Fascinating paper estimating the magnitude of the role the genetics still has to play in PD

Hum Mol Genet. 2012 Aug 13. [Epub ahead of print]

Using Genome-wide Complex Trait Analysis to quantify 'missing heritability' in Parkinson's disease.

Keller MF, Saad M, Bras JM, Bettella F, Nicolaou N, Simón-Sánchez J, Mittag F, Buechel F, Sharma M, Gibbs JR, Schulte C, Moskvina V, Durr A, Holmans P, Kilarski LL, Guerreiro R, Hernandez D, Brice A, Ylikotila P, Stefánsson H, Majamaa K, Morris HR, Williams N, Gasser T, Heutink P, Wood N, Hardy J, Martinez M, Singleton AB, Nalls MA; for the International Parkinson’s Disease Genomics Consortium (IPDGC) and The Wellcome Trust Case Control Consortium 2 (WTCCC2).

Source

Laboratory of Neurogenetics, National Institute on Aging, National Institutes of Health, Bethesda, Maryland, United States of America.

Abstract

Genome-wide association studies (GWAS) 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 7,096 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%, 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 3-5%) and indicates there are substantially more risk loci to be identified. Our results suggest that while GWAS is 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.