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

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...

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Bibliographic Details
Published in:	Human molecular genetics 2012-11, Vol.21 (22), p.4996-5009
Main Authors:	KELLER, Margaux F, SAAD, Mohamad, SCHULTE, Claudia, MOSKVINA, Valentina, DURR, Alexandra, HOLMANS, Peter, KILARSKI, Laura L, GUERREIRO, Rita, HERNANDEZ, Dena G, BRICE, Alexis, YLIKOTILA, Pauli, STEFANSSON, Hreinn, BRAS, Jose, MAJAMAA, Kari, MORRIS, Huw R, WILLIAMS, Nigel, GASSER, Thomas, HEUTINK, Peter, WOOD, Nicholas W, HARDY, John, MARTINEZ, Maria, SINGLETON, Andrew B, NALLS, Michael A, BETTELLA, Francesco, NICOLAOU, Nayia, SIMON-SANCHEZ, Javier, MITTAG, Florian, BÜCHEL, Finja, SHARMA, Manu, RAPHAEL GIBBS, J
Format:	Article
Language:	eng
Subjects:	Adult Aged Aged, 80 and over Association Studies Biological and medical sciences Degenerative and inherited degenerative diseases of the nervous system. Leukodystrophies. Prion diseases Female Fundamental and applied biological sciences. Psychology Genetic Predisposition to Disease Genetic Variation Genetics of eukaryotes. Biological and molecular evolution Genome-Wide Association Study Humans Male Medical sciences Middle Aged Molecular and cellular biology Multifactorial Inheritance Neurology Parkinson Disease - genetics Quantitative Trait, Heritable Whites - genetics
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Summary:	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.
ISSN:	0964-6906 1460-2083