Publications by authors named "Maarten Kooyman"

11 Publications

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Genome-wide association analyses of risk tolerance and risky behaviors in over 1 million individuals identify hundreds of loci and shared genetic influences.

Nat Genet 2019 02 14;51(2):245-257. Epub 2019 Jan 14.

Department of Economics, University of Toronto, Toronto, Ontario, Canada.

Humans vary substantially in their willingness to take risks. In a combined sample of over 1 million individuals, we conducted genome-wide association studies (GWAS) of general risk tolerance, adventurousness, and risky behaviors in the driving, drinking, smoking, and sexual domains. Across all GWAS, we identified hundreds of associated loci, including 99 loci associated with general risk tolerance. We report evidence of substantial shared genetic influences across risk tolerance and the risky behaviors: 46 of the 99 general risk tolerance loci contain a lead SNP for at least one of our other GWAS, and general risk tolerance is genetically correlated ([Formula: see text] ~ 0.25 to 0.50) with a range of risky behaviors. Bioinformatics analyses imply that genes near SNPs associated with general risk tolerance are highly expressed in brain tissues and point to a role for glutamatergic and GABAergic neurotransmission. We found no evidence of enrichment for genes previously hypothesized to relate to risk tolerance.
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http://dx.doi.org/10.1038/s41588-018-0309-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6713272PMC
February 2019

Genome-wide Analyses Identify KIF5A as a Novel ALS Gene.

Authors:
Aude Nicolas Kevin P Kenna Alan E Renton Nicola Ticozzi Faraz Faghri Ruth Chia Janice A Dominov Brendan J Kenna Mike A Nalls Pamela Keagle Alberto M Rivera Wouter van Rheenen Natalie A Murphy Joke J F A van Vugt Joshua T Geiger Rick A Van der Spek Hannah A Pliner Shankaracharya Bradley N Smith Giuseppe Marangi Simon D Topp Yevgeniya Abramzon Athina Soragia Gkazi John D Eicher Aoife Kenna Gabriele Mora Andrea Calvo Letizia Mazzini Nilo Riva Jessica Mandrioli Claudia Caponnetto Stefania Battistini Paolo Volanti Vincenzo La Bella Francesca L Conforti Giuseppe Borghero Sonia Messina Isabella L Simone Francesca Trojsi Fabrizio Salvi Francesco O Logullo Sandra D'Alfonso Lucia Corrado Margherita Capasso Luigi Ferrucci Cristiane de Araujo Martins Moreno Sitharthan Kamalakaran David B Goldstein Aaron D Gitler Tim Harris Richard M Myers Hemali Phatnani Rajeeva Lochan Musunuri Uday Shankar Evani Avinash Abhyankar Michael C Zody Julia Kaye Steven Finkbeiner Stacia K Wyman Alex LeNail Leandro Lima Ernest Fraenkel Clive N Svendsen Leslie M Thompson Jennifer E Van Eyk James D Berry Timothy M Miller Stephen J Kolb Merit Cudkowicz Emily Baxi Michael Benatar J Paul Taylor Evadnie Rampersaud Gang Wu Joanne Wuu Giuseppe Lauria Federico Verde Isabella Fogh Cinzia Tiloca Giacomo P Comi Gianni Sorarù Cristina Cereda Philippe Corcia Hannu Laaksovirta Liisa Myllykangas Lilja Jansson Miko Valori John Ealing Hisham Hamdalla Sara Rollinson Stuart Pickering-Brown Richard W Orrell Katie C Sidle Andrea Malaspina John Hardy Andrew B Singleton Janel O Johnson Sampath Arepalli Peter C Sapp Diane McKenna-Yasek Meraida Polak Seneshaw Asress Safa Al-Sarraj Andrew King Claire Troakes Caroline Vance Jacqueline de Belleroche Frank Baas Anneloor L M A Ten Asbroek José Luis Muñoz-Blanco Dena G Hernandez Jinhui Ding J Raphael Gibbs Sonja W Scholz Mary Kay Floeter Roy H Campbell Francesco Landi Robert Bowser Stefan M Pulst John M Ravits Daniel J L MacGowan Janine Kirby Erik P Pioro Roger Pamphlett James Broach Glenn Gerhard Travis L Dunckley Christopher B Brady Neil W Kowall Juan C Troncoso Isabelle Le Ber Kevin Mouzat Serge Lumbroso Terry D Heiman-Patterson Freya Kamel Ludo Van Den Bosch Robert H Baloh Tim M Strom Thomas Meitinger Aleksey Shatunov Kristel R Van Eijk Mamede de Carvalho Maarten Kooyman Bas Middelkoop Matthieu Moisse Russell L McLaughlin Michael A Van Es Markus Weber Kevin B Boylan Marka Van Blitterswijk Rosa Rademakers Karen E Morrison A Nazli Basak Jesús S Mora Vivian E Drory Pamela J Shaw Martin R Turner Kevin Talbot Orla Hardiman Kelly L Williams Jennifer A Fifita Garth A Nicholson Ian P Blair Guy A Rouleau Jesús Esteban-Pérez Alberto García-Redondo Ammar Al-Chalabi Ekaterina Rogaeva Lorne Zinman Lyle W Ostrow Nicholas J Maragakis Jeffrey D Rothstein Zachary Simmons Johnathan Cooper-Knock Alexis Brice Stephen A Goutman Eva L Feldman Summer B Gibson Franco Taroni Antonia Ratti Cinzia Gellera Philip Van Damme Wim Robberecht Pietro Fratta Mario Sabatelli Christian Lunetta Albert C Ludolph Peter M Andersen Jochen H Weishaupt William Camu John Q Trojanowski Vivianna M Van Deerlin Robert H Brown Leonard H van den Berg Jan H Veldink Matthew B Harms Jonathan D Glass David J Stone Pentti Tienari Vincenzo Silani Adriano Chiò Christopher E Shaw Bryan J Traynor John E Landers

