127 research outputs found

    Rare variants in single-minded 1 (SIM1) are associated with severe obesity

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    Single-minded 1 (SIM1) is a basic helix-loop-helix transcription factor involved in the development and function of the paraventricular nucleus of the hypothalamus. Obesity has been reported in Sim1 haploinsufficient mice and in a patient with a balanced translocation disrupting SIM1. We sequenced the coding region of SIM1 in 2,100 patients with severe, early onset obesity and in 1,680 controls. Thirteen different heterozygous variants in SIM1 were identified in 28 unrelated severely obese patients. Nine of the 13 variants significantly reduced the ability of SIM1 to activate a SIM1-responsive reporter gene when studied in stably transfected cells coexpressing the heterodimeric partners of SIM1 (ARNT or ARNT2). SIM1 variants with reduced activity cosegregated with obesity in extended family studies with variable penetrance. We studied the phenotype of patients carrying variants that exhibited reduced activity in vitro. Variant carriers exhibited increased ad libitum food intake at a test meal, normal basal metabolic rate, and evidence of autonomic dysfunction. Eleven of the 13 probands had evidence of a neurobehavioral phenotype. The phenotypic similarities between patients with SIM1 deficiency and melanocortin 4 receptor (MC4R) deficiency suggest that some of the effects of SIM1 deficiency on energy homeostasis are mediated by altered melanocortin signaling.Shwetha Ramachandrappa, Anne Raimondo, Anna M.G. Cali, Julia M. Keough, Elana Henning, Sadia Saeed, Amanda Thompson, Sumedha Garg, Elena G. Bochukova, Soren Brage, Victoria Trowse, Eleanor Wheeler, Adrienne E. Sullivan, Mehul Dattani, Peter E. Clayton, Vippan Datta, John B. Bruning, Nick J. Wareham, Stephen O'Rahilly, Daniel J. Peet, Ines Barroso, Murray L. Whielaw and I. Sadaf Farooq

    Exome Sequencing Identifies Genes and Gene Sets Contributing to Severe Childhood Obesity, Linking PHIP Variants to Repressed POMC Transcription.

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    Obesity is genetically heterogeneous with monogenic and complex polygenic forms. Using exome and targeted sequencing in 2,737 severely obese cases and 6,704 controls, we identified three genes (PHIP, DGKI, and ZMYM4) with an excess burden of very rare predicted deleterious variants in cases. In cells, we found that nuclear PHIP (pleckstrin homology domain interacting protein) directly enhances transcription of pro-opiomelanocortin (POMC), a neuropeptide that suppresses appetite. Obesity-associated PHIP variants repressed POMC transcription. Our demonstration that PHIP is involved in human energy homeostasis through transcriptional regulation of central melanocortin signaling has potential diagnostic and therapeutic implications for patients with obesity and developmental delay. Additionally, we found an excess burden of predicted deleterious variants involving genes nearest to loci from obesity genome-wide association studies. Genes and gene sets influencing obesity with variable penetrance provide compelling evidence for a continuum of causality in the genetic architecture of obesity, and explain some of its missing heritability

    Rare Variant Analysis of Human and Rodent Obesity Genes in Individuals with Severe Childhood Obesity

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    A. Palotie on työryhmÀn UK10K Consortium jÀsen.Obesity is a genetically heterogeneous disorder. Using targeted and whole-exome sequencing, we studied 32 human and 87 rodent obesity genes in 2,548 severely obese children and 1,117 controls. We identified 52 variants contributing to obesity in 2% of cases including multiple novel variants in GNAS, which were sometimes found with accelerated growth rather than short stature as described previously. Nominally significant associations were found for rare functional variants in BBS1, BBS9, GNAS, MKKS, CLOCK and ANGPTL6. The p.S284X variant in ANGPTL6 drives the association signal (rs201622589, MAF similar to 0.1%, odds ratio = 10.13, p-value = 0.042) and results in complete loss of secretion in cells. Further analysis including additional case-control studies and population controls (N = 260,642) did not support association of this variant with obesity (odds ratio = 2.34, p-value = 2.59 x 10(-3)), highlighting the challenges of testing rare variant associations and the need for very large sample sizes. Further validation in cohorts with severe obesity and engineering the variants in model organisms will be needed to explore whether human variants in ANGPTL6 and other genes that lead to obesity when deleted in mice, do contribute to obesity. Such studies may yield druggable targets for weight loss therapies.Peer reviewe

