Genetic aetiology of glycaemic traits: approaches and insights.

Glycaemic traits such as fasting and post-challenge glucose and insulin measures, as well as glycated haemoglobin (HbA1c), are used to diagnose and monitor diabetes. These traits are risk factors for cardiovascular disease even below the diabetic threshold, and their study can additionally yield insights into the pathophysiology of type 2 diabetes. To date, a diverse set of genetic approaches have led to the discovery of over 97 loci influencing glycaemic traits. In this review, we will focus on recent advances in the genetic aetiology of glycaemic traits, and the resulting biological insights. We will provide a brief overview of results ranging from common, to low- and rare-frequency variant-trait association studies, studies leveraging the diversity across populations, and studies harnessing the power of genetic and genomic approaches to gain insights into the biological underpinnings of these traits

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

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

A. Palotie on työryhmän UK10K Consortium jäsen.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

Rare and low-frequency coding variants alter human adult height

Height is a highly heritable, classic polygenic trait with ~700 common associated variants identified so far through genome - wide association studies . Here , we report 83 height - associated coding variants with lower minor allele frequenc ies ( range of 0.1 - 4.8% ) and effects of up to 2 16 cm /allele ( e.g. in IHH , STC2 , AR and CRISPLD2 ) , >10 times the average effect of common variants . In functional follow - up studies, rare height - increasing alleles of STC2 (+1 - 2 cm/allele) compromise d proteolytic inhibition of PAPP - A and increased cleavage of IGFBP - 4 in vitro , resulting in higher bioavailability of insulin - like growth factors . The se 83 height - associated variants overlap genes mutated in monogenic growth disorders and highlight new biological candidates ( e.g. ADAMTS3, IL11RA, NOX4 ) and pathways ( e.g . proteoglycan/ glycosaminoglycan synthesis ) involved in growth . Our results demonstrate that sufficiently large sample sizes can uncover rare and low - frequency variants of moderate to large effect associated with polygenic human phenotypes , and that these variants implicate relevant genes and pathways

The trans-ancestral genomic architecture of glycemic traits

Glycemic traits are used to diagnose and monitor type 2 diabetes and cardiometabolic health. To date, most genetic studies of glycemic traits have focused on individuals of European ancestry. Here we aggregated genome-wide association studies comprising up to 281,416 individuals without diabetes (30% non-European ancestry) for whom fasting glucose, 2-h glucose after an oral glucose challenge, glycated hemoglobin and fasting insulin data were available. Trans-ancestry and single-ancestry meta-analyses identified 242 loci (99 novel; P < 5 x 10(-8)), 80% of which had no significant evidence of between-ancestry heterogeneity. Analyses restricted to individuals of European ancestry with equivalent sample size would have led to 24 fewer new loci. Compared with single-ancestry analyses, equivalent-sized trans-ancestry fine-mapping reduced the number of estimated variants in 99% credible sets by a median of 37.5%. Genomic-feature, gene-expression and gene-set analyses revealed distinct biological signatures for each trait, highlighting different underlying biological pathways. Our results increase our understanding of diabetes pathophysiology by using trans-ancestry studies for improved power and resolution. A trans-ancestry meta-analysis of GWAS of glycemic traits in up to 281,416 individuals identifies 99 novel loci, of which one quarter was found due to the multi-ancestry approach, which also improves fine-mapping of credible variant sets.Peer reviewe

Whole-genome sequence-based analysis of thyroid function

Tiina Paunio on työryhmän UK10K Consortium jäsen.Normal thyroid function is essential for health, but its genetic architecture remains poorly understood. Here, for the heritable thyroid traits thyrotropin (TSH) and free thyroxine (FT4), we analyse whole-genome sequence data from the UK10K project (N = 2,287). Using additional whole-genome sequence and deeply imputed data sets, we report meta-analysis results for common variants (MAF >= 1%) associated with TSH and FT4 (N = 16,335). For TSH, we identify a novel variant in SYN2 (MAF = 23.5%, P = 6.15 x 10(-9)) and a new independent variant in PDE8B (MAF = 10.4%, P = 5.94 x 10(-14)). For FT4, we report a low-frequency variant near B4GALT6/ SLC25A52 (MAF = 3.2%, P = 1.27 x 10(-9)) tagging a rare TTR variant (MAF = 0.4%, P = 2.14 x 10(-11)). All common variants explain >= 20% of the variance in TSH and FT4. Analysis of rare variants (MAFPeer reviewe

Bi-allelic Loss-of-Function CACNA1B Mutations in Progressive Epilepsy-Dyskinesia.