Neuron 2018 03;97(6):1268-1283.e6

Department of Neurology, University of Massachusetts Medical School, Worcester, MA 01605, USA. Electronic address:

To identify novel genes associated with ALS, we undertook two lines of investigation. We carried out a genome-wide association study comparing 20,806 ALS cases and 59,804 controls. Independently, we performed a rare variant burden analysis comparing 1,138 index familial ALS cases and 19,494 controls. Through both approaches, we identified kinesin family member 5A (KIF5A) as a novel gene associated with ALS. Interestingly, mutations predominantly in the N-terminal motor domain of KIF5A are causative for two neurodegenerative diseases: hereditary spastic paraplegia (SPG10) and Charcot-Marie-Tooth type 2 (CMT2). In contrast, ALS-associated mutations are primarily located at the C-terminal cargo-binding tail domain and patients harboring loss-of-function mutations displayed an extended survival relative to typical ALS cases. Taken together, these results broaden the phenotype spectrum resulting from mutations in KIF5A and strengthen the role of cytoskeletal defects in the pathogenesis of ALS.
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http://dx.doi.org/10.1016/j.neuron.2018.02.027DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5867896PMC
March 2018

Detection of long repeat expansions from PCR-free whole-genome sequence data.

Genome Res 2017 11 8;27(11):1895-1903. Epub 2017 Sep 8.

Illumina Incorporated, San Diego, California 92122, USA.

Identifying large expansions of short tandem repeats (STRs), such as those that cause amyotrophic lateral sclerosis (ALS) and fragile X syndrome, is challenging for short-read whole-genome sequencing (WGS) data. A solution to this problem is an important step toward integrating WGS into precision medicine. We developed a software tool called ExpansionHunter that, using PCR-free WGS short-read data, can genotype repeats at the locus of interest, even if the expanded repeat is larger than the read length. We applied our algorithm to WGS data from 3001 ALS patients who have been tested for the presence of the repeat expansion with repeat-primed PCR (RP-PCR). Compared against this truth data, ExpansionHunter correctly classified all (212/212, 95% CI [0.98, 1.00]) of the expanded samples as either expansions (208) or potential expansions (4). Additionally, 99.9% (2786/2789, 95% CI [0.997, 1.00]) of the wild-type samples were correctly classified as wild type by this method with the remaining three samples identified as possible expansions. We further applied our algorithm to a set of 152 samples in which every sample had one of eight different pathogenic repeat expansions, including those associated with fragile X syndrome, Friedreich's ataxia, and Huntington's disease, and correctly flagged all but one of the known repeat expansions. Thus, ExpansionHunter can be used to accurately detect known pathogenic repeat expansions and provides researchers with a tool that can be used to identify new pathogenic repeat expansions.
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http://dx.doi.org/10.1101/gr.225672.117DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5668946PMC
November 2017

Non-additive genome-wide association scan reveals a new gene associated with habitual coffee consumption.

Sci Rep 2016 08 25;6:31590. Epub 2016 Aug 25.

Institute for Maternal and Child Health IRCCS Burlo Garofolo, Trieste Italy.

Coffee is one of the most consumed beverages world-wide and one of the primary sources of caffeine intake. Given its important health and economic impact, the underlying genetics of its consumption has been widely studied. Despite these efforts, much has still to be uncovered. In particular, the use of non-additive genetic models may uncover new information about the genetic variants driving coffee consumption. We have conducted a genome-wide association study in two Italian populations using additive, recessive and dominant models for analysis. This has uncovered a significant association in the PDSS2 gene under the recessive model that has been replicated in an independent cohort from the Netherlands (ERF). The identified gene has been shown to negatively regulate the expression of the caffeine metabolism genes and can thus be linked to coffee consumption. Further bioinformatics analysis of eQTL and histone marks from Roadmap data has evidenced a possible role of the identified SNPs in regulating PDSS2 gene expression through enhancers present in its intron. Our results highlight a novel gene which regulates coffee consumption by regulating the expression of the genes linked to caffeine metabolism. Further studies will be needed to clarify the biological mechanism which links PDSS2 and coffee consumption.
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http://dx.doi.org/10.1038/srep31590DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4997959PMC
August 2016

A Genome-Wide Association Study in isolated populations reveals new genes associated to common food likings.

