and serum (n = 574) from two cohorts (U-BIOPRED and ADEPT) of mild-moderate and severe asthma by SOMAscan. Using least absolute shrinkage and selection operator (LASSO)-penalised logistic regression in a stability selection framework, we sought sparse sets of proteins associated with either eosinophilic or neutrophilic asthma with and without adjustment for established clinical factors including oral corticosteroid use and forced expiratory volume.FindingsWe identified 13 serum proteins associated with eosinophilic asthma, including 7 (PAPP-A, TARC/CCL17, ALT/GPT, IgE, CCL28, CO8A1, and IL5-Rα) that were stably selected while adjusting for clinical factors yielding an AUC of 0.84 (95% CI: 0.83–0.84) compared to 0.62 (95% CI: 0.61–0.63) for clinical factors only. Sputum protein analysis selected only PAPP-A (AUC = 0.81 [95% CI: 0.80–0.81]). 12 serum proteins were associated with neutrophilic asthma, of which 5 (MMP-9, EDAR, GIIE/PLA2G2E, IL-1-R4/IL1RL1, and Elafin) complemented clinical factors increasing the AUC from 0.63 (95% CI: 0.58–0.67) for the model with clinical factors only to 0.89 (95% CI: 0.89–0.90). Our model did not select any sputum proteins associated with neutrophilic status.InterpretationTargeted serum proteomic profiles are a non-invasive and scalable approach for subtyping of neutrophilic and eosinophilic asthma and for future functional understanding of these phenotypes.FundingU-BIOPRED has received funding from the Innovative Medicines Initiative (IMI) Joint Undertaking under grant agreement no. 115010, resources of which a

Journal article

Bodinier B, Vuckovic D, Rodrigues S, Filippi S, Chiquet J, Chadeau Met al., 2023, Automated calibration of consensus weighted distance-based clustering approaches using sharp, Bioinformatics, Vol: 39, ISSN: 1367-4803

Motivation:In consensus clustering, a clustering algorithm is used in combination with a subsampling procedure to detect stable clusters. Previous studies on both simulated and real data suggest that consensus clustering outperforms native algorithms.Results:We extend here consensus clustering to allow for attribute weighting in the calculation of pairwise distances using existing regularised approaches. We propose a procedure for the calibration of the number of clusters (and regularisation parameter) by maximising the sharp score, a novel stability score calculated directly from consensus clustering outputs, making it extremely computationally competitive. Our simulation study shows better clustering performances of (i) approaches calibrated by maximising the sharp score compared to existing calibration scores, and (ii) weighted compared to unweighted approaches in the presence of features that do not contribute to cluster definition. Application on real gene expression data measured in lung tissue reveals clear clusters corresponding to different lung cancer subtypes.Availability and implementation:The R package sharp (version ≥ 1.4.3) is available on CRAN at https://CRAN.R-project.org/package=sharp.

Journal article

Bortz J, Guariglia A, Klaric L, Ward P, Geer M, Chadeau M, Vuckovic D, Joshi Pet al., 2023, Biological age estimation using circulating blood biomarkers, Communications Biology, Vol: 6, ISSN: 2399-3642

Biological age captures physiological deterioration better than chronological age and is amenable to interventions. Blood-based biomarkers have been identified as suitable candidates for biological age estimation. This study aims to improve biological age estimation using machine learning models and a feature-set of 60 circulating biomarkers available from the UK Biobank (n = 306,116). We implement an Elastic-Net derived Cox model with 25 selected biomarkers to predict mortality risk (C-Index = 0.778; 95% CI [0.767–0.788]), which outperforms the well-known blood-biomarker based PhenoAge model (C-Index = 0.750; 95% CI [0.739–0.761]), providing a C-Index lift of 0.028 representing an 11% relative increase in predictive value. Importantly, we then show that using common clinical assay panels, with few biomarkers, alongside imputation and the model derived on the full set of biomarkers, does not substantially degrade predictive accuracy from the theoretical maximum achievable for the available biomarkers. Biological age is estimated as the equivalent age within the same-sex population which corresponds to an individual’s mortality risk. Values ranged between 20-years younger and 20-years older than individuals’ chronological age, exposing the magnitude of ageing signals contained in blood markers. Thus, we demonstrate a practical and cost-efficient method of estimating an improved measure of Biological Age, available to the general population.

Journal article

Stefanucci L, Collins JH, Sims MC, Barrio-Hernandez I, Sun L, Burren O, Perfetto L, Bender I, Callahan TJ, Fleming K, Guerrero JA, Hermjakob H, Martin MJ, Stephenson JD, Paneerselvam K, Petrovski S, Porras P, Robinson PN, Wang Q, Watkins X, Frontini M, Laskowski RA, Beltrao P, Di Angelantonio E, Gomez K, Laffan M, Ouwehand WH, Mumford AD, Freson K, Carss KJ, Downes K, Gleadall NS, Megy K, Bruford E, Vuckovic Det al., 2023, The effects of pathogenic variants for inherited hemostasis disorders in 140,214 UK Biobank participants., Blood

Rare genetic diseases affect millions, and identifying causal DNA variants is essential for patient care. Therefore, it is imperative to estimate the effect of each independent variant and improve their pathogenicity classification. Our study of 140,214 unrelated UK Biobank (UKB) participants found each carries a median of 7 variants previously reported as pathogenic or likely pathogenic. We focused on 967 diagnostic-grade genes (DGGs) variants for rare bleeding, thrombotic, and platelet disorders (BTPDs) observed in 12,367 UKB participants. By association analysis, for a subset of these variants, we estimated effect sizes for platelet count and volume, and odds ratios for bleeding and thrombosis. Variants causal of some autosomal recessive platelet disorders revealed phenotypic consequences in carriers. Loss-of-function variants in MPL, which cause chronic amegakaryocytic thrombocytopenia if biallelic, were unexpectedly associated with increased platelet counts in carriers. We also demonstrated that common variants identified by genome-wide association studies (GWAS) for platelet count or thrombosis risk may influence the penetrance of rare variants in BTPD DGGs on their associated hemostasis disorders. Network-propagation analysis applied to an interactome of 18,410 nodes and 571,917 edges showed that GWAS variants with large effect sizes are enriched in DGGs and their first-order interactors. Finally, we illustrate the modifying effect of polygenic scores for platelet count and thrombosis risk on disease severity in participants carrying rare variants in TUBB1, or PROC and PROS1, respectively. Our findings demonstrate the power of association analyses using large population datasets in improving pathogenicity classifications of rare variants.

