Browsing by Author "Tromp, Gerard"

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    1000 Genomes-based metaanalysis identifies 10 novel loci for kidney function
    (Nature Research, 2017) Gorski, Mathias; Van Der Most, Peter J.; Teumer, Alexander; Chu, Audrey Y.; Li, Man; Mijatovic, Vladan; Nolte, Ilja M.; Cocca, Massimiliano; Taliun, Daniel; Gomez, Felicia; Li, Yong; Tayo, Bamidele; Tin, Adrienne; Feitosa, Mary F.; Aspelund, Thor; Attia, John; Biffar, Reiner; Bochud, Murielle; Boerwinkle, Eric; Borecki, Ingrid; Bottinger, Erwin P.; Chen, Ming-Huei; Chouraki, Vincent; Ciullo, Marina; Coresh, Josef; Cornelis, Marilyn C.; Curhan, Gary C.; d’Adamo, Adamo Pio; Dehghan, Abbas; Dengler, Laura; Ding, Jingzhong; Eiriksdottir, Gudny; Endlich, Karlhans; Enroth, Stefan; Esko, Tonu; Franco, Oscar H.; Gasparini, Paolo; Gieger, Christian; Girotto, Giorgia; Gottesman, Omri; Gudnason, Vilmundur; Gyllensten, Ulf; Hancock, Stephen J.; Harris, Tamara B.; Helmer, Catherine; Hollerer, Simon; Hofer, Edith; Hofman, Albert; Holliday, Elizabeth G.; Homuth, Georg; Hu, Frank B.; Huth, Cornelia; Hutri-Kahonen, Nina; Hwang, Shih-Jen; Imboden, Medea; Johansson, Asa; Kahonen, Mika; Konig, Wolfgang; Kramer, Holly; Kramer, Bernhard K.; Kumar, Ashish; Kutalik, Zoltan; Lambert, Jean-Charles; Launer, Lenore J.; Lehtimaki, Terho; De Borst, Martin H.; Navis, Gerjan; Swertz, Morris; Liu, Yongmei; Lohman, Kurt; Loos, Ruth J. F.; Lu, Yingchang; Lyytikainen, Leo-Pekka; McEvoy, Mark A.; Meisinger, Christa; Meitinger, Thomas; Metspalu, Andres; Metzger, Marie; Mihailov, Evelin; Mitchell, Paul; Nauck, Matthias; Oldehinkel, Albertine J.; Olden, Matthias; Penninx, Brenda W. J. H.; Pistis, Giorgio; Pramstaller, Peter P.; Probst-Hensch, Nicole; Raitakari, Olli T.; Rettig, Rainer; Ridker, Paul M.; Rivadeneira, Fernando; Robino, Antonietta; Rosas, Sylvia E.; Ruderfer, Douglas; Ruggiero, Daniela; Saba, Yasaman; Sala, Cinzia; Schmidt, Helena; Schmidt, Reinhold; Scott, Rodney J.; Sedaghat, Sanaz; Smith, Albert V.; Sorice, Rossella; Stengel, Benedicte; Stracke, Sylvia; Strauch, Konstantin; Toniolo, Daniela; Uitterlinden, Andre G.; Ulivi, Sheila; Viikari, Jorma S.; Volker, Uwe; Vollenweider, Peter; Volzke, Henry; Vuckovic, Dragana; Waldenberger, Melanie; Wang, Jie Jin; Yang, Qiong; Chasman, Daniel I.; Tromp, Gerard; Snieder, Harold; Heid, Iris M.; Fox, Caroline S.; Kottgen, Anna; Pattaro, Cristian; Boger, Carsten A.; Fuchsberger, Christian
    HapMap imputed genome-wide association studies (GWAS) have revealed >50 loci at which common variants with minor allele frequency >5% are associated with kidney function. GWAS using more complete reference sets for imputation, such as those from The 1000 Genomes project, promise to identify novel loci that have been missed by previous efforts. To investigate the value of such a more complete variant catalog, we conducted a GWAS meta-analysis of kidney function based on the estimated glomerular filtration rate (eGFR) in 110,517 European ancestry participants using 1000 Genomes imputed data. We identified 10 novel loci with p-value < 5 × 10−8 previously missed by HapMap-based GWAS. Six of these loci (HOXD8, ARL15, PIK3R1, EYA4, ASTN2, and EPB41L3) are tagged by common SNPs unique to the 1000 Genomes reference panel. Using pathway analysis, we identified 39 significant (FDR < 0.05) genes and 127 significantly (FDR < 0.05) enriched gene sets, which were missed by our previous analyses. Among those, the 10 identified novel genes are part of pathways of kidney development, carbohydrate metabolism, cardiac septum development and glucose metabolism. These results highlight the utility of re-imputing from denser reference panels, until whole-genome sequencing becomes feasible in large samples.
