Population isolates such as those in Finland benefit genetic research because deleterious alleles are often concentrated on a small number of low-frequency variants (0.1% ≤ minor allele frequency < ...5%). These variants survived the founding bottleneck rather than being distributed over a large number of ultrarare variants. Although this effect is well established in Mendelian genetics, its value in common disease genetics is less explored
. FinnGen aims to study the genome and national health register data of 500,000 Finnish individuals. Given the relatively high median age of participants (63 years) and the substantial fraction of hospital-based recruitment, FinnGen is enriched for disease end points. Here we analyse data from 224,737 participants from FinnGen and study 15 diseases that have previously been investigated in large genome-wide association studies (GWASs). We also include meta-analyses of biobank data from Estonia and the United Kingdom. We identified 30 new associations, primarily low-frequency variants, enriched in the Finnish population. A GWAS of 1,932 diseases also identified 2,733 genome-wide significant associations (893 phenome-wide significant (PWS), P < 2.6 × 10
) at 2,496 (771 PWS) independent loci with 807 (247 PWS) end points. Among these, fine-mapping implicated 148 (73 PWS) coding variants associated with 83 (42 PWS) end points. Moreover, 91 (47 PWS) had an allele frequency of <5% in non-Finnish European individuals, of which 62 (32 PWS) were enriched by more than twofold in Finland. These findings demonstrate the power of bottlenecked populations to find entry points into the biology of common diseases through low-frequency, high impact variants.
Association of Common Variation in the HNF1α Gene Region With Risk of Type 2 Diabetes
Wendy Winckler 1 2 3 ,
Noël P. Burtt 3 ,
Johan Holmkvist 4 ,
Camilla Cervin 4 ,
Paul I.W. de Bakker 1 2 3 ,
Maria ...Sun 1 3 ,
Peter Almgren 4 ,
Tiinamaija Tuomi 5 ,
Daniel Gaudet 6 ,
Thomas J. Hudson 7 ,
Kristin G. Ardlie 8 ,
Mark J. Daly 3 ,
Joel N. Hirschhorn 2 3 9 ,
David Altshuler 1 2 3 10 11 and
Leif Groop 4 5
1 Department of Molecular Biology, Center for Human Genetic Research, Massachusetts General Hospital, Boston, Massachusetts
2 Department of Genetics, Harvard Medical School, Boston, Massachusetts
3 Program in Medical and Population Genetics, Broad Institute of MIT and Harvard, Cambridge, Massachusetts
4 Department of Endocrinology, University Hospital MAS, Lund University, Malmö, Sweden
5 Department of Medicine, Helsinki University Central Hospital, Folkhalsan Genetic Institute, Folkhalsan Research Center, and
Research Program for Molecular Medicine, University of Helsinki, Helsinki, Finland
6 University of Montreal Community Genomic Center, Chicoutimi Hospital, Montreal, Quebec, Canada
7 McGill University and Genome Quebec Innovation Centre, Montreal, Quebec, Canada
8 Genomics Collaborative, Inc., Cambridge, Massachusetts
9 Divisions of Genetics and Endocrinology, Children’s Hospital, Boston, Massachusetts
10 Diabetes Unit, Massachusetts General Hospital, Boston, Massachusetts
11 Department of Medicine, Harvard Medical School, Boston, Massachusetts
Address correspondence and reprint requests to David Altshuler, Department of Molecular Biology, Massachusetts General Hospital,
Boston, MA 02114. E-mail: altshuler{at}molbio.mgh.harvard.edu . Or Leif Groop, Department of Endocrinology, University Hospital MAS, Lund University, Malmö, Sweden. E-mail: leif.groop{at}endo.mas.lu.se
Abstract
It is currently unclear how often genes that are mutated to cause rare, early-onset monogenic forms of disease also harbor
common variants that contribute to the more typical polygenic form of each disease. The gene for MODY3 diabetes, HNF1 α, lies in a region that has shown linkage to late-onset type 2 diabetes (12q24, NIDDM2 ), and previous association studies have suggested a weak trend toward association for common missense variants in HNF1 α with glucose-related traits. Based on genotyping of 79 common SNPs in the 118 kb spanning HNF1 α, we selected 21 haplotype tag single nucleotide polymorphisms (SNPs) and genotyped them in >4,000 diabetic patients and
control subjects from Sweden, Finland, and Canada. Several SNPs from the coding region and 5′ of the gene demonstrated nominal
association with type 2 diabetes, with the most significant marker (rs1920792) having an odds ratio of 1.17 and a P value of 0.002. We then genotyped three SNPs with the strongest evidence for association to type 2 diabetes (rs1920792, I27L,
and A98V) in an additional 4,400 type 2 diabetic and control subjects from North America and Poland and compared our results
with those of the original sample and of Weedon et al. None of the results were consistently observed across all samples,
with the possible exception of a modest association of the rare (3–5%) A98V variant. These results indicate that common variants
in HNF1 α either play no role in type 2 diabetes, a very small role, or a role that cannot be consistently observed without consideration
of as yet unmeasured genetic or environmental modifiers.
