Publications
1182 results found
Imatoh T, Yengo L, Rocheleau G, et al., 2018, <i>ALDH2</i> Polymorphism rs671, but Not <i>ADH1B</i> Polymorphism rs1229984, Increases Risk for Hypo-HDL-Cholesterolemia in a/a Carriers Compared to the G/G Carriers, LIPIDS, Vol: 53, Pages: 797-807, ISSN: 0024-4201
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- Citations: 6
Abderrahmani A, Yengo L, Caiazzo R, et al., 2018, Increased Hepatic PDGF-AA Signaling Mediates Liver Insulin Resistance in Obesity-Associated Type 2 Diabetes, DIABETES, Vol: 67, Pages: 1310-1321, ISSN: 0012-1797
In type 2 diabetes (T2D), hepatic insulin resistance is strongly associated with nonalcoholic fatty liver disease (NAFLD). In this study, we hypothesized that the DNA methylome of livers from patients with T2D compared with livers of individuals with normal plasma glucose levels can unveil some mechanism of hepatic insulin resistance that could link to NAFLD. Using DNA methylome and transcriptome analyses of livers from obese individuals, we found that hypomethylation at a CpG site in PDGFA (encoding platelet-derived growth factor α) and PDGFA overexpression are both associated with increased T2D risk, hyperinsulinemia, increased insulin resistance, and increased steatohepatitis risk. Genetic risk score studies and human cell modeling pointed to a causative effect of high insulin levels on PDGFA CpG site hypomethylation, PDGFA overexpression, and increased PDGF-AA secretion from the liver. We found that PDGF-AA secretion further stimulates its own expression through protein kinase C activity and contributes to insulin resistance through decreased expression of insulin receptor substrate 1 and of insulin receptor. Importantly, hepatocyte insulin sensitivity can be restored by PDGF-AA–blocking antibodies, PDGF receptor inhibitors, and by metformin, opening therapeutic avenues. Therefore, in the liver of obese patients with T2D, the increased PDGF-AA signaling contributes to insulin resistance, opening new therapeutic avenues against T2D and possibly NAFLD.
Montagne L, Derhourhi M, Piton A, et al., 2018, CoDE-seq, an augmented whole-exome sequencing, enables the accurate detection of CNVs and mutations in Mendelian obesity and intellectual disability, MOLECULAR METABOLISM, Vol: 13, Pages: 1-9, ISSN: 2212-8778
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- Citations: 7
Feitosa M, Kraja A, Zhang W, et al., 2018, Novel genetic associations for blood pressure identified via gene-alcohol interaction in up to 570,000 individuals across multiple ancestries, PLoS ONE, Vol: 13, ISSN: 1932-6203
Heavy alcohol consumption is an established risk factor for hypertension; the mechanism by which alcohol consumption impact blood pressure (BP) regulation remains unknown. We hypothesized that a genome-wide association study accounting for gene-alcohol consumption interaction for BP might identify additional BP loci and contribute to the understanding of alcohol-related BP regulation. We conducted a large two-stage investigation incorporating joint testing of main genetic effects and single nucleotide variant (SNV)-alcohol consumption interactions. In Stage 1, genome-wide discovery meta-analyses in ≈131K individuals across several ancestry groups yielded 3,514 SNVs (245 loci) with suggestive evidence of association (P < 1.0 x 10-5). In Stage 2, these SNVs were tested for independent external replication in ≈440K individuals across multiple ancestries. We identified and replicated (at Bonferroni correction threshold) five novel BP loci (380 SNVs in 21 genes) and 49 previously reported BP loci (2,159 SNVs in 109 genes) in European ancestry, and in multi-ancestry meta-analyses (P < 5.0 x 10-8). For African ancestry samples, we detected 18 potentially novel BP loci (P < 5.0 x 10-8) in Stage 1 that warrant further replication. Additionally, correlated meta-analysis identified eight novel BP loci (11 genes). Several genes in these loci (e.g., PINX1, GATA4, BLK, FTO and GABBR2) have been previously reported to be associated with alcohol consumption. These findings provide insights into the role of alcohol consumption in the genetic architecture of hypertension.
