DOCSLIB.ORG

Gene and Gene-Set Analysis for Genome-Wide Association Studies

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Gene and Gene-Set Analysis for Genome-Wide Association Studies Gene and Gene-Set Analysis for Genome-Wide Association Studies Inti Pedroso National Institute of Health Research Specialist Biomedical Research Centre for Mental Health & Medical Research Council Social, Genetic and Developmental Psychiatry Research Centre King’s College London This dissertation is submitted for the degree of Doctor of Philosophy March 2011 1 Abstract Genome-wide association studies (GWAS) have identified hundreds of loci at very stringent levels of statistical significance across many different human traits. However, it is now clear that very large samples (n~104-105) are needed to find the majority of genetic variants underlying risk for most human diseases. Therefore, the field has engaged itself in a race to increase study sample sizes with some studies yielding very successful results but also studies which provide little or no new insights. This project started early on in this new wave of studies and I decided to use an alternative approach that uses prior biological knowledge to improve both interpretation and power of GWAS. The project aimed to a) implement and develop new gene-based methods to derive gene-level statistics to use GWAS in well established system biology tools; b) use of these gene-level statistics in networks and gene-set analyses of GWAS data; c) mine GWAS of neuropsychiatric disorders using gene, gene-sets and integrative biology analyses with gene-expression studies; and d) explore the ability of these methods to improve the analysis GWAS on disease sub-phenotypes which usually suffer of very small sample sizes. In this project, we focused on the analysis of GWAS on bipolar disorder, however, GWAS from other complex disorders have also been analysed and are used throughout to compare the performance of the methods developed across disorders that, very likely, have different genetic architecture. From the analysis of these datasets, it was possible to draw that conclusion. I found that from a computational perspective the application of gene and gene-set analyses to GWAS is feasible, even if calculation are performed on a common desktop computer. As a genetic mapping tool, gene-based association of GWAS provides a 2 ABSTRACT valuable complement to single SNP analysis methods. It highlights true disease loci and generates an accurate association statistic for a gene. These gene-level statistics proved of merit for meta-analyses of GWAS and integrative analysis with protein-protein interaction networks or gene-expression studies. In this thesis, I combined results of GWAS and gene-expression studies and I was able to provide evidence of association with bipolar disorder for a protein-interaction network. This demonstrates the possibility of using GWAS to extract information of biological systems from GWAS of neuropsychiatric disorders. This information is not readily available from single SNP analyses, which also suffer from genetic heterogeneity. Therefore, these methods help to improve the interpretation and provide an alternative to the simple but costly method of improving statistical power through increasing sample size. This ability proved to be important in our analyses of bipolar disorder sub-phenotypes, which had significantly smaller sample sizes. Analysis of disease sub-phenotypes showed that some genetic associations are shared with the main diagnosis but that others seemed largely specific of the sub-phenotypes. This evidence allowed me to suggest that genetic association with sub-phenotypes should be analysed as both a follow up of association in a main diagnosis and an independently if the available evidence is suggesting that the genetic associations observed are largely independent of those with the main diagnosis (e.g., as we found for age of onset of bipolar disorder). Despite the interesting new insights and potential relevance, our gene and gene-set associations with bipolar disorder require replication in independent samples. This is especially important for the association found with the bipolar disorder sub-phenotypes because their smaller samples size also makes them more prone to be false positives. This project has also generated the software suite FORGE which allows users to perform fast and robust analysis of GWAS, allowing researchers with a lack of 3 ABSTRACT experience in bioinformatics and statistical genetics, e.g. wet-laboratory based biologists, to perform both gene-based and pathway analyses to generate a systems biology interpretation of their phenotype of interest. 