Ncomms-18-33880B

Total Page:16

File Type:pdf, Size:1020Kb

Ncomms-18-33880B ORE Open Research Exeter TITLE Genome-wide association study identifies genetic loci for self-reported habitual sleep duration supported by accelerometer-derived estimates. AUTHORS Dashti, HS; Jones, SE; Wood, AR; et al. JOURNAL Nature Communications DEPOSITED IN ORE 15 March 2019 This version available at http://hdl.handle.net/10871/36477 COPYRIGHT AND REUSE Open Research Exeter makes this work available in accordance with publisher policies. A NOTE ON VERSIONS The version presented here may differ from the published version. If citing, you are advised to consult the published version for pagination, volume/issue and date of publication NCOMMS-18-33880B Dashti et al. a. Sleep Duration Heritability = 9.8 (0.1)% ) P ( 10 log - Chromosome 1 2 3 4 5 6 7 10 11 14 17 19 C1orf94 PAX8 PCCB BANK1 PAM SCAND3 FOXP2 LOC100131719 HSD17B12 LOC100128215 LOC644157 PIN1 GJB5 LOC100130100 PPP2R3A LCORL PPIP5K2 LOC646160 MAD1L1 GPR26 METT5D1 PRKD1 LOC644172 OLFM2 PDE4B VRK2 STAG1 LOC645174 GIN1 FAM83B CHCHD3 MLLT10 OR2BH1P NOVA1 PER1 DAB1 GALNT3 MSL2 PRKG2 C5ORF30 MEA1 DNAJC1 C11orf63 ADCK1 PFAS DPYD SCN1A BBX, CCSER1 SLC6A3 PPP2R5D 8 SKIDA1 DRD2 FRDAP SMAD5-AS5 20 TTC21B LOC285205 LOC100132531 SMAD5-AS2 PPP1R3B EGR2 PTPRJ RTN1 VAMP2 YWHAB PABPCP2 LOC100128160 LOC285577 SRF PABPC1L LOC100129150 ADO OR4X2 12 FNTB AURKB LOC100133235 FOXP1 FGFR4 CUL7 FGF8 OR4B1 GPX2 ARHGEF15 SLC8A1 IL20RB V27 SLC34A1 DNPH1 KSR2 9 NFKB2 OR4S1 MVK MAX RANGRF MBOAT2 NPM1P17 RGS14 CUL9 ZCCHC7 PITX3 OR4X1 CHURC1 BORCS6 MAP2 ERC2 LMAN2 MRPL2 KCTD10 MRPL41 PSD TRPC6 UBE3B RAB15 CTC1, SPOPL PRELID1 TTBK1 EHMT1 FBXW4 ARCN1 TMEM107 GPD2 NSD1 RRP36 MYO1H ARRDC1-AS1 GBF1 KMT2A MMAB 15 KRBA2 NR4A2 MXD3 KLC4 ARRDC1 LDB1 IFT46 SEMA6D SLC25A35 CDC25C KLHDC3 DPH7 BTRC TMEM25 CA10 EGR1 LOC100129847 ZMYND19 NOLC1 LOC729790 LOC339209 ETF1 LOC100128159 NPM3 LOC646195 GFRA3 PNRC1 MGEA5 BUD13 16 18 HSPA9 KIFC1, PHF1 PPRC1 POLL GRM5 FTO TCF4 NME5 SYNGAP1 KCNIP2 CUEDC2 CA10 CDC23 GNAO1 CUTA FBXL15 LOC339209 RBFOX1 KIF20A C10ORF76 MYRF BRD8 HPS6 V27 TMEM258 PRKCB FAM53C C10orf95 FEN1 REEP2 ELOVL3 FADS1 KDM3B FADS3 SMAD5 RAB3IL1 SMAD5-AS1 FADS2 b. TRPC7 2 3 5 PAX8 AMT, APEH, RHOA, DAG1, SMAD5 11 GNAI2, GNAT1, GPX1, HYAL1, Short Sleep METT5D1 Heritability = 7.9 (0.1)% LOC100130100 SMAD5-AS1 OR2BH1P MST1, MST1R, SEMA3F, TRPC7 LOC647016 TCTA, UBA7 USP4, IFRD2, NDUFS3 LOC100131953 POLS 1 SEMA3B, HYAL3 HYAL2, BSN, LOC100130063 7 PSMC3 LMOD1 CACNA2D2, IP6K1, RBM6, FOXP2 PTPRJ PAM MAD1L1 RAPSN IPO9 RBM5, TRAIP, NPRL2, PPIP5K2 6 NAV1 SLC38A3, CYB561D2, GIN1 ZSCAN12 CELF1 16 18 ZSCAN31 NUP160 TCF4 SHISA4 TMEM115, RASSF1, TUSC2, c5orf30 GNAO1 22 