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. 2023 Oct 20:8:483.
doi: 10.12688/wellcomeopenres.18754.1. eCollection 2023.

Large-scale exome array summary statistics resources for glycemic traits to aid effector gene prioritization

Sara M Willems #  1   2 Natasha H J Ng #  3   4 Juan Fernandez  5 Rebecca S Fine  6   7   8   9 Eleanor Wheeler  1   10 Jennifer Wessel  8   11   12   13 Hidetoshi Kitajima  5 Gaelle Marenne  10 Xueling Sim  14   15 Hanieh Yaghootkar  16 Shuai Wang  17 Sai Chen  15 Yuning Chen  17 Yii-Der Ida Chen  18 Niels Grarup  19 Ruifang Li-Gao  20 Tibor V Varga  21 Jennifer L Asimit  10   22 Shuang Feng  23 Rona J Strawbridge  24   25 Erica L Kleinbrink  26   27 Tarunveer S Ahluwalia  28   29 Ping An  30 Emil V Appel  19 Dan E Arking  31 Juha Auvinen  32   33 Lawrence F Bielak  34 Nathan A Bihlmeyer  35 Jette Bork-Jensen  19 Jennifer A Brody  36   37 Archie Campbell  38 Audrey Y Chu  39 Gail Davies  40   41 Ayse Demirkan  42 James S Floyd  36   37 Franco Giulianini  39 Xiuqing Guo  18 Stefan Gustafsson  43 Anne U Jackson  15 Johanna Jakobsdottir  44 Marjo-Riitta Järvelin  32   45   46 Richard A Jensen  36   37 Stavroula Kanoni  47 Sirkka Keinanen-Kiukaanniemi  48   49 Man Li  50   51 Yingchang Lu  52   53 Jian'an Luan  1 Alisa K Manning  54   55 Jonathan Marten  56 Karina Meidtner  57   58 Dennis O Mook-Kanamori  20   59 Taulant Muka  42   60 Giorgio Pistis  61   62 Bram Prins  10 Kenneth M Rice  36   63 Serena Sanna  61   64 Albert Vernon Smith  44   65 Jennifer A Smith  34   66 Lorraine Southam  5   10   67 Heather M Stringham  15 Vinicius Tragante  68 Sander W van der Laan  69 Helen R Warren  47   70 Jie Yao  18 Andrianos M Yiorkas  71   72 Weihua Zhang  73   74 Wei Zhao  34 Mariaelisa Graff  75 Heather M Highland  75   76 Anne E Justice  75 Eirini Marouli  47 Carolina Medina-Gomez  42   77 Saima Afaq  45 Wesam A Alhejily  47   78 Najaf Amin  42 Folkert W Asselbergs  79   80 Lori L Bonnycastle  81 Michiel L Bots  82 Ivan Brandslund  83   84 Ji Chen  10 John Danesh  85 Renée de Mutsert  20 Abbas Dehghan  42   45   86 Tapani Ebeling  87 Paul Elliott  45   88   89 EPIC-Interact ConsortiumAliki-Eleni Farmaki  90   91 Jessica D Faul  66 Paul W Franks  7   21   92 Steve Franks  93 Andreas Fritsche  58   94 Anette P Gjesing  19 Mark O Goodarzi  95 Vilmundur Gudnason  44   65 Göran Hallmans  96 Tamara B Harris  44 Karl-Heinz Herzig  97   98 Marie-France Hivert  99   100 Torben Jørgensen  101   102   103 Marit E Jørgensen  29   104 Pekka Jousilahti  105 Eero Kajantie  105   106   107   108 Maria Karaleftheri  109 Sharon L R Kardia  34 Leena Kinnunen  105 Heikki A Koistinen  105   110   111 Pirjo Komulainen  112 Peter Kovacs  113   114 Johanna Kuusisto  115 Markku Laakso  115 Leslie A Lange  116 Lenore J Launer  117 Aaron Leong  118 Jaana Lindström  105 Jocelyn E Manning Fox  119   120 Satu Männistö  105 Nisa M Maruthur  51   121   122 Leena Moilanen  123 Antonella Mulas  61   124 Mike A Nalls  125   126 Matthew Neville  3 James S Pankow  127 Alison Pattie  41 Eva R B Petersen  83 Hannu Puolijoki  128 Asif Rasheed  129 Paul Redmond  41 Frida Renström  21   96 Michael Roden  58   130   131 Danish Saleheen  129   132 Juha Saltevo  133 Kai Savonen  112   134 Sylvain Sebert  46   48 Tea Skaaby  101 Kerrin S Small  135 Alena Stančáková  115 Jakob Stokholm  28 Konstantin Strauch  136 E-Shyong Tai  14   137   138 Kent D Taylor  18 Betina H Thuesen  101 Anke Tönjes  139 Emmanouil Tsafantakis  140 Tiinamaija Tuomi  141   142   143   144 Jaakko Tuomilehto  105   145   146 Understanding Society Scientific GroupMatti Uusitupa  147 Marja Vääräsmäki  106   148 Ilonca Vaartjes  82 Magdalena Zoledziewska  61 Goncalo Abecasis  62 Beverley Balkau  149 Hans Bisgaard  28 Alexandra I Blakemore  71   72 Matthias Blüher  139   150 Heiner Boeing  151 Eric Boerwinkle  152 Klaus Bønnelykke  28 Erwin P Bottinger  52 Mark J Caulfield  47   70 John C Chambers  45   74   153 Daniel I Chasman  39   154   155 Ching-Yu Cheng  156   157   158 Francis S Collins  81 Josef Coresh  51   122 Francesco Cucca  61   124 Gert J de Borst  159 Ian J Deary  40   41 George Dedoussis  90 Panos Deloukas  47   160 Hester M den Ruijter  161 Josée Dupuis  17   162 Michele K Evans  117 Ele Ferrannini  163 Oscar H Franco  42   60 Harald Grallert  58   164 Torben Hansen  19   165 Andrew T Hattersley  166 Caroline Hayward  56 Joel N Hirschhorn  7   8   167 Arfan Ikram  42 Erik Ingelsson  168   169   170 Fredrik Karpe  3   171 Kay-Tee Kaw  172 Wieland Kiess  173 Jaspal S Kooner  74   153   174 Antje Körner  173 Timo Lakka  112   134   175 Claudia Langenberg  1 Lars Lind  176 Cecilia M Lindgren  5   177 Allan Linneberg  101   178 Leonard Lipovich  27   179 Ching-Ti Liu  17 Jun Liu  42 Yongmei Liu  180 Ruth J F Loos  52   181 Patrick E MacDonald  119   120 Karen L Mohlke  182 Andrew D Morris  183 Patricia B Munroe  47   70 Alison Murray  184 Sandosh Padmanabhan  185 Colin N A Palmer  186 Gerard Pasterkamp  161   187 Oluf Pedersen  19 Patricia A Peyser  34 Ozren Polasek  188 David Porteous  38 Michael A Province  30 Bruce M Psaty  36   37   189 Rainer Rauramaa  112 Paul M Ridker  39   154   190 Olov Rolandsson  191 Patrik Rorsman  3   171 Frits R Rosendaal  20 Igor Rudan  183 Veikko Salomaa  105 Matthias B Schulze  57   58 Robert Sladek  192   193 Blair H Smith  186 Timothy D Spector  135 John M Starr  40   194 Michael Stumvoll  139 Cornelia M van Duijn  42 Mark Walker  195 Nick J Wareham  1 David R Weir  66 James G Wilson  196 Tien Yin Wong  156   157   158 Eleftheria Zeggini  10   67   197 Alan B Zonderman  117 Jerome I Rotter  18 Andrew P Morris  198 Michael Boehnke  15 Jose C Florez  55   199   200 Mark I McCarthy  3   5   171   201 James B Meigs  118   200 Anubha Mahajan  5   201 Robert A Scott  1 Anna L Gloyn  3   5   171   202 Inês Barroso  1   10   203
Affiliations