Journal article

Akbari P, Vuckovic D, Stefanucci L, Jiang T, Kundu K, Kreuzhuber R, Bao EL, Collins JH, Downes K, Grassi L, Guerrero JA, Kaptoge S, Knight JC, Meacham S, Sambrook J, Seyres D, Stegle O, Verboon JM, Walter K, Watkins NA, Danesh J, Roberts DJ, Di Angelantonio E, Sankaran VG, Frontini M, Burgess S, Kuijpers T, Peters JE, Butterworth AS, Ouwehand WH, Soranzo N, Astle WJet al., 2023, A genome-wide association study of blood cell morphology identifies cellular proteins implicated in disease aetiology, NATURE COMMUNICATIONS, Vol: 14

Journal article

Burley K, Fitzgibbon L, van Heel D, Vuckovic D, Mumford ADet al., 2023, <i>PIK3R3</i> is a candidate regulator of platelet count in people of Bangladeshi ancestry, RESEARCH AND PRACTICE IN THROMBOSIS AND HAEMOSTASIS, Vol: 7

Journal article

Garmendia AT, Gkouzionis I, Triantafyllidis CP, Dimakopoulos V, Liliopoulos S, Vuckovic D, Paseiro-Garcia L, Chadeau-Hyam Met al., 2023, Towards personalised early prediction of Intra-Operative Hypotension following anesthesia using Deep Learning and phenotypic heterogeneity

<jats:title>Abstract</jats:title><jats:p>Intra-Operative Hypotension (IOH) is a haemodynamic abnormality that is commonly observed in operating theatres following general anesthesia and associates with life-threatening post-operative complications. Using Long Short Term Memory (LSTM) models applied to Electronic Health Records (EHR) and time-series intra-operative data in 604 patients that underwent colorectal surgery we predicted the instant risk of IOH events within the next five minutes. K-means clustering was used to group patients based on pre-clinical data. As part of a sensitivity analysis, the model was also trained on patients clustered according to Mean artelial Blood Pressure (MBP) time-series trends at the start of the operation using K-means with Dynamic Time Warping. The baseline LSTM model trained on all patients yielded a test set Area Under the Curve (AUC) value of 0.83. In contrast, training the model on smaller sized clusters (grouped by EHR) improved the AUC value (0.85). Similarly, the AUC was increased by 4.8% (0.87) when training the model on clusters grouped by MBP. The encouraging results of the baseline model demonstrate the applicability of the approach in a clinical setting. Furthermore, the increased predictive performance of the model after being trained using a clustering approach first, paves the way for a more personalised patient stratification approach to IOH prediction using clinical data.</jats:p>

Journal article

Rowland B, Venkatesh S, Tardaguila M, Wen J, Rosen JD, Tapia AL, Sun Q, Graff M, Vuckovic D, Lettre G, Sankaran VG, Voloudakis G, Roussos P, Huffman JE, Reiner AP, Soranzo N, Raffield LM, Li Yet al., 2022, Transcriptome-wide association study in UK biobank Europeans identifies associations with blood cell traits, Human Molecular Genetics, Vol: 31, Pages: 2333-2347, ISSN: 0964-6906

Previous genome-wide association studies (GWAS) of hematological traits have identified over 10 000 distinct trait-specific risk loci. However, at these loci, the underlying causal mechanisms remain incompletely characterized. To elucidate novel biology and better understand causal mechanisms at known loci, we performed a transcriptome-wide association study (TWAS) of 29 hematological traits in 399 835 UK Biobank (UKB) participants of European ancestry using gene expression prediction models trained from whole blood RNA-seq data in 922 individuals. We discovered 557 gene-trait associations for hematological traits distinct from previously reported GWAS variants in European populations. Among the 557 associations, 301 were available for replication in a cohort of 141 286 participants of European ancestry from the Million Veteran Program (MVP). Of these 301 associations, 108 replicated at a strict Bonferroni adjusted threshold ($\alpha$ = 0.05/301). Using our TWAS results, we systematically assigned 4261 out of 16 900 previously identified hematological trait GWAS variants to putative target genes. Compared to coloc, our TWAS results show reduced specificity and increased sensitivity in external datasets to assign variants to target genes.

Journal article

Okbay A, Wu Y, Wang N, Jayashankar H, Bennett M, Nehzati SM, Sidorenko J, Kweon H, Goldman G, Gjorgjieva T, Jiang Y, Hicks B, Tian C, Hinds DA, Ahlskog R, Magnusson PKE, Oskarsson S, Hayward C, Campbell A, Porteous DJ, Freese J, Herd P, 23andMe Research Team, Social Science Genetic Association Consortium, Watson C, Jala J, Conley D, Koellinger PD, Johannesson M, Laibson D, Meyer MN, Lee JJ, Kong A, Yengo L, Cesarini D, Turley P, Visscher PM, Beauchamp JP, Benjamin DJ, Young AIet al., 2022, Polygenic prediction of educational attainment within and between families from genome-wide association analyses in 3 million individuals., Nat Genet, Vol: 54, Pages: 437-449

We conduct a genome-wide association study (GWAS) of educational attainment (EA) in a sample of ~3 million individuals and identify 3,952 approximately uncorrelated genome-wide-significant single-nucleotide polymorphisms (SNPs). A genome-wide polygenic predictor, or polygenic index (PGI), explains 12-16% of EA variance and contributes to risk prediction for ten diseases. Direct effects (i.e., controlling for parental PGIs) explain roughly half the PGI's magnitude of association with EA and other phenotypes. The correlation between mate-pair PGIs is far too large to be consistent with phenotypic assortment alone, implying additional assortment on PGI-associated factors. In an additional GWAS of dominance deviations from the additive model, we identify no genome-wide-significant SNPs, and a separate X-chromosome additive GWAS identifies 57.