CEPH, Centre d’Etude du Polymorphisme Humain
GCI, Genomics Collaborative, Inc
LD, linkage disequilibrium
MODY, maturity-onset diabetes of the young
SNP, single nucleotide polymorphism
Footnotes
D.A. and L.G. jointly supervised this project.
J.N.H. has received consulting fees from Correlagen. D.A. has served on advisory panels for and received consulting fees from
Genomics Collaborative, Inc. L.G. has served on advisory panels for and received consulting fees from Aventis-Sanofi, Bristol-Myers
Squibb, Kowa, and Roche.
Accepted February 16, 2005.
Received November 24, 2004.
DIABETES
Association Testing in 9,000 People Fails to Confirm the Association of the Insulin Receptor Substrate-1 G972R Polymorphism
With Type 2 Diabetes
Jose C. Florez 1 2 3 4 ,
Marketa Sjögren 5 ,
Noël ...Burtt 3 ,
Marju Orho-Melander 5 ,
Steve Schayer 3 ,
Maria Sun 1 3 ,
Peter Almgren 5 ,
Ulf Lindblad 6 ,
Tiinamaija Tuomi 7 ,
Daniel Gaudet 8 ,
Thomas J. Hudson 9 ,
Mark J. Daly 3 ,
Kristin G. Ardlie 10 ,
Joel N. Hirschhorn 3 11 12 ,
David Altshuler 1 2 3 4 11 and
Leif Groop 5
1 Department of Molecular Biology, Massachusetts General Hospital, Boston, Massachusetts
2 Diabetes Unit, Massachusetts General Hospital, Boston, Massachusetts
3 Program in Medical and Population Genetics, Broad Institute of Harvard and MIT, Cambridge, Massachusetts
4 Department of Medicine, Harvard Medical School, Boston, Massachusetts
5 Department of Endocrinology, University Hospital MAS, Lund University, Malmö, Sweden
6 Department of Community Medicine, University Hospital MAS, Lund University, Malmö, Sweden
7 Department of Medicine, Helsinki University Central Hospital; Folkhalsan Genetic Institute, Folkhalsan Research Center; and
Research Program for Molecular Medicine, University of Helsinki, Helsinki, Finland
8 University of Montreal Community Genomic Center, Chicoutimi Hospital, Quebec, Canada
9 McGill University and Genome Quebec Innovation Centre, Montreal, Canada
10 Genomics Collaborative, Cambridge, Massachusetts
11 Department of Genetics, Harvard Medical School, Boston, Massachusetts
12 Divisions of Genetics and Endocrinology, Children’s Hospital, Boston, Massachusetts
Address correspondence and reprint requests to David Altshuler, Department of Molecular Biology, Massachusetts General Hospital,
Boston, MA 02114. E-mail: altshuler{at}molbio.mgh.harvard.edu
Leif Groop, Department of Endocrinology, University Hospital MAS, Lund University, Malmö, Sweden. E-mail: leif.groop{at}endo.mas.lu.se
Abstract
The insulin receptor substrate (IRS)-1 is an important component of the insulin signal transduction cascade. Several reports
suggest that a Gly→Arg change in codon 972 is associated with type 2 diabetes and related traits, and a recent meta-analysis
reported a modest but nominally significant association with type 2 diabetes (odds ratio OR 1.25 in favor of carriers of
the Arg allele 95% CI 1.05–1.48). To test the reproducibility of the model in a recent meta-analysis, we examined genotype-phenotype
correlation in three large Caucasian samples (not previously reported for this variant) totaling 9,000 individuals (estimated
to have >95% power to obtain a P < 0.05 for the OR of 1.25 estimated in the meta-analysis). In our combined sample, comprising 4,279 case and 3,532 control
subjects, as well as 1,189 siblings discordant for type 2 diabetes, G972R was not associated with type 2 diabetes (OR 0.96
0.84–1.10, P = 0.60). Genotype at G972R had no significant effect on various measures of insulin secretion or insulin resistance in a