4 Acknowledgements I am most grateful to everyone that contributed to the work of this thesis, for giving advice and encouragement. First of all, I would like to thank my supervisor Gerome Breen. His imagination and scientific twist has always prompted me to look at scientific problems from a different perspective. I would also like to thank Mike Barnes, who never spared words for strong criticism but also to give support and prompt me to be critic, ruthless and grasp beyond the obvious. Finally, Tom Price, who joined my PhD supervision in the last turn of the race but provided timely and sharp advice and time for enjoyable scientific conversation. Thanks to you since you have helped the most. Of all others I shall first thank my wife Katherinne for cooking and gardening our love. I must thank my parents for giving me space to be or not to be and for giving me a whole life of love. I must also thanks my grand mother and my uncle Gonzalo for bringing passion into this endless route of discovery and for making me believe in people and what they can do. Thanks to you because you made me what I am. I cannot forget my friends, both in Chile and UK, who have helped me from the beginning and continue we help me now. In particular I would like to thank Anbarasu L, James R, Sara Campos, Sarah Cohen, Sarah Jugurnauth, Margarita, Shaza, Katherine Tansey, Ursula, Cerisse, Jo G, Carol Shum, Sietske, Evangelos Vassos, Bahare Azadi, Chloe Wong and many other people has made my years as student in the SGDP a pleasure. Because of my sweet tooth I must also express my gratitude to the Fellowship of the Cake, for baking every month and sharing with me the sweetest of the friendships. 5 ACKNOWLEDGMENTS Thanks to David Collier, Cathryn Lewis, Stuart Newman, Ammar Al-Chalabi, all member of the Depression Genetic Consortium led by Prof Peter McGuffin and many others for small and big gestures of help or advice. I am immensely grateful for the funding provided by the NIHR BRC for Mental Health and the Overseas Research Studentship Award Scheme. Finally, I must thank the patients and researchers who gathered the data I analysed. Without their hard work this thesis would not be possible. 6 Do we need to do all of this? As a first year PhD student I used to ask myself, Do we need all these genetic studies? Are they relevant at all? Are we just teasing ourselves while pleasing our big brain’s curiosity? Is the answer relevant just because we want to know it or can know it? Unfortunately, I still have no answer. I have learnt that beyond pleasing ourselves genetic research can be tremendously valuable when a real breakthrough occurs and people out there benefit from it. That is the reason I focused my efforts in help transforming GWAS into something useful to biologists, with the hope that it would help produce solutions for real people. I do think more genetic research should be funded and more genetic findings should end up in systems biology efforts, where causality and complexity can be studied systematically. At the end of the day complexity is at the heart of why we are in love with biology and why is so challenging. However, I have also learnt that we may be missing an important bit in here. Many diseases do have a strong genetic liability but also have an important environmental contribution. Many have risen in frequency beyond what genetics alone can explain. We inherited our genes and not our diseases. Changes in lifestyle may be an important aspect of decreasing disease morbidity, probably in some cases beyond what genetic findings can do. I think human genetics will soon face great scientific challenges. The massive amounts of incoming data will enable us to test some of our wildest hypothesis with an accuracy and depth not seen before. New sequencing technologies are opening the door to systematic and almost complete sampling of genetic material. Protein measurements may probably follow soon as will quantifications and sequencing of lipids and sugars. Biology will move aways from a reductionistic science into a data driven technology. 7 DO WE NEED TO DO ALL OF THIS? Quantitative models will supersede speculation and medicine will become a science of forecasting, in the same way that weather forecasting changed in the last 80 years. Nevertheless, I think you still will have to go to the gym to lose weight and get fit for the summer. 8 Contents I INTRODUCTION 27 1.1 GENETIC MAPPING 28 1.1.1 Sweet and sour: From Mendelian to complex diseases 28 1.1.2 The common disease-common variant model 30 1.1.3 Construction of public resources to analyse human genetic variation 31 1.1.4 Genome-wide association studies 33 1.2 CHALLENGES OF CURRENT GWAS 39 1.3 GENE-SET AND NETWORK ANALYSES 42 1.4 GSA AND NETWORK ANALYSES IN GWAS 46 1.5 OBJECTIVES OF THIS THESIS 51 II A SOFTWARE SUITE TO PERFORM GENE-BASED AND GENE- SET ANALYSES OF GENOME-WIDE ASSOCIATION STUDIES 53 2.1 INTRODUCTION 54 2.2 GENE-BASED ANALYSES OF GWAS 55 2.3 GENE-SET ANALYSES 56 2.3.1 Different null hypotheses 57 2.3.2 Using SNP versus gene-based statistics 57 2.3.3 Methods implemented in FORGE 57 2.4 METHODS 58 9 CONTENTS 2.4.1 Region or gene-wide statistics 58 2.4.1.1 Sidak’s correction of the minimum p-value 58 2.4.1.2 Fisher’s method to combine correlated p-values 58 2.4.1.3 Fixed-effect z-score statistic 58 2.4.1.4 Random-effect z-score statistic 59 2.4.1.5 Significance of gene-wide statistics by sampling from a MND 60 2.5 GENE-SET ANALYSES 62 2.5.1 SNP to gene-sets strategy 62 2.5.1.1 Gene-sets analysis with gene p-values 62 2.5.1.2 Reducing the number of simulations needed of p-values << 1.