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    Appetitive and reactive aggression are differentially associated with the STin2 genetic variant in the serotonin transporter gene
    (Nature Research (part of Springer Nature), 2018-04) Hemmings, Sian M. J.; Xulu, Khethelo; Sommer, Jessica; Hinsberger, Martina; Malan-Muller, Stefanie; Tromp, Gerard; Elbert, Thomas; Weierstall, Roland; Seedat, Soraya
    Appetitive aggression is a sub-category of instrumental aggression, characterised by the primary intrinsic enjoyment of aggressive activity. Aggression is heritable, and serotonergic and monoaminergic neurotransmitter systems have been found to contribute to the underlying molecular mechanisms. The aim of this study was to investigate the role that genetic variants in the serotonin transporter (SLC6A4) and monoamine oxidase A (MAOA) genes play in the aetiology of appetitive aggression in South African Xhosa males (n = 290). SLC6A4 5-HTTLPR, rs25531, and STin2 variants, as well as MAOA-uVNTR were investigated for their association with levels of appetitive aggression using Poisson regression analysis. The STin2 VNTR12 allele was found to be associated with increased levels of appetitive aggression (p = 0.003), but with decreased levels of reactive aggression (p = 7 × 10−5). This study is the first to investigate genetic underpinnings of appetitive aggression in a South African population, with preliminary evidence suggesting that SCL6A4 STin2 variants play a role in its aetiology, and may also be important in differentiating between appetitive and reactive aggression. Although the results require replication, they shed some preliminary light on the molecular dichotomy that may underlie the two forms of aggression.
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    The complete genome sequence of the African buffalo (Syncerus caffer)
    (BioMed Central, 2016-12-07) Glanzmann, Brigitte; Moller, Marlo; Le Roex, Nikki; Tromp, Gerard; Hoal, Eileen G.; Van Helden, Paul D.
    Background: The African buffalo (Syncerus caffer) is an important role player in the savannah ecosystem. It has become a species of relevance because of its role as a wildlife maintenance host for an array of infectious and zoonotic diseases some of which include corridor disease, foot-and-mouth disease and bovine tuberculosis. To date, no complete genome sequence for S. caffer had been available for study and the genomes of other species such as the domestic cow (Bos taurus) had been used as a proxy for any genetics analysis conducted on this species. Here, the high coverage genome sequence of the African buffalo (S. caffer) is presented. Results: A total of 19,765 genes were predicted and 19,296 genes could be successfully annotated to S. caffer while 469 genes remained unannotated. Moreover, in order to extend a detailed annotation of S. caffer, gene clusters were constructed using twelve additional mammalian genomes. The S. caffer genome contains 10,988 gene clusters, of which 62 are shared exclusively between B. taurus and S. caffer. Conclusions: This study provides a unique genomic perspective for the S. caffer, allowing for the identification of novel variants that may play a role in the natural history and physiological adaptations.
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    Detection of tuberculosis recurrence, diagnosis and treatment response by a blood transcriptomic risk signature in HIV-infected persons on antiretroviral therapy
    (Frontiers Media, 2019) Darboe, Fatoumatta; Mbandi, Stanley Kimbung; Naidoo, Kogieleum; Yende-Zuma, Nonhlanhla; Lewis, Lara; Thompson, Ethan G.; Duffy, Fergal J.; Fishe, Michelle; Filander, Elizabeth; Van Rooyen, Michele; Bilek, Nicole; Mabwe, Simbarashe; McKinnon, Lyle R.; Chegou, Novel; Loxton, Andre; Walzl, Gerhard; Tromp, Gerard; Padayatchi, Nesri; Govender, Dhineshree; Hatherill, Mark; Karim, Salim Abdool; Zak, Daniel E.; Penn-Nicholson, Adam; Scriba, Thomas J.; SATVI Clinical Immunology Team
    ENGLISH ABSTRACT: HIV-infected individuals are at high risk of tuberculosis disease and those with prior tuberculosis episodes are at even higher risk of disease recurrence. A non-sputum biomarker that identifies individuals at highest tuberculosis risk would allow targeted microbiological testing and appropriate treatment and also guide need for prolonged therapy. We determined the utility of a previously developed whole blood transcriptomic correlate of risk (COR) signature for (1) predicting incident recurrent tuberculosis, (2) tuberculosis diagnosis and (3) its potential utility for tuberculosis treatment monitoring in HIV-infected individuals. We retrieved cryopreserved blood specimens from three previously completed clinical studies and measured the COR signature by quantitative microfluidic real-time-PCR. The signature differentiated recurrent tuberculosis progressors from non-progressors within 3 months of diagnosis with an area under the Receiver-operating characteristic (ROC) curve (AUC) of 0.72 (95% confidence interval (CI), 0.58–0.85) amongst HIV-infected individuals on antiretroviral therapy (ART). Twenty-five of 43 progressors (58%) were asymptomatic at microbiological diagnosis and thus had subclinical disease. The signature showed excellent diagnostic discrimination between HIV-uninfected tuberculosis cases and controls (AUC 0.97; 95%CI 0.94–1). Performance was lower in HIV-infected individuals (AUC 0.83; 95%CI 0.81–0.96) and signature scores were directly associated with HIV viral loads. Tuberculosis treatment response in HIV-infected individuals on ART with a new recurrent tuberculosis diagnosis was also assessed. Signature scores decreased significantly during treatment. However, pre-treatment scores could not differentiate between those who became sputum negative before and after 2 months. Direct application of the unmodified blood transcriptomic COR signature detected subclinical and active tuberculosis by blind validation in HIV-infected individuals. However, prognostic performance for recurrent tuberculosis, and performance as diagnostic and as treatment monitoring tool in HIV-infected persons was inferior to published results from HIV-negative cohorts. Our results suggest that performance of transcriptomic signatures comprising interferon stimulated genes are negatively affected in HIV-infected individuals, especially in those with incompletely suppressed viral loads.