Journal article

Xu Y, Vuckovic D, Ritchie SC, Akbari P, Jiang T, Grealey J, Butterworth AS, Ouwehand WH, Roberts DJ, Di Angelantonio E, Danesh J, Soranzo N, Inouye Met al., 2022, Machine learning optimized polygenic scores for blood cell traits identify sex-specific trajectories and genetic correlations with disease, Cell Genomics, Vol: 2

Genetic association studies for blood cell traits, which are key indicators of health and immune function, have identified several hundred associations and defined a complex polygenic architecture. Polygenic scores (PGSs) for blood cell traits have potential clinical utility in disease risk prediction and prevention, but designing PGS remains challenging and the optimal methods are unclear. To address this, we evaluated the relative performance of 6 methods to develop PGS for 26 blood cell traits, including a standard method of pruning and thresholding (P + T) and 5 learning methods: LDpred2, elastic net (EN), Bayesian ridge (BR), multilayer perceptron (MLP) and convolutional neural network (CNN). We evaluated these optimized PGSs on blood cell trait data from UK Biobank and INTERVAL. We find that PGSs designed using common machine learning methods EN and BR show improved prediction of blood cell traits and consistently outperform other methods. Our analyses suggest EN/BR as the top choices for PGS construction, showing improved performance for 25 blood cell traits in the external validation, with correlations with the directly measured traits increasing by 10%–23%. Ten PGSs showed significant statistical interaction with sex, and sex-specific PGS stratification showed that all of them had substantial variation in the trajectories of blood cell traits with age. Genetic correlations between the PGSs for blood cell traits and common human diseases identified well-known as well as new associations. We develop machine learning-optimized PGS for blood cell traits, demonstrate their relationships with sex, age, and disease, and make these publicly available as a resource.

Journal article

Collins J, Astle WJ, Megy K, Mumford AD, Vuckovic Det al., 2021, Advances in understanding the pathogenesis of hereditary macrothrombocytopenia, BRITISH JOURNAL OF HAEMATOLOGY, Vol: 195, Pages: 25-45, ISSN: 0007-1048

Journal article

Puan KJ, San Luis B, Yusof N, Kumar D, Andiappan AK, Lee W, Cajic S, Vuckovic D, Chan JD, Döllner T, Hou HW, Jiang Y, Tian C, 23andMe Research Team, Rapp E, Poidinger M, Wang DY, Soranzo N, Lee B, Rötzschke Oet al., 2021, FUT6 deficiency compromises basophil function by selectively abrogating their sialyl-Lewis x expression., Commun Biol, Vol: 4

Sialyl-Lewis x (sLex, CD15s) is a tetra-saccharide on the surface of leukocytes required for E-selectin-mediated rolling, a prerequisite for leukocytes to migrate out of the blood vessels. Here we show using flow cytometry that sLex expression on basophils and mast cell progenitors depends on fucosyltransferase 6 (FUT6). Using genetic association data analysis and qPCR, the cell type-specific defect was associated with single nucleotide polymorphisms (SNPs) in the FUT6 gene region (tagged by rs17855739 and rs778798), affecting coding sequence and/or expression level of the mRNA. Heterozygous individuals with one functional FUT6 gene harbor a mixed population of sLex+ and sLex- basophils, a phenomenon caused by random monoallelic expression (RME). Microfluidic assay demonstrated FUT6-deficient basophils rolling on E-selectin is severely impaired. FUT6 null alleles carriers exhibit elevated blood basophil counts and a reduced itch sensitivity against insect bites. FUT6-deficiency thus dampens the basophil-mediated allergic response in the periphery, evident also in lower IgE titers and reduced eosinophil counts.

Journal article

Bowden S, Bodinier B, Kalliala I, Zuber V, Vuckovic D, Doulgeraki T, Whitaker M, Wielscher M, Cartwright R, Tsilidis K, Bennett P, Jarvelin M-R, Flanagan J, Chadeau M, Kyrgiou M, FinnGen consortiumet al., 2021, Genetic variation in cervical preinvasive and invasive disease: a genome-wide association study, The Lancet Oncology, Vol: 22, Pages: 548-557, ISSN: 1213-9432

Background: Most uterine cervical high-risk HPV infections (hrHPV) are transient, with only a small 3fraction developing into cervical cancer. Family aggregation studies and heritability estimates suggest 4a significant inherited genetic component. Candidate gene studies and previous genome-wide 5association studies (GWAS) report associations between the human leukocyte antigen (HLA) region 6and cervical cancer. 78Methods: Adopting a genome-wide approach, we compared the genetic variation in women with 9invasive cervical cancer (ICC) and cervical intra-epithelial neoplasia (CIN) grade 3, to that in healthy 10controls using the largest reported cohort of unrelated European individuals (N=150,314)to date. We 11sought for replication in a second large independent dataset (N=128,123). We further performed a two-12sample Mendelian Randomisation approach to explore the role of risk factors in the genetic risk of 13cervical cancer.1415Findings: In our analysis (N=4,769 CIN3 and ICC cases; N=145,545 controls), of the (N=9,600,464) 16assayed and imputed SNPs, six independent variants were found associated with CIN3and ICC. These 17included novel loci rs10175462(PAX8; OR=0.87(95%CI=0.84-0.91); P=1.07x10-9) and rs27069 18(CLPTM1L;OR=0.88(95%CI=0.84-0.92); P=2.51x10-9), and previously reported signals at rs9272050 19(HLA-DQA1;OR=1.27(95%CI=1.21-1.32); P=2.51x10-28), rs6938453 (MICA;OR=0.7920(95%CI=0.75-0.83); P=1.97x10-17), rs55986091 (HLA-DQB1;OR=0.66(95%CI=0.60-0.72); 21P=6.42x10-22) and rs9266183 (HLA-B;OR=0.73(95%CI=0.64-0.83); P=1.53x10-6). Mendelian 22randomisation further supported the complementary role of smoking, age at first pregnancy, and number 23of sexual partners in the risk of developing cervical cancer.2425Interpretation: Our results provide substantial new evidence for genetic susceptibility to cervical cancer, 26including PAX8, CLPTM1LandHLA genes, suggesting disruption in apoptotic and immun

Journal article

Simcoe M, Valdes A, Liu F, Furlotte NA, Evans DM, Hemani G, Ring SM, Smith GD, Duffy DL, Zhu G, Gordon SD, Medland SE, Vuckovic D, Girotto G, Sala C, Catamo E, Concas MP, Brumat M, Gasparini P, Toniolo D, Cocca M, Robino A, Yazar S, Hewitt A, Wu W, Kraft P, Hammond CJ, Shi Y, Chen Y, Zeng C, Klaver CCW, Uitterlinden AG, Ikram MA, Hamer MA, van Duijn CM, Nijsten T, Han J, Mackey DA, Martin NG, Cheng C-Y, Hinds DA, Spector TD, Kayser M, Hysi PGet al., 2021, Genome-wide association study in almost 195,000 individuals identifies 50 previously unidentified genetic loci for eye color, SCIENCE ADVANCES, Vol: 7, ISSN: 2375-2548

