Author
Maria Sterner
Bio: Maria Sterner is an academic researcher from Lund University. The author has contributed to research in topics: Exome sequencing & CDKAL1. The author has an hindex of 6, co-authored 6 publications receiving 2932 citations.
Topics: Exome sequencing, CDKAL1, Type 2 diabetes, TCF7L2, Exome
Papers
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TL;DR: The discovery of associated variants in unsuspected genes and outside coding regions illustrates the ability of genome-wide association studies to provide potentially important clues to the pathogenesis of common diseases.
Abstract: New strategies for prevention and treatment of type 2 diabetes (T2D) require improved insight into disease etiology. We analyzed 386,731 common single-nucleotide polymorphisms (SNPs) in 1464 patients with T2D and 1467 matched controls, each characterized for measures of glucose metabolism, lipids, obesity, and blood pressure. With collaborators (FUSION and WTCCC/UKT2D), we identified and confirmed three loci associated with T2D-in a noncoding region near CDKN2A and CDKN2B, in an intron of IGF2BP2, and an intron of CDKAL1-and replicated associations near HHEX and in SLC30A8 found by a recent whole-genome association study. We identified and confirmed association of a SNP in an intron of glucokinase regulatory protein (GCKR) with serum triglycerides. The discovery of associated variants in unsuspected genes and outside coding regions illustrates the ability of genome-wide association studies to provide potentially important clues to the pathogenesis of common diseases.
2,813 citations
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University of Copenhagen1, University of California, Berkeley2, Wellcome Trust Centre for Human Genetics3, Lund University4, Centre national de la recherche scientifique5, University of Eastern Finland6, University of Oxford7, Aarhus University8, VU University Medical Center9, University of Dundee10, University of Helsinki11, University of Exeter12, Umeå University13, Chinese PLA General Hospital14, Queen Mary University of London15, South China University of Technology16, Steno Diabetes Center17, IRSA18, French Institute of Health and Medical Research19, Claude Bernard University Lyon 120, Leiden University Medical Center21, Newcastle University22, Broad Institute23, Harvard University24, Imperial College London25, National Institute for Health Research26, University of Southern Denmark27, Glostrup Hospital28, Aalborg University29
TL;DR: Exome sequencing is applied as a basis for finding genetic determinants of metabolic traits and show the existence of low-frequency and common coding polymorphisms with impact on common metabolic traits.
Abstract: Aims/hypothesis Human complex metabolic traits are in part regulated by genetic determinants. Here we applied exome sequencing to identify novel associations of coding polymorphisms at minor allele frequencies (MAFs) >1% with common metabolic phenotypes. Methods The study comprised three stages. We performed medium-depth (8×) whole exome sequencing in 1,000 cases with type 2 diabetes, BMI >27.5 kg/m 2 and hypertension and in 1,000 controls (stage 1). We selected 16,192 polymorphisms nominally associated (p<0.05) with case–control status, from four selected annotation categories or from
130 citations
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TL;DR: LADA and adult-onset type 1 diabetes share genetic risk variants with type 2 diabetes, supporting the idea of a hybrid form of diabetes and distinguishing them from patients with classical young-onsets type 1 Diabetes.
Abstract: Latent autoimmune diabetes in adults (LADA) is phenotypically a hybrid of type 1 and type 2 diabetes. Genetically LADA is poorly characterised but does share genetic predisposition with type 1 diabetes. We aimed to improve the genetic characterisation of LADA and hypothesised that type 2 diabetes-associated gene variants also predispose to LADA, and that the associations would be strongest in LADA patients with low levels of GAD autoantibodies (GADA). We assessed 41 type 2 diabetes-associated gene variants in Finnish (phase I) and Swedish (phase II) patients with LADA (n = 911) or type 1 diabetes (n = 406), all diagnosed after the age of 35 years, as well as in non-diabetic control individuals 40 years or older (n = 4,002). Variants in the ZMIZ1 (rs12571751, p = 4.1 × 10−5) and TCF7L2 (rs7903146, p = 5.8 × 10−4) loci were strongly associated with LADA. Variants in the KCNQ1 (rs2237895, p = 0.0012), HHEX (rs1111875, p = 0.0024 in Finns) and MTNR1B (rs10830963, p = 0.0039) loci showed the strongest association in patients with low GADA, supporting the hypothesis that the disease in these patients is more like type 2 diabetes. In contrast, variants in the KLHDC5 (rs10842994, p = 9.5 × 10−4 in Finns), TP53INP1 (rs896854, p = 0.005), CDKAL1 (rs7756992, p = 7.0 × 10−4; rs7754840, p = 8.8 × 10−4) and PROX1 (rs340874, p = 0.003) loci showed the strongest association in patients with high GADA. For type 1 diabetes, a strong association was seen for MTNR1B (rs10830963, p = 3.2 × 10−6) and HNF1A (rs2650000, p = 0.0012). LADA and adult-onset type 1 diabetes share genetic risk variants with type 2 diabetes, supporting the idea of a hybrid form of diabetes and distinguishing them from patients with classical young-onset type 1 diabetes.
