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| 001 | 136668 | ||
| 005 | 20240321220120.0 | ||
| 024 | 7 | _ | |a 10.1093/hmg/dds335 |2 doi |
| 024 | 7 | _ | |a pmid:22892372 |2 pmid |
| 024 | 7 | _ | |a pmc:PMC3576713 |2 pmc |
| 024 | 7 | _ | |a 0964-6906 |2 ISSN |
| 024 | 7 | _ | |a 1460-2083 |2 ISSN |
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| 037 | _ | _ | |a DZNE-2020-02990 |
| 041 | _ | _ | |a English |
| 082 | _ | _ | |a 570 |
| 100 | 1 | _ | |a Keller, Margaux F |b 0 |
| 245 | _ | _ | |a Using genome-wide complex trait analysis to quantify 'missing heritability' in Parkinson's disease. |
| 260 | _ | _ | |a Oxford |c 2012 |b Oxford Univ. Press |
| 264 | _ | 1 | |3 online |2 Crossref |b Oxford University Press (OUP) |c 2012-08-13 |
| 264 | _ | 1 | |3 print |2 Crossref |b Oxford University Press (OUP) |c 2012-11-15 |
| 336 | 7 | _ | |a article |2 DRIVER |
| 336 | 7 | _ | |a Output Types/Journal article |2 DataCite |
| 336 | 7 | _ | |a Journal Article |b journal |m journal |0 PUB:(DE-HGF)16 |s 1593691364_9583 |2 PUB:(DE-HGF) |
| 336 | 7 | _ | |a ARTICLE |2 BibTeX |
| 336 | 7 | _ | |a JOURNAL_ARTICLE |2 ORCID |
| 336 | 7 | _ | |a Journal Article |0 0 |2 EndNote |
| 520 | _ | _ | |a 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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| 588 | _ | _ | |a Dataset connected to CrossRef, PubMed, |
| 650 | _ | 2 | |a Adult |2 MeSH |
| 650 | _ | 2 | |a Aged |2 MeSH |
| 650 | _ | 2 | |a Aged, 80 and over |2 MeSH |
| 650 | _ | 2 | |a European Continental Ancestry Group: genetics |2 MeSH |
| 650 | _ | 2 | |a Female |2 MeSH |
| 650 | _ | 2 | |a Genetic Predisposition to Disease |2 MeSH |
| 650 | _ | 2 | |a Genetic Variation |2 MeSH |
| 650 | _ | 2 | |a Genome-Wide Association Study |2 MeSH |
| 650 | _ | 2 | |a Humans |2 MeSH |
| 650 | _ | 2 | |a Male |2 MeSH |
| 650 | _ | 2 | |a Middle Aged |2 MeSH |
| 650 | _ | 2 | |a Multifactorial Inheritance |2 MeSH |
| 650 | _ | 2 | |a Parkinson Disease: genetics |2 MeSH |
| 650 | _ | 2 | |a Quantitative Trait, Heritable |2 MeSH |
| 700 | 1 | _ | |a Saad, Mohamad |b 1 |
| 700 | 1 | _ | |a Bras, Jose |b 2 |
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| 999 | C | 5 | |a 10.1093/brain/awp044 |9 -- missing cx lookup -- |2 Crossref |o 10.1093/brain/awp044 |
| 999 | C | 5 | |a 10.1056/NEJMoa0901281 |9 -- missing cx lookup -- |2 Crossref |o 10.1056/NEJMoa0901281 |
| 999 | C | 5 | |a 10.1056/NEJMra0905980 |9 -- missing cx lookup -- |2 Crossref |o 10.1056/NEJMra0905980 |
| 999 | C | 5 | |a 10.1016/j.tig.2010.07.004 |9 -- missing cx lookup -- |2 Crossref |o 10.1016/j.tig.2010.07.004 |
| 999 | C | 5 | |a 10.1371/journal.pone.0024945 |9 -- missing cx lookup -- |2 Crossref |o 10.1371/journal.pone.0024945 |
| 999 | C | 5 | |a 10.1016/S0140-6736(10)62345-8 |9 -- missing cx lookup -- |2 Crossref |o 10.1016/S0140-6736(10)62345-8 |
| 999 | C | 5 | |a 10.1038/ng.487 |9 -- missing cx lookup -- |2 Crossref |o 10.1038/ng.487 |
| 999 | C | 5 | |a 10.1371/journal.pgen.1002142 |9 -- missing cx lookup -- |2 Crossref |o 10.1371/journal.pgen.1002142 |
| 999 | C | 5 | |a 10.1007/s00439-008-0582-9 |9 -- missing cx lookup -- |2 Crossref |o 10.1007/s00439-008-0582-9 |
| 999 | C | 5 | |a 10.1038/ejhg.2010.254 |9 -- missing cx lookup -- |2 Crossref |o 10.1038/ejhg.2010.254 |
| 999 | C | 5 | |a 10.1016/S1474-4422(06)70578-6 |9 -- missing cx lookup -- |2 Crossref |o 10.1016/S1474-4422(06)70578-6 |
| 999 | C | 5 | |a 10.1016/S1474-4422(10)70184-8 |9 -- missing cx lookup -- |2 Crossref |o 10.1016/S1474-4422(10)70184-8 |
| 999 | C | 5 | |a 10.1371/journal.pgen.1000415 |9 -- missing cx lookup -- |2 Crossref |o 10.1371/journal.pgen.1000415 |
| 999 | C | 5 | |a 10.1093/hmg/ddq469 |9 -- missing cx lookup -- |2 Crossref |o 10.1093/hmg/ddq469 |
| 999 | C | 5 | |a 10.1146/annurev.genom.9.081307.164242 |9 -- missing cx lookup -- |2 Crossref |o 10.1146/annurev.genom.9.081307.164242 |
| 999 | C | 5 | |a 10.1002/gepi.20533 |9 -- missing cx lookup -- |2 Crossref |o 10.1002/gepi.20533 |
| 999 | C | 5 | |a 10.1038/ng1847 |9 -- missing cx lookup -- |2 Crossref |o 10.1038/ng1847 |
| 999 | C | 5 | |a 10.1086/519795 |9 -- missing cx lookup -- |2 Crossref |o 10.1086/519795 |
| 999 | C | 5 | |a 10.1136/bmj.39343.408449.80 |9 -- missing cx lookup -- |2 Crossref |o 10.1136/bmj.39343.408449.80 |
| 999 | C | 5 | |a 10.1136/bmj.327.7414.557 |9 -- missing cx lookup -- |2 Crossref |o 10.1136/bmj.327.7414.557 |
| 999 | C | 5 | |a 10.2307/3001666 |9 -- missing cx lookup -- |2 Crossref |o 10.2307/3001666 |
| 999 | C | 5 | |y 2011 |2 Crossref |o 2011 |
| Library | Collection | CLSMajor | CLSMinor | Language | Author |
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