DPYD NAT6, GMPPB, ZMYND10, ZKSCAN3 FNBP4 4 8 15 MGAT3 ) PDE4B RNF123, CAMKV, NICN1, SLC39A8 HCRTR2 MTCH2 USP49 PCMTD1 KBTBD4 SEMA6D SYNGR P C1orf94 GJB5 MON1A, LSMEM2, AMIGO3 PRKG2 ( PXDNL TAB1 FAM212A, ACTBP13 LAMA2 AGBL2 17 10 C1QTNF4 SHISA6 log - 0 2 4 ) P ( 10 6 log - 8 1 11 17 CAMTA1 MPZL2 KIAA1267 5 JAML 16 LOC644246 RAB3C FTO PDE4D MPZL3 10 C11orf63 GUCY2E 2 PAX8 12 LOC100130100 Long Sleep Heritability = 4.7 (0.1)% OB:oadnlt_yls_atvtn_gpoenculd rece g_protein_coupled_ activating_ denylate_cyclase_ GO_Bp:go_a KEGG_LONG_TERM_DEPRESSION a. b. REACTOME_TRANSMISSION_ACROSS_CHEMICAL_SYNAPSES uae_ n_esiaoahMt oei_arly_progenitor poiesis_ea ene_sets:ivanova_heMato Curated_g EnrichMent -log10(P) MAGMA Curated_gene_sets:ivanova_heMatopoiesis GO_Bp:go_response_to_auditory_stiMulus REACTOME_NEURONAL_SYSTEM 0 1 2 3 4 5 6 7 GO_cc:go_soMatodendritic_coMpartMent GO_Bp:go_adenylate_cyclase_activating GO_Bp:go_Mechanosensory_Behavior ptor_ signa ling_ pathway GO_Bp:go_suBpalliuM_developMent OB:orepnet_u t ry_stiMulus ito esponse_to_aud GO_Bp:go_r Oc:osMtd dii_oprM nt ndritic_coMpartMe GO_cc:go_soMatode GO_Bp:go_striatuM_developMent OB:oMehnsnoyBh vior echanosensory_Beha GO_Bp:go_M OB:osBpliM developMent palliuM_ GO_Bp:go_suB GO_Mf:go_dopaMine_Binding GO_Bp:go_synaptic GO_cc:go_neuron_projection OB:osriatuM_developMent GO_Bp:go_str KEGG_MAPK_SIGNALING_PATHWAY _ OB:osnp ic_signaling GO_Bp:go_synapt GO_Mf:go_d opaMine_Binding Oc:onu on_projection GO_cc:go_neur gpcr_signaling_pathway _ early_progenitor signaling KEGG_GAP_JUNCTION PASCAL REACTOME_SIGNALING_BY_FGFR_I N_DISEASE REACTOME_HEMOSTASIS 0 EnrichMent EnrichMent REACTOME_SIGNALING_BY_FGFR 2 REACTOME_MEIOTIC_SYNAPSIS REACTOME_PLC_BETA_MEDIATED_EVENTS 4 - log 6 10 ( P ) 8 c. EnrichMent -log10(P) ADRB2 ALDH1A3 ASCL1 BBS2 BBS4 BCL11B CNTNAP2 DLX1 DLX2 DRD1 DRD2 DRD3 DRD4 DRD5 ETV1 MAGMA FGF8 FOXP2 GLI3 GPR21 GPR52 GSX2 HTT INHBA MKKS NRXN1 NRXN2 OGDH RARB SECISBP2 SHANK3 SLC1A3 SLC6A3 SLITRK5 SLITRK6 STRA6 STRBP TH Sleep Duration * * * * * * Short Sleep * * * * * * * Long Sleep * * * * * * * * a. b. MR Test Inverse variance weighted Weighted median MR Egger 0.10 ● ● ● ● 0.05 ● ● ● ● ● ● ● ● ● ● ● ● ● ● ●● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● ● 0.00 ● ● ● ● ● ● ●● ● ● ● ● ● SNP effect on Schizophrenia || id:22 on Schizophrenia SNP effect ● ● ● ● ● ● ● ● ● ● ● SNP effect on schizophrenia, log odds ● ● −0.05 1.0 1.5 2.0 2.5 SNP effect on mrbase_sleepduration_schizophrenia.csv SNP effect on sleep duration, minutes MR effect of sleep duration on schizophrenia, log odds c. d. SNP effect on sleep duration, minutes SNP effect on schizophrenia, log odds MR effect of schizophrenia on sleep duration, minutes.
