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Meta-analysis of Ge...
Meta-analysis of Gene-Level Associations for Rare Variants Based on Single-Variant Statistics
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Hu, Yi-Juan (författare)
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Berndt, Sonja I. (författare)
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- Gustafsson, Stefan (författare)
- Uppsala universitet,Institutionen för medicinska vetenskaper,Science for Life Laboratory, SciLifeLab
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- Ganna, Andrea (författare)
- Karolinska Institutet,Uppsala universitet,Institutionen för medicinska vetenskaper,Science for Life Laboratory, SciLifeLab
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Hirschhorn, Joel (författare)
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North, Kari E. (författare)
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- Ingelsson, Erik (författare)
- Uppsala universitet,Institutionen för medicinska vetenskaper,Science for Life Laboratory, SciLifeLab
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Lin, Dan-Yu (författare)
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(creator_code:org_t)
- Elsevier BV, 2013
- 2013
- Engelska.
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Ingår i: American Journal of Human Genetics. - : Elsevier BV. - 0002-9297 .- 1537-6605. ; 93:2, s. 236-248
- Relaterad länk:
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http://www.cell.com/...
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https://urn.kb.se/re...
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https://doi.org/10.1...
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http://kipublication...
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Abstract
Ämnesord
Stäng
- 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.
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