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

M.F. Keller, M. Saad, J. Bras, F. Bettella, N. Nicolaou, J. Simon-Sanchez, F. Mittag, F. Buchel, M. Sharma, J.R. Gibbs, C. Schulte, V. Moskvina, A. Durr, P. Holmans, L.L. Kilarski, R. Guerreiro, D.G. Hernandez, A. Brice, P. Ylikotila, H. StefanssonK. Majamaa, H.R. Morris, N. Williams, T. Gasser, P. Heutink, N.W. Wood, J. Hardy, M. Martinez, A.B. Singleton, M.A. Nalls, Karen Morrison

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123 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 - 01 Nov 2012

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