Recommended publications
  • Sean Raspet – Molecules
    1. Commercial name: Fructaplex© IUPAC Name: 2-(3,3-dimethylcyclohexyl)-2,5,5-trimethyl-1,3-dioxane SMILES: CC1(C)CCCC(C1)C2(C)OCC(C)(C)CO2 Molecular weight: 240.39 g/mol Volume (cubic Angstroems): 258.88 Atoms number (non-hydrogen): 17 miLogP: 4.43 Structure: Biological Properties: Predicted Druglikenessi: GPCR ligand -0.23 Ion channel modulator -0.03 Kinase inhibitor -0.6 Nuclear receptor ligand 0.15 Protease inhibitor -0.28 Enzyme inhibitor 0.15 Commercial name: Fructaplex© IUPAC Name: 2-(3,3-dimethylcyclohexyl)-2,5,5-trimethyl-1,3-dioxane SMILES: CC1(C)CCCC(C1)C2(C)OCC(C)(C)CO2 Predicted Olfactory Receptor Activityii: OR2L13 83.715% OR1G1 82.761% OR10J5 80.569% OR2W1 78.180% OR7A2 77.696% 2. Commercial name: Sylvoxime© IUPAC Name: N-[4-(1-ethoxyethenyl)-3,3,5,5tetramethylcyclohexylidene]hydroxylamine SMILES: CCOC(=C)C1C(C)(C)CC(CC1(C)C)=NO Molecular weight: 239.36 Volume (cubic Angstroems): 252.83 Atoms number (non-hydrogen): 17 miLogP: 4.33 Structure: Biological Properties: Predicted Druglikeness: GPCR ligand -0.6 Ion channel modulator -0.41 Kinase inhibitor -0.93 Nuclear receptor ligand -0.17 Protease inhibitor -0.39 Enzyme inhibitor 0.01 Commercial name: Sylvoxime© IUPAC Name: N-[4-(1-ethoxyethenyl)-3,3,5,5tetramethylcyclohexylidene]hydroxylamine SMILES: CCOC(=C)C1C(C)(C)CC(CC1(C)C)=NO Predicted Olfactory Receptor Activity: OR52D1 71.900% OR1G1 70.394% 0R52I2 70.392% OR52I1 70.390% OR2Y1 70.378% 3. Commercial name: Hyperflor© IUPAC Name: 2-benzyl-1,3-dioxan-5-one SMILES: O=C1COC(CC2=CC=CC=C2)OC1 Molecular weight: 192.21 g/mol Volume
    [Show full text]
  • A Multi-Cancer Gene Signature Associated with Stromal Activation
    A multi-cancer gene signature associated with stromal activation Sandra Orsulic, PhD Professor of OB/GYN Women’s Cancer Program Samuel Oschin Comprehensive Cancer Institute I Cedars-Sinai Medical Center 1100011010 1001000101 0101001011 Stroma/ECM Prevents Tumor Invasion Stroma/ECM Promotes Tumor Invasion Tumor Growth and Stroma Remodeling Ki67-positive dividing cells cancer cells stroma Tumor Growth and Stroma Remodeling Ki67-positive dividing cells cancer • proliferative cells • targeted by chemotherapy • genetically unstable stroma Tumor Growth and Stroma Remodeling Ki67-positive dividing cells cancer • proliferating cells • targeted by chemotherapy • genetically unstable • slow proliferating • not targeted by chemotherapy stroma • genetically stable • enhanced remodeling predicts poor outcome Expression of COL11A1 in Intratumoral Stroma COL11A1 + Fat 0% Peritumoral ~1% stroma Tumor ~50% Intratumoral 100µm stroma COL11A1 Expression in Cancers and Corresponding Normal Tissues Smooth Muscle Actin Expression in Cancers and Corresponding Normal Tissues COL11A1 Expression in Cancers and Corresponding Normal Tissues BREAST COLORECTAL 1 2 1 2 3 1. Breast (61) 1. Colon (19) 2. Invasive ductal breast 2. Rectum (3) carcinoma (389) 3. Colon adenocarcinoma (101) Overexpression gene rank: 1 Overexpression gene rank: 3 (in top 1%) (in top 1%) p-value: 1.15E-73 p-value: 2.19E-44 t-test: 33.769 t-test: 27.871 fold change: 40.542 fold change: 32.796 Increase in COL11A1 Levels During Cancer Progression Breast Cancer Ductal Invasive Carcinoma Ductal In
    [Show full text]
  • The Hypothalamus As a Hub for SARS-Cov-2 Brain Infection and Pathogenesis