set of Scandinavian samples in whom we had detailed phenotypic data. In contrast, the well-documented associations of peroxisome
proliferator-activated receptor γ P12A and Kir6.2 E23K with type 2 diabetes are both robustly observed in these 9,000 subjects,
including an additional (previously unpublished) confirmation of Kir6.2 E23K and type 2 diabetes in the Polish and North American
samples (combined OR 1.15 1.05–1.26, P = 0.001). Despite genotyping 9,000 people and >95% power to reproduce the estimated OR from the recent meta-analysis, we
were unable to replicate the association of the IRS-1 G972R polymorphism with type 2 diabetes.
HOMA, homeostasis model assessment
HOMA-β, HOMA of β-cell function
HOMA-IR, HOMA of insulin resistance
IRS, insulin receptor substrate
ISI, insulin sensitivity index
OGTT, oral glucose tolerance test
PI3K, phosphatidylinositol 3-kinase
PPAR, peroxisome proliferator-activated receptor
Footnotes
D.A. and L.G. jointly supervised this project.
In the past, D.A. has been on an advisory panel for and received consulting fees from Genomics Collaborative.
Accepted August 23, 2004.
Received July 9, 2004.
DIABETES
Diabetes is presently classified into two main forms, type 1 and type 2 diabetes, but type 2 diabetes in particular is highly heterogeneous. A refined classification could provide a powerful tool to ...individualise treatment regimens and identify individuals with increased risk of complications at diagnosis.
We did data-driven cluster analysis (k-means and hierarchical clustering) in patients with newly diagnosed diabetes (n=8980) from the Swedish All New Diabetics in Scania cohort. Clusters were based on six variables (glutamate decarboxylase antibodies, age at diagnosis, BMI, HbA
, and homoeostatic model assessment 2 estimates of β-cell function and insulin resistance), and were related to prospective data from patient records on development of complications and prescription of medication. Replication was done in three independent cohorts: the Scania Diabetes Registry (n=1466), All New Diabetics in Uppsala (n=844), and Diabetes Registry Vaasa (n=3485). Cox regression and logistic regression were used to compare time to medication, time to reaching the treatment goal, and risk of diabetic complications and genetic associations.
We identified five replicable clusters of patients with diabetes, which had significantly different patient characteristics and risk of diabetic complications. In particular, individuals in cluster 3 (most resistant to insulin) had significantly higher risk of diabetic kidney disease than individuals in clusters 4 and 5, but had been prescribed similar diabetes treatment. Cluster 2 (insulin deficient) had the highest risk of retinopathy. In support of the clustering, genetic associations in the clusters differed from those seen in traditional type 2 diabetes.
We stratified patients into five subgroups with differing disease progression and risk of diabetic complications. This new substratification might eventually help to tailor and target early treatment to patients who would benefit most, thereby representing a first step towards precision medicine in diabetes.
Swedish Research Council, European Research Council, Vinnova, Academy of Finland, Novo Nordisk Foundation, Scania University Hospital, Sigrid Juselius Foundation, Innovative Medicines Initiative 2 Joint Undertaking, Vasa Hospital district, Jakobstadsnejden Heart Foundation, Folkhälsan Research Foundation, Ollqvist Foundation, and Swedish Foundation for Strategic Research.