Journal article

Bell S, Rigas AS, Magnusson MK, Ferkingstad E, Allara E, Bjornsdottir G, Ramond A, Sorensen E, Halldorsson GH, Paul DS, Eggertsson HP, Burgdorf KS, Howson JMM, Thorner LW, Kristmundsdottir S, Astle WJ, Erikstrup C, Sigurdsson JK, Vuckovic D, Dinh KM, Tragante V, Surendran P, Pedersen OB, Vidarsson B, Jiang T, Paarup HM, Onundarson PT, Akbari P, Nielsen KR, Lund SH, Juliusson K, Magnusson M, Frigge ML, Oddsson A, Olafsson I, Kaptoge S, Hjalgrim H, Runarsson G, Wood AM, Jonsdottir I, Hansen TF, Sigurdardottir O, Stefansson H, Rye D, Peters JE, Westergaard D, Holm H, Soranzo N, Banasik K, Thorleifsson G, Ouwehand WH, Thorsteinsdottir U, Roberts DJ, Sulem P, Butterworth AS, Gudbjartsson DF, Danesh J, Brunak S, Di Angelantonio E, Ullum H, Stefansson Ket al., 2021, A genome-wide meta-analysis yields 46 new loci associating with biomarkers of iron homeostasis, Communications Biology, Vol: 4, Pages: 1-14, ISSN: 2399-3642

Iron is essential for many biological functions and iron deficiency and overload have major health implications. We performed a meta-analysis of three genome-wide association studies from Iceland, the UK and Denmark of blood levels of ferritin (N = 246,139), total iron binding capacity (N = 135,430), iron (N = 163,511) and transferrin saturation (N = 131,471). We found 62 independent sequence variants associating with iron homeostasis parameters at 56 loci, including 46 novel loci. Variants at DUOX2, F5, SLC11A2 and TMPRSS6 associate with iron deficiency anemia, while variants at TF, HFE, TFR2 and TMPRSS6 associate with iron overload. A HBS1L-MYB intergenic region variant associates both with increased risk of iron overload and reduced risk of iron deficiency anemia. The DUOX2 missense variant is present in 14% of the population, associates with all iron homeostasis biomarkers, and increases the risk of iron deficiency anemia by 29%. The associations implicate proteins contributing to the main physiological processes involved in iron homeostasis: iron sensing and storage, inflammation, absorption of iron from the gut, iron recycling, erythropoiesis and bleeding/menstruation.

Journal article

Chen M-H, Raffield LM, Mousas A, Sakaue S, Huffman JE, Moscati A, Trivedi B, Jiang T, Akbari P, Vuckovic D, Bao EL, Zhong X, Manansala R, Laplante V, Chen M, Lo KS, Qian H, Lareau CA, Beaudoin M, Hunt KA, Akiyama M, Bartz TM, Ben-Shlomo Y, Beswick A, Bork-Jensen J, Bottinger EP, Brody JA, van Rooij FJA, Chitrala K, Cho K, Choquet H, Correa A, Danesh J, Di Angelantonio E, Dimou N, Ding J, Elliott P, Esko T, Evans MK, Floyd JS, Broer L, Grarup N, Guo MH, Greinacher A, Haessler J, Hansen T, Howson JMM, Huang QQ, Huang W, Jorgenson E, Kacprowski T, Kahonen M, Kamatani Y, Kanai M, Karthikeyan S, Koskeridis F, Lange LA, Lehtimaki T, Lerch MM, Linneberg A, Liu Y, Lyytikainen L-P, Manichaikul A, Martin HC, Matsuda K, Mohlke KL, Mononen N, Murakami Y, Nadkarni GN, Nauck M, Nikus K, Ouwehand WH, Pankratz N, Pedersen O, Preuss M, Psaty BM, Raitakari OT, Roberts DJ, Rich SS, Rodriguez BAT, Rosen JD, Rotter JI, Schubert P, Spracklen CN, Surendran P, Tang H, Tardif J-C, Trembath RC, Ghanbari M, Volker U, Volzke H, Watkins NA, Zonderman AB, Wilson PWF, Li Y, Butterworth AS, Gauchat J-F, Chiang CWK, Li B, Loos RJF, Astle WJ, Evangelou E, van Heel DA, Sankaran VG, Okada Y, Soranzo N, Johnson AD, Reiner AP, Auer PL, Lettre Get al., 2020, Trans-ethnic and Ancestry-Specific Blood-Cell Genetics in 746,667 Individuals from 5 Global Populations, CELL, Vol: 182, Pages: 1198-1213.E14, ISSN: 0092-8674

Most loci identified by GWASs have been found in populations of European ancestry (EUR). In trans-ethnic meta-analyses for 15 hematological traits in 746,667 participants, including 184,535 non-EUR individuals, we identified 5,552 trait-variant associations at p < 5 × 10 −9, including 71 novel associations not found in EUR populations. We also identified 28 additional novel variants in ancestry-specific, non-EUR meta-analyses, including an IL7 missense variant in South Asians associated with lymphocyte count in vivo and IL-7 secretion levels in vitro. Fine-mapping prioritized variants annotated as functional and generated 95% credible sets that were 30% smaller when using the trans-ethnic as opposed to the EUR-only results. We explored the clinical significance and predictive value of trans-ethnic variants in multiple populations and compared genetic architecture and the effect of natural selection on these blood phenotypes between populations. Altogether, our results for hematological traits highlight the value of a more global representation of populations in genetic studies.