    Hundreds of variants clustered in genomic loci and biological pathways affect human height

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    Most common human traits and diseases have a polygenic pattern of inheritance: DNA sequence variants at many genetic loci influence the phenotype. Genome-wide association (GWA) studies have identified more than 600 variants associated with human traits, but these typically explain small fractions of phenotypic variation, raising questions about the use of further studies. Here, using 183,727 individuals, we show that hundreds of genetic variants, in at least 180 loci, influence adult height, a highly heritable and classic polygenic trait. The large number of loci reveals patterns with important implications for genetic studies of common human diseases and traits. First, the 180 loci are not random, but instead are enriched for genes that are connected in biological pathways (P = 0.016) and that underlie skeletal growth defects (P < 0.001). Second, the likely causal gene is often located near the most strongly associated variant: in 13 of 21 loci containing a known skeletal growth gene, that gene was closest to the associated variant. Third, at least 19 loci have multiple independently associated variants, suggesting that allelic heterogeneity is a frequent feature of polygenic traits, that comprehensive explorations of already-discovered loci should discover additional variants and that an appreciable fraction of associated loci may have been identified. Fourth, associated variants are enriched for likely functional effects on genes, being over-represented among variants that alter amino-acid structure of proteins and expression levels of nearby genes. Our data explain approximately 10% of the phenotypic variation in height, and we estimate that unidentified common variants of similar effect sizes would increase this figure to approximately 16% of phenotypic variation (approximately 20% of heritable variation). Although additional approaches are needed to dissect the genetic architecture of polygenic human traits fully, our findings indicate that GWA studies can identify large numbers of loci that implicate biologically relevant genes and pathways.

    New genetic loci implicated in fasting glucose homeostasis and their impact on type 2 diabetes risk.

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    Levels of circulating glucose are tightly regulated. To identify new loci influencing glycemic traits, we performed meta-analyses of 21 genome-wide association studies informative for fasting glucose, fasting insulin and indices of beta-cell function (HOMA-B) and insulin resistance (HOMA-IR) in up to 46,186 nondiabetic participants. Follow-up of 25 loci in up to 76,558 additional subjects identified 16 loci associated with fasting glucose and HOMA-B and two loci associated with fasting insulin and HOMA-IR. These include nine loci newly associated with fasting glucose (in or near ADCY5, MADD, ADRA2A, CRY2, FADS1, GLIS3, SLC2A2, PROX1 and C2CD4B) and one influencing fasting insulin and HOMA-IR (near IGF1). We also demonstrated association of ADCY5, PROX1, GCK, GCKR and DGKB-TMEM195 with type 2 diabetes. Within these loci, likely biological candidate genes influence signal transduction, cell proliferation, development, glucose-sensing and circadian regulation. Our results demonstrate that genetic studies of glycemic traits can identify type 2 diabetes risk loci, as well as loci containing gene variants that are associated with a modest elevation in glucose levels but are not associated with overt diabetes

    Pathogenic huntingtin inhibits fast axonal transport by activating JNK3 and phosphorylating kinesin