The last decade has revealed hundreds of genetic variants associated with type 2 diabetes, many especially with insulin secretion. However, the evidence for their single or combined effect on ...beta-cell function relies mostly on genetic association of the variants or genetic risk scores with simple traits, and few have been functionally fully characterized even in cell or animal models. Translating the measured traits into human physiology is not straightforward: none of the various indices for beta-cell function or insulin sensitivity recapitulates the dynamic interplay between glucose sensing, endogenous glucose production, insulin production and secretion, insulin clearance, insulin resistance—to name just a few factors. Because insulin sensitivity is a major determinant of physiological need of insulin, insulin secretion should be evaluated in parallel with insulin sensitivity. On the other hand, multiple physiological or pathogenic processes can either mask or unmask subtle defects in beta-cell function. Even in monogenic diabetes, a clearly pathogenic genetic variant can result in different phenotypic characteristics—or no phenotype at all. In this review, we evaluate the methods available for studying beta-cell function in humans, critically examine the evidence linking some identified variants to a specific beta-cell phenotype, and highlight areas requiring further study.
Display omitted
•Human physiology of the diabetes-related genetic variants has been poorly characterized.•Few variants can unequivocally be interpreted to be associated with defective insulin secretion.•Instead of directly studying the dysregulated processes, we have to rely on proxies.•Insulin secretion should be evaluated in parallel with insulin sensitivity.•The tests cannot distinguish between reduced beta cell mass and function.
To characterize type 2 diabetes (T2D)-associated variation across the allele frequency spectrum, we conducted a meta-analysis of genome-wide association data from 26,676 T2D case and 132,532 control ...subjects of European ancestry after imputation using the 1000 Genomes multiethnic reference panel. Promising association signals were followed up in additional data sets (of 14,545 or 7,397 T2D case and 38,994 or 71,604 control subjects). We identified 13 novel T2D-associated loci (
< 5 × 10
), including variants near the
,
, and
genes. Our analysis brought the total number of independent T2D associations to 128 distinct signals at 113 loci. Despite substantially increased sample size and more complete coverage of low-frequency variation, all novel associations were driven by common single nucleotide variants. Credible sets of potentially causal variants were generally larger than those based on imputation with earlier reference panels, consistent with resolution of causal signals to common risk haplotypes. Stratification of T2D-associated loci based on T2D-related quantitative trait associations revealed tissue-specific enrichment of regulatory annotations in pancreatic islet enhancers for loci influencing insulin secretion and in adipocytes, monocytes, and hepatocytes for insulin action-associated loci. These findings highlight the predominant role played by common variants of modest effect and the diversity of biological mechanisms influencing T2D pathophysiology.
The genetic architecture of type 2 diabetes Teslovich, Tanya M; Mahajan, Anubha; Fontanillas, Pierre ...
Nature (London),
08/2016, Letnik:
536, Številka:
7614
Journal Article
Recenzirano
Odprti dostop
The genetic architecture of common traits, including the number, frequency, and effect sizes of inherited variants that contribute to individual risk, has been long debated. Genome-wide association ...studies have identified scores of common variants associated with type 2 diabetes, but in aggregate, these explain only a fraction of the heritability of this disease. Here, to test the hypothesis that lower-frequency variants explain much of the remainder, the GoT2D and T2D-GENES consortia performed whole-genome sequencing in 2,657 European individuals with and without diabetes, and exome sequencing in 12,940 individuals from five ancestry groups. To increase statistical power, we expanded the sample size via genotyping and imputation in a further 111,548 subjects. Variants associated with type 2 diabetes after sequencing were overwhelmingly common and most fell within regions previously identified by genome-wide association studies. Comprehensive enumeration of sequence variation is necessary to identify functional alleles that provide important clues to disease pathophysiology, but large-scale sequencing does not support the idea that lower-frequency variants have a major role in predisposition to type 2 diabetes.