Journal article

Vuckovic D, Bao EL, Akbari P, Lareau CA, Mousas A, Jiang T, Chen M-H, Raffield LM, Tardaguila M, Huffman JE, Ritchie SC, Megy K, Ponstingl H, Penkett CJ, Albers PK, Wigdor EM, Sakaue S, Moscati A, Manansala R, Lo KS, Qian H, Akiyama M, Bartz TM, Ben-Shlomo Y, Beswick A, Bork-Jensen J, Bottinger EP, Brody JA, van Rooij FJA, Chitrala KN, Wilson PWF, Choquet H, Danesh J, Di Angelantonio E, Dimou N, Ding J, Elliott P, Esko T, Evans MK, Felix SB, Floyd JS, Broer L, Grarup N, Guo MH, Guo Q, Greinacher A, Haessler J, Hansen T, Howson JMM, Huang W, Jorgenson E, Kacprowski T, Kahonen M, Kamatani Y, Kanai M, Karthikeyan S, Koskeridis F, Lange LA, Lehtimaki T, Linneberg A, Liu Y, Lyytikainen L-P, Manichaikul A, Matsuda K, Mohlke KL, Mononen N, Murakami Y, Nadkarni GN, Nikus K, Pankratz N, Pedersen O, Preuss M, Psaty BM, Raitakari OT, Rich SS, Rodriguez BAT, Rosen JD, Rotter JI, Schubert P, Spracklen CN, Surendran P, Tang H, Tardif J-C, Ghanbari M, Volker U, Volzke H, Watkins NA, Weiss S, Cai N, Kundu K, Watt SB, Walter K, Zonderman AB, Cho K, Li Y, Loos RJF, Knight JC, Georges M, Stegle O, Evangelou E, Okada Y, Roberts DJ, Inouye M, Johnson AD, Auer PL, Astle WJ, Reiner AP, Butterworth AS, Ouwehand WH, Lettre G, Sankaran VG, Soranzo Net al., 2020, The polygenic and monogenic basis of blood traits and diseases, Cell, Vol: 182, Pages: 1214-1231.e11, ISSN: 0092-8674

Blood cells play essential roles in human health, underpinning physiological processes such as immunity, oxygen transport, and clotting, which when perturbed cause a significant global health burden. Here we integrate data from UK Biobank and a large-scale international collaborative effort, including data for 563,085 European ancestry participants, and discover 5,106 new genetic variants independently associated with 29 blood cell phenotypes covering a range of variation impacting hematopoiesis. We holistically characterize the genetic architecture of hematopoiesis, assess the relevance of the omnigenic model to blood cell phenotypes, delineate relevant hematopoietic cell states influenced by regulatory genetic variants and gene networks, identify novel splice-altering variants mediating the associations, and assess the polygenic prediction potential for blood traits and clinical disorders at the interface of complex and Mendelian genetics. These results show the power of large-scale blood cell trait GWAS to interrogate clinically meaningful variants across a wide allelic spectrum of human variation.

Journal article

Cocca M, Barbieri C, Concas MP, Robino A, Brumat M, Gandin I, Trudu M, Sala CF, Vuckovic D, Girotto G, Matullo G, Polasek O, Kolcic I, Gasparini P, Soranzo N, Toniolo D, Mezzavilla Met al., 2020, A bird's-eye view of Italian genomic variation through whole-genome sequencing, EUROPEAN JOURNAL OF HUMAN GENETICS, Vol: 28, Pages: 435-444, ISSN: 1018-4813

Journal article

Nagtegaal AP, Broer L, Zilhao NR, Jakobsdottir J, Bishop CE, Brumat M, Christiansen MW, Cocca M, Gao Y, Heard-Costa NL, Evans DS, Pankratz N, Pratt SR, Price TR, Spankovich C, Stimson MR, Valle K, Vuckovic D, Wells H, Eiriksdottir G, Fransen E, Ikram MA, Li C-M, Longstreth WT, Steves C, Van Camp G, Correa A, Cruickshanks KJ, Gasparini P, Girotto G, Kaplan RC, Nalls M, Schweinfurth JM, Seshadri S, Sotoodehnia N, Tranah GJ, Uitterlinden AG, Wilson JG, Gudnason V, Hoffman HJ, Williams FMK, Goedegebure Aet al., 2019, Genome-wide association meta-analysis identifies five novel loci for age-related hearing impairment, SCIENTIFIC REPORTS, Vol: 9, ISSN: 2045-2322

Journal article

Turcot V, Lu Y, Highland HM, Schurmann C, Justice AE, Fine RS, Bradfield JP, Esko T, Giri A, Graff M, Guo X, Hendricks AE, Karaderi T, Lempradl A, Locke AE, Mahajan A, Marouli E, Sivapalaratnam S, Young KL, Alfred T, Feitosa MF, Masca NGD, Manning AK, Medina-Gomez C, Mudgal P, Ng MCY, Reiner AP, Vedantam S, Willems SM, Winkler TW, Abecasis G, Aben KK, Alam DS, Alharthi SE, Allison M, Amouyel P, Asselbergs FW, Auer PL, Balkau B, Bang LE, Barroso I, Bastarache L, Benn M, Bergmann S, Bielak LF, Bluher M, Boehnke M, Boeing H, Boerwinkle E, Boger CA, Bork-Jensen J, Bots ML, Bottinger EP, Bowden DW, Brandslund I, Breen G, Brilliant MH, Broer L, Brumat M, Burt AA, Butterworth AS, Campbell PT, Cappellani S, Carey DJ, Catamo E, Caulfield MJ, Chambers JC, Chasman DI, Chen Y-DI, Chowdhury R, Christensen C, Chu AY, Cocca M, Collins FS, Cook JP, Corley J, Galbany JC, Cox AJ, Crosslin DS, Cuellar-Partida G, D'Eustacchio A, Danesh J, Davies G, Bakker PIW, Groot MCH, Mutsert R, Deary IJ, Dedoussis G, Demerath EW, Heijer M, Hollander AI, Ruijter HM, Dennis JG, Denny JC, Di Angelantonio E, Drenos F, Du M, Dube M-P, Dunning AM, Easton DF, Edwards TL, Ellinghaus D, Ellinor PT, Elliott P, Evangelou E, Farmaki A-E, Farooqi IS, Faul JD, Fauser S, Feng S, Ferrannini E, Ferrieres J, Florez JC, Ford I, Fornage M, Franco OH, Franke A, Franks PW, Friedrich N, Frikke-Schmidt R, Galesloot TE, Gan W, Gandin I, Gasparini P, Gibson J, Giedraitis V, Gjesing AP, Gordon-Larsen P, Gorski M, Grabe H-J, Grant SFA, Grarup N, Griffiths HL, Grove ML, Gudnason V, Gustafsson S, Haessler J, Hakonarson H, Hammerschlag AR, Hansen T, Harris KM, Harris TB, Hattersley AT, Have CT, Hayward C, He L, Heard-Costa NL, Heath AC, Heid IM, Helgeland O, Hernesniemi J, Hewitt AW, Holmen OL, Hovingh GK, Howson JMM, Hu Y, Huang PL, Huffman JE, Ikram MA, Ingelsson E, Jackson AU, Jansson J-H, Jarvik GP, Jensen GB, Jia Y, Johansson S, Jorgensen ME, Jorgensen T, Jukema JW, Kahali B, Kahn RS, Kahonen M, Kamstrup PR, Kanoni S, Kapriet al., 2019, Protein-altering variants associated with body mass index implicate pathways that control energy intake and expenditure in obesity, Nature Genetics, Vol: 51, Pages: 1191-1192, ISSN: 1061-4036