Canouil M, Balkau B, Roussel R, et al., 2018, Jointly Modelling Single Nucleotide Polymorphisms With Longitudinal and Time-to-Event Trait: An Application to Type 2 Diabetes and Fasting Plasma Glucose, FRONTIERS IN GENETICS, Vol: 9, ISSN: 1664-8021
In observational cohorts, longitudinal data are collected with repeated measurements at predetermined time points for many biomarkers, along with other variables measured at baseline. In these cohorts, time until a certain event of interest occurs is reported and very often, a relationship will be observed between some biomarker repeatedly measured over time and that event. Joint models were designed to efficiently estimate statistical parameters describing this relationship by combining a mixed model for the longitudinal biomarker trajectory and a survival model for the time until occurrence of the event, using a set of random effects to account for the relationship between the two types of data. In this paper, we discuss the implementation of joint models in genetic association studies. First, we check model consistency based on different simulation scenarios, by varying sample sizes, minor allele frequencies and number of repeated measurements. Second, using genotypes assayed with the Metabochip DNA arrays (Illumina) from about 4,500 individuals recruited in the French cohort D.E.S.I.R. (Data from an Epidemiological Study on the Insulin Resistance syndrome), we assess the feasibility of implementing the joint modelling approach in a real high-throughput genomic dataset. An alternative model approximating the joint model, called the Two-Step approach (TS), is also presented. Although the joint model shows more precise and less biased estimators than its alternative counterpart, the TS approach results in much reduced computational times, and could thus be used for testing millions of SNPs at the genome-wide scale.
Campanella G, Gunter MJ, Polidoro S, et al., 2018, Epigenome-wide association study of adiposity and future risk of obesity-related diseases, INTERNATIONAL JOURNAL OF OBESITY, Vol: 42, Pages: 2022-2035, ISSN: 0307-0565
Griscelli F, Ezanno H, Soubeyrand M, et al., 2018, Generation of an induced pluripotent stem cell (iPSC) line from a patient with maturity-onset diabetes of the young type 3 (MODY3) carrying a hepatocyte nuclear factor 1-alpha (<i>HNF1A</i>) mutation, STEM CELL RESEARCH, Vol: 29, Pages: 56-59, ISSN: 1873-5061
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- Citations: 15
Froguel P, 2018, Obesity genes in obesity, BULLETIN DE L ACADEMIE NATIONALE DE MEDECINE, Vol: 202, Pages: 1037-1040, ISSN: 0001-4079
Hancili S, Bonnefond A, Philippe J, et al., 2018, A novel <i>NEUROG3</i> mutation in neonatal diabetes associated with a neuro-intestinal syndrome, PEDIATRIC DIABETES, Vol: 19, Pages: 381-387, ISSN: 1399-543X
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- Citations: 13
Rivas MA, Avila BE, Koskela J, et al., 2018, Insights into the genetic epidemiology of Crohn's and rare diseases in the Ashkenazi Jewish population., PLoS Genet, Vol: 14
As part of a broader collaborative network of exome sequencing studies, we developed a jointly called data set of 5,685 Ashkenazi Jewish exomes. We make publicly available a resource of site and allele frequencies, which should serve as a reference for medical genetics in the Ashkenazim (hosted in part at https://ibd.broadinstitute.org, also available in gnomAD at http://gnomad.broadinstitute.org). We estimate that 34% of protein-coding alleles present in the Ashkenazi Jewish population at frequencies greater than 0.2% are significantly more frequent (mean 15-fold) than their maximum frequency observed in other reference populations. Arising via a well-described founder effect approximately 30 generations ago, this catalog of enriched alleles can contribute to differences in genetic risk and overall prevalence of diseases between populations. As validation we document 148 AJ enriched protein-altering alleles that overlap with "pathogenic" ClinVar alleles (table available at https://github.com/macarthur-lab/clinvar/blob/master/output/clinvar.tsv), including those that account for 10-100 fold differences in prevalence between AJ and non-AJ populations of some rare diseases, especially recessive conditions, including Gaucher disease (GBA, p.Asn409Ser, 8-fold enrichment); Canavan disease (ASPA, p.Glu285Ala, 