Load more

Recommended publications
  • Wo 2010/075007 A2
    (12) INTERNATIONAL APPLICATION PUBLISHED UNDER THE PATENT COOPERATION TREATY (PCT) (19) World Intellectual Property Organization International Bureau (10) International Publication Number (43) International Publication Date 1 July 2010 (01.07.2010) WO 2010/075007 A2 (51) International Patent Classification: (81) Designated States (unless otherwise indicated, for every C12Q 1/68 (2006.01) G06F 19/00 (2006.01) kind of national protection available): AE, AG, AL, AM, C12N 15/12 (2006.01) AO, AT, AU, AZ, BA, BB, BG, BH, BR, BW, BY, BZ, CA, CH, CL, CN, CO, CR, CU, CZ, DE, DK, DM, DO, (21) International Application Number: DZ, EC, EE, EG, ES, FI, GB, GD, GE, GH, GM, GT, PCT/US2009/067757 HN, HR, HU, ID, IL, IN, IS, JP, KE, KG, KM, KN, KP, (22) International Filing Date: KR, KZ, LA, LC, LK, LR, LS, LT, LU, LY, MA, MD, 11 December 2009 ( 11.12.2009) ME, MG, MK, MN, MW, MX, MY, MZ, NA, NG, NI, NO, NZ, OM, PE, PG, PH, PL, PT, RO, RS, RU, SC, SD, (25) Filing Language: English SE, SG, SK, SL, SM, ST, SV, SY, TJ, TM, TN, TR, TT, (26) Publication Language: English TZ, UA, UG, US, UZ, VC, VN, ZA, ZM, ZW. (30) Priority Data: (84) Designated States (unless otherwise indicated, for every 12/3 16,877 16 December 2008 (16.12.2008) US kind of regional protection available): ARIPO (BW, GH, GM, KE, LS, MW, MZ, NA, SD, SL, SZ, TZ, UG, ZM, (71) Applicant (for all designated States except US): DODDS, ZW), Eurasian (AM, AZ, BY, KG, KZ, MD, RU, TJ, W., Jean [US/US]; 938 Stanford Street, Santa Monica, TM), European (AT, BE, BG, CH, CY, CZ, DE, DK, EE, CA 90403 (US).
    [Show full text]
  • University of Copenhagen, Copenhagen, Denmark, Citation: Pereira J, Johnson WE, O’Brien SJ, 8
    Evolutionary genomics and adaptive evolution of the hedgehog gene family (Shh, Ihh and Dhh) in vertebrates Pereira, Joana; Johnson, Warren E.; O'Brien, Stephen J.; Jarvis, Erich D; Zhang, Guojie; Gilbert, M. Thomas P.; Vasconcelos, Vitor; Antunes, Agostinho Published in: PloS one DOI: 10.1371/journal.pone.0074132 Publication date: 2014 Document version Publisher's PDF, also known as Version of record Document license: CC BY Citation for published version (APA): Pereira, J., Johnson, W. E., O'Brien, S. J., Jarvis, E. D., Zhang, G., Gilbert, M. T. P., Vasconcelos, V., & Antunes, A. (2014). Evolutionary genomics and adaptive evolution of the hedgehog gene family (Shh, Ihh and Dhh) in vertebrates. PloS one, 9(12), [e74132]. https://doi.org/10.1371/journal.pone.0074132 Download date: 24. sep.. 2021 RESEARCH ARTICLE Evolutionary Genomics and Adaptive Evolution of the Hedgehog Gene Family (Shh, Ihh and Dhh) in Vertebrates Joana Pereira1¤, Warren E. Johnson2, Stephen J. O’Brien3,4, Erich D. Jarvis5, Guojie Zhang6, M. Thomas P. Gilbert7, Vitor Vasconcelos1,8, Agostinho Antunes1,8* 1. CIIMAR/CIMAR, Interdisciplinary Centre of Marine and Environmental Research, University of Porto, Porto, Portugal, 2. Smithsonian Conservation Biology Institute, National Zoological Park, Front Royal, Virginia, United States of America, 3. Theodosius Dobzhansky Center for Genome Bioinformatics, St. Petersburg State University, St. Petersburg, Russia, 4. Oceanographic Center, N. Ocean Drive, Nova Southeastern University, Ft. Lauderdale, Florida, United States of America, 5. Howard Hughes Medical Institute, Department of Neurobiology, Duke University Medical Center, Durham, North Carolina, United States of America, 6. BGI-Shenzhen, Beishan Industrial Zoon, Yantian District, Shenzhen, China, 7.