The occurrence of non-epileptic hyperkinetic movements in the context of developmental epileptic encephalopathies is an increasingly recognized phenomenon. Identification of causative mutations provides an important insight into common pathogenic mechanisms that cause both seizures and abnormal motor control. We report bi-allelic loss-of-function CACNA1B variants in six children from three unrelated families whose affected members present with a complex and progressive neurological syndrome. All affected individuals presented with epileptic encephalopathy, severe neurodevelopmental delay (often with regression), and a hyperkinetic movement disorder. Additional neurological features included postnatal microcephaly and hypotonia. Five children died in childhood or adolescence (mean age of death: 9 years), mainly as a result of secondary respiratory complications. CACNA1B encodes the pore-forming subunit of the pre-synaptic neuronal voltage-gated calcium channel Cav2.2/N-type, crucial for SNARE-mediated neurotransmission, particularly in the early postnatal period. Bi-allelic loss-of-function variants in CACNA1B are predicted to cause disruption of Ca2+ influx, leading to impaired synaptic neurotransmission. The resultant effect on neuronal function is likely to be important in the development of involuntary movements and epilepsy. Overall, our findings provide further evidence for the key role of Cav2.2 in normal human neurodevelopment.MAK is funded by an NIHR Research Professorship and receives funding from the Wellcome Trust, Great Ormond Street Children's Hospital Charity, and Rosetrees Trust. E.M. received funding from the Rosetrees Trust (CD-A53) and Great Ormond Street Hospital Children's Charity. K.G. received funding from Temple Street Foundation. A.M. is funded by Great Ormond Street Hospital, the National Institute for Health Research (NIHR), and Biomedical Research Centre. F.L.R. and D.G. are funded by Cambridge Biomedical Research Centre. K.C. and A.S.J. are funded by NIHR Bioresource for Rare Diseases. The DDD Study presents independent research commissioned by the Health Innovation Challenge Fund (grant number HICF-1009-003), a parallel funding partnership between the Wellcome Trust and the Department of Health, and the Wellcome Trust Sanger Institute (grant number WT098051). We acknowledge support from the UK Department of Health via the NIHR comprehensive Biomedical Research Centre award to Guy's and St. Thomas' National Health Service (NHS) Foundation Trust in partnership with King's College London. This research was also supported by the NIHR Great Ormond Street Hospital Biomedical Research Centre. J.H.C. is in receipt of an NIHR Senior Investigator Award. The research team acknowledges the support of the NIHR through the Comprehensive Clinical Research Network. The views expressed are those of the author(s) and not necessarily those of the NHS, the NIHR, Department of Health, or Wellcome Trust. E.R.M. acknowledges support from NIHR Cambridge Biomedical Research Centre, an NIHR Senior Investigator Award, and the University of Cambridge has received salary support in respect of E.R.M. from the NHS in the East of England through the Clinical Academic Reserve. I.E.S. is supported by the National Health and Medical Research Council of Australia (Program Grant and Practitioner Fellowship)

Statistical methods to combine SNP and CNV indorma in genome-wide association studies: an application to bladder cancer

Tesis doctoral inédita realizada conjuntamente en la Universidad Autónoma de Madrid, Facultad de Medicina, Departamento de Medicina Preventiva y Salud Pública y la Université Paris-Sud XI, Faculté de Médicine. Los laboratorios del la investigación fueron: Centro Nacional de Investigaciones Oncológicas (CNIO) y French National Institute of Health and Medical Research (Inserm). Fecha de lectura: 28 de Septiembre de 2012

Utilisation conjointe de l'information apportée par les différents polymorphismes, SNPs et CNVs, dans les études d'association pangénomique : application au cancer de la vessie