    bioRxiv preprint doi: https://doi.org/10.1101/2020.06.08.139329; this version posted June 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-NC-ND 4.0 International license. The hypothalamus as a hub for SARS-CoV-2 brain infection and pathogenesis Sreekala Nampoothiri1,2#, Florent Sauve1,2#, Gaëtan Ternier1,2ƒ, Daniela Fernandois1,2 ƒ, Caio Coelho1,2, Monica ImBernon1,2, Eleonora Deligia1,2, Romain PerBet1, Vincent Florent1,2,3, Marc Baroncini1,2, Florence Pasquier1,4, François Trottein5, Claude-Alain Maurage1,2, Virginie Mattot1,2‡, Paolo GiacoBini1,2‡, S. Rasika1,2‡*, Vincent Prevot1,2‡* 1 Univ. Lille, Inserm, CHU Lille, Lille Neuroscience & Cognition, DistAlz, UMR-S 1172, Lille, France 2 LaBoratorY of Development and PlasticitY of the Neuroendocrine Brain, FHU 1000 daYs for health, EGID, School of Medicine, Lille, France 3 Nutrition, Arras General Hospital, Arras, France 4 Centre mémoire ressources et recherche, CHU Lille, LiCEND, Lille, France 5 Univ. Lille, CNRS, INSERM, CHU Lille, Institut Pasteur de Lille, U1019 - UMR 8204 - CIIL - Center for Infection and ImmunitY of Lille (CIIL), Lille, France. # and ƒ These authors contriButed equallY to this work. ‡ These authors directed this work *Correspondence to: [email protected] and [email protected] Short title: Covid-19: the hypothalamic hypothesis 1 bioRxiv preprint doi: https://doi.org/10.1101/2020.06.08.139329; this version posted June 19, 2020. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity.
    [Show full text]
  • Genetic Variants Affecting Equivalent Protein Family Positions Reflect
    www.nature.com/scientificreports OPEN Genetic variants afecting equivalent protein family positions refect human diversity Received: 13 January 2017 Francesco Raimondi1,2, Matthew J. Betts1,2, Qianhao Lu1,2, Asuka Inoue3,4, J. Silvio Gutkind5 & Accepted: 13 September 2017 Robert B. Russell 1,2 Published: xx xx xxxx Members of diverse protein families often perform overlapping or redundant functions meaning that diferent variations within them could refect diferences between individual organisms. We investigated likely functional positions within aligned protein families that contained a signifcant enrichment of nonsynonymous variants in genomes of healthy individuals. We identifed more than a thousand enriched positions across hundreds of family alignments with roles indicative of mammalian individuality, including sensory perception and the immune system. The most signifcant position is the Arginine from the Olfactory receptor “DRY” motif, which has more variants in healthy individuals than all other positions in the proteome. Odorant binding data suggests that these variants lead to receptor inactivity, and they are mostly mutually exclusive with other loss-of-function (stop/frameshift) variants. Some DRY Arginine variants correlate with smell preferences in sub-populations and all 2,504 humans studied contain a unique spectrum of active and inactive receptors. The many other variant enriched positions, across hundreds of other families might also provide insights into individual diferences. Genomes from healthy individuals1 illuminate both diseases2 and attributes of individuals or human popula- tions3,4. Perhaps the most common current use of these genomes is to provide background mutation rates to assess candidate disease mutations or to identify likely deleterious genetic alterations5,6. For example, ExAC6 is frequently used by human geneticists to determine variant frequency during searches for disease causing mutations.