    [Show full text]
  • Molecular and Physiological Basis for Hair Loss in Near Naked Hairless and Oak Ridge Rhino-Like Mouse Models: Tracking the Role of the Hairless Gene
    University of Tennessee, Knoxville TRACE: Tennessee Research and Creative Exchange Doctoral Dissertations Graduate School 5-2006 Molecular and Physiological Basis for Hair Loss in Near Naked Hairless and Oak Ridge Rhino-like Mouse Models: Tracking the Role of the Hairless Gene Yutao Liu University of Tennessee - Knoxville Follow this and additional works at: https://trace.tennessee.edu/utk_graddiss Part of the Life Sciences Commons Recommended Citation Liu, Yutao, "Molecular and Physiological Basis for Hair Loss in Near Naked Hairless and Oak Ridge Rhino- like Mouse Models: Tracking the Role of the Hairless Gene. " PhD diss., University of Tennessee, 2006. https://trace.tennessee.edu/utk_graddiss/1824 This Dissertation is brought to you for free and open access by the Graduate School at TRACE: Tennessee Research and Creative Exchange. It has been accepted for inclusion in Doctoral Dissertations by an authorized administrator of TRACE: Tennessee Research and Creative Exchange. For more information, please contact [email protected]. To the Graduate Council: I am submitting herewith a dissertation written by Yutao Liu entitled "Molecular and Physiological Basis for Hair Loss in Near Naked Hairless and Oak Ridge Rhino-like Mouse Models: Tracking the Role of the Hairless Gene." I have examined the final electronic copy of this dissertation for form and content and recommend that it be accepted in partial fulfillment of the requirements for the degree of Doctor of Philosophy, with a major in Life Sciences. Brynn H. Voy, Major Professor We have read this dissertation and recommend its acceptance: Naima Moustaid-Moussa, Yisong Wang, Rogert Hettich Accepted for the Council: Carolyn R.
    [Show full text]
  • A Computational Approach for Defining a Signature of Β-Cell Golgi Stress in Diabetes Mellitus
    Page 1 of 781 Diabetes A Computational Approach for Defining a Signature of β-Cell Golgi Stress in Diabetes Mellitus Robert N. Bone1,6,7, Olufunmilola Oyebamiji2, Sayali Talware2, Sharmila Selvaraj2, Preethi Krishnan3,6, Farooq Syed1,6,7, Huanmei Wu2, Carmella Evans-Molina 1,3,4,5,6,7,8* Departments of 1Pediatrics, 3Medicine, 4Anatomy, Cell Biology & Physiology, 5Biochemistry & Molecular Biology, the 6Center for Diabetes & Metabolic Diseases, and the 7Herman B. Wells Center for Pediatric Research, Indiana University School of Medicine, Indianapolis, IN 46202; 2Department of BioHealth Informatics, Indiana University-Purdue University Indianapolis, Indianapolis, IN, 46202; 8Roudebush VA Medical Center, Indianapolis, IN 46202. *Corresponding Author(s): Carmella Evans-Molina, MD, PhD ([email protected]) Indiana University School of Medicine, 635 Barnhill Drive, MS 2031A, Indianapolis, IN 46202, Telephone: (317) 274-4145, Fax (317) 274-4107 Running Title: Golgi Stress Response in Diabetes Word Count: 4358 Number of Figures: 6 Keywords: Golgi apparatus stress, Islets, β cell, Type 1 diabetes, Type 2 diabetes 1 Diabetes Publish Ahead of Print, published online August 20, 2020 Diabetes Page 2 of 781 ABSTRACT The Golgi apparatus (GA) is an important site of insulin processing and granule maturation, but whether GA organelle dysfunction and GA stress are present in the diabetic β-cell has not been tested. We utilized an informatics-based approach to develop a transcriptional signature of β-cell GA stress using existing RNA sequencing and microarray datasets generated using human islets from donors with diabetes and islets where type 1(T1D) and type 2 diabetes (T2D) had been modeled ex vivo. To narrow our results to GA-specific genes, we applied a filter set of 1,030 genes accepted as GA associated.
