1Department of Anthropology, Temple University, 2Laboratory of Neurogenetics, NIH-NIA
Saturday 4:00-4:15, Parlors
Genome-wide association studies have been successful at identifying 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 developed by Peter Visscher used to estimate phenotypic variance of complex traits explained by genome-wide SNPs, including those not associated with the trait in a Genome Wide Association Study (GWAS). We applied this method to 8 cohorts containing 6,057 case and 17,471 control individuals of European ancestry, in order to examine the missing heritability present in Parkinson’s disease, a neurodegenerative disorder affecting between 1-2% of individuals over the age of 65. We meta-analyzed our initial results to produce more robust and generalizable heritability estimates for PD types. Our results identify 36% (p = 6.47E-06) of phenotypic variance associated with all types of PD, 51% (p = 3.91E-04) phenotypic variance associated with early onset PD, and 39% (p = 2.55E-05) phenotypic variance associated with late onset PD. This is a substantial increase from the genetic variance identified by GWAS alone (between 1-3%). Our results suggest that while GWAS is a useful tool in identifying some of the most common variants associated with complex disease, a large portion of the heritability associated with disease traits remains unattributed.
The authors recieved no specific funding for this work.