12-fold enrichment); and Tay-Sachs disease (HEXA, c.1421+1G>C, 27-fold enrichment; p.Tyr427IlefsTer5, 12-fold enrichment). We next sought to use this catalog, of well-established relevance to Mendelian disease, to explore Crohn's disease, a common disease with an estimated two to four-fold excess prevalence in AJ. We specifically attempt to evaluate whether strong acting rare alleles, particularly protein-truncating or otherwise large effect-size alleles, enriched by the same founder-effect, contribute excess genetic risk to Crohn's disease in AJ, and find that ten rare genetic risk factors in NOD2 and LRRK2 are enriched in AJ (p < 0.005), including s
Mahajan A, Wessel J, Willems SM, et al., 2018, Refining the accuracy of validated target identification through coding variant fine-mapping in type 2 diabetes, Nature Genetics, Vol: 50, Pages: 559-559, ISSN: 1061-4036
We aggregated coding variant data for 81,412 type 2 diabetes cases and 370,832 controls of diverse ancestry, identifying 40 coding variant association signals (P < 2.2 × 10−7); of these, 16 map outside known risk-associated loci. We make two important observations. First, only five of these signals are driven by low-frequency variants: even for these, effect sizes are modest (odds ratio ≤1.29). Second, when we used large-scale genome-wide association data to fine-map the associated variants in their regional context, accounting for the global enrichment of complex trait associations in coding sequence, compelling evidence for coding variant causality was obtained for only 16 signals. At 13 others, the associated coding variants clearly represent ‘false leads’ with potential to generate erroneous mechanistic inference. Coding variant associations offer a direct route to biological insight for complex diseases and identification of validated therapeutic targets; however, appropriate mechanistic inference requires careful specification of their causal contribution to disease predisposition.
Hegron A, Plouffe B, Bonnefond A, et al., 2018, Identification of key regions mediating human melatonin type 1 receptor biased signaling revealed by natural variants, Experimental Biology Meeting, Publisher: FEDERATION AMER SOC EXP BIOL, ISSN: 0892-6638
Plouffe B, Karamitri A, Flock T, et al., 2018, Identification of residues in human melatonin type 2 receptor involved in signaling selectivity or general signal transmission using natural variants, Experimental Biology Meeting, Publisher: FEDERATION AMER SOC EXP BIOL, ISSN: 0892-6638
Saeed S, Arslan M, Froguel P, 2018, Genetics of Obesity in Consanguineous Populations: Toward Precision Medicine and the Discovery of Novel Obesity Genes, OBESITY, Vol: 26, Pages: 474-484, ISSN: 1930-7381
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- Citations: 20
Sung YJ, Lehne B, Scott WR, et al., 2018, A large-scale multi-ancestry genome-wide study accounting for smoking bahavior identifies multiple genome-wide significant loci for systolic and diastolic blood pressure, American Journal of Human Genetics, Vol: 102, Pages: 375-400, ISSN: 0002-9297
Genome-wide association analysis advanced understanding of blood pressure (BP), a major risk factor for vascular conditions such as coronary heart disease and stroke. Accounting for smoking behavior may help identify novel BP loci and extend our knowledge of its genetic architecture. We performed genome-wide association meta-analyses of systolic and diastolic BP incorporating gene-smoking interactionsin 610,091 individuals. Stage 1 analysis examined ~18.8 million SNPs and small insertion/deletion variants in 129,913 individuals from four ancestries (European, African, Asian, and Hispanic) with follow-upanalysis of promising variants in 480,178 additional individuals from five ancestries. Weidentified 15 new loci that were genome-wide significant (P < 5×10-8) in Stage 1 and formally replicated in Stage 2. A combined Stage 1 and 2 meta-analysis identified 66 additional genome-wide significant loci ( 13, 35, and 18 loci in European, African and trans-ancestry, respectively). A total of 56 known BP loci were also identified by our results (P < 5×10-8).O f the newly identified loci, 10 showed significant interaction with smoking status, but none of them were replicated in Stage 2. Several loci were identified in African ancestry, highlighting the importance of genetic studies in diverse populations. The identified loci show strong evidence for regulatory features and support shared pathophysiology with cardiometabolic and addiction traits. They also highlight a role in BP regulation for biological candidates such as modulators of vascular structure and function (CDKN1B, BCAR1-CFDP1, PXDN, EEA1), ciliopathies(SDCCAG8,RPGRIP1L), telomere maintenance (TNKS, PINX1, AKTIP), and central dopaminergic signaling (MSRA, EBF2)