    [Show full text]
  • Supplementary Data
    SUPPLEMENTARY METHODS 1) Characterisation of OCCC cell line gene expression profiles using Prediction Analysis for Microarrays (PAM) The ovarian cancer dataset from Hendrix et al (25) was used to predict the phenotypes of the cell lines used in this study. Hendrix et al (25) analysed a series of 103 ovarian samples using the Affymetrix U133A array platform (GEO: GSE6008). This dataset comprises clear cell (n=8), endometrioid (n=37), mucinous (n=13) and serous epithelial (n=41) primary ovarian carcinomas and samples from 4 normal ovaries. To build the predictor, the Prediction Analysis of Microarrays (PAM) package in R environment was employed (http://rss.acs.unt.edu/Rdoc/library/pamr/html/00Index.html). When more than one probe described the expression of a given gene, we used the probe with the highest median absolute deviation across the samples. The dataset from Hendrix et al. (25) and the dataset of OCCC cell lines described in this manuscript were then overlaid on the basis of 11536 common unique HGNC gene symbols. Only the 99 primary ovarian cancers samples and the four normal ovary samples were used to build the predictor. Following leave one out cross-validation, a predictor based upon 126 genes was able to identify correctly the four distinct phenotypes of primary ovarian tumour samples with a misclassification rate of 18.3%. This predictor was subsequently applied to the expression data from the 12 OCCC cell lines to determine the likeliest phenotype of the OCCC cell lines compared to primary ovarian cancers. Posterior probabilities were estimated for each cell line in comparison to the following phenotypes: clear cell, endometrioid, mucinous and serous epithelial.
    [Show full text]
  • Us 2018 / 0305689 A1
    US 20180305689A1 ( 19 ) United States (12 ) Patent Application Publication ( 10) Pub . No. : US 2018 /0305689 A1 Sætrom et al. ( 43 ) Pub . Date: Oct. 25 , 2018 ( 54 ) SARNA COMPOSITIONS AND METHODS OF plication No . 62 /150 , 895 , filed on Apr. 22 , 2015 , USE provisional application No . 62/ 150 ,904 , filed on Apr. 22 , 2015 , provisional application No. 62 / 150 , 908 , (71 ) Applicant: MINA THERAPEUTICS LIMITED , filed on Apr. 22 , 2015 , provisional application No. LONDON (GB ) 62 / 150 , 900 , filed on Apr. 22 , 2015 . (72 ) Inventors : Pål Sætrom , Trondheim (NO ) ; Endre Publication Classification Bakken Stovner , Trondheim (NO ) (51 ) Int . CI. C12N 15 / 113 (2006 .01 ) (21 ) Appl. No. : 15 /568 , 046 (52 ) U . S . CI. (22 ) PCT Filed : Apr. 21 , 2016 CPC .. .. .. C12N 15 / 113 ( 2013 .01 ) ; C12N 2310 / 34 ( 2013. 01 ) ; C12N 2310 /14 (2013 . 01 ) ; C12N ( 86 ) PCT No .: PCT/ GB2016 /051116 2310 / 11 (2013 .01 ) $ 371 ( c ) ( 1 ) , ( 2 ) Date : Oct . 20 , 2017 (57 ) ABSTRACT The invention relates to oligonucleotides , e . g . , saRNAS Related U . S . Application Data useful in upregulating the expression of a target gene and (60 ) Provisional application No . 62 / 150 ,892 , filed on Apr. therapeutic compositions comprising such oligonucleotides . 22 , 2015 , provisional application No . 62 / 150 ,893 , Methods of using the oligonucleotides and the therapeutic filed on Apr. 22 , 2015 , provisional application No . compositions are also provided . 62 / 150 ,897 , filed on Apr. 22 , 2015 , provisional ap Specification includes a Sequence Listing . SARNA sense strand (Fessenger 3 ' SARNA antisense strand (Guide ) Mathew, Si Target antisense RNA transcript, e . g . NAT Target Coding strand Gene Transcription start site ( T55 ) TY{ { ? ? Targeted Target transcript , e .