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    Discovery and replication of SNP-SNP interactions for quantitative lipid traits in over 60,000 individuals
    (BioMed Central, 2017) Holzinger, Emily R.; Verma, Shefali S.; Moore, Carrie B.; Hall, Molly; De, Rishika; Gilbert-Diamond, Diane; Lanktree, Matthew B.; Pankratz, Nathan; Amuzu, Antoinette; Burt, Amber; Dale, Caroline; Dudek, Scott; Furlong, Clement E.; Gaunt, Tom R.; Kim, Daniel Seung; Riess, Helene; Sivapalaratnam, Suthesh; Tragante, Vinicius; Van Iperen, Erik P. A.; Brautba, Ariel; Carrell, David S.; Crosslin, David R.; Jarvik, Gail P.; Kuivaniemi, Helena; Kullo, Iftikhar J.; Larson, Eric B.; Rasmussen-Torvik, Laura J.; Tromp, Gerard; Baumert, Jens; Cruickshanks, Karen J.; Farrall, Martin; Hingorani, Aroon D.; Hovingh, G. K.; Kleber, Marcus E.; Klein, Barbara E.; Klein, Ronald; Koenig, Wolfgang; Lange, Leslie A.; Mӓrz, Winfried; North, Kari E.; Onland-Moret, N. Charlotte; Reiner, Alex P.; Talmud, Philippa J.; Van Der Schouw, Yvonne T.; Wilson, James G.; Kivimaki, Mika; Kumari, Meena; Moore, Jason H.; Drenos, Fotios; Asselbergs, Folkert W.; Keating, Brendan J.; Ritchie, Marylyn D.
    Background: The genetic etiology of human lipid quantitative traits is not fully elucidated, and interactions between variants may play a role. We performed a gene-centric interaction study for four different lipid traits: low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), total cholesterol (TC), and triglycerides (TG). Results: Our analysis consisted of a discovery phase using a merged dataset of five different cohorts (n = 12,853 to n = 16,849 depending on lipid phenotype) and a replication phase with ten independent cohorts totaling up to 36,938 additional samples. Filters are often applied before interaction testing to correct for the burden of testing all pairwise interactions. We used two different filters: 1. A filter that tested only single nucleotide polymorphisms (SNPs) with a main effect of p < 0.001 in a previous association study. 2. A filter that only tested interactions identified by Biofilter 2.0. Pairwise models that reached an interaction significance level of p < 0.001 in the discovery dataset were tested for replication. We identified thirteen SNP-SNP models that were significant in more than one replication cohort after accounting for multiple testing. Conclusions: These results may reveal novel insights into the genetic etiology of lipid levels. Furthermore, we developed a pipeline to perform a computationally efficient interaction analysis with multi-cohort replication.
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    Distinct host-immune response toward species related intracellular mycobacterial killing : a transcriptomic study
    (Taylor & Francis, 2020) Madhvi, Abhilasha; Mishra, Hridesh; Chegoua, Novel N.; Tromp, Gerard; Van Heerden, Carel J.; Pietersen, R. D.; Leisching, Gina; Baker, Bienyameen
    The comparison of the host immune response when challenged with pathogenic and nonpatho- genic species of mycobacteria can provide answers to the unresolved question of how pathogens subvert or inhibit an effective response. We infected human monocyte derived macrophages (hMDMs) with different species of mycobacteria, in increasing order of pathogenicity, i.e. M. smegmatis, M. bovis BCG, and M. tuberculosis R179 that had been cultured in the absence of detergents. RNA was isolated post-infection and transcriptomic analysis using amplicons (Ampliseq) revealed 274 differentially expressed genes (DEGs) across three species, out of which we selected 19 DEGs for further validation. We used qRT-PCR to confirm the differential expression of 19 DEGs. We studied biological network through Ingenuity Pathway Analysis® (IPA) which revealed up-regulated pathways of the interferon and interleukin family related to the killing of M. smegmatis. Apart from interferon and interleukin family, we found one up-regulated (EIF2AK2) and two down-regulated (MT1A and TRIB3) genes as unique potential targets found by Ampliseq and qRT-PCR which may be involved in the intracellular mycobacterial killing. The roles of these genes have not previously been described in tuberculosis. Multiplex ELISA of culture supernatants showed increased host immune response toward M. smegmatis as compared to M. bovis BCG and M.tb R179. These results enhance our understanding of host immune response against M.tb infection.