Foucan L, Larifla L, Durand E, et al., 2018, High Prevalence of Rare Monogenic Forms of Obesity in Obese Guadeloupean Afro-Caribbean Children, JOURNAL OF CLINICAL ENDOCRINOLOGY & METABOLISM, Vol: 103, Pages: 539-545, ISSN: 0021-972X
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- Citations: 19
Rabhi N, Hannon SA, Gromada X, et al., 2018, <i>Cdkn2a</i> deficiency promotes adipose tissue browning, MOLECULAR METABOLISM, Vol: 8, Pages: 65-76, ISSN: 2212-8778
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- Citations: 26
Flannick J, Fuchsberger C, Mahajan A, et al., 2018, Erratum: Sequence data and association statistics from 12,940 type 2 diabetes cases and controls, Scientific Data, Vol: 5, ISSN: 2052-4463
Saeed S, Bonnefond A, Tamanini F, et al., 2018, Loss-of-function mutations in ADCY3 cause monogenic severe obesity, NATURE GENETICS, Vol: 50, Pages: 175-179, ISSN: 1061-4036
Study of monogenic forms of obesity has demonstrated the pivotal role of the central leptin–melanocortin pathway in controlling energy balance, appetite and body weight1. The majority of loss-of-function mutations (mostly recessive or co-dominant) have been identified in genes that are directly involved in leptin–melanocortin signaling. These genes, however, only explain obesity in <5% of cases, predominantly from outbred populations2. We previously showed that, in a consanguineous population in Pakistan, recessive mutations in known obesity-related genes explain ~30% of cases with severe obesity3,4,5. These data suggested that new monogenic forms of obesity could also be identified in this population. Here we identify and functionally characterize homozygous mutations in the ADCY3 gene encoding adenylate cyclase 3 in children with severe obesity from consanguineous Pakistani families, as well as compound heterozygous mutations in a severely obese child of European-American descent. These findings highlight ADCY3 as an important mediator of energy homeostasis and an attractive pharmacological target in the treatment of obesity.
Lebenthal Y, Shvalb NF, Gozlan Y, et al., 2018, The unique clinical spectrum of maturity onset diabetes of the young type 3, DIABETES RESEARCH AND CLINICAL PRACTICE, Vol: 135, Pages: 18-22, ISSN: 0168-8227
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- Citations: 5
Bril G, Vaxillaire M, Gruber N, et al., 2018, Life Changing Decisions Due to Etiological Genetic Diagnosis in Families of Children with Maturity Onset Diabetes of the Young (MODY), Publisher: KARGER, Pages: 208-209, ISSN: 1663-2818
Flannick J, Froguel P, Prokopenko I, et al., 2017, Sequence data and association statistics from 12,940 type 2 diabetes cases and controls, Scientific Data, Vol: 4, ISSN: 2052-4463
To investigate the genetic basis of type 2 diabetes (T2D) to high resolution, the GoT2D and T2D-GENES consortia catalogued variation from whole-genome sequencing of 2,657 European individuals and exome sequencing of 12,940 individuals of multiple ancestries. Over 27M SNPs, indels, and structural variants were identified, including 99% of low-frequency (minor allele frequency [MAF] 0.1–5%) non-coding variants in the whole-genome sequenced individuals and 99.7% of low-frequency coding variants in the whole-exome sequenced individuals. Each variant was tested for association with T2D in the sequenced individuals, and, to increase power, most were tested in larger numbers of individuals (>80% of low-frequency coding variants in ~82 K Europeans via the exome chip, and ~90% of low-frequency non-coding variants in ~44 K Europeans via genotype imputation). The variants, genotypes, and association statistics from these analyses provide the largest reference to date of human genetic information relevant to T2D, for use in activities such as T2D-focused genotype imputation, functional characterization of variants or genes, and other novel analyses to detect associations between sequence variation and T2D.