    [Show full text]
  • Multi-Tissue Probabilistic Fine-Mapping of Transcriptome-Wide Association Study Identifies Cis-Regulated Genes for Miserableness
    bioRxiv preprint doi: https://doi.org/10.1101/682633; this version posted June 26, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. Multi-tissue probabilistic fine-mapping of transcriptome-wide association study identifies cis-regulated genes for miserableness Calwing Liao1,2 BSc, Veikko Vuokila2, Alexandre D Laporte2 BSc, Dan Spiegelman2 MSc, Patrick A. Dion2,3 PhD, Guy A. Rouleau1,2,3 * MD, PhD 1Department oF Human Genetics, McGill University, Montréal, Quebec, Canada 2Montreal Neurological Institute, McGill University, Montréal, Quebec, Canada 3Department oF Neurology and Neurosurgery, McGill University, Montréal, Quebec, Canada Short summary: The First transcriptome-wide association study oF miserableness identiFies many genes including c7orf50 implicated in the trait. Word count: 1,522 excluding abstract and reFerences Tables: 3 Keywords: Miserableness, transcriptome-wide association study, TWAS *Correspondence: Dr. Guy A. Rouleau Montreal Neurological Institute and Hospital Department oF Neurology and Neurosurgery 3801 University Street, Montreal, QC Canada H3A 2B4. Tel: +1 514 398 2690 Fax: +1 514 398 8248 E-mail: [email protected] bioRxiv preprint doi: https://doi.org/10.1101/682633; this version posted June 26, 2019. The copyright holder for this preprint (which was not certified by peer review) is the author/funder, who has granted bioRxiv a license to display the preprint in perpetuity. It is made available under aCC-BY-ND 4.0 International license. Abstract (141 words) Miserableness is a behavioural trait that is characterized by strong negative Feelings in an individual.
    [Show full text]
  • Explorations in Olfactory Receptor Structure and Function by Jianghai
    Explorations in Olfactory Receptor Structure and Function by Jianghai Ho Department of Neurobiology Duke University Date:_______________________ Approved: ___________________________ Hiroaki Matsunami, Supervisor ___________________________ Jorg Grandl, Chair ___________________________ Marc Caron ___________________________ Sid Simon ___________________________ [Committee Member Name] Dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Neurobiology in the Graduate School of Duke University 2014 ABSTRACT Explorations in Olfactory Receptor Structure and Function by Jianghai Ho Department of Neurobiology Duke University Date:_______________________ Approved: ___________________________ Hiroaki Matsunami, Supervisor ___________________________ Jorg Grandl, Chair ___________________________ Marc Caron ___________________________ Sid Simon ___________________________ [Committee Member Name] An abstract of a dissertation submitted in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the Department of Neurobiology in the Graduate School of Duke University 2014 Copyright by Jianghai Ho 2014 Abstract Olfaction is one of the most primitive of our senses, and the olfactory receptors that mediate this very important chemical sense comprise the largest family of genes in the mammalian genome. It is therefore surprising that we understand so little of how olfactory receptors work. In particular we have a poor idea of what chemicals are detected by most of the olfactory receptors in the genome, and for those receptors which we have paired with ligands, we know relatively little about how the structure of these ligands can either activate or inhibit the activation of these receptors. Furthermore the large repertoire of olfactory receptors, which belong to the G protein coupled receptor (GPCR) superfamily, can serve as a model to contribute to our broader understanding of GPCR-ligand binding, especially since GPCRs are important pharmaceutical targets.
    [Show full text]
  • Exome Array Analysis Identifies New Loci and Low-Frequency Variants Influencing Insulin Processing and Secretion
    LETTERS Exome array analysis identifies new loci and low-frequency variants influencing insulin processing and secretion Jeroen R Huyghe1, Anne U Jackson1, Marie P Fogarty2, Martin L Buchkovich2, Alena Stančáková3, Heather M Stringham1, Xueling Sim1, Lingyao Yang1, Christian Fuchsberger1, Henna Cederberg3, Peter S Chines4, Tanya M Teslovich1, Jane M Romm5, Hua Ling5, Ivy McMullen5, Roxann Ingersoll5, Elizabeth W Pugh5, Kimberly F Doheny5, Benjamin M Neale6–8, Mark J Daly6–8, Johanna Kuusisto3, Laura J Scott1, Hyun Min Kang1, Francis S Collins4, Gonçalo R Abecasis1, Richard M Watanabe9,10, Michael Boehnke1,11, Markku Laakso3,11 & Karen L Mohlke2,11 Insulin secretion has a crucial role in glucose homeostasis, HumanExome Beadchip. Clinical characteristics of 8,229 analyzed and failure to secrete sufficient insulin is a hallmark of nondiabetic study participants are summarized in Supplementary type 2 diabetes. Genome-wide association studies (GWAS) Table 1. Among 242,071 variants passing quality control, 89,864 have identified loci contributing to insulin processing and (38.1%) were variable in the studied individuals; of these, 71,077 were secretion1,2; however, a substantial fraction of the genetic nonsynonymous, nonsense or located in splice sites (Supplementary contribution remains undefined. To examine low-frequency Table 2). We tested 59,029 variants with MAF > 0.05% for associa- (minor allele frequency (MAF) 0.5–5%) and rare (MAF < tion with insulin processing, secretion and glycemic traits, assuming 0.5%) nonsynonymous variants, we analyzed exome array additive allelic effects and using a linear mixed model to account for data in 8,229 nondiabetic Finnish males using the Illumina relatedness among study participants5.
