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

Keller, Marguax F., Saad, Mohamad, Bras, Jose, Bettella, Francesco, Nicolaou, Nayia, Simon-Sanchez, Javier, Mittag, Florian, Buchel, Finja, Sharma, Manu, Gibbs, J. Raphael, Schulte, Claudia, Escott-Price, Valentina ORCID: https://orcid.org/0000-0003-1784-5483, Durr, Alexandra, Holmans, Peter Alan ORCID: https://orcid.org/0000-0003-0870-9412, Kilarski, Laura, Guerreiro, Rita, Hernandez, Dena G., Brice, Alexis, Ylikotila, Pauli, Stefansson, Hreinn, Majamaa, Kari, Morris, Huw Rees, Williams, Nigel Melville ORCID: https://orcid.org/0000-0003-1177-6931, Gasser, Thomas, Heutink, Peter, Wood, Nicholas W., Hardy, John, Martinez, Maria, Singleton, Andrew B. and Nalls, Michael A. 2012. Using genome-wide complex trait analysis to quantify 'missing heritability' in Parkinson's disease. Human Molecular Genetics 21 (22) , pp. 4996-5009. 10.1093/hmg/dds335

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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.

Item Type:	Article
Date Type:	Publication
Status:	Published
Schools:	MRC Centre for Neuropsychiatric Genetics and Genomics (CNGG) Medicine Systems Immunity Research Institute (SIURI) Neuroscience and Mental Health Research Institute (NMHRI)
Subjects:	Q Science > QH Natural history > QH426 Genetics R Medicine > R Medicine (General) R Medicine > RC Internal medicine > RC0321 Neuroscience. Biological psychiatry. Neuropsychiatry
Publisher:	Oxford University Press
ISSN:	0964-6906
Last Modified:	09 Jun 2023 06:45
URI:	https://orca.cardiff.ac.uk/id/eprint/41921

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