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    Distinct serum biosignatures are associated with different tuberculosis treatment outcomes
    (Elsevier, 2019) Ronachera, Katharina; Chegoua, Novel N.; Kleynhansa, Leanie; Siawayac, Joel F. Djoba; Du Plessis, Nelita; Loxton, Andre G.; Maasdorp, Elizna; Tromp, Gerard; Kidd, Martin; Stanleya, Kim; Kriela, Magdalena; Menezesa, Angela; Gutschmidta, Andrea; Van Der Spuya, Gian D.; Warrena, Robin M.; Dietzee, Reynaldo; Okweraf, Alphonse; Thielg, Bonnie; Belisleh, John T.; Cliffi, Jacqueline M.; Boomg, W. Henry; Johnsong, John L.; Van Heldena, Paul D.; Dockrelli, Hazel M.; Walzla, Gerhard
    ENGLISH ABSTRACT: Biomarkers for TB treatment response and outcome are needed. This study characterize changes in immune profiles during TB treatment, define biosignatures associated with treatment outcomes, and explore the feasibility of predictive models for relapse. Seventy-two markers were measured by multiplex cytokine array in serum samples from 78 cured, 12 relapsed and 15 failed treatment patients from South Africa before and during therapy for pulmonary TB. Promising biosignatures were evaluated in a second cohort from Uganda/Brazil consisting of 17 relapse and 23 cured patients. Thirty markers changed significantly with different response patterns during TB treatment in cured patients. The serum biosignature distinguished cured from relapse patients and a combination of two clinical (time to positivity in liquid culture and BMI) and four immunological parameters (TNF-β, sIL-6R, IL-12p40 and IP-10) at diagnosis predicted relapse with a 75% sensitivity (95%CI 0.38–1) and 85% specificity (95%CI 0.75–0.93). This biosignature was validated in an independent Uganda/Brazil cohort correctly classifying relapse patients with 83% (95%CI 0.58–1) sensitivity and 61% (95%CI 0.39–0.83) specificity. A characteristic biosignature with value as predictor of TB relapse was identified. The repeatability and robustness of these biomarkers require further validation in well-characterized cohorts.
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    Editorial: FDA-Approved Drug Repositioning for P-Glycoprotein Overexpressing Resistant Cancer
    (Frontiers Media S.A, 2021-03) Yoon, Sungpil; Wang, Xiaoju; Vongpunsawad, Sompong; Tromp, Gerard; Kuivaniemi, Helena
    Anticancer drugs are an essential part of cancer treatment. Cancer cells can, however, develop resistance to these drugs by e.g., P-glycoprotein 1 (P-gp) overexpression or accumulation of mutations in the genes part of growth signaling pathways, apoptotic pathways, or repair system. Intrinsically, metastatic cancers, advanced-stage cancers, or stem cell-like cancers are usually drug-resistant and difficult to treat using current anticancer drugs. The overexpression of P-gp, also known as multidrug resistance protein 1 (MDR1) or ATP-binding cassette sub-family B member 1 (ABCB1), is one of the well-known mechanisms of resistance to anticancer drugs. Stem cell-like cancers often overexpress P-gp on their membranes, which results in inefficient treatment using the currently available anticancer drugs (1). It is, therefore, important to investigate novel therapeutic options to treat the P-gp overexpressing drug-resistant cancer cells. Identifying the mechanisms for targeting these cancers can overcome the inefficiencies of current anticancer drugs and lead to better outcomes for patients with P-gp overexpressing cancers.
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    eMERGE Phenome-Wide Association Study (PheWAS) identifies clinical associations and pleiotropy for stop-gain variants
    (BioMed Central, 2016) Verma, Anurag; Verma, Shefali S.; Pendergrass, Sarah A.; Crawford, Dana C.; Crosslin, David R.; Kuivaniemi, Helena; Bush, William S.; Bradford, Yuki; Kullo, Iftikhar; Bielinski, Suzette J.; Li, Rongling; Denny, Joshua C.; Peissig, Peggy; Hebbring, Scott; De Andrade, Mariza; Ritchie, Marylyn D.; Tromp, Gerard
    Background: We explored premature stop-gain variants to test the hypothesis that variants, which are likely to have a consequence on protein structure and function, will reveal important insights with respect to the phenotypes associated with them. We performed a phenome-wide association study (PheWAS) exploring the association between a selected list of functional stop-gain genetic variants (variation resulting in truncated proteins or in nonsense-mediated decay) and an extensive group of diagnoses to identify novel associations and uncover potential pleiotropy. Results: In this study, we selected 25 stop-gain variants: 5 stop-gain variants with previously reported phenotypic associations, and a set of 20 putative stop-gain variants identified using dbSNP. For the PheWAS, we used data from the electronic MEdical Records and GEnomics (eMERGE) Network across 9 sites with a total of 41,057 unrelated patients. We divided all these samples into two datasets by equal proportion of eMERGE site, sex, race, and genotyping platform. We calculated single effect associations between these 25 stop-gain variants and ICD-9 defined case-control diagnoses. We also performed stratified analyses for samples of European and African ancestry. Associations were adjusted for sex, site, genotyping platform and the first three principal components to account for global ancestry. We identified previously known associations, such as variants in LPL associated with hyperglyceridemia indicating that our approach was robust. We also found a total of three significant associations with p < 0.01 in both datasets, with the most significant replicating result being LPL SNP rs328 and ICD-9 code 272. 1 “Disorder of Lipoid metabolism” (pdiscovery = 2.59x10-6, preplicating = 2.7x10-4). The other two significant replicated associations identified by this study are: variant rs1137617 in KCNH2 gene associated with ICD-9 code category 244 “Acquired Hypothyroidism” (pdiscovery = 5.31x103, preplicating = 1.15x10-3) and variant rs12060879 in DPT gene associated with ICD-9 code category 996 “Complications peculiar to certain specified procedures” (pdiscovery = 8. 65x103, preplicating = 4.16x10-3).
