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

2012; Oxford University Press; Volume: 21; Issue: 22 Linguagem: Inglês

10.1093/hmg/dds335

1460-2083

Margaux F. Keller, Mohamad Saad, José Brás, Francesco Bettella, Nayia Nicolaou, Javier Simón‐Sánchez, Florian Mittag, Finja Büchel, Manu Sharma, J. Raphael Gibbs, Joshua Shulman, Valentina Moskvina, Alexandra Dürr, Peter Holmans, Laura L. Kilarski, Rita Guerreiro, D. G. Hernandez, Alexis Brice, Pauli Ylikotila, Hreinn Stefánsson, Kari Majamaa, Huw R. Morris, N. Williams, Thomas Gasser, Peter Heutink, Nicholas Wood, John Hardy, María Martínez, Andrew Singleton, Michael A. Nalls,

Genetic Associations and Epidemiology

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.