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

Keller, Margaux F., Saad, Mohamad, Bras, Jose, Bettella, Francesco, Nicolaou, Nayia, Simon-Sanchez, Javier, Mittag, Florian, Buchel, Finja, Sharma, Manu, Gibbs, J. Raphael, Schulte, Claudia, Moskvina, Valentina, Durr, Alexandra, Holmans, Peter, Kilarski, Laura L., Guerreiro, Rita, Hernandez, Dena G., Brice, Alexis, Ylikotila, Pauli, Stefansson, Hreinn, Majamaa, Kari, Morris, Huw R., Williams, Nigel, Gasser, Thomas, Heutink, Peter, Wood, Nicholas W., Hardy, John, Martinez, Maria, Singleton, Andrew B., Nalls, Michael A., for International Parkinson's Disease Geonomics Consortium (IPDGC), for The Welcome Trust Case Control Consortium 2 (WTCCC2) and Brown, Matthew A. (2012) Using genome-wide complex trait analysis to quantify 'missing heritability' in Parkinson's disease. Human Molecular Genetics, 21 22: 4996-5009. doi:10.1093/hmg/dds335


Author Keller, Margaux F.
Saad, Mohamad
Bras, Jose
Bettella, Francesco
Nicolaou, Nayia
Simon-Sanchez, Javier
Mittag, Florian
Buchel, Finja
Sharma, Manu
Gibbs, J. Raphael
Schulte, Claudia
Moskvina, Valentina
Durr, Alexandra
Holmans, Peter
Kilarski, Laura L.
Guerreiro, Rita
Hernandez, Dena G.
Brice, Alexis
Ylikotila, Pauli
Stefansson, Hreinn
Majamaa, Kari
Morris, Huw R.
Williams, Nigel
Gasser, Thomas
Heutink, Peter
Wood, Nicholas W.
Hardy, John
Martinez, Maria
Singleton, Andrew B.
Nalls, Michael A.
for International Parkinson's Disease Geonomics Consortium (IPDGC)
for The Welcome Trust Case Control Consortium 2 (WTCCC2)
Brown, Matthew A.
Title Using genome-wide complex trait analysis to quantify 'missing heritability' in Parkinson's disease
Journal name Human Molecular Genetics   Check publisher's open access policy
ISSN 0964-6906
1460-2083
Publication date 2012-11
Sub-type Article (original research)
DOI 10.1093/hmg/dds335
Open Access Status DOI
Volume 21
Issue 22
Start page 4996
End page 5009
Total pages 14
Place of publication Oxford, United Kingdom
Publisher Oxford University Press
Language eng
Formatted 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.
Q-Index Code C1
Q-Index Status Provisional Code
Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collection: UQ Diamantina Institute Publications
 
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