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    Epistatic gene-based interaction analyses for glaucoma in eMERGE and NEIGHBOR Consortium
    (Public Library of Science, 2016) Verma, Shefali Setia; Bailey, Jessica N. Cooke; Lucas, Anastasia; Bradford, Yuki; Linneman, James G.; Hauser, Michael A.; Pasquale, Louis R.; Peissig, Peggy L.; Brilliant, Murray H.; McCarty, Catherine A.; Haines, Jonathan L.; Wiggs, Janey L.; Vrabec, Tamara R.; Tromp, Gerard; Ritchie, Marylyn D.; eMERGE Network; NEIGHBOR Consortium
    Primary open angle glaucoma (POAG) is a complex disease and is one of the major leading causes of blindness worldwide. Genome-wide association studies have successfully identified several common variants associated with glaucoma; however, most of these variants only explain a small proportion of the genetic risk. Apart from the standard approach to identify main effects of variants across the genome, it is believed that gene-gene interactions can help elucidate part of the missing heritability by allowing for the test of interactions between genetic variants to mimic the complex nature of biology. To explain the etiology of glaucoma, we first performed a genome-wide association study (GWAS) on glaucoma case-control samples obtained from electronic medical records (EMR) to establish the utility of EMR data in detecting non-spurious and relevant associations; this analysis was aimed at confirming already known associations with glaucoma and validating the EMR derived glaucoma phenotype. Our findings from GWAS suggest consistent evidence of several known associations in POAG. We then performed an interaction analysis for variants found to be marginally associated with glaucoma (SNPs with main effect p-value <0.01) and observed interesting findings in the electronic MEdical Records and GEnomics Network (eMERGE) network dataset. Genes from the top epistatic interactions from eMERGE data (Likelihood Ratio Test i.e. LRT p-value <1e-05) were then tested for replication in the NEIGHBOR consortium dataset. To replicate our findings, we performed a gene-based SNP-SNP interaction analysis in NEIGHBOR and observed significant gene-gene interactions (p-value <0.001) among the top 17 gene-gene models identified in the discovery phase. Variants from gene-gene interaction analysis that we found to be associated with POAG explain 3.5% of additional genetic variance in eMERGE dataset above what is explained by the SNPs in genes that are replicated from previous GWAS studies (which was only 2.1% variance explained in eMERGE dataset); in the NEIGHBOR dataset, adding replicated SNPs from gene-gene interaction analysis explain 3.4% of total variance whereas GWAS SNPs alone explain only 2.8% of variance. Exploring gene-gene interactions may provide additional insights into many complex traits when explored in properly designed and powered association studies.
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    Evaluating the accuracy of imputation methods in a five-way admixed population
    (Frontiers Media, 2019) Schurz, Haiko; Muller, Stephanie J.; Van Helden, Paul David; Tromp, Gerard; Hoal, Eileen G.; Kinnear, Craig J.; Moller, Marlo
    Genotype imputation is a powerful tool for increasing statistical power in an association analysis. Meta-analysis of multiple study datasets also requires a substantial overlap of SNPs for a successful association analysis, which can be achieved by imputation. Quality of imputed datasets is largely dependent on the software used, as well as the reference populations chosen. The accuracy of imputation of available reference populations has not been tested for the five-way admixed South African Colored (SAC) population. In this study, imputation results obtained using three freely-accessible methods were evaluated for accuracy and quality. We show that the African Genome Resource is the best reference panel for imputation of missing genotypes in samples from the SAC population, implemented via the freely accessible Sanger Imputation Server.
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    Genetic association of lipids and lipid drug targets with abdominal aortic aneurysm : a meta-analysis
    (American Medical Association, 2018) Harrison, Seamus C.; Holmes, Michael V.; Burgess, Stephen; Asselbergs, Folkert W.; Jones, Gregory T.; Baas, Annette F.; Van 't Hof, F. N.; De Bakker, Paul I. W.; Blankensteijn, Jan D.; Powell, Janet T.; Saratzis, Athanasios; De Borst, Gert J.; Swerdlow, Daniel I.; Van der Graaf, Yolanda; Van Rij, Andre M.; Carey, David J.; Elmore, James R.; Tromp, Gerard; Kuivaniemi, Helena; Sayers, Robert D.; Samani, Nilesh J.; Bown, Matthew J.; Humphries, Steve E.