    [Show full text]
  • Distinguishing Pleiotropy from Linked QTL Between Milk Production Traits
    Cai et al. Genet Sel Evol (2020) 52:19 https://doi.org/10.1186/s12711-020-00538-6 Genetics Selection Evolution RESEARCH ARTICLE Open Access Distinguishing pleiotropy from linked QTL between milk production traits and mastitis resistance in Nordic Holstein cattle Zexi Cai1*†, Magdalena Dusza2†, Bernt Guldbrandtsen1, Mogens Sandø Lund1 and Goutam Sahana1 Abstract Background: Production and health traits are central in cattle breeding. Advances in next-generation sequencing technologies and genotype imputation have increased the resolution of gene mapping based on genome-wide association studies (GWAS). Thus, numerous candidate genes that afect milk yield, milk composition, and mastitis resistance in dairy cattle are reported in the literature. Efect-bearing variants often afect multiple traits. Because the detection of overlapping quantitative trait loci (QTL) regions from single-trait GWAS is too inaccurate and subjective, multi-trait analysis is a better approach to detect pleiotropic efects of variants in candidate genes. However, large sample sizes are required to achieve sufcient power. Multi-trait meta-analysis is one approach to deal with this prob- lem. Thus, we performed two multi-trait meta-analyses, one for three milk production traits (milk yield, protein yield and fat yield), and one for milk yield and mastitis resistance. Results: For highly correlated traits, the power to detect pleiotropy was increased by multi-trait meta-analysis com- pared with the subjective assessment of overlapping of single-trait QTL confdence intervals. Pleiotropic efects of lead single nucleotide polymorphisms (SNPs) that were detected from the multi-trait meta-analysis were confrmed by bivariate association analysis. The previously reported pleiotropic efects of variants within the DGAT1 and MGST1 genes on three milk production traits, and pleiotropic efects of variants in GHR on milk yield and fat yield were con- frmed.
    [Show full text]
  • Androgen Receptor Binding Sites Identified by a GREF GATA Model
    doi:10.1016/j.jmb.2005.09.009 J. Mol. Biol. (2005) 353, 763–771 COMMUNICATION Androgen Receptor Binding Sites Identified by a GREF_GATA Model Katsuaki Masuda1, Thomas Werner2, Shilpi Maheshwari1 Matthias Frisch2, Soyon Oh1, Gyorgy Petrovics1, Klaus May2 Vasantha Srikantan1, Shiv Srivastava1 and Albert Dobi1* 1Center for Prostate Disease Changes in transcriptional regulation can be permissive for tumor Research, Department of progression by allowing for selective growth advantage of tumor cells. Surgery, Uniformed Services Tumor suppressors can effectively inhibit this process. The PMEPA1 gene, a University, Rockville, MD potent inhibitor of prostate cancer cell growth is an androgen-regulated 20852, USA gene. We addressed the question of whether or not androgen receptor can directly bind to specific PMEPA1 promoter upstream sequences. To test this 2Genomatix Software GmbH hypothesis we extended in silico prediction of androgen receptor binding D-80339 Munich, Germany sites by a modeling approach and verified the actual binding by in vivo chromatin immunoprecipitation assay. Promoter upstream sequences of highly androgen-inducible genes were examined from microarray data of prostate cancer cells for transcription factor binding sites (TFBSs). Results were analyzed to formulate a model for the description of specific androgen receptor binding site context in these sequences. In silico analysis and subsequent experimental verification of the selected sequences suggested that a model that combined a GREF and a GATA TFBS was sufficient for predicting a class of functional androgen receptor binding sites. The GREF matrix family represents androgen receptor, glucocorticoid receptor and progesterone receptor binding sites and the GATA matrix family represents GATA binding protein 1–6 binding sites.
