Discovery and Fine-Mapping of Glycaemic and Obesity-Related Trait Loci Using High-Density Imputation

Horikoshi, Momoko; Mägi, Reedik; Bunt, Martijn van de; Surakka, Ida; Sarin, Antti‐Pekka; Mahajan, Anubha; Marullo, Letizia; Þorleifsson, Guðmar; Hägg, Sara; Hottenga, Jouke‐Jan; Ladenvall, Claes; Ried, Janina S.; Winkler, Thomas; Willems, Sara M.; Pervjakova, Natalia; Esko, Tõnu; Beekman, Marian; Nelson, Christopher P.; Willenborg, Christina; Wiltshire, Steven; Ferreira, Teresa; Fernández, Juan Carlos Delgado; Gaulton, Kyle J.; Steinthórsdóttir, Valgerður; Hamsten, Anders; Magnusson, Patrik K. E.; Willemsen, Gonneke; Milaneschi, Yuri; Robertson, Neil; Groves, Christopher J.; Bennett, Amanda J.; Lehtimäki, Terho; Viikari, Jorma; Rung, Johan; Lyssenko, Valeriya; Perola, Markus; Heid, Iris M.; Herder, Christian; Grallert, Harald; Müller‐Nurasyid, Martina; Roden, Michael; Hyppönen, Elina; Isaacs, Aaron; Leeuwen, Elisabeth M. van; Karssen, Lennart C.; Mihailov, Evelin; Houwing-Duistermaat, Jeanine J.; Craen, Anton J. M. de; Deelen, Joris; Havulinna, Aki S.; Blades, Matthew; Hengstenberg, Christian; Erdmann, Jeanette; Schunkert, Heribert; Kaprio, Jaakko; Tobin, Martin D.; Samani, Nilesh J.; Lind, Lars; Salomaa, Veikko; Lindgren, Cecilia M.; Slagboom, P. Eline; Metspalu, Andres; Duijn, Cornelia M. van; Eriksson, Johan G.; Peters, Annette; Gieger, Christian; Jula, Antti; Groop, Leif; Raitakari, Olli T.; Power, Chris; Penninx, Brenda W. J. H.; Geus, Eco J. C. de; Smit, Johannes H.; Boomsma, Dorret I.; Pedersen, Nancy L.; Ingelsson, Erik; Þorsteinsdóttir, Unnur; Stefánsson, Kári; Ripatti, Samuli; Prokopenko, Inga; McCarthy, Mark I.; Morris, Andrew P.

doi:10.1371/journal.pgen.1005230

Cited by 80 publications

(67 citation statements)

References 67 publications

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“…As a supplement to our in-house analyses of the Danish populations, we performed a lookup of the effect of p.R270H (rs116454156) in the online-available data (http://diagram-consortium.org/2015_ENGAGE_1KG/) based on recently published articles 30 31. The lookup was performed in summary results of genome-wide association studies (GWAS) from the European Network for Genetic and Genomic Epidemiology (ENGAGE) consortium from where the authors have used 1000G imputation to find novel genetic associations with BMI and FPG in a large number of individuals.…”

Section: Resultsmentioning

confidence: 99%

Functional and genetic epidemiological characterisation of theFFAR4(GPR120) p.R270H variant in the Danish population

Vestmar¹,

Andersson

Christensen

et al. 2016

J Med Genet

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Section: Resultsmentioning

confidence: 99%

Functional and genetic epidemiological characterisation of theFFAR4(GPR120) p.R270H variant in the Danish population

Vestmar¹,

Andersson

Christensen

et al. 2016

J Med Genet

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“…html); GIANT Consortium (https://www.broadinstitute.org/collaboration/giant/index) 17 for body mass index, waist circumference, and WHR; global lipids genetics consortium (http://csg.sph.umich.edu//abecasis/ public/lipids2013) 18 for lipids; ENGAGE 1000 Genome Consortium 19,20 (http://diagram-consortium.org/2015_ENGAGE_1KG/) for fasting insulin, lipids, and WHR; and CARDIoGRAM Consortium and CARDIoGRAMplusC4D (http://www.cardiogramplusc4d.org/) 1000 Genome Consortium 21,22 for CHD. They were used in enrichment analyses and to obtain the effects of genetic variants on TL, metabolic risk factors, and CHD.…”

