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Meta-Analysis
. 2012 Nov 15;21(22):4996-5009.
doi: 10.1093/hmg/dds335. Epub 2012 Aug 13.

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

Margaux F Keller  1 , Mohamad Saad, Jose Bras, Francesco Bettella, Nayia Nicolaou, Javier Simón-Sánchez, Florian Mittag, Finja Büchel, Manu Sharma, J Raphael Gibbs, Claudia Schulte, Valentina Moskvina, Alexandra Durr, Peter Holmans, Laura L Kilarski, Rita Guerreiro, Dena G Hernandez, Alexis Brice, Pauli Ylikotila, Hreinn Stefánsson, Kari Majamaa, Huw R Morris, Nigel Williams, Thomas Gasser, Peter Heutink, Nicholas W Wood, John Hardy, Maria Martinez, Andrew B Singleton, Michael A Nalls; International Parkinson's Disease Genomics Consortium (IPDGC); Wellcome Trust Case Control Consortium 2 (WTCCC2)
Collaborators, Affiliations
Meta-Analysis

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

Margaux F Keller et al. Hum Mol Genet. .

Erratum in

  • Hum Mol Genet. 2013 Apr 15;22(8):1696
  • Hum Mol Genet. 2013 Jul 15;22(14):2973

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.

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Figures

Figure 1.
Figure 1.
Forest plots of heritability estimates across cohorts. Cohort-specific heritability estimates are shown in blue, the size of the square is proportional to the size of the study. Confidence intervals of the summary heritability estimates are shown as red diamonds, with the centerline of each diamond representing the summary heritability estimate for that particular subset of data.
See this image and copyright information in PMC

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