Rev Endocr Metab Disord 2016 06;17(2):209-19

Institute for Maternal and Child Health - IRCCS "Burlo Garofolo", Trieste, Italy.

Food preferences are the first factor driving food choice and thus nutrition. They involve numerous different senses such as taste and olfaction as well as various other factors such as personal experiences and hedonistic aspects. Although it is clear that several of these have a genetic basis, up to now studies have focused mostly on the effects of polymorphisms of taste receptor genes. Therefore, we have carried out one of the first large scale (4611 individuals) GWAS on food likings assessed for 20 specific food likings belonging to 4 different categories (vegetables, fatty, dairy and bitter). A two-step meta-analysis using three different isolated populations from Italy for the discovery step and two populations from The Netherlands and Central Asia for replication, revealed 15 independent genome-wide significant loci (p < 5 × 10(-8)) for 12 different foods. None of the identified genes coded for either taste or olfactory receptors suggesting that genetics impacts in determining food likings in a much broader way than simple differences in taste perception. These results represent a further step in uncovering the genes that underlie liking of common foods that in the end will greatly help understanding the genetics of human nutrition in general.
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http://dx.doi.org/10.1007/s11154-016-9354-3DOI Listing
June 2016

JAG: A Computational Tool to Evaluate the Role of Gene-Sets in Complex Traits.

Genes (Basel) 2015 May 14;6(2):238-51. Epub 2015 May 14.

Department of Complex Trait Genetics, Neuroscience Campus Amsterdam, VU University & VU Medical Center, Amsterdam 1081HV, The Netherlands.

Gene-set analysis has been proposed as a powerful tool to deal with the highly polygenic architecture of complex traits, as well as with the small effect sizes typically found in GWAS studies for complex traits. We developed a tool, Joint Association of Genetic variants (JAG), which can be applied to Genome Wide Association (GWA) data and tests for the joint effect of all single nucleotide polymorphisms (SNPs) located in a user-specified set of genes or biological pathway. JAG assigns SNPs to genes and incorporates self-contained and/or competitive tests for gene-set analysis. JAG uses permutation to evaluate gene-set significance, which implicitly controls for linkage disequilibrium, sample size, gene size, the number of SNPs per gene and the number of genes in the gene-set. We conducted a power analysis using the Wellcome Trust Case Control Consortium (WTCCC) Crohn's disease data set and show that JAG correctly identifies validated gene-sets for Crohn's disease and has more power than currently available tools for gene-set analysis. JAG is a powerful, novel tool for gene-set analysis, and can be freely downloaded from the CTG Lab website.
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http://dx.doi.org/10.3390/genes6020238DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4488663PMC
May 2015

Genome-wide association analysis on five isolated populations identifies variants of the HLA-DOA gene associated with white wine liking.

Eur J Hum Genet 2015 Dec 11;23(12):1717-22. Epub 2015 Mar 11.

Institute for Maternal and Child Health, IRCCS, Burlo, Garofolo.

Wine is the most popular alcoholic beverage around the world and because of its importance in society has been widely studied. Understanding what drives its flavor has been a quest for decades but much is still unknown and will be determined at least in part by individual taste preferences. Recently studies in the genetics of taste have uncovered the role of different genes in the determination of food preferences giving new insight on its physiology. In this context we have performed a genome-wide association study on red and white wine liking using three isolated populations collected in Italy, and replicated our results on two additional populations coming from the Netherland and Central Asia for a total of 3885 samples. We have found a significant association (P=2.1 × 10(-8)) between white wine liking and rs9276975:C>T a polymorphism in the HLA-DOA gene encoding a non-canonical MHC II molecule, which regulates other MHC II molecules. The same association was also found with red wine liking (P=8.3 × 10(-6)). Sex-separated analysis have also revealed that the effect of HLA-DOA is twice as large in women as compared to men suggesting an interaction between this polymorphism and gender. Our results are one of the first examples of genome-wide association between liking of a commonly consumed food and gene variants. Moreover, our results suggest a role of the MHC system in the determination of food preferences opening new insight in this field in general.
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http://dx.doi.org/10.1038/ejhg.2015.34DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC4795214PMC
December 2015

Association analysis of bitter receptor genes in five isolated populations identifies a significant correlation between TAS2R43 variants and coffee liking.