Section: Summary Of Gwas Datamentioning

confidence: 99%

Exploring the Causal Pathway From Telomere Length to Coronary Heart Disease

Zhan

Karlsson

et al. 2017

Circulation Research

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In this study, we aim to examine metabolic risk factors as possible mediators in the causal relationship between genetically determined TL and CHD using a network MR method 12 from summarized genome-wide association study (GWAS) data. The rationale for focusing on metabolic risk factors is based on the following: (1) these metabolic risk factors explain a large proportion Molecular Medicine© 2017 American Heart Association, Inc. Rationale: Observational studies have found shorter leukocyte telomere length (TL) to be a risk factor for coronary heart disease (CHD), and recently the association was suggested to be causal. However, the relationship between TL and common metabolic risk factors for CHD is not well understood. Whether these risk factors could explain pathways from TL to CHD warrants further attention.Objective: To examine whether metabolic risk factors for CHD mediate the causal pathway from short TL to increased risk of CHD using a network Mendelian randomization design. Methods and Results:Summary statistics from several genome-wide association studies were used in a 2-sample Mendelian randomization study design. Network Mendelian randomization analysis-an approach using genetic variants as the instrumental variables for both the exposure and mediator to infer causality-was performed to examine the causal association between telomeres and CHD and metabolic risk factors. Summary statistics from the ENGAGE Telomere Consortium were used (n=37 684) as a TL genetic instrument, CARDIoGRAMplusC4D Consortium data were used (case=22 233 and control=64 762) for CHD, and other consortia data were used for metabolic traits (fasting insulin, triglyceride, total cholesterol, low-density lipoprotein cholesterol, fasting glucose, diabetes mellitus, glycohemoglobin, body mass index, waist circumference, and waist:hip ratio). One-unit increase of genetically determined TL was associated with −0.07 (95% confidence interval, −0.01 to −0.12; P=0.01) lower log-transformed fasting insulin (pmol/L) and 21% lower odds (95% confidence interval, 3-35; P=0.02) of CHD. Higher genetically determined log-transformed fasting insulin level was associated with higher CHD risk (odds ratio, 1.86; 95% confidence interval, 1.01-3.41; P=0.04). Conclusions:

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“…Credible intervals were defined by the boundaries of the 99% credible sets of variants [24] from DIAGRAM (96 loci) [25] and ENGAGE (16 loci) [26] consortium data, respectively (ESM Table 10). A subset of 15 loci was considered to influence type 2 diabetes via beta cell dysfunction; these loci included ones causing hyperglycaemia, reduced insulin processing and secretion, and reduced fasting proinsulin levels [27,28] (ESM Table 11, ESM Methods).…”

Section: Flow Cytometrymentioning

confidence: 99%

“…We repeated the enrichment test using a subset of 15 type 2 diabetes GWAS loci for which the Table 10), and for all differentially expressed genes in only physiological type 2 diabetes loci [T2D (beta cell)] (ESM Table 11). We consider beta cell function loci as 15 loci influencing hyperglycaemia, beta cell function and insulin processing [26,27]. The y-axis represents the results of the hypergeometric test in permuted p values (−log 10 ).…”

Section: −10mentioning

confidence: 99%

Patterns of differential gene expression in a cellular model of human islet development, and relationship to type 2 diabetes predisposition

et al. 2018

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Aims/hypothesis Most type 2 diabetes-associated genetic variants identified via genome-wide association studies (GWASs) appear to act via the pancreatic islet. Observed defects in insulin secretion could result from an impact of these variants on islet development and/or the function of mature islets. Most functional studies have focused on the latter, given limitations regarding access to human fetal islet tissue. Capitalising upon advances in in vitro differentiation, we characterised the transcriptomes of human induced pluripotent stem cell (iPSC) lines differentiated along the pancreatic endocrine lineage, and explored the contribution of altered islet development to the pathogenesis of type 2 diabetes. Methods We performed whole-transcriptome RNA sequencing of human iPSC lines from three independent donors, at baseline and at seven subsequent stages during in vitro islet differentiation. Differentially expressed genes (q < 0.01, log 2 fold change [FC] > 1) were assigned to the stages at which they were most markedly upregulated. We used these data to characterise upstream transcription factors directing different stages of development, and to explore the relationship between RNA expression profiles and genes mapping to type 2 diabetes GWAS signals.Results We identified 9409 differentially expressed genes across all stages, including many known markers of islet development. Integration of differential expression data with information on transcription factor motifs highlighted the potential contribution of REST to islet development. Over 70% of genes mapping within type 2 diabetes-associated credible intervals showed peak differential expression during islet development, and type 2 diabetes GWAS loci of largest effect (including TCF7L2; log 2 FC = 1.2; q = 8.5 × 10 −10 ) were notably enriched in genes differentially expressed at the posterior foregut stage (q = 0.002), as calculated by gene set enrichment analyses. In a complementary analysis of enrichment, genes differentially expressed in the final, beta-like cell stage of in vitro differentiation were significantly enriched (hypergeometric test, permuted p value <0.05) for genes within the credible intervals of type 2 diabetes GWAS loci. Conclusions/interpretation The present study characterises RNA expression profiles during human islet differentiation, identifies potential transcriptional regulators of the differentiation process, and suggests that the inherited predisposition to type 2 diabetes is partly mediated through modulation of islet development.Marta Perez-Alcantara and Christian Honoré contributed equally to this study.Electronic supplementary material The online version of this article (https://doi.org/10.1007/s00125-018-4612-4) contains peer-reviewed but unedited supplementary material, which is available to authorised users.

show abstract

Discovery and Fine-Mapping of Glycaemic and Obesity-Related Trait Loci Using High-Density Imputation

Cited by 80 publications

References 67 publications

Functional and genetic epidemiological characterisation of theFFAR4(GPR120) p.R270H variant in the Danish population

Functional and genetic epidemiological characterisation of theFFAR4(GPR120) p.R270H variant in the Danish population

Exploring the Causal Pathway From Telomere Length to Coronary Heart Disease

Patterns of differential gene expression in a cellular model of human islet development, and relationship to type 2 diabetes predisposition

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