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    Author Posting. © The Author(s), 2009. This is the author's version of the work. It is posted here by permission of Nature America for personal use, not for redistribution. The definitive version was published in Nature Neuroscience 12 (2009): 864-871, doi:10.1038/nn.2346.Selected vulnerability of neurons in Huntington’s disease (HD) suggests alterations in a cellular process particularly critical for neuronal function. Supporting this idea, pathogenic Htt (polyQ-Htt) inhibits fast axonal transport (FAT) in various cellular and animal HD models (mouse and squid), but the molecular basis of this effect remains unknown. Here we show that polyQ-Htt inhibits FAT through a mechanism involving activation of axonal JNK. Accordingly, increased activation of JNK was observed in vivo in cellular and animal HD models. Additional experiments indicate that polyQ-Htt effects on FAT are mediated by the neuron-specific JNK3, and not ubiquitously expressed JNK1, providing a molecular basis for neuron-specific pathology in HD. Mass spectrometry identified a residue in the kinesin-1 motor domain phosphorylated by JNK3, and this modification reduces kinesin-1 binding to microtubules. These data identify JNK3 as a critical mediator of polyQ-Htt toxicity and provides a molecular basis for polyQ-Htt-induced inhibition of FAT.This work was supported by 2007/2008 MBL summer fellowship to GM; an HDSA grant to GM; NIH grants MH066179 to GB; and ALSA, Muscular Dystrophy Association, and NIH (NS23868, NS23320, NS41170) grants to STB

    The association between circulating 25-hydroxyvitamin D metabolites and type 2 diabetes in European populations: A meta-analysis and Mendelian randomisation analysis

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    Funder: NIHR Biomedical Research Centre Cambridge: Nutrition, Diet, and Lifestyle Research Theme; Grant(s): IS-BRC-1215-20014Background: Prior research suggested a differential association of 25-hydroxyvitamin D (25(OH)D) metabolites with type 2 diabetes (T2D), with total 25(OH)D and 25(OH)D3 inversely associated with T2D, but the epimeric form (C3-epi-25(OH)D3) positively associated with T2D. Whether or not these observational associations are causal remains uncertain. We aimed to examine the potential causality of these associations using Mendelian randomisation (MR) analysis. Methods and findings: We performed a meta-analysis of genome-wide association studies for total 25(OH)D (N = 120,618), 25(OH)D3 (N = 40,562), and C3-epi-25(OH)D3 (N = 40,562) in participants of European descent (European Prospective Investigation into Cancer and Nutrition [EPIC]–InterAct study, EPIC-Norfolk study, EPIC-CVD study, Ely study, and the SUNLIGHT consortium). We identified genetic variants for MR analysis to investigate the causal association of the 25(OH)D metabolites with T2D (including 80,983 T2D cases and 842,909 non-cases). We also estimated the observational association of 25(OH)D metabolites with T2D by performing random effects meta-analysis of results from previous studies and results from the EPIC-InterAct study. We identified 10 genetic loci associated with total 25(OH)D, 7 loci associated with 25(OH)D3 and 3 loci associated with C3-epi-25(OH)D3. Based on the meta-analysis of observational studies, each 1–standard deviation (SD) higher level of 25(OH)D was associated with a 20% lower risk of T2D (relative risk [RR]: 0.80; 95% CI 0.77, 0.84; p < 0.001), but a genetically predicted 1-SD increase in 25(OH)D was not significantly associated with T2D (odds ratio [OR]: 0.96; 95% CI 0.89, 1.03; p = 0.23); this result was consistent across sensitivity analyses. In EPIC-InterAct, 25(OH)D3 (per 1-SD) was associated with a lower risk of T2D (RR: 0.81; 95% CI 0.77, 0.86; p < 0.001), while C3-epi-25(OH)D3 (above versus below lower limit of quantification) was positively associated with T2D (RR: 1.12; 95% CI 1.03, 1.22; p = 0.006), but neither 25(OH)D3 (OR: 0.97; 95% CI 0.93, 1.01; p = 0.14) nor C3-epi-25(OH)D3 (OR: 0.98; 95% CI 0.93, 1.04; p = 0.53) was causally associated with T2D risk in the MR analysis. Main limitations include the lack of a non-linear MR analysis and of the generalisability of the current findings from European populations to other populations of different ethnicities. Conclusions: Our study found discordant associations of biochemically measured and genetically predicted differences in blood 25(OH)D with T2D risk. The findings based on MR analysis in a large sample of European ancestry do not support a causal association of total 25(OH)D or 25(OH)D metabolites with T2D and argue against the use of vitamin D supplementation for the prevention of T2D

    Integrative genomic analysis implicates limited peripheral adipose storage capacity in the pathogenesis of human insulin resistance.