In the HTML version of this article initially published, the author groups ‘CHD Exome+ Consortium’, ‘EPIC-CVD Consortium’, ‘ExomeBP Consortium’, ‘Global Lipids Genetic Consortium’, ‘GoT2D Genes Consortium’, ‘EPIC InterAct Consortium’, ‘INTERVAL Study’, ‘ReproGen Consortium’, ‘T2D-Genes Consortium’, ‘The MAGIC Investigators’ and ‘Understanding Society Scientific Group’ appeared at the end of the author list but should have appeared earlier in the list, after author Krina T. Zondervan. The errors have been corrected in the HTML version of the article.

Journal article

Hysi PG, Valdes AM, Liu F, Furlotte NA, Evans DM, Bataille V, Visconti A, Hemani G, McMahon G, Ring SM, Smith GD, Duffy DL, Zhu G, Gordon SD, Medland SE, Lin BD, Willemsen G, Hottenga JJ, Vuckovic D, Girotto G, Gandin I, Sala C, Concas MP, Brumat M, Gasparini P, Toniolo D, Cocca M, Robino A, Yazar S, Hewitt AW, Chen Y, Zeng C, Uitterlinden AG, Ikram MA, Hamer MA, van Duijn CM, Nijsten T, Mackey DA, Falchi M, Boomsma DI, Martin NG, Hinds DA, Kayser M, Spector TDet al., 2019, Genome-wide association meta-analysis of individuals of European ancestry identifies new loci explaining a substantial fraction of hair color variation and heritability (vol 50, pg 652, 2018), NATURE GENETICS, Vol: 51, Pages: 1190-1190, ISSN: 1061-4036

Journal article

de Vries PS, Brown MR, Bentley AR, Sung YJ, Winkler TW, Ntalla I, Schwander K, Kraja AT, Guo X, Franceschini N, Cheng C-Y, Sim X, Vojinovic D, Huffman JE, Musani SK, Li C, Feitosa MF, Richard MA, Noordam R, Aschard H, Bartz TM, Bielak LF, Deng X, Dorajoo R, Lohman KK, Manning AK, Rankinen T, Smith AV, Tajuddin SM, Evangelou E, Graff M, Alver M, Boissel M, Chai JF, Chen X, Divers J, Gandin I, Gao C, Goel A, Hagemeijer Y, Harris SE, Hartwig FP, He M, Horimoto ARVR, Hsu F-C, Jackson AU, Kasturiratne A, Komulainen P, Kühnel B, Laguzzi F, Lee JH, Luan J, Lyytikäinen L-P, Matoba N, Nolte IM, Pietzner M, Riaz M, Said MA, Scott RA, Sofer T, Stancáková A, Takeuchi F, Tayo BO, van der Most PJ, Varga TV, Wang Y, Ware EB, Wen W, Yanek LR, Zhang W, Zhao JH, Afaq S, Amin N, Amini M, Arking DE, Aung T, Ballantyne C, Boerwinkle E, Broeckel U, Campbell A, Canouil M, Charumathi S, Chen Y-DI, Connell JM, de Faire U, de Las Fuentes L, de Mutsert R, de Silva HJ, Ding J, Dominiczak AF, Duan Q, Eaton CB, Eppinga RN, Faul JD, Fisher V, Forrester T, Franco OH, Friedlander Y, Ghanbari M, Giulianini F, Grabe HJ, Grove ML, Gu CC, Harris TB, Heikkinen S, Heng C-K, Hirata M, Hixson JE, Howard BV, Ikram MA, InterAct Consortium, Jacobs DR, Johnson C, Jonas JB, Kammerer CM, Katsuya T, Khor CC, Kilpeläinen TO, Koh W-P, Koistinen HA, Kolcic I, Kooperberg C, Krieger JE, Kritchevsky SB, Kubo M, Kuusisto J, Lakka TA, Langefeld CD, Langenberg C, Launer LJ, Lehne B, Lemaitre RN, Li Y, Liang J, Liu J, Liu K, Loh M, Louie T, Mägi R, Manichaikul AW, McKenzie CA, Meitinger T, Metspalu A, Milaneschi Y, Milani L, Mohlke KL, Mosley TH, Mukamal KJ, Nalls MA, Nauck M, Nelson CP, Sotoodehnia N, O'Connell JR, Palmer ND, Pazoki R, Pedersen NL, Peters A, Peyser PA, Polasek O, Poulter N, Raffel LJ, Raitakari OT, Reiner AP, Rice TK, Rich SS, Robino A, Robinson JG, Rose LM, Rudan I, Schmidt CO, Schreiner PJ, Scott WR, Sever P, Shi Y, Sidney S, Sims M, Smith BH, Smith JA, Snieder H, Starr JM, Strauch K, Tan N, Taylor KDet al., 2019, Multi-ancestry genome-wide association study of lipid levels incorporating gene-alcohol interactions, American Journal of Epidemiology, Vol: 188, Pages: 1033-1054, ISSN: 1476-6256

An individual's lipid profile is influenced by genetic variants and alcohol consumption, but the contribution of interactions between these exposures has not been studied. We therefore incorporated gene-alcohol interactions into a multi-ancestry genome-wide association study of levels of high-density lipoprotein cholesterol, low-density lipoprotein cholesterol, and triglycerides. We included 45 studies in Stage 1 (genome-wide discovery) and 66 studies in Stage 2 (focused follow-up), for a total of 394,584 individuals from five ancestry groups. Genetic main and interaction effects were jointly assessed by a 2 degrees of freedom (DF) test, and a 1 DF test was used to assess the interaction effects alone. Variants at 495 loci were at least suggestively associated (P < 1 × 10-6) with lipid levels in Stage 1 and were evaluated in Stage 2, followed by combined analyses of Stage 1 and Stage 2. In the combined analysis of Stage 1 and Stage 2, 147 independent loci were associated with lipid levels at P < 5 × 10-8 using 2 DF tests, of which 18 were novel. No genome-wide significant associations were found testing the interaction effect alone. The novel loci included several genes (PCSK5, VEGFB, and A1CF) with a putative role in lipid metabolism based on existing evidence from cellular and experimental models.