Les variations en nombre de copies (CNV) sont des gains ou pertes d’une séquence d’ADN et peuvent avoir un rôle dans la susceptibilité à certaines maladies. Les CNVs peuvent être détectés par les puces de SNPs de haute résolution en analysant les intensités des allèles avec des algorithmes de détection des CNVs tels que CNV partition, PennCNV et QuantiSNP. Dans cette thèse, nous avons évalué les performances de ces outils pour la détection des CNVs au niveau pangénomique et pour les tests d'association. Nous avons également étudié des stratégies d'association combinant les informations de l'allèle et du nombre de copies pour des SNP situés dans des CNV. Nous avons appliqué ces outils pour mener une étude d’association pan-génomique avec les CNV en utilisant les données de l'étude espagnole du cancer de lavessie (SBC)/EPICURO générées par la puce Illumina 1M.Nos résultats montrent une faible fiabilité et une faible sensibilité des algorithmes de détection des CNV. Dans la région du gène GSTM1 où un CNV très fréquent existe qui est associé au risque de cancer de la vessie, nous avons constaté que les algorithmes de détection des CNV ont de faibles performances. Néanmoins, l’utilisation de la mesure d'intensité des allèles dans les tests d'association peut alors être une alternative intéressante car cela nous a permis de détecter cette association connue. Pour les SNPs situés dans des CNVs, nous avons étudié plusieurs stratégies de tests d'association et nous avons montré que la plus puissante était d’utiliser un modèle avec deux termes correspondant respectivement à la somme et à la différence du nombre de copies des deux allèles. Finalement, en appliquant ces stratégies à l'étude (SBC)/EPICURO, nous avons identifié des CNVs potentiellement associés au risque de cancer de la vessie, ainsi que des SNP dont l'allèle et le nombre de copies pourraient être impliqués dans le risque de cancer de la vessie.Copy number variations (CNVs) are losses or gains of DNA sequences that may play a role in specific disease susceptibility. CNVs can be detected by high-resolution SNP-arrays through the analysis of allele intensities with CNV calling algorithms such as CNVpartition, PennCNV and QuantiSNP. In this thesis, we identified and assessed the performances of available tools for CNV calling and for association testing, at the genome-wide level. We also investigatedassociation strategies that combine information on both the allele and the number of copies for SNPs located in CNV regions. We applied these tools to conduct a genome-wide association study with CNV using data from the Spanish Bladder Cancer (SBC)/EPICURO Study generated by the Illumina 1M SNP-array. Our results showed a low reliability and a low sensitivity of the investigated CNV calling algorithms applied to SNP-array data. The GSTM1 locus shows a very frequent CNV that is associated with bladder cancer (BC) risk. We reported that the calling algorithms performed very poorly in identifying this CNV. We proposed using allele intensity measures (LRR) as a screening step to assess association as it allowed the detection of the GSTM1 CNV association with BC. To combine the allele and the number of copies for SNPs located in CNV regions, we investigated several strategies of association testing and we showed that the more powerfulone used a two-term model with the sum and the difference of the number of copies of both alleles. Finally, by applying these strategies to the (SBC)/EPICURO Study, we identified CNV regions potentially associated with BC risk, as well as SNPs for which both the allele and the number of copies could be involved in BC risk

Ravages: An R package for the simulation and analysis of rare variants in multicategory phenotypes

Abstract Current software packages for the analysis and the simulations of rare variants are only available for binary and continuous traits. Ravages provides solutions in a single R package to perform rare variant association tests for multicategory, binary and continuous phenotypes, to simulate datasets under different scenarios and to compute statistical power. Association tests can be run in the whole genome thanks to C++ implementation of most of the functions, using either RAVA‐FIRST, a recently developed strategy to filter and analyse genome‐wide rare variants, or user‐defined candidate regions. Ravages also includes a simulation module that generates genetic data for cases who can be stratified into several subgroups and for controls. Through comparisons with existing programmes, we show that Ravages complements existing tools and will be useful to study the genetic architecture of complex diseases. Ravages is available on the CRAN at https://cran.r-project.org/web/packages/Ravages/ and maintained on Github at https://github.com/genostats/Ravages