Large-scale exome array summary statistics resources for glycemic traits to aid effector gene prioritization

Sara M Willems et al. Wellcome Open Res. .

Abstract

Background: Genome-wide association studies for glycemic traits have identified hundreds of loci associated with these biomarkers of glucose homeostasis. Despite this success, the challenge remains to link variant associations to genes, and underlying biological pathways.

Methods: To identify coding variant associations which may pinpoint effector genes at both novel and previously established genome-wide association loci, we performed meta-analyses of exome-array studies for four glycemic traits: glycated hemoglobin (HbA1c, up to 144,060 participants), fasting glucose (FG, up to 129,665 participants), fasting insulin (FI, up to 104,140) and 2hr glucose post-oral glucose challenge (2hGlu, up to 57,878). In addition, we performed network and pathway analyses.

Results: Single-variant and gene-based association analyses identified coding variant associations at more than 60 genes, which when combined with other datasets may be useful to nominate effector genes. Network and pathway analyses identified pathways related to insulin secretion, zinc transport and fatty acid metabolism. HbA1c associations were strongly enriched in pathways related to blood cell biology.

Conclusions: Our results provided novel glycemic trait associations and highlighted pathways implicated in glycemic regulation. Exome-array summary statistic results are being made available to the scientific community to enable further discoveries.

Keywords: effector genes; exome chip; genetic discovery; glycaemic traits; summary statistics resources.