PLoS One 2014 19;9(3):e92065. Epub 2014 Mar 19.

Institute for Maternal and Child Health, Istituto Di Ricovero e Cura a Carattere Scientifico "Burlo Garofolo," Trieste, Italy; Department of Medical, Surgical and Health Sciences, University of Trieste, Trieste, Italy.

Coffee, one of the most popular beverages in the world, contains many different physiologically active compounds with a potential impact on people's health. Despite the recent attention given to the genetic basis of its consumption, very little has been done in understanding genes influencing coffee preference among different individuals. Given its markedly bitter taste, we decided to verify if bitter receptor genes (TAS2Rs) variants affect coffee liking. In this light, 4066 people from different parts of Europe and Central Asia filled in a field questionnaire on coffee liking. They have been consequently recruited and included in the study. Eighty-eight SNPs covering the 25 TAS2R genes were selected from the available imputed ones and used to run association analysis for coffee liking. A significant association was detected with three SNP: one synonymous and two functional variants (W35S and H212R) on the TAS2R43 gene. Both variants have been shown to greatly reduce in vitro protein activity. Surprisingly the wild type allele, which corresponds to the functional form of the protein, is associated to higher liking of coffee. Since the hTAS2R43 receptor is sensible to caffeine, we verified if the detected variants produced differences in caffeine bitter perception on a subsample of people coming from the FVG cohort. We found a significant association between differences in caffeine perception and the H212R variant but not with the W35S, which suggests that the effect of the TAS2R43 gene on coffee liking is mediated by caffeine and in particular by the H212R variant. No other significant association was found with other TAS2R genes. In conclusion, the present study opens new perspectives in the understanding of coffee liking. Further studies are needed to clarify the role of the TAS2R43 gene in coffee hedonics and to identify which other genes and pathways are involved in its genetics.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0092065PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3960174PMC
December 2015

The PinkThing for analysing ChIP profiling data in their genomic context.

BMC Res Notes 2013 Apr 4;6:133. Epub 2013 Apr 4.

CMBI-Centre for Molecular and Biomolecular Informatics, Nijmegen Centre for Molecular Life Sciences, PO Box 9101, 6500HB Nijmegen, Netherlands.

Background: Current epigenetic research makes frequent use of whole-genome ChIP profiling for determining the in vivo binding of proteins, e.g. transcription factors and histones, to DNA. Two important and recurrent questions for these large scale analyses are: 1) What is the genomic distribution of a set of binding sites? and 2) Does this genomic distribution differ significantly from another set of sites?

Findings: We exemplify the functionality of the PinkThing by analysing a ChIP profiling dataset of cohesin binding sites. We show the subset of cohesin sites with no CTCF binding have a characteristic genomic distribution different from the set of all cohesin sites.

Conclusions: The PinkThing is a web application for fast and easy analysis of the context of genomic loci, such as peaks from ChIP profiling experiments. The output of the PinkThing analysis includes: categorisation of position relative to genes (intronic, exonic, 5' near, 3' near 5' far, 3' far and distant), distance to the closest annotated 3' and 5' end of genes, direction of transcription of the nearest gene, and the option to include other genomic elements like ESTs and CpG islands. The PinkThing enables easy statistical comparison between experiments, i.e. experimental versus background sets, reporting over- and underrepresentation as well as p-values for all comparisons. Access and use of the PinkThing is free and open (without registration) to all users via the website: http://pinkthing.cmbi.ru.nl
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http://dx.doi.org/10.1186/1756-0500-6-133DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3674928PMC
April 2013

MetiTree: a web application to organize and process high-resolution multi-stage mass spectrometry metabolomics data.

Bioinformatics 2012 Oct 31;28(20):2707-9. Epub 2012 Jul 31.

Netherlands Metabolomics Centre, Leiden, The Netherlands.

Unlabelled: Identification of metabolites using high-resolution multi-stage mass spectrometry (MS(n)) data is a significant challenge demanding access to all sorts of computational infrastructures. MetiTree is a user-friendly, web application dedicated to organize, process, share, visualize and compare MS(n) data. It integrates several features to export and visualize complex MS(n) data, facilitating the exploration and interpretation of metabolomics experiments. A dedicated spectral tree viewer allows the simultaneous presentation of three related types of MS(n) data, namely, the spectral data, the fragmentation tree and the fragmentation reactions. MetiTree stores the data in an internal database to enable searching for similar fragmentation trees and matching against other MS(n) data. As such MetiTree contains much functionality that will make the difficult task of identifying unknown metabolites much easier.

Availability: MetiTree is accessible at http://www.MetiTree.nl. The source code is available at https://github.com/NetherlandsMetabolomicsCentre/metitree/wiki.
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http://dx.doi.org/10.1093/bioinformatics/bts486DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3467742PMC
October 2012