    [Show full text]
  • Supplemental Information
    Supplemental information Dissection of the genomic structure of the miR-183/96/182 gene. Previously, we showed that the miR-183/96/182 cluster is an intergenic miRNA cluster, located in a ~60-kb interval between the genes encoding nuclear respiratory factor-1 (Nrf1) and ubiquitin-conjugating enzyme E2H (Ube2h) on mouse chr6qA3.3 (1). To start to uncover the genomic structure of the miR- 183/96/182 gene, we first studied genomic features around miR-183/96/182 in the UCSC genome browser (http://genome.UCSC.edu/), and identified two CpG islands 3.4-6.5 kb 5’ of pre-miR-183, the most 5’ miRNA of the cluster (Fig. 1A; Fig. S1 and Seq. S1). A cDNA clone, AK044220, located at 3.2-4.6 kb 5’ to pre-miR-183, encompasses the second CpG island (Fig. 1A; Fig. S1). We hypothesized that this cDNA clone was derived from 5’ exon(s) of the primary transcript of the miR-183/96/182 gene, as CpG islands are often associated with promoters (2). Supporting this hypothesis, multiple expressed sequences detected by gene-trap clones, including clone D016D06 (3, 4), were co-localized with the cDNA clone AK044220 (Fig. 1A; Fig. S1). Clone D016D06, deposited by the German GeneTrap Consortium (GGTC) (http://tikus.gsf.de) (3, 4), was derived from insertion of a retroviral construct, rFlpROSAβgeo in 129S2 ES cells (Fig. 1A and C). The rFlpROSAβgeo construct carries a promoterless reporter gene, the β−geo cassette - an in-frame fusion of the β-galactosidase and neomycin resistance (Neor) gene (5), with a splicing acceptor (SA) immediately upstream, and a polyA signal downstream of the β−geo cassette (Fig.
    [Show full text]
  • The Discovery and Regulation of Modes of Exocytosis Through the Lens of Computer Vision
    THE DISCOVERY AND REGULATION OF MODES OF EXOCYTOSIS THROUGH THE LENS OF COMPUTER VISION Fabio Urbina A thesis submitted to the faculty at the University of North Carolina at Chapel Hill in partial fulfillment of the requirements for the degree of Doctor of Philosophy in the department of Cell Biology and Physiology in the School of Medicine. Chapel Hill 2020 Approved by: Stephanie Gupton Patrick Brennwald Doug Cyr Keith Burridge Shawn Gomez © 2020 Fabio Urbina ALL RIGHTS RESERVED ii ABSTRACT Fabio Urbina: Discovery and Regulation of the Modes of Exocytosis through the lens of computer vision (Under the direction of Stephanie Gupton) The formation of the nervous system involves establishing complex networks of synaptic connections between proper partners, which requires the rapid expansion of the plasma membrane surface area as neurons grow. Critical to the expansion of the plasma membrane is exocytic vesicle fusion, a regulated mechanism driven by soluble N-ethylmaleimide-sensitive factor attachment proteins receptors (SNAREs). Multiple modes of exocytosis have been proposed, with full-vesicle fusion (FVF) and kiss-and-run (KNR) being the best described. The basis of SNARE-mediated fusion, the opening of a fusion pore, and its contribution to plasma membrane expansion remains enigmatic, as vesicle fusion is spatially small and temporally fast. We exploited TIRF microscopy to image VAMP-pHluorin mediated exocytosis in murine embryonic cortical neurons and developed computer-vision software and statistical tools to perform unbiased, efficient identification of exocytic events and uncover spatiotemporal aspects of exocytosis during neuron development. We further developed novel classification algorithms to describe. and classify individual exocytic events.