Fasting Versus Postload Plasma Glucose Concentration and the Risk for Future Type 2 Diabetes
Results from the Botnia Study
Muhammad A. Abdul-Ghani , MD, PHD 1 ,
Valeriya Lyssenko , MD, PHD 2 ,
...Tiinamaija Tuomi , MD, PHD 2 ,
Ralph A. DeFronzo , MD 1 and
Leif Groop , MD 2
1 Division of Diabetes, University of Texas Health Science Center at San Antonio, San Antonio, Texas
2 Department of Clinical Sciences, Diabetes and Endocrinology, and Lund University Diabetes Center, Lund University, Malmö,
Sweden
Corresponding author: Muhammad Abdul-Ghani, abdulghani{at}uthscsa.edu
Abstract
OBJECTIVE —The purpose of this study was to assess the efficacy of the postload plasma glucose concentration in predicting future risk
of type 2 diabetes, compared with prediction models based on measurement of the fasting plasma glucose (FPG) concentration.
RESEARCH DESIGN AND METHODS —A total of 2,442 subjects from the Botnia Study, who were free of type 2 diabetes at baseline, received an oral glucose tolerance
test (OGTT) at baseline and after 7–8 years of follow-up. Future risk for type 2 diabetes was assessed with area under the
receiver-operating characteristic curve for prediction models based up measurement of the FPG concentration 1 ) with or without a 1-h plasma glucose concentration during the OGTT and 2 ) with or without the metabolic syndrome.
RESULTS —Prediction models based on measurement of the FPG concentration were weak predictors for the risk of future type 2 diabetes.
Addition of a 1-h plasma glucose concentration markedly enhanced prediction of the risk of future type 2 diabetes. A cut point
of 155 mg/dl for the 1-h plasma glucose concentration during the OGTT and presence of the metabolic syndrome were used to
stratify subjects in each glucose tolerance group into low, intermediate, and high risk for future type 2 diabetes.
CONCLUSIONS —The plasma glucose concentration at 1 h during the OGTT is a strong predictor of future risk for type 2 diabetes and adds
to the prediction power of models based on measurements made during the fasting state. A plasma glucose cut point of 155 mg/dl
plus the Adult Treatment Panel III criteria for the metabolic syndrome can be used to stratify nondiabetic subjects into low-,
intermediate-, and high-risk groups.
Footnotes
Published ahead of print at http://care.diabetesjournals.org on 18 November 2008.
Readers may use this article as long as the work is properly cited, the use is educational and not for profit, and the work
is not altered. See http://creativecommons.org/licenses/by-nc-nd/3.0/ for details.
The costs of publication of this article were defrayed in part by the payment of page charges. This article must therefore
be hereby marked “advertisement” in accordance with 18 U.S.C Section 1734 solely to indicate this fact.
Accepted October 30, 2008.
Received July 8, 2008.
DIABETES CARE
OBJECTIVE:--The purpose of this study was to assess the efficacy of the postload plasma glucose concentration in predicting future risk of type 2 diabetes, compared with prediction models based on ...measurement of the fasting plasma glucose (FPG) concentration. RESEARCH DESIGN AND METHODS--A total of 2,442 subjects from the Botnia Study, who were free of type 2 diabetes at baseline, received an oral glucose tolerance test (OGTT) at baseline and after 7-8 years of follow-up. Future risk for type 2 diabetes was assessed with area under the receiver-operating characteristic curve for prediction models based up measurement of the FPG concentration 1) with or without a 1-h plasma glucose concentration during the OGTT and 2) with or without the metabolic syndrome. RESULTS:--Prediction models based on measurement of the FPG concentration were weak predictors for the risk of future type 2 diabetes. Addition of a 1-h plasma glucose concentration markedly enhanced prediction of the risk of future type 2 diabetes. A cut point of 155 mg/dl for the 1-h plasma glucose concentration during the OGTT and presence of the metabolic syndrome were used to stratify subjects in each glucose tolerance group into low, intermediate, and high risk for future type 2 diabetes. CONCLUSIONS:--The plasma glucose concentration at 1 h during the OGTT is a strong predictor of future risk for type 2 diabetes and adds to the prediction power of models based on measurements made during the fasting state. A plasma glucose cut point of 155 mg/dl plus the Adult Treatment Panel III criteria for the metabolic syndrome can be used to stratify nondiabetic subjects into low-, intermediate-, and high-risk groups.
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