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    Insulin resistance is a key mediator of obesity-related cardiometabolic disease, yet the mechanisms underlying this link remain obscure. Using an integrative genomic approach, we identify 53 genomic regions associated with insulin resistance phenotypes (higher fasting insulin levels adjusted for BMI, lower HDL cholesterol levels and higher triglyceride levels) and provide evidence that their link with higher cardiometabolic risk is underpinned by an association with lower adipose mass in peripheral compartments. Using these 53 loci, we show a polygenic contribution to familial partial lipodystrophy type 1, a severe form of insulin resistance, and highlight shared molecular mechanisms in common/mild and rare/severe insulin resistance. Population-level genetic analyses combined with experiments in cellular models implicate CCDC92, DNAH10 and L3MBTL3 as previously unrecognized molecules influencing adipocyte differentiation. Our findings support the notion that limited storage capacity of peripheral adipose tissue is an important etiological component in insulin-resistant cardiometabolic disease and highlight genes and mechanisms underpinning this link.This study was funded by the UK Medical Research Council through grants MC_UU_12015/1, MC_PC_13046, MC_PC_13048 and MR/L00002/1. This work was supported by the MRC Metabolic Diseases Unit (MC_UU_12012/5) and the Cambridge NIHR Biomedical Research Centre and EU/EFPIA Innovative Medicines Initiative Joint Undertaking (EMIF grant 115372). Funding for the InterAct project was provided by the EU FP6 program (grant LSHM_CT_2006_037197). This work was funded, in part, through an EFSD Rising Star award to R.A.S. supported by Novo Nordisk. D.B.S. is supported by Wellcome Trust grant 107064. M.I.M. is a Wellcome Trust Senior Investigator and is supported by the following grants from the Wellcome Trust: 090532 and 098381. M.v.d.B. is supported by a Novo Nordisk postdoctoral fellowship run in partnership with the University of Oxford. I.B. is supported by Wellcome Trust grant WT098051. S.O'R. acknowledges funding from the Wellcome Trust (Wellcome Trust Senior Investigator Award 095515/Z/11/Z and Wellcome Trust Strategic Award 100574/Z/12/Z)

    Meta-analysis of Gene-Level Associations for Rare Variants Based on Single-Variant Statistics

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    Meta-analysis of genome-wide association studies (GWASs) has led to the discoveries of many common variants associated with complex human diseases. There is a growing recognition that identifying “causal” rare variants also requires large-scale meta-analysis. The fact that association tests with rare variants are performed at the gene level rather than at the variant level poses unprecedented challenges in the meta-analysis. First, different studies may adopt different gene-level tests, so the results are not compatible. Second, gene-level tests require multivariate statistics (i.e., components of the test statistic and their covariance matrix), which are difficult to obtain. To overcome these challenges, we propose to perform gene-level tests for rare variants by combining the results of single-variant analysis (i.e., p values of association tests and effect estimates) from participating studies. This simple strategy is possible because of an insight that multivariate statistics can be recovered from single-variant statistics, together with the correlation matrix of the single-variant test statistics, which can be estimated from one of the participating studies or from a publicly available database. We show both theoretically and numerically that the proposed meta-analysis approach provides accurate control of the type I error and is as powerful as joint analysis of individual participant data. This approach accommodates any disease phenotype and any study design and produces all commonly used gene-level tests. An application to the GWAS summary results of the Genetic Investigation of ANthropometric Traits (GIANT) consortium reveals rare and low-frequency variants associated with human height. The relevant software is freely available
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