    [Show full text]
  • Supplementary Table S4. FGA Co-Expressed Gene List in LUAD
    Supplementary Table S4. FGA co-expressed gene list in LUAD tumors Symbol R Locus Description FGG 0.919 4q28 fibrinogen gamma chain FGL1 0.635 8p22 fibrinogen-like 1 SLC7A2 0.536 8p22 solute carrier family 7 (cationic amino acid transporter, y+ system), member 2 DUSP4 0.521 8p12-p11 dual specificity phosphatase 4 HAL 0.51 12q22-q24.1histidine ammonia-lyase PDE4D 0.499 5q12 phosphodiesterase 4D, cAMP-specific FURIN 0.497 15q26.1 furin (paired basic amino acid cleaving enzyme) CPS1 0.49 2q35 carbamoyl-phosphate synthase 1, mitochondrial TESC 0.478 12q24.22 tescalcin INHA 0.465 2q35 inhibin, alpha S100P 0.461 4p16 S100 calcium binding protein P VPS37A 0.447 8p22 vacuolar protein sorting 37 homolog A (S. cerevisiae) SLC16A14 0.447 2q36.3 solute carrier family 16, member 14 PPARGC1A 0.443 4p15.1 peroxisome proliferator-activated receptor gamma, coactivator 1 alpha SIK1 0.435 21q22.3 salt-inducible kinase 1 IRS2 0.434 13q34 insulin receptor substrate 2 RND1 0.433 12q12 Rho family GTPase 1 HGD 0.433 3q13.33 homogentisate 1,2-dioxygenase PTP4A1 0.432 6q12 protein tyrosine phosphatase type IVA, member 1 C8orf4 0.428 8p11.2 chromosome 8 open reading frame 4 DDC 0.427 7p12.2 dopa decarboxylase (aromatic L-amino acid decarboxylase) TACC2 0.427 10q26 transforming, acidic coiled-coil containing protein 2 MUC13 0.422 3q21.2 mucin 13, cell surface associated C5 0.412 9q33-q34 complement component 5 NR4A2 0.412 2q22-q23 nuclear receptor subfamily 4, group A, member 2 EYS 0.411 6q12 eyes shut homolog (Drosophila) GPX2 0.406 14q24.1 glutathione peroxidase
    [Show full text]
  • (P -Value<0.05, Fold Change≥1.4), 4 Vs. 0 Gy Irradiation
    Table S1: Significant differentially expressed genes (P -Value<0.05, Fold Change≥1.4), 4 vs. 0 Gy irradiation Genbank Fold Change P -Value Gene Symbol Description Accession Q9F8M7_CARHY (Q9F8M7) DTDP-glucose 4,6-dehydratase (Fragment), partial (9%) 6.70 0.017399678 THC2699065 [THC2719287] 5.53 0.003379195 BC013657 BC013657 Homo sapiens cDNA clone IMAGE:4152983, partial cds. [BC013657] 5.10 0.024641735 THC2750781 Ciliary dynein heavy chain 5 (Axonemal beta dynein heavy chain 5) (HL1). 4.07 0.04353262 DNAH5 [Source:Uniprot/SWISSPROT;Acc:Q8TE73] [ENST00000382416] 3.81 0.002855909 NM_145263 SPATA18 Homo sapiens spermatogenesis associated 18 homolog (rat) (SPATA18), mRNA [NM_145263] AA418814 zw01a02.s1 Soares_NhHMPu_S1 Homo sapiens cDNA clone IMAGE:767978 3', 3.69 0.03203913 AA418814 AA418814 mRNA sequence [AA418814] AL356953 leucine-rich repeat-containing G protein-coupled receptor 6 {Homo sapiens} (exp=0; 3.63 0.0277936 THC2705989 wgp=1; cg=0), partial (4%) [THC2752981] AA484677 ne64a07.s1 NCI_CGAP_Alv1 Homo sapiens cDNA clone IMAGE:909012, mRNA 3.63 0.027098073 AA484677 AA484677 sequence [AA484677] oe06h09.s1 NCI_CGAP_Ov2 Homo sapiens cDNA clone IMAGE:1385153, mRNA sequence 3.48 0.04468495 AA837799 AA837799 [AA837799] Homo sapiens hypothetical protein LOC340109, mRNA (cDNA clone IMAGE:5578073), partial 3.27 0.031178378 BC039509 LOC643401 cds. [BC039509] Homo sapiens Fas (TNF receptor superfamily, member 6) (FAS), transcript variant 1, mRNA 3.24 0.022156298 NM_000043 FAS [NM_000043] 3.20 0.021043295 A_32_P125056 BF803942 CM2-CI0135-021100-477-g08 CI0135 Homo sapiens cDNA, mRNA sequence 3.04 0.043389246 BF803942 BF803942 [BF803942] 3.03 0.002430239 NM_015920 RPS27L Homo sapiens ribosomal protein S27-like (RPS27L), mRNA [NM_015920] Homo sapiens tumor necrosis factor receptor superfamily, member 10c, decoy without an 2.98 0.021202829 NM_003841 TNFRSF10C intracellular domain (TNFRSF10C), mRNA [NM_003841] 2.97 0.03243901 AB002384 C6orf32 Homo sapiens mRNA for KIAA0386 gene, partial cds.
