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

DOI: http://doi.org/10.1093/hmg/dds335 Journal: Hum Mol Genet E-pub date: 15 Nov 2012 Authors: MF Keller, M Saad, J Bras, F Bettella, N Nicolaou, J Simón-Sánchez, F Mittag, F Büchel, M Sharma, JR Gibbs, C Schulte, V Moskvina, A Durr, P Holmans, LL Kilarski, R Guerreiro, DG Hernandez, A Brice, P Ylikotila, H Stefánsson, K Majamaa, HR Morris, N Williams, T Gasser, P Heutink, NW Wood, J Hardy, M Martinez, AB Singleton, MA Nalls, International Parkinson's Disease Genomics Consortium (IPDGC), Wellcome Trust Case Control Consortium 2 (WTCCC2)

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

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.