55 citations
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TL;DR: In this paper, a joint Nordic quality assessment (QA) round was organized between 11 laboratories in the Nordic and Baltic countries to survey the quality of SNP genotyping, and the results from each laboratory were compared to this genotype.
Abstract: To survey the quality of SNP genotyping, a joint Nordic quality assessment (QA) round was organized between 11 laboratories in the Nordic and Baltic countries. The QA round involved blinded genotyping of 47 DNA samples for 18 or six randomly selected SNPs. The methods used by the participating laboratories included all major platforms for small- to medium-size SNP genotyping. The laboratories used their standard procedures for SNP assay design, genotyping, and quality control. Based on the joint results from all laboratories, a consensus genotype for each DNA sample and SNP was determined by the coordinator of the survey, and the results from each laboratory were compared to this genotype. The overall genotyping accuracy achieved in the survey was excellent. Six laboratories delivered genotype data that were in full agreement with the consensus genotype. The average accuracy per SNP varied from 99.1 to 100% between the laboratories, and it was frequently 100% for the majority of the assays for which SNP genotypes were reported. Lessons from the survey are that special attention should be given to the quality of the DNA samples prior to genotyping, and that a conservative approach for calling the genotypes should be used to achieve a high accuracy.
24 citations
01 Nov 2012
TL;DR: In this paper, the authors applied exome sequencing to identify novel associations of coding polymorphisms at minor allele frequencies (MAFs) > 1% with common metabolic phenotypes, including type 2 diabetes, BMI >27.5 kg/m2 and hypertension.
Abstract: Aims/hypothesisHuman complex metabolic traits are in part regulated by genetic determinants. Here we applied exome sequencing to identify novel associations of coding polymorphisms at minor allele frequencies (MAFs) >1% with common metabolic phenotypes.MethodsThe study comprised three stages. We performed medium-depth (8×) whole exome sequencing in 1,000 cases with type 2 diabetes, BMI >27.5 kg/m2 and hypertension and in 1,000 controls (stage 1). We selected 16,192 polymorphisms nominally associated (p < 0.05) with case–control status, from four selected annotation categories or from loci reported to associate with metabolic traits. These variants were genotyped in 15,989 Danes to search for association with 12 metabolic phenotypes (stage 2). In stage 3, polymorphisms showing potential associations were genotyped in a further 63,896 Europeans.ResultsExome sequencing identified 70,182 polymorphisms with MAF >1%. In stage 2 we identified 51 potential associations with one or more of eight metabolic phenotypes covered by 45 unique polymorphisms. In meta-analyses of stage 2 and stage 3 results, we demonstrated robust associations for coding polymorphisms in CD300LG (fasting HDL-cholesterol: MAF 3.5%, p = 8.5 × 10−14), COBLL1 (type 2 diabetes: MAF 12.5%, OR 0.88, p = 1.2 × 10−11) and MACF1 (type 2 diabetes: MAF 23.4%, OR 1.10, p = 8.2 × 10−10).Conclusions/interpretationWe applied exome sequencing as a basis for finding genetic determinants of metabolic traits and show the existence of low-frequency and common coding polymorphisms with impact on common metabolic traits. Based on our study, coding polymorphisms with MAF above 1% do not seem to have particularly high effect sizes on the measured metabolic traits.
15 citations
Cited by
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TL;DR: This work introduces PLINK, an open-source C/C++ WGAS tool set, and describes the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation, which focuses on the estimation and use of identity- by-state and identity/descent information in the context of population-based whole-genome studies.
Abstract: Whole-genome association studies (WGAS) bring new computational, as well as analytic, challenges to researchers. Many existing genetic-analysis tools are not designed to handle such large data sets in a convenient manner and do not necessarily exploit the new opportunities that whole-genome data bring. To address these issues, we developed PLINK, an open-source C/C++ WGAS tool set. With PLINK, large data sets comprising hundreds of thousands of markers genotyped for thousands of individuals can be rapidly manipulated and analyzed in their entirety. As well as providing tools to make the basic analytic steps computationally efficient, PLINK also supports some novel approaches to whole-genome data that take advantage of whole-genome coverage. We introduce PLINK and describe the five main domains of function: data management, summary statistics, population stratification, association analysis, and identity-by-descent estimation. In particular, we focus on the estimation and use of identity-by-state and identity-by-descent information in the context of population-based whole-genome studies. This information can be used to detect and correct for population stratification and to identify extended chromosomal segments that are shared identical by descent between very distantly related individuals. Analysis of the patterns of segmental sharing has the potential to map disease loci that contain multiple rare variants in a population-based linkage analysis.
26,280 citations
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TL;DR: This study has demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in theBritish population is generally modest.