    [Show full text]
  • Online Supporting Information S2: Proteins in Each Negative Pathway
    Online Supporting Information S2: Proteins in each negative pathway Index Proteins ADO,ACTA1,DEGS2,EPHA3,EPHB4,EPHX2,EPOR,EREG,FTH1,GAD1,HTR6, IGF1R,KIR2DL4,NCR3,NME7,NOTCH1,OR10S1,OR2T33,OR56B4,OR7A10, Negative_1 OR8G1,PDGFC,PLCZ1,PROC,PRPS2,PTAFR,SGPP2,STMN1,VDAC3,ATP6V0 A1,MAPKAPK2 DCC,IDS,VTN,ACTN2,AKR1B10,CACNA1A,CHIA,DAAM2,FUT5,GCLM,GNAZ Negative_2 ,ITPA,NEU4,NTF3,OR10A3,PAPSS1,PARD3,PLOD1,RGS3,SCLY,SHC1,TN FRSF4,TP53 Negative_3 DAO,CACNA1D,HMGCS2,LAMB4,OR56A3,PRKCQ,SLC25A5 IL5,LHB,PGD,ADCY3,ALDH1A3,ATP13A2,BUB3,CD244,CYFIP2,EPHX2,F CER1G,FGD1,FGF4,FZD9,HSD17B7,IL6R,ITGAV,LEFTY1,LIPG,MAN1C1, Negative_4 MPDZ,PGM1,PGM3,PIGM,PLD1,PPP3CC,TBXAS1,TKTL2,TPH2,YWHAQ,PPP 1R12A HK2,MOS,TKT,TNN,B3GALT4,B3GAT3,CASP7,CDH1,CYFIP1,EFNA5,EXTL 1,FCGR3B,FGF20,GSTA5,GUK1,HSD3B7,ITGB4,MCM6,MYH3,NOD1,OR10H Negative_5 1,OR1C1,OR1E1,OR4C11,OR56A3,PPA1,PRKAA1,PRKAB2,RDH5,SLC27A1 ,SLC2A4,SMPD2,STK36,THBS1,SERPINC1 TNR,ATP5A1,CNGB1,CX3CL1,DEGS1,DNMT3B,EFNB2,FMO2,GUCY1B3,JAG Negative_6 2,LARS2,NUMB,PCCB,PGAM1,PLA2G1B,PLOD2,PRDX6,PRPS1,RFXANK FER,MVD,PAH,ACTC1,ADCY4,ADCY8,CBR3,CLDN16,CPT1A,DDOST,DDX56 ,DKK1,EFNB1,EPHA8,FCGR3A,GLS2,GSTM1,GZMB,HADHA,IL13RA2,KIR2 Negative_7 DS4,KLRK1,LAMB4,LGMN,MAGI1,NUDT2,OR13A1,OR1I1,OR4D11,OR4X2, OR6K2,OR8B4,OXCT1,PIK3R4,PPM1A,PRKAG3,SELP,SPHK2,SUCLG1,TAS 1R2,TAS1R3,THY1,TUBA1C,ZIC2,AASDHPPT,SERPIND1 MTR,ACAT2,ADCY2,ATP5D,BMPR1A,CACNA1E,CD38,CYP2A7,DDIT4,EXTL Negative_8 1,FCER1G,FGD3,FZD5,ITGAM,MAPK8,NR4A1,OR10V1,OR4F17,OR52D1,O R8J3,PLD1,PPA1,PSEN2,SKP1,TACR3,VNN1,CTNNBIP1 APAF1,APOA1,CARD11,CCDC6,CSF3R,CYP4F2,DAPK1,FLOT1,GSTM1,IL2
    [Show full text]
  • Supplemental Table S18. Cellular Process Enrichment Analysis Output
    Supplemental Table S18.
    [Show full text]
  • Table S2. Primers and Probes Used for Qpcr
    Table S2.
    [Show full text]