Please use this identifier to cite or link to this item:
https://hdl.handle.net/2440/88220
Citations | ||
Scopus | Web of Science® | Altmetric |
---|---|---|
?
|
?
|
Full metadata record
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Hu, Y. | - |
dc.contributor.author | Berndt, S. | - |
dc.contributor.author | Gustafsson, S. | - |
dc.contributor.author | Ganna, A. | - |
dc.contributor.author | Genetic Investigation of ANthropometric Traits (GIANT) Consortium, | - |
dc.contributor.author | Hirschhorn, J. | - |
dc.contributor.author | North, K. | - |
dc.contributor.author | Ingelsson, E. | - |
dc.contributor.author | Lin, D. | - |
dc.date.issued | 2013 | - |
dc.identifier.citation | American Journal of Human Genetics, 2013; 93(2):236-248 | - |
dc.identifier.issn | 0002-9297 | - |
dc.identifier.issn | 1537-6605 | - |
dc.identifier.uri | http://hdl.handle.net/2440/88220 | - |
dc.description | Lyle J. Plamer is a member of the GIANT Consortium | - |
dc.description.abstract | Meta-analysis of genome-wide association studies (GWASs) has led to the discoveries of many common variants associated with complex human diseases. There is a growing recognition that identifying "causal" rare variants also requires large-scale meta-analysis. The fact that association tests with rare variants are performed at the gene level rather than at the variant level poses unprecedented challenges in the meta-analysis. First, different studies may adopt different gene-level tests, so the results are not compatible. Second, gene-level tests require multivariate statistics (i.e., components of the test statistic and their covariance matrix), which are difficult to obtain. To overcome these challenges, we propose to perform gene-level tests for rare variants by combining the results of single-variant analysis (i.e., p values of association tests and effect estimates) from participating studies. This simple strategy is possible because of an insight that multivariate statistics can be recovered from single-variant statistics, together with the correlation matrix of the single-variant test statistics, which can be estimated from one of the participating studies or from a publicly available database. We show both theoretically and numerically that the proposed meta-analysis approach provides accurate control of the type I error and is as powerful as joint analysis of individual participant data. This approach accommodates any disease phenotype and any study design and produces all commonly used gene-level tests. An application to the GWAS summary results of the Genetic Investigation of ANthropometric Traits (GIANT) consortium reveals rare and low-frequency variants associated with human height. The relevant software is freely available. | - |
dc.description.statementofresponsibility | Yi-Juan Hu, Sonja I. Berndt, Stefan Gustafsson, Andrea Ganna, Genetic Investigation of ANthropometric Traits, GIANT, Consortium, Joel Hirschhorn, Kari E. North, Erik Ingelsson, and Dan-Yu Lin | - |
dc.language.iso | en | - |
dc.publisher | University of Chicago Press | - |
dc.rights | © 2013 by The American Society of Human Genetics. All rights reserved | - |
dc.source.uri | http://dx.doi.org/10.1016/j.ajhg.2013.06.011 | - |
dc.subject | No keywords specified | - |
dc.title | Meta-analysis of gene-level associations for rare variants based on single-variant statistics | - |
dc.type | Journal article | - |
dc.identifier.doi | 10.1016/j.ajhg.2013.06.011 | - |
pubs.publication-status | Published | - |
Appears in Collections: | Aurora harvest 7 Translational Health Science publications |
Files in This Item:
There are no files associated with this item.
Items in DSpace are protected by copyright, with all rights reserved, unless otherwise indicated.