    Importance Risk factors for abdominal aortic aneurysm (AAA) are largely unknown, which has hampered the development of nonsurgical treatments to alter the natural history of disease. Objective To investigate the association between lipid-associated single-nucleotide polymorphisms (SNPs) and AAA risk. Design, Setting, and Participants Genetic risk scores, composed of lipid trait–associated SNPs, were constructed and tested for their association with AAA using conventional (inverse-variance weighted) mendelian randomization (MR) and data from international AAA genome-wide association studies. Sensitivity analyses to account for potential genetic pleiotropy included MR-Egger and weighted median MR, and multivariable MR method was used to test the independent association of lipids with AAA risk. The association between AAA and SNPs in loci that can act as proxies for drug targets was also assessed. Data collection took place between January 9, 2015, and January 4, 2016. Data analysis was conducted between January 4, 2015, and December 31, 2016. Exposures Genetic elevation of low-density lipoprotein cholesterol (LDL-C), high-density lipoprotein cholesterol (HDL-C), and triglycerides (TG). Main Outcomes and Measures The association between genetic risk scores of lipid-associated SNPs and AAA risk, as well as the association between SNPs in lipid drug targets (HMGCR, CETP, and PCSK9) and AAA risk. Results Up to 4914 cases and 48 002 controls were included in our analysis. A 1-SD genetic elevation of LDL-C was associated with increased AAA risk (odds ratio [OR], 1.66; 95% CI, 1.41-1.96; P = 1.1 × 10−9). For HDL-C, a 1-SD increase was associated with reduced AAA risk (OR, 0.67; 95% CI, 0.55-0.82; P = 8.3 × 10−5), whereas a 1-SD increase in triglycerides was associated with increased AAA risk (OR, 1.69; 95% CI, 1.38-2.07; P = 5.2 × 10−7). In multivariable MR analysis and both MR-Egger and weighted median MR methods, the association of each lipid fraction with AAA risk remained largely unchanged. The LDL-C–reducing allele of rs12916 in HMGCR was associated with AAA risk (OR, 0.93; 95% CI, 0.89-0.98; P = .009). The HDL-C–raising allele of rs3764261 in CETP was associated with lower AAA risk (OR, 0.89; 95% CI, 0.85-0.94; P = 3.7 × 10−7). Finally, the LDL-C–lowering allele of rs11206510 in PCSK9 was weakly associated with a lower AAA risk (OR, 0.94; 95% CI, 0.88-1.00; P = .04), but a second independent LDL-C–lowering variant in PCSK9 (rs2479409) was not associated with AAA risk (OR, 0.97; 95% CI, 0.92-1.02; P = .28). Conclusions and Relevance The MR analyses in this study lend support to the hypothesis that lipids play an important role in the etiology of AAA. Analyses of individual genetic variants used as proxies for drug targets support LDL-C lowering as a potential effective treatment strategy for preventing and managing AAA.
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    Multi-phenotype genome-wide association study of clades causing tuberculosis in a Ghanaian- and South African cohort
    (Elsevier Inc., 2021-04) Müller, Stephanie J.; Haiko, Schurz; Tromp, Gerard; Van der Spuy, Gian D.; Hoal, Eileen G.; Van Helden, Paul D.; Owusu-Dabo, Ellis; Meyer, Christian G.; Muntau, Birgit; Thye, Thorsten; Niemann, Stefan; Warren, Robin M.; Streicher, Elizabeth; Muller, Marlo; Kinnear, Craig
    Despite decades of research and advancements in diagnostics and treatment, tuberculosis remains a major public health concern. New computational methods are needed to interrogate the intersection of host- and bacterial genomes. Paired host genotype datum and infecting bacterial isolate information were analysed for associations using a multinomial logistic regression framework implemented in SNPTest. A cohort of 853 admixed South African participants and a Ghanaian cohort of 1359 participants were included. Two directly genotyped variants, namely rs529920 and rs41472447, were identified in the Ghanaian cohort as being statistically significantly associated with risk for infection with strains of different members of the MTBC. Thus, a multinomial logistic regression using paired host-pathogen data may prove valuable for investigating the complex relationships driving infectious disease.
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    Phenome-wide association study to explore relationships between immune system related genetic loci and complex traits and diseases
    (Public Library of Science, 2016) Verma, Anurag; Basile, Anna O.; Bradford, Yuki; Kuivaniemi, Helena; Tromp, Gerard; Carey, David; Gerhard, Glenn S.; Crowe, James E.; Ritchie, Marylyn D.; Pendergrass, Sarah A.
    This study highlights the utility of using PheWAS in conjunction with EHRs to discover new genotypic-phenotypic associations for immune-system related genetic loci.
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    Quantitative 18F-FDG PET-CT scan characteristics correlate with tuberculosis treatment response
    (SpringerOpen (part of Springer Nature), 2020-02-10) Malherbe, Stephanus T.; Chen, Ray Y.; Dupont, Patrick; Kant, Ilse; Kriel, Magdalena; Loxton, Andre G.; Smith, Bronwyn; Beltran, Caroline G. G.; Van Zyl, Susan; McAnda, Shirely; Abrahams, Charmaine; Maasdorp, Elizna; Doruyter, Alex; Via, Laura E.; Barry, Clifton E.; Alland, David; Richards, Stephanie G.; Ellman, Annare; Peppard, Thomas; Belisle, John; Tromp, Gerard; Ronacher, Katharina; Warwick, James M.; Winter, Jill; Walzl, Gerhard
    Background: There is a growing interest in the use of F-18 FDG PET-CT to monitor tuberculosis (TB) treatment response. Tuberculosis lung lesions are often complex and diffuse, with dynamic changes during treatment and persisting metabolic activity after apparent clinical cure. This poses a challenge in quantifying scan-based markers of burden of disease and disease activity. We used semi-automated, whole lung quantification of lung lesions to analyse serial FDG PET-CT scans from the Catalysis TB Treatment Response Cohort to identify characteristics that best correlated with clinical and microbiological outcomes. Results: Quantified scan metrics were already associated with clinical outcomes at diagnosis and 1 month after treatment, with further improved accuracy to differentiate clinical outcomes after standard treatment duration (month 6). A high cavity volume showed the strongest association with a risk of treatment failure (AUC 0.81 to predict failure at diagnosis), while a suboptimal reduction of the total glycolytic activity in lung lesions during treatment had the strongest association with recurrent disease (AUC 0.8 to predict pooled unfavourable outcomes). During the first year after TB treatment lesion burden reduced; but for many patients, there were continued dynamic changes of individual lesions. Conclusions: Quantification of FDG PET-CT images better characterised TB treatment outcomes than qualitative scan patterns and robustly measured the burden of disease. In future, validated metrics may be used to stratify patients and help evaluate the effectiveness of TB treatment modalities.
