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Keller, M.F.* ; Saad, M.* ; Bras, J.* ; Bettella, F.* ; Nicolaou, N.* ; Simon-Sanchez, J.* ; Mittag, F.* ; Buchel, F.* ; Sharma, M.* ; Gibbs, J.R.* ; Schulte, C.* ; Moskvina, V.* ; Dürr, A.* ; Holmans, P.* ; Kilarski, L.L.* ; Guerreiro, R.* ; Hernandez, D.G.* ; Brice, A.* ; Ylikotila, P.* ; Stefansson, H.* ; Majamaa, K.* ; Morris, H.R.* ; Williams, N.* ; Gasser, T.* ; Heutink, P.* ; Wood, N.W.* ; Hardy, J.* ; Martinez, M.* ; Singleton, A.B.* ; Nalls, M.A.* ; International Parkinson's Disease Genomics Consortium (IPDGC) (Illig, T. ; Lichtner, P.) ; Wellcome Trust Case Control Consortium 2 (WTCCC2) (*)

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

Hum. Mol. Genet. 21, 4996-5009 (2012)
DOI
Open Access Green möglich sobald Postprint bei der ZB eingereicht worden ist.
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.
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Publikationstyp Artikel: Journalartikel
Dokumenttyp Wissenschaftlicher Artikel
ISSN (print) / ISBN 0964-6906
e-ISSN 1460-2083
Quellenangaben Band: 21, Heft: 22, Seiten: 4996-5009 Artikelnummer: , Supplement: ,
Verlag Oxford University Press
Begutachtungsstatus Peer reviewed