Using genome-wide complex trait analysis to quantify ‘missing heritability’ in Parkinson's disease

Using genome-wide complex trait analysis to quantify ‘missing heritability’ in Parkinson's disease

Margaux F. Keller1,2, Mohamad Saad3,4, Jose Bras5, Francesco Bettella7, Nayia Nicolaou8, Javier Simón-Sánchez8, Florian Mittag3, Finja Büchel3, Manu Sharma9,10, J. Raphael Gibbs1,5, Claudia Schulte9,10, Valentina Moskvina11,12, Alexandra Durr13,14,15,16, Peter Holmans11,12, Laura L. Kilarski11,12, Rita Guerreiro5, Dena G. Hernandez1,5, Alexis Brice13,14,15,16, Pauli Ylikotila17, Hreinn Stefánsson7, Kari Majamaa18, Huw R. Morris11,12, Nigel Williams11,12, Thomas Gasser9,10, Peter Heutink7, Nicholas W. Wood5,6, John Hardy5, Maria Martinez3,4, Andrew B. Singleton1 and Michael A. Nalls1,* for the International Parkinson's Disease Genomics Consortium (IPDGC) and The Wellcome Trust Case Control Consortium 2 (WTCCC2)†
+ Author Affiliations

1Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, MD, USA,
2Department of Biological Anthropology, Temple University, Philadelphia, PA, USA,
3Institut National de la Sante et de la Recherche Medicale, UMR 1043, Centre de Physiopathologie de Toulouse-Purpan, Toulouse, France,
4Paul Sabatier University, Toulouse, France
5Department of Molecular Neuroscience, Institute of Neurology and
6UCL Genetics Institute, University College London, London, UK
7deCODE genetics, Scientific Services, Sturlugata 8, IS-101 Reykjavik, Iceland,
8Department of Clinical Genetics, Section of Medical Genomics, VU University Medical Centre, Amsterdam, The Netherlands,
9Department for Neurodegenerative Diseases, Hertie Institute for Clinical Brain Research, University of Tubingen, Tübingen, Germany,
10Deutsches Zentrum fur Neurodegenerative Erkrangungen (German Center for Neurodegenerative Diseases), Tubingen, Germany
11Institute of Psychological Medicine and Clinical Neurosciences and
12Medical Research Council Centre for Neuropsychiatric Genetics and Genomics, Cardiff University School of Medicine, Cardiff, UK
13Université Pierre et Marie Curie-Paris, Centre de Recherche de l'Institut du Cerveau et de la Moelle Epinière, UMR-S975, Paris, France,
14Département de Génétique, AP-HP, Hôpital de la Salpêtrière, Paris, France,
15Institut National de la Sante et de la Recherche Medicale, UMR-S975 (Formerly UMR-S679), Paris, France,
16Centre National de la Recherche Scientifique, UMR-7225, Paris, France,
17Department of Neurology, Turku University Hospital and University of Turku, Finland and
18Department of Clinical Medicine, Neurology, University of Oulu, Finland
↵*To whom correspondence should be addressed at: Molecular Genetics Section, Laboratory of Neurogenetics, NIA, NIH Building 35, 35 Convent Drive, Bethesda, MD 20892, USA. Tel: +1 3014513831; Fax: +1 3014517295; Email: nallsm@mail.nih.gov
Received April 20, 2012.
Revision received July 23, 2012.
Accepted August 1, 2012.
Abstract

Genome-wide association studies (GWASs) have been successful at identifying single-nucleotide polymorphisms (SNPs) highly associated with common traits; however, a great deal of the heritable variation associated with common traits remains unaccounted for within the genome. Genome-wide complex trait analysis (GCTA) is a statistical method that applies a linear mixed model to estimate phenotypic variance of complex traits explained by genome-wide SNPs, including those not associated with the trait in a GWAS. We applied GCTA to 8 cohorts containing 7096 case and 19 455 control individuals of European ancestry in order to examine the missing heritability present in Parkinson's disease (PD). We meta-analyzed our initial results to produce robust heritability estimates for PD types across cohorts. Our results identify 27% (95% CI 17–38, P = 8.08E − 08) phenotypic variance associated with all types of PD, 15% (95% CI −0.2 to 33, P = 0.09) phenotypic variance associated with early-onset PD and 31% (95% CI 17–44, P = 1.34E − 05) phenotypic variance associated with late-onset PD. This is a substantial increase from the genetic variance identified by top GWAS hits alone (between 3 and 5%) and indicates there are substantially more risk loci to be identified. Our results suggest that although GWASs are a useful tool in identifying the most common variants associated with complex disease, a great deal of common variants of small effect remain to be discovered.

Published by Oxford University Press 2012