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    Quantitative 18F-FDG PET-CT scan characteristics correlate with tuberculosis treatment response
    (SpringerOpen (part of Springer Nature), 2020) Malherbe, Stephanus T.; Chen, Ray Y.; Dupont, Patrick; Kant, Ilse; Kriel, Magdalena; Loxton, Andre G.; Smith, Bronwyn; Beltran, Caroline G. G.; Van Zyl, Susan; McAnda, Shirely; Abrahams, Charmaine; Maasdorp, Elizna; Doruyter, Alex; Via, Laura E.; Barry, Clifton E.; Alland, David; Griffith- Richards, Stephanie; Ellman, Annare; Peppard, Thomas; Belisle, John; Tromp, Gerard; Ronacher, Katharina; Warwick, James M.; Winter, Jill; Walzl, Gerhard
    Background: There is a growing interest in the use of F-18 FDG PET-CT to monitor tuberculosis (TB) treatment response. Tuberculosis lung lesions are often complex and diffuse, with dynamic changes during treatment and persisting metabolic activity after apparent clinical cure. This poses a challenge in quantifying scan-based markers of burden of disease and disease activity. We used semi-automated, whole lung quantification of lung lesions to analyse serial FDG PET-CT scans from the Catalysis TB Treatment Response Cohort to identify characteristics that best correlated with clinical and microbiological outcomes. Results: Quantified scan metrics were already associated with clinical outcomes at diagnosis and 1 month after treatment, with further improved accuracy to differentiate clinical outcomes after standard treatment duration (month 6). A high cavity volume showed the strongest association with a risk of treatment failure (AUC 0.81 to predict failure at diagnosis), while a suboptimal reduction of the total glycolytic activity in lung lesions during treatment had the strongest association with recurrent disease (AUC 0.8 to predict pooled unfavourable outcomes). During the first year after TB treatment lesion burden reduced; but for many patients, there were continued dynamic changes of individual lesions. Conclusions: Quantification of FDG PET-CT images better characterised TB treatment outcomes than qualitative scan patterns and robustly measured the burden of disease. In future, validated metrics may be used to stratify patients and help evaluate the effectiveness of TB treatment modalities.
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    A serum circulating miRNA signature for short-term risk of progression to active tuberculosis among household contacts
    (Frontiers Media, 2018) Duffy, Fergal J.; Thompson, Ethan; Downing, Katrina; Suliman, Sara; Mayanja-Kizza, Harriet; Boom, W. Henry; Thiel, Bonnie; Weiner III, January; Kaufmann, Stefan H. E.; Dover, Drew; Tabb, David L.; Dockrell, Hazel M.; Ottenhoff, Tom H. M.; Tromp, Gerard; Scriba, Thomas J.; Zak, Daniel E.; Walzl, Gerhard; GC6-74 Consortium
    ENGLISH ABSTRACT: Biomarkers that predict who among recently Mycobacterium tuberculosis (MTB)-exposed individuals will progress to active tuberculosis are urgently needed. Intracellular microRNAs (miRNAs) regulate the host response to MTB and circulating miRNAs (c-miRNAs) have been developed as biomarkers for other diseases. We performed machine-learning analysis of c-miRNA measurements in the serum of adult household contacts (HHCs) of TB index cases from South Africa and Uganda and developed a c-miRNA-based signature of risk for progression to active TB. This c-miRNA-based signature significantly discriminated HHCs within 6 months of progression to active disease from HHCs that remained healthy in an independent test set [ROC area under the ROC curve (AUC) 0.74, progressors < 6 Mo to active TB and ROC AUC 0.66, up to 24 Mo to active TB], and complements the predictions of a previous cellular mRNA-based signature of TB risk.