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Conflict of interest statement

Competing interests: Rebecca S. Fine: Rebecca S. Fine is currently employed by Vertex Pharmaceuticals Incorporated. Audrey Y Chu: Currently employed by GlaxoSmithkline. Dennis O. Mook-Kanamori: Dennis Mook-Kanamori is working as a part-time clinical research consultant for Metabolon, Inc. Paul W. Franks: PWF has been a paid consultant for Eli Lilly and Sanofi Aventis and has received research support from several pharmaceutical companies as part of a European Union Innovative Medicines Initiative (IMI) project. Mike A. Nalls: Dr. Mike A. Nalls is supported by a consulting contract between Data Tecnica International LLC and the National Institute on Aging (NIA), National Institutes of Health (NIH), Bethesda, MD, USA. Dr. Nalls also consults for Illumina Inc., the Michael J. Fox Foundation, and the University of California Healthcare. Mark J. Caulfield: MJC is Chief Scientist for Genomics England, a UK government company. Joel N. Hirschhorn: JHN is on the scientific advisory board of Camp4 Therapeutics. Erik Ingelsson: Erik Ingelsson is now an employee of GlaxoSmithKline. Anubha Mahajan: Anubha Mahajan is an employee of Genentech since January 2020, and a holder of Roche stock. Mark I McCarthy: The views expressed in this article are those of the author(s) and not necessarily those of the NHS, the NIHR, or the Department of Health. MMcC has served on advisory panels for Pfizer, NovoNordisk and Zoe Global, has received honoraria from Merck, Pfizer, Novo Nordisk and Eli Lilly, and research funding from Abbvie, Astra Zeneca, Boehringer Ingelheim, Eli Lilly, Janssen, Merck, NovoNordisk, Pfizer, Roche, Sanofi Aventis, Servier, and Takeda. As of June 2019, MMcC is an employee of Genentech, and a holder of Roche stock. Inês Barroso: IB and spouse declare stock ownership in GlaxoSmithkline and Incyte Ltd. James B. Meigs: JBM serves as an Academic Associate for Quest Diagnostics R&D Bruce M Psaty serves on the Steering Committee of the Yale Open Data Access Project funded by Johnson & Johnson. Dr. Sander W. van der Laan has received Roche funding for unrelated work. Matthias Blüher received honoraria as a consultant and speaker from Amgen, AstraZeneca, Bayer, Boehringer-Ingelheim, Lilly, Novo Nordisk, Novartis, Pfizer and Sanofi. Vinicius Tragante: VT became an employee of deCODE genetics/Amgen Inc. after the conclusion of this work Dr Franco is employed by ErasmusAGE, a center for aging research across the life course funded by Nestlé Nutrition (Nestec Ltd.) and Metagenics.

Figures

Figure 1.
Figure 1.. Network and pathway analyses identify relevant gene sets regulating glycemia using two different methods for variant associations with P <1 × 10 -5.
( A–B) The networks represent composite networks for ( A) HbA1c and ( B) FG, from the GeneMANIA analysis using genes with variant associations at P <1 × 10 -5 for each trait as input. Nodes outlined in red correspond to genes from the input list. Other nodes correspond to related genes based on 50 default databases. Based on the network, GO terms and Reactome pathways that were significantly enriched are depicted. To summarize these results, the most significant term of all calculated terms within the same group is represented. Barplots with the Bonferroni-adjusted -log10(p-values) of the most significant terms within each group are are shown. Each group was assigned a specific color; if a gene is present in more than one term, it is displayed in more than one color. ( C–D) Heatmaps showing EC-DEPICT results from analysis of ( C) all traits except HbA1c and ( D) FG. The columns represent the input genes for the analysis. In ( C), these are genes with variant associations of P <1 × 10 -5 for FG, FI, and/or 2hGlu, and in ( D) these are genes with variant associations of P <1 × 10 -5 for FG. Rows in the heatmap represent significant meta-gene sets (FDR <0.05). The color of each square indicates DEPICT’s z-score for membership of that gene in that gene set, where dark red means “very likely a member” and dark blue means “very unlikely a member.” The gene set annotations indicate whether that meta-gene set was significant at FDR <0.05 or not significant (n.s.) for each of the other EC-DEPICT analyses. For heatmap intensity and EC-DEPICT P-values, the meta-gene set values are taken from the most significantly enriched member gene set. The gene variant annotations are as follows: (1) the European minor allele frequency (MAF) of the input variant, where rare is MAF <1%, low-frequency is MAF 1–5%, and common is MAF >5%, 2) whether the gene has an Online Mendelian Inheritance in Man (OMIM) annotation as causal for a diabetes/glycemic-relevant syndrome or blood disorder, 3) to 6) whether each variant was significant ( P <2 × 10 -7), suggestively significant ( P <1 × 10 -5), or not significant in Europeans for each of the four traits, and 7) whether each variant was included in the analysis or excluded by filters (see Methods). AWS: array-wide significant.

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