Journal article

Justice AE, Karaderi T, Highland HM, Young KL, Graff M, Lu Y, Turcot V, Auer PL, Fine RS, Guo X, Schurmann C, Lempradl A, Marouli E, Mahajan A, Winkler TW, Locke AE, Medina-Gomez C, Esko T, Vedantam S, Giri A, Lo KS, Alfred T, Mudgal P, Ng MCY, Heard-Costa NL, Feitosa MF, Manning AK, Willems SM, Sivapalaratnam S, Abecasis G, Alam DS, Allison M, Amouyel P, Arzumanyan Z, Balkau B, Bastarache L, Bergmann S, Bielak LF, Blüher M, Boehnke M, Boeing H, Boerwinkle E, Böger CA, Bork-Jensen J, Bottinger EP, Bowden DW, Brandslund I, Broer L, Burt AA, Butterworth AS, Caulfield MJ, Cesana G, Chambers JC, Chasman DI, Chen Y-DI, Chowdhury R, Christensen C, Chu AY, Collins FS, Cook JP, Cox AJ, Crosslin DS, Danesh J, de Bakker PIW, Denus SD, Mutsert RD, Dedoussis G, Demerath EW, Dennis JG, Denny JC, Angelantonio ED, Dörr M, Drenos F, Dubé M-P, Dunning AM, Easton DF, Elliott P, Evangelou E, Farmaki A-E, Feng S, Ferrannini E, Ferrieres J, Florez JC, Fornage M, Fox CS, Franks PW, Friedrich N, Gan W, Gandin I, Gasparini P, Giedraitis V, Girotto G, Gorski M, Grallert H, Grarup N, Grove ML, Gustafsson S, Haessler J, Hansen T, Hattersley AT, Hayward C, Heid IM, Holmen OL, Hovingh GK, Howson JMM, Hu Y, Hung Y-J, Hveem K, Ikram MA, Ingelsson E, Jackson AU, Jarvik GP, Jia Y, Jørgensen T, Jousilahti P, Justesen JM, Kahali B, Karaleftheri M, Kardia SLR, Karpe F, Kee F, Kitajima H, Komulainen P, Kooner JS, Kovacs P, Krämer BK, Kuulasmaa K, Kuusisto J, Laakso M, Lakka TA, Lamparter D, Lange LA, Langenberg C, Larson EB, Lee NR, Lee W-J, Lehtimäki T, Lewis CE, Li H, Li J, Li-Gao R, Lin L-A, Lin X, Lind L, Lindström J, Linneberg A, Liu C-T, Liu DJ, Luan J, Lyytikäinen L-P, MacGregor S, Mägi R, Männistö S, Marenne G, Marten J, Masca NGD, McCarthy MI, Meidtner K, Mihailov E, Moilanen L, Moitry M, Mook-Kanamori DO, Morgan A, Morris AP, Müller-Nurasyid M, Munroe PB, Narisu N, Nelson CP, Neville M, Ntalla I, O'Connell JR, Owen KR, Pedersen O, Peloso GM, Pennell CE, Perola M, Perry JA, Perry JRB, Pers THet al., 2019, Protein-coding variants implicate novel genes related to lipid homeostasis contributing to body-fat distribution, Nature Genetics, Vol: 51, Pages: 452-469, ISSN: 1061-4036

Body-fat distribution is a risk factor for adverse cardiovascular health consequences. We analyzed the association of body-fat distribution, assessed by waist-to-hip ratio adjusted for body mass index, with 228,985 predicted coding and splice site variants available on exome arrays in up to 344,369 individuals from five major ancestries (discovery) and 132,177 European-ancestry individuals (validation). We identified 15 common (minor allele frequency, MAF ≥5%) and nine low-frequency or rare (MAF <5%) coding novel variants. Pathway/gene set enrichment analyses identified lipid particle, adiponectin, abnormal white adipose tissue physiology and bone development and morphology as important contributors to fat distribution, while cross-trait associations highlight cardiometabolic traits. In functional follow-up analyses, specifically in Drosophila RNAi-knockdowns, we observed a significant increase in the total body triglyceride levels for two genes (DNAH10 and PLXND1). We implicate novel genes in fat distribution, stressing the importance of interrogating low-frequency and protein-coding variants.

Journal article

Linner RK, Biroli P, Kong E, Meddens FW, Wedow R, Fontana MA, Lebreton M, Tino SP, Abdellaoui A, Hammerschlag AR, Nivard MG, Okbay A, Rietveld CA, Timshel PN, Trzaskowski M, de Vlaming R, Zund CL, Bao Y, Buzdugan L, Caplin AH, Chen C-Y, Eibich P, Fontanillas P, Gonzalez JR, Joshi PK, Karhunen V, Kleinman A, Levin RZ, Lill CM, Meddens GA, Muntane G, Sanchez-Roige S, van Rooij FJ, Taskesen E, Wu Y, Zhang F, Agee M, Alipanahi B, Bell RK, Bryc K, Elson SL, Furlotte NA, Huber KE, Litterman NK, McCreight JC, McIntyre MH, Mountain JL, Northover CAM, Pitts SJ, Sathirapongsasuti JF, Sazonova OV, Shelton JF, Shringarpure S, Tian C, Tung JY, Vacic V, Wilson CH, Agbessi M, Ahsan H, Alves I, Andiappan A, Awadalla P, Battle A, Beutner F, Bonder MJ, Boomsma DI, Christiansen M, Claringbould A, Deelen P, Esko T, Fave M-J, Franke L, Frayling T, Gharib SA, Gibson G, Heijmans B, Hemani G, Jansen R, Kahonen M, Kalnapenkis A, Kasela S, Kettunen J, Kim Y, Kirsten H, Kovacs P, Krohn K, Kronberg-Guzman J, Kukushkina V, Kutalik Z, Lee B, Lehtimaki T, Loeffler M, Marigorta UM, Metspalu A, Milani L, Montgomery GW, Mueller-Nurasyid M, Nauck M, Penninx B, Perola M, Pervjakova N, Pierce B, Powell J, Prokisch H, Psaty BM, Raitakari O, Ring S, Ripatti S, Rotzchke O, Rueger S, Saha A, Scholz M, Schramm K, Seppala I, Stumvoll M, Sullivan P, Hoen P-B, Teumer A, Thiery J, Tong L, Tonjes A, van Dongen J, van Meurs J, Verlouw J, Visscher PM, Voelker U, Vosa U, Westra H-J, Yaghootkar H, Yang J, Zeng B, Lee JJ, Pers TH, Turley P, Chen G-B, Emilsson V, Oskarsson S, Pickrell JK, Thom K, Timshel P, Ahluwalia TS, Bacelis J, Baumbach C, Bjornsdottir G, Brandsma JH, Concas MP, Derringer J, Furlotte NA, Galesloot TE, Girotto G, Gupta R, Hall LM, Harris SE, Hofer E, Horikoshi M, Huffman JE, Kaasik K, Kalafati IP, Kong A, Lahti J, van der Lee SJ, de Leeuw C, Lind PA, Lindgren K-O, Liu T, Mangino M, Marten J, Mihailov E, Miller MB, van der Most PJ, Oldmeadow C, Payton A, Pervjakova N, Peyrot WJ, Qian Y, Raitakari Oet al., 2019, Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences, NATURE GENETICS, Vol: 51, Pages: 245-+, ISSN: 1061-4036