    [Show full text]
  • Testing for Parallel Genomic and Epigenomic Footprints of Adaptation to Urban Life in a Passerine Bird
    SUPPLEMENTARY INFORMATION Testing for parallel genomic and epigenomic footprints of adaptation to urban life in a passerine bird Authors: Aude E. Caizergues1*, Jeremy Le Luyer2, Arnaud Grégoire1, Marta Szulkin3, Juan-Carlos Señar4, Anne Charmantier1†, Charles Perrier5† 1 CEFE, Univ Montpellier, CNRS, Univ Paul Valéry Montpellier 3, EPHE, IRD, Montpellier, France 2 Ifremer, UMR EIO 241, Centre du Pacifique, Taravao, Tahiti, Polynésie française, France 3 Centre of New Technologies, University of Warsaw, S. Banacha 2c, 02-097 Warsaw, Poland 4 Museu de Ciències Naturals de Barcelona, Parc Ciutadella, 08003 Barcelona, Spain 5 CBGP, INRAe, CIRAD, IRD, Montpellier SupAgro, Univ. Montpellier, Montpellier, France † shared senior authorship * Corresponding author: Aude E. Caizergues, 1919 route de Mende, 34293 Montpellier cedex 5, FRANCE Email : [email protected] SUPPLEMENTARY TABLES Table S1: Redundancy analysis (RDA) performed on the genetic data including the Z chromosome. adjusted R- P-value RDA1 RDA2 RDA3 RDA4 squared % variance explained by axes Full RDA 0.018 0.001 0.024 0.021 0.021 0.019 Variables Biplot scores City – Montpellier 0.57 -0.819 0.059 -0.039 0.001 City – Warsaw 0.381 0.883 0.214 0.17 Habitat – Urban 0.001 -0.21 -0.1 0.968 -0.089 Sex – Male 0.004 0.184 0.143 -0.0358 -0.972 % variance explained by axe Partial RDA for city 0.012 0.001 0.025 0.022 Variable Biplot scores City – Montpellier 0.63 -0.776 0.001 City – Warsaw 0.356 0.934 Partial RDA for % variance explained by axe 0.004 0.001 habitat 0.022 Variable Biplot score Habitat – Urban 0.001 0.999 % variance explained by axe Partial RDA for sex 0.002 0.004 0.02 Variable Biplot score Sex – Male 0.005 -0.999 Table S2: Redundancy analysis (RDA) performed on the genetic data without Z chromosome.
    [Show full text]
  • Looking for Missing Proteins in the Proteome Of
    Looking for Missing Proteins in the Proteome of Human Spermatozoa: An Update Yves Vandenbrouck, Lydie Lane, Christine Carapito, Paula Duek, Karine Rondel, Christophe Bruley, Charlotte Macron, Anne Gonzalez de Peredo, Yohann Coute, Karima Chaoui, et al. To cite this version: Yves Vandenbrouck, Lydie Lane, Christine Carapito, Paula Duek, Karine Rondel, et al.. Looking for Missing Proteins in the Proteome of Human Spermatozoa: An Update. Journal of Proteome Research, American Chemical Society, 2016, 15 (11), pp.3998-4019. 10.1021/acs.jproteome.6b00400. hal-02191502 HAL Id: hal-02191502 https://hal.archives-ouvertes.fr/hal-02191502 Submitted on 19 Mar 2021 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. Journal of Proteome Research 1 2 3 Looking for missing proteins in the proteome of human spermatozoa: an 4 update 5 6 Yves Vandenbrouck1,2,3,#,§, Lydie Lane4,5,#, Christine Carapito6, Paula Duek5, Karine Rondel7, 7 Christophe Bruley1,2,3, Charlotte Macron6, Anne Gonzalez de Peredo8, Yohann Couté1,2,3, 8 Karima Chaoui8, Emmanuelle Com7, Alain Gateau5, AnneMarie Hesse1,2,3, Marlene 9 Marcellin8, Loren Méar7, Emmanuelle MoutonBarbosa8, Thibault Robin9, Odile Burlet- 10 Schiltz8, Sarah Cianferani6, Myriam Ferro1,2,3, Thomas Fréour10,11, Cecilia Lindskog12,Jérôme 11 1,2,3 7,§ 12 Garin , Charles Pineau .
    [Show full text]