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

Margaux F. Keller, Mohamad Saad, Jose Bras, Francesco Bettella, Nayia Nicolaou, Javier Simón-Sánchez, Florian Mittag, Finja Büchel, Manu Sharma, J. Raphael Gibbs, Claudia Schulte, Valentina Moskvina, Alexandra Durr, Peter Holmans, Laura L. Kilarski, Rita Guerreiro, Dena G. Hernandez, Alexis Brice, Pauli Ylikotila, Hreinn StefánssonKari Majamaa, Huw R. Morris, Nigel Williams, Thomas Gasser, Peter Heutink, Nicholas W. Wood, John Hardy, Maria Martinez, Andrew B. Singleton, Michael A. Nalls, Vincent Plagnol, Una Marie Sheerin, Suzanne Lesage, Sigurlaug Sveinbjörnsdóttir, Sampath Arepalli, Roger Barker, Yoav Ben-Shlomo, Henk W. Berendse, Daniela Berg, Kailash Bhatia, Rob M.A. de Bie, Alessandro Biffi, Bas Bloem, Zoltan Bochdanovits, Michael Bonin, Kathrin Brockmann, Janet Brooks, David J. Burn, Gavin Charlesworth, Honglei Chen, Patrick F. Chinnery, Sean Chong, Carl E. Clarke, Mark R. Cookson, J. Mark Cooper, Jean Christophe Corvol, Carl Counsell, Philippe Damier, Jean François Dartigues, Panos Deloukas, Günther Deuschl, David T. Dexter, Karin D. van Dijk, Allissa Dillman, Frank Durif, Alexandra Dürr, Sarah Edkins, Jonathan R. Evans, Thomas Foltynie, Jianjun Gao, Michelle Gardner, Alison Goate, Emma Gray, Ómar Gústafsson, Clare Harris, Jacobus J. van Hilten, Albert Hofman, Albert Hollenbeck, Janice Holton, Michele Hu, Xuemei Huang, Heiko Huber, Gavin Hudson, Sarah E. Hunt, Johanna Huttenlocher, Thomas Illig, Pálmi V. Jónsson, Jean Charles Lambert, Cordelia Langford, Andrew Lees, Peter Lichtner, Patricia Limousin, Grisel Lopez, Delia Lorenz, Alisdair McNeill, Catriona Moorby, Matthew Moore, Karen E. Morrison, Ese Mudanohwo, Sean S. O'Sullivan, Justin Pearson, Joel S. Perlmutter, Hjörvar Pétursson, Pierre Pollak, Bart Post, Simon C. Potter, Bernard Ravina, Tamas Revesz, Olaf Riess, Fernando Rivadeneira, Patrizia Rizzu, Mina Ryten, Stephen J. Sawcer, Anthony Schapira, Hans Scheffer, Karen Shaw, Ira Shoulson, Ellen Sidransky, Colin Smith, Chris C.A. Spencer, Stacy Steinberg, Joanna D. Stockton, Amy Strange, Kevin Talbot, Carlie M. Tanner, Avazeh Tashakkori-Ghanbaria, François Tison, Daniah Trabzuni, Bryan J. Traynor, André G. Uitterlinden, Daan Velseboer, Marie Vidailhet, Robert Walker, Bart van de Warrenburg, Mirdhu Wickremaratchi, Caroline H. Williams-Gray, Sophie Winder-Rhodes, Kári Stefánsson, Peter Donnelly, Ines Barroso, Jenefer M. Blackwell, Elvira Bramon, Matthew A. Brown, Juan P. Casas, Aiden Corvin, Audrey Duncanson, Janusz Jankowski, Hugh S. Markus, Christopher G. Mathew, Colin N.A. Palmer, Robert Plomin, Anna Rautanen, Richard C. Trembath, Ananth C. Viswanathan, Gavin Band, Céline Bellenguez, Colin Freeman, Garrett Hellenthal, Eleni Giannoulatou, Matti Pirinen, Richard Pearson, Zhan Su, Damjan Vukcevic, Rhian Gwilliam, Hannah Blackburn, Suzannah J. Bumpstead, Serge Dronov, Matthew Gillman, Naomi Hammond, Alagurevathi Jayakumar, Owen T. McCann, Jennifer Liddle, Radhi Ravindrarajah, Michelle Ricketts, Matthew Waller, Paul Weston, Sara Widaa, Pamela Whittaker, Mark I. McCarthy

Research output: Contribution to journalArticle

102 Citations (Scopus)

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.

Original languageEnglish
Pages (from-to)4996-5009
Number of pages14
JournalHuman molecular genetics
Volume21
Issue number22
DOIs
Publication statusPublished - Nov 2012

    Fingerprint

ASJC Scopus subject areas

  • Molecular Biology
  • Genetics
  • Genetics(clinical)

Cite this

Keller, M. F., Saad, M., Bras, J., Bettella, F., Nicolaou, N., Simón-Sánchez, J., Mittag, F., Büchel, F., Sharma, M., Gibbs, J. R., Schulte, C., Moskvina, V., Durr, A., Holmans, P., Kilarski, L. L., Guerreiro, R., Hernandez, D. G., Brice, A., Ylikotila, P., ... McCarthy, M. I. (2012). Using genome-wide complex trait analysis to quantify 'missing heritability' in Parkinson's disease. Human molecular genetics, 21(22), 4996-5009. https://doi.org/10.1093/hmg/dds335