Bonnefond A, Froguel P, 2017, Disentangling the Role of Melatonin and its Receptor MTNR1B in Type 2 Diabetes: Still a Long Way to Go?, CURRENT DIABETES REPORTS, Vol: 17, ISSN: 1534-4827
Márquez-Luna C, Loh P-R, South Asian Type 2 Diabetes SAT2D Consortium, et al., 2017, Multiethnic polygenic risk scores improve risk prediction in diverse populations., Genet Epidemiol, Vol: 41, Pages: 811-823
Methods for genetic risk prediction have been widely investigated in recent years. However, most available training data involves European samples, and it is currently unclear how to accurately predict disease risk in other populations. Previous studies have used either training data from European samples in large sample size or training data from the target population in small sample size, but not both. Here, we introduce a multiethnic polygenic risk score that combines training data from European samples and training data from the target population. We applied this approach to predict type 2 diabetes (T2D) in a Latino cohort using both publicly available European summary statistics in large sample size (Neff = 40k) and Latino training data in small sample size (Neff = 8k). Here, we attained a >70% relative improvement in prediction accuracy (from R2 = 0.027 to 0.047) compared to methods that use only one source of training data, consistent with large relative improvements in simulations. We observed a systematically lower load of T2D risk alleles in Latino individuals with more European ancestry, which could be explained by polygenic selection in ancestral European and/or Native American populations. We predict T2D in a South Asian UK Biobank cohort using European (Neff = 40k) and South Asian (Neff = 16k) training data and attained a >70% relative improvement in prediction accuracy, and application to predict height in an African UK Biobank cohort using European (N = 113k) and African (N = 2k) training data attained a 30% relative improvement. Our work reduces the gap in polygenic risk prediction accuracy between European and non-European target populations.
Solimena M, Schulte AM, Marselli L, et al., 2017, Systems biology of the IMIDIA biobank from organ donors and pancreatectomised patients defines a novel transcriptomic signature of islets from individuals with type 2 diabetes., Diabetologia, Vol: 61, Pages: 641-657, ISSN: 0012-186X
AIMS/HYPOTHESIS: Pancreatic islet beta cell failure causes type 2 diabetes in humans. To identify transcriptomic changes in type 2 diabetic islets, the Innovative Medicines Initiative for Diabetes: Improving beta-cell function and identification of diagnostic biomarkers for treatment monitoring in Diabetes (IMIDIA) consortium ( www.imidia.org ) established a comprehensive, unique multicentre biobank of human islets and pancreas tissues from organ donors and metabolically phenotyped pancreatectomised patients (PPP). METHODS: Affymetrix microarrays were used to assess the islet transcriptome of islets isolated either by enzymatic digestion from 103 organ donors (OD), including 84 non-diabetic and 19 type 2 diabetic individuals, or by laser capture microdissection (LCM) from surgical specimens of 103 PPP, including 32 non-diabetic, 36 with type 2 diabetes, 15 with impaired glucose tolerance (IGT) and 20 with recent-onset diabetes (<1 year), conceivably secondary to the pancreatic disorder leading to surgery (type 3c diabetes). Bioinformatics tools were used to (1) compare the islet transcriptome of type 2 diabetic vs non-diabetic OD and PPP as well as vs IGT and type 3c diabetes within the PPP group; and (2) identify transcription factors driving gene co-expression modules correlated with insulin secretion ex vivo and glucose tolerance in vivo. Selected genes of interest were validated for their expression and function in beta cells. RESULTS: Comparative transcriptomic analysis identified 19 genes differentially expressed (false discovery rate ≤0.05, fold change ≥1.5) in type 2 diabetic vs non-diabetic islets from OD and PPP. Nine out of these 19 dysregulated genes were not previously reported to be dysregulated in type 2 diabetic islets. Signature genes included TMEM37, which inhibited Ca2+-influx and insulin secretion in beta cells, and ARG2 and PPP1R1A, which promoted insulin secretion. Systems biology approaches identified HNF1A, PDX1 and REST as drivers o