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    A sex-stratified genome-wide association study of tuberculosis using a multi-ethnic genotyping array
    (Frontiers Media, 2019) Schurz, Haiko; Kinnear, Craig J.; Gignoux, Chris; Wojcik, Genevieve; Van Helden, Paul D.; Tromp, Gerard; Henn, Brenna; Hoal, Eileen G.; Moller, Marlo
    Tuberculosis (TB), caused by Mycobacterium tuberculosis, is a complex disease with a known human genetic component. Males seem to be more affected than females and in most countries the TB notification rate is twice as high in males than in females. While socio-economic status, behavior and sex hormones influence the male bias they do not fully account for it. Males have only one copy of the X chromosome, while diploid females are subject to X chromosome inactivation. In addition, the X chromosome codes for many immune-related genes, supporting the hypothesis that X-linked genes could contribute to TB susceptibility in a sex-biased manner. We report the first TB susceptibility genome-wide association study (GWAS) with a specific focus on sex-stratified autosomal analysis and the X chromosome. A total of 810 individuals (410 cases and 405 controls) from an admixed South African population were genotyped using the Illumina Multi Ethnic Genotyping Array, specifically designed as a suitable platform for diverse and admixed populations. Association testing was done on the autosome (8,27,386 variants) and X chromosome (20,939 variants) in a sex stratified and combined manner. SNP association testing was not statistically significant using a stringent cut-off for significance but revealed likely candidate genes that warrant further investigation. A genome wide interaction analysis detected 16 significant interactions. Finally, the results highlight the importance of sex-stratified analysis as strong sex-specific effects were identified on both the autosome and X chromosome.
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    Shared genetic risk factors of intracranial, abdominal, and thoracic aneurysms
    (American Heart Association, 2016-07-14) Van 't Hof, Femke N. G.; Ruigrok, Ynte M.; Lee, Cue Hyunkyu; Ripke, Stephan; Anderson, Graig; De Andrade, Mariza; Tromp, Gerard
    Background: Intracranial aneurysms (IAs), abdominal aortic aneurysms (AAAs), and thoracic aortic aneurysms (TAAs) all have a familial predisposition. Given that aneurysm types are known to co‐occur, we hypothesized that there may be shared genetic risk factors for IAs, AAAs, and TAAs. Methods and Results: We performed a mega‐analysis of 1000 Genomes Project‐imputed genome‐wide association study (GWAS) data of 4 previously published aneurysm cohorts: 2 IA cohorts (in total 1516 cases, 4305 controls), 1 AAA cohort (818 cases, 3004 controls), and 1 TAA cohort (760 cases, 2212 controls), and observed associations of 4 known IA, AAA, and/or TAA risk loci (9p21, 18q11, 15q21, and 2q33) with consistent effect directions in all 4 cohorts. We calculated polygenic scores based on IA‐, AAA‐, and TAA‐associated SNPs and tested these scores for association to case‐control status in the other aneurysm cohorts; this revealed no shared polygenic effects. Similarly, linkage disequilibrium–score regression analyses did not show significant correlations between any pair of aneurysm subtypes. Last, we evaluated the evidence for 14 previously published aneurysm risk single‐nucleotide polymorphisms through collaboration in extended aneurysm cohorts, with a total of 6548 cases and 16 843 controls (IA) and 4391 cases and 37 904 controls (AAA), and found nominally significant associations for IA risk locus 18q11 near RBBP8 to AAA (odds ratio [OR]=1.11; P=4.1×10−5) and for TAA risk locus 15q21 near FBN1 to AAA (OR=1.07; P=1.1×10−3). Conclusions: Although there was no evidence for polygenic overlap between IAs, AAAs, and TAAs, we found nominally significant effects of two established risk loci for IAs and TAAs in AAAs. These two loci will require further replication.
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    Systematic review of genetic factors in the etiology of esophageal squamous cell carcinoma in African populations
    (Frontiers Media, 2019) Simba, Hannah; Kuivaniemi, Helena; Lutje, Vittoria; Tromp, Gerard; Sewram, Vikash
    Background: Esophageal squamous cell carcinoma (ESCC), one of the most aggressive cancers, is endemic in Sub-Saharan Africa, constituting a major health burden. It has the most divergence in cancer incidence globally, with high prevalence reported in East Asia, Southern Europe, and in East and Southern Africa. Its etiology is multifactorial, with lifestyle, environmental, and genetic risk factors. Very little is known about the role of genetic factors in ESCC development and progression among African populations. The study aimed to systematically assess the evidence on genetic variants associated with ESCC in African populations. Methods: We carried out a comprehensive search of all African published studies up to April 2019, using PubMed, Embase, Scopus, and African Index Medicus databases. Quality assessment and data extraction were carried out by two investigators. The strength of the associations was measured by odds ratios and 95% confidence intervals. Results: Twenty-three genetic studies on ESCC in African populations were included in the systematic review. They were carried out on Black and admixed South African populations, as well as on Malawian, Sudanese, and Kenyan populations. Most studies were candidate gene studies and included DNA sequence variants in 58 different genes. Only one study carried out whole-exome sequencing of 59 ESCC patients. Sample sizes varied from 18 to 880 cases and 88 to 939 controls. Altogether, over 100 variants in 37 genes were part of 17 case-control genetic association studies to identify susceptibility loci for ESCC. In these studies, 25 variants in 20 genes were reported to have a statistically significant association. In addition, eight studies investigated changes in cancer tissues and identified somatic alterations in 17 genes and evidence of loss of heterozygosity, copy number variation, and microsatellite instability. Two genes were assessed for both genetic association and somatic mutation. Conclusions: Comprehensive large-scale studies on the genetic basis of ESCC are still lacking in Africa. Sample sizes in existing studies are too small to draw definitive conclusions about ESCC etiology. Only a small number of African populations have been analyzed, and replication and validation studies are missing. The genetic etiology of ESCC in Africa is, therefore, still poorly defined.