Journal article

Morgan A, Vuckovic D, Krishnamoorthy N, Rubinato E, Ambrosetti U, Castorina P, Franzè A, Vozzi D, La Bianca M, Cappellani S, Di Stazio M, Gasparini P, Girotto Get al., 2019, Next-generation sequencing identified SPATC1L as a possible candidate gene for both early-onset and age-related hearing loss, European Journal of Human Genetics, Vol: 27, Pages: 70-79, ISSN: 1018-4813

Hereditary hearing loss (HHL) and age-related hearing loss (ARHL) are two major sensory diseases affecting millions of people worldwide. Despite many efforts, additional HHL-genes and ARHL genetic risk factors still need to be identified. To fill this gap a large genomic screening based on next-generation sequencing technologies was performed. Whole exome sequencing in a 3-generation Italian HHL family and targeted re-sequencing in 464 ARHL patients were performed. We detected three variants in SPATC1L: a nonsense allele in an HHL family and a frameshift insertion and a missense variation in two unrelated ARHL patients. In silico molecular modelling of all variants suggested a significant impact on the structural stability of the protein itself, likely leading to deleterious effects and resulting in truncated isoforms. After demonstrating Spatc1l expression in mice inner ear, in vitro functional experiments were performed confirming the results of the molecular modelling studies. Finally, a candidate-gene population-based statistical study in cohorts from Caucasus and Central Asia revealed a statistically significant association of SPATC1L with normal hearing function at low and medium hearing frequencies. Overall, the amount of different genetic data presented here (variants with early-onset and late-onset hearing loss in addition to genetic association with normal hearing function), together with relevant functional evidence, likely suggest a role of SPATC1L in hearing function and loss.

Journal article

Evangelou E, Warren HR, Mosen-Ansorena D, Mifsud B, Pazoki R, Gao H, Ntritsos G, Dimou N, Cabrera CP, Karaman I, Fu LN, Evangelou M, Witkowska K, Tzanis E, Hellwege JN, Giri A, Edwards DRV, Sun YV, Cho K, Gaziano JM, Wilson PWF, Tsao PS, Kovesdy CP, Esko T, Magi R, Milani L, Almgren P, Boutin T, Debette S, Ding J, Giulianini F, Holliday EG, Jackson AU, Li-Gao R, Lin W-Y, Luan J, Mangino M, Oldmeadow C, Prins BP, Qian Y, Sargurupremraj M, Shah N, Surendran P, Theriault S, Verweij N, Willems SM, Zhao J-H, Amouyel P, Connell J, de Mutsert R, Doney ASF, Farrall M, Menni C, Morris AD, Noordam R, Pare G, Poulter NR, Shields DC, Stanton A, Thom S, Abecasis G, Amin N, Arking DE, Ayers KL, Barbieri CM, Batini C, Bis JC, Blake T, Bochud M, Boehnke M, Boerwinkle E, Boomsma DI, Bottinger EP, Braund PS, Brumat M, Campbell A, Campbell H, Chakravarti A, Chambers JC, Chauhan G, Ciullo M, Cocca M, Collins F, Cordell HJ, Davies G, de Borst MH, de Geus EJ, Deary IJ, Deelen J, Del Greco FM, Demirkale CY, Dorr M, Ehret GB, Elosua R, Enroth S, Erzurumluoglu AM, Ferreira T, Franberg M, Franco OH, Gandin I, Gasparini P, Giedraitis V, Gieger C, Girotto G, Goel A, Gow AJ, Gudnason V, Guo X, Gyllensten U, Hamsten A, Harris TB, Harris SE, Hartman CA, Havulinna AS, Hicks AA, Hofer E, Hofman A, Hottenga J-J, Huffman JE, Hwang S-J, Ingelsson E, James A, Jansen R, Jarvelin M-R, Joehanes R, Johansson A, Johnson AD, Joshi PK, Jousilahti P, Jukema JW, Jula A, Kahonen M, Kathiresan S, Keavney BD, Khaw K-T, Knekt P, Knight J, Kolcic I, Kooner JS, Koskinen S, Kristiansson K, Kutalik Z, Laan M, Larson M, Launer LJ, Lehne B, Lehtimaki T, Liewald DCM, Lin L, Lind L, Lindgren CM, Liu Y, Loos RJF, Lopez LM, Lu Y, Lyytikainen L-P, Mahajan A, Mamasoula C, Marrugat J, Marten J, Milaneschi Y, Morgan A, Morris AP, Morrison AC, Munson PJ, Nalls MA, Nandakumar P, Nelson CP, Niiranen T, Nolte IM, Nutile T, Oldehinkel AJ, Oostra BA, O'Reilly PF, Org E, Padmanabhan S, Palmas W, Palotie A, Pattie A, Penninx BWJH, Perolet al., 2018, Genetic analysis of over 1 million people identifies 535 new loci associated with blood pressure traits (vol 50, pg 1412, 2018), NATURE GENETICS, Vol: 50, Pages: 1755-1755, ISSN: 1061-4036

Journal article

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