Abstract: There is increasing evidence that genome-wide association ( GWA) studies represent a powerful approach to the identification of genes involved in common human diseases. We describe a joint GWA study ( using the Affymetrix GeneChip 500K Mapping Array Set) undertaken in the British population, which has examined similar to 2,000 individuals for each of 7 major diseases and a shared set of similar to 3,000 controls. Case-control comparisons identified 24 independent association signals at P < 5 X 10(-7): 1 in bipolar disorder, 1 in coronary artery disease, 9 in Crohn's disease, 3 in rheumatoid arthritis, 7 in type 1 diabetes and 3 in type 2 diabetes. On the basis of prior findings and replication studies thus-far completed, almost all of these signals reflect genuine susceptibility effects. We observed association at many previously identified loci, and found compelling evidence that some loci confer risk for more than one of the diseases studied. Across all diseases, we identified a large number of further signals ( including 58 loci with single-point P values between 10(-5) and 5 X 10(-7)) likely to yield additional susceptibility loci. The importance of appropriately large samples was confirmed by the modest effect sizes observed at most loci identified. This study thus represents a thorough validation of the GWA approach. It has also demonstrated that careful use of a shared control group represents a safe and effective approach to GWA analyses of multiple disease phenotypes; has generated a genome-wide genotype database for future studies of common diseases in the British population; and shown that, provided individuals with non-European ancestry are excluded, the extent of population stratification in the British population is generally modest. Our findings offer new avenues for exploring the pathophysiology of these important disorders. We anticipate that our data, results and software, which will be widely available to other investigators, will provide a powerful resource for human genetics research.
9,244 citations
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Tanya M. Teslovich1, Kiran Musunuru, Albert V. Smith2, Andrew C. Edmondson3 +215 more•Institutions (46)
TL;DR: The results identify several novel loci associated with plasma lipids that are also associated with CAD and provide the foundation to develop a broader biological understanding of lipoprotein metabolism and to identify new therapeutic opportunities for the prevention of CAD.
Abstract: Plasma concentrations of total cholesterol, low-density lipoprotein cholesterol, high-density lipoprotein cholesterol and triglycerides are among the most important risk factors for coronary artery disease (CAD) and are targets for therapeutic intervention. We screened the genome for common variants associated with plasma lipids in >100,000 individuals of European ancestry. Here we report 95 significantly associated loci (P < 5 x 10(-8)), with 59 showing genome-wide significant association with lipid traits for the first time. The newly reported associations include single nucleotide polymorphisms (SNPs) near known lipid regulators (for example, CYP7A1, NPC1L1 and SCARB1) as well as in scores of loci not previously implicated in lipoprotein metabolism. The 95 loci contribute not only to normal variation in lipid traits but also to extreme lipid phenotypes and have an impact on lipid traits in three non-European populations (East Asians, South Asians and African Americans). Our results identify several novel loci associated with plasma lipids that are also associated with CAD. Finally, we validated three of the novel genes-GALNT2, PPP1R3B and TTC39B-with experiments in mouse models. Taken together, our findings provide the foundation to develop a broader biological understanding of lipoprotein metabolism and to identify new therapeutic opportunities for the prevention of CAD.
3,469 citations
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University of Oxford1, Wellcome Trust Centre for Human Genetics2, University of Michigan3, Fred Hutchinson Cancer Research Center4, Duke University5, University of Ottawa6, Foundation for Research & Technology – Hellas7, Tufts University8, Harvard University9, Broad Institute10, Boston Children's Hospital11
TL;DR: This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management.
Abstract: The past year has witnessed substantial advances in understanding the genetic basis of many common phenotypes of biomedical importance. These advances have been the result of systematic, well-powered, genome-wide surveys exploring the relationships between common sequence variation and disease predisposition. This approach has revealed over 50 disease-susceptibility loci and has provided insights into the allelic architecture of multifactorial traits. At the same time, much has been learned about the successful prosecution of association studies on such a scale. This Review highlights the knowledge gained, defines areas of emerging consensus, and describes the challenges that remain as researchers seek to obtain more complete descriptions of the susceptibility architecture of biomedical traits of interest and to translate the information gathered into improvements in clinical management.
2,908 citations
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TL;DR: A number of recent improvements to theNHGRI Catalog of Published Genome-Wide Association Studies are presented, including novel ways for users to interact with the Catalog and changes to the curation infrastructure.
Abstract: The National Human Genome Research Institute (NHGRI) Catalog of Published Genome-Wide Association Studies (GWAS) Catalog provides a publicly available manually curated collection of published GWAS assaying at least 100000 singlenucleotide polymorphisms (SNPs) and all SNP-trait associations with P <110 5 . The Catalog includes 1751 curated publications of 11912 SNPs. In addition to the SNP-trait association data, the Catalog also publishes a quarterly diagram of all SNP-trait associations mapped to the SNPs’ chromosomal locations. The Catalog can be accessed via a tabular web interface, via a dynamic visualization on the human karyotype, as a downloadable tab-delimited file and as an OWL knowledge base. This article presents a number of recent improvements to the Catalog, including novel ways for users to interact with the Catalog and changes to the curation infrastructure.
2,755 citations