Rabhi N, Hannou SA, Froguel P, et al., 2017, Cofactors As Metabolic Sensors Driving Cell Adaptation in Physiology and Disease, FRONTIERS IN ENDOCRINOLOGY, Vol: 8, ISSN: 1664-2392
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- Citations: 13
Balkau B, Roussel R, Wagner S, et al., 2017, Transmission of Type 2 diabetes to sons and daughters: the DESIR cohort, DIABETIC MEDICINE, Vol: 34, Pages: 1615-1622, ISSN: 0742-3071
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- Citations: 4
Schormair B, Zhao C, Bell S, et al., 2017, Identification of novel risk loci for restless legs syndrome in genome-wide association studies in individuals of European ancestry: a meta-analysis, LANCET NEUROLOGY, Vol: 16, Pages: 898-907, ISSN: 1474-4422
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- Citations: 140
Wheeler E, Leong A, Liu C-T, et al., 2017, Impact of common genetic determinants of Hemoglobin A1c on type 2 diabetes risk and diagnosis in ancestrally diverse populations: A transethnic genome-wide meta-analysis., PLoS Medicine, Vol: 14, ISSN: 1549-1277
BACKGROUND: Glycated hemoglobin (HbA1c) is used to diagnose type 2 diabetes (T2D) and assess glycemic control in patients with diabetes. Previous genome-wide association studies (GWAS) have identified 18 HbA1c-associated genetic variants. These variants proved to be classifiable by their likely biological action as erythrocytic (also associated with erythrocyte traits) or glycemic (associated with other glucose-related traits). In this study, we tested the hypotheses that, in a very large scale GWAS, we would identify more genetic variants associated with HbA1c and that HbA1c variants implicated in erythrocytic biology would affect the diagnostic accuracy of HbA1c. We therefore expanded the number of HbA1c-associated loci and tested the effect of genetic risk-scores comprised of erythrocytic or glycemic variants on incident diabetes prediction and on prevalent diabetes screening performance. Throughout this multiancestry study, we kept a focus on interancestry differences in HbA1c genetics performance that might influence race-ancestry differences in health outcomes. METHODS & FINDINGS: Using genome-wide association meta-analyses in up to 159,940 individuals from 82 cohorts of European, African, East Asian, and South Asian ancestry, we identified 60 common genetic variants associated with HbA1c. We classified variants as implicated in glycemic, erythrocytic, or unclassified biology and tested whether additive genetic scores of erythrocytic variants (GS-E) or glycemic variants (GS-G) were associated with higher T2D incidence in multiethnic longitudinal cohorts (N = 33,241). Nineteen glycemic and 22 erythrocytic variants were associated with HbA1c at genome-wide significance. GS-G was associated with higher T2D risk (incidence OR = 1.05, 95% CI 1.04-1.06, per HbA1c-raising allele, p = 3 × 10-29); whereas GS-E was not (OR = 1.00, 95% CI 0.99-1.01, p = 0.60). In Europeans and Asians, erythrocytic variants in aggregate had only modest effects on the diagnostic
Bonnefond A, Derhourhi M, Montagne L, et al., 2017, StarSeq, an innovative method based on NGS for accurate detection of punctual mutations and copy number variants in obese children, 53rd Annual Meeting of the European-Association-for-the-Study-of-Diabetes (EASD), Publisher: SPRINGER, Pages: S132-S133, ISSN: 0012-186X
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