Composite likelihood-based meta-analysis of breast cancer association studies
Composite likelihood-based meta-analysis of breast cancer association studies
For detecting low risk disease variants in genome-wide association panels, meta-analysis is a powerful strategy to increase power. We apply a composite likelihood-based method, which models association with disease in regions defined on a linkage disequilibrium map and combines the evidence across multiple genome-wide samples. This fixed region approach has the advantage that, as only one statistical test is made per region, there is no increased multiple testing penalty in higher marker density panels. Imputation of missing genotypes is also advantageous to increase coverage. Meta-analysis of three breast cancer data sets combines evidence from samples that show heterogeneity in phenotype and, particularly, in marker coverage. The FGFR2 gene has the highest rank, consistent with previous analysis of one of these samples and supported by the small number of early-onset breast cancer cases included. The 8q24 breast cancer region also ranks highly and is supported by evidence from both early-onset and post-menopausal breast cancer samples. The PIK3AP1 gene region is highlighted in this analysis as a strong candidate for further study
377-382
Politopoulos, Ioannis
0d757d1d-1fef-4dcb-bd64-c554940c3843
Gibson, Jane
855033a6-38f3-4853-8f60-d7d4561226ae
Tapper, William
9d5ddc92-a8dd-4c78-ac67-c5867b62724c
Ennis, Sarah
7b57f188-9d91-4beb-b217-09856146f1e9
Eccles, Diana
5b59bc73-11c9-4cf0-a9d5-7a8e523eee23
Collins, Andrew
7daa83eb-0b21-43b2-af1a-e38fb36e2a64
May 2011
Politopoulos, Ioannis
0d757d1d-1fef-4dcb-bd64-c554940c3843
Gibson, Jane
855033a6-38f3-4853-8f60-d7d4561226ae
Tapper, William
9d5ddc92-a8dd-4c78-ac67-c5867b62724c
Ennis, Sarah
7b57f188-9d91-4beb-b217-09856146f1e9
Eccles, Diana
5b59bc73-11c9-4cf0-a9d5-7a8e523eee23
Collins, Andrew
7daa83eb-0b21-43b2-af1a-e38fb36e2a64
Politopoulos, Ioannis, Gibson, Jane, Tapper, William, Ennis, Sarah, Eccles, Diana and Collins, Andrew
(2011)
Composite likelihood-based meta-analysis of breast cancer association studies.
Journal of Human Genetics, 56 (5), .
(doi:10.1038/jhg.2011.23).
(PMID:21390041)
Abstract
For detecting low risk disease variants in genome-wide association panels, meta-analysis is a powerful strategy to increase power. We apply a composite likelihood-based method, which models association with disease in regions defined on a linkage disequilibrium map and combines the evidence across multiple genome-wide samples. This fixed region approach has the advantage that, as only one statistical test is made per region, there is no increased multiple testing penalty in higher marker density panels. Imputation of missing genotypes is also advantageous to increase coverage. Meta-analysis of three breast cancer data sets combines evidence from samples that show heterogeneity in phenotype and, particularly, in marker coverage. The FGFR2 gene has the highest rank, consistent with previous analysis of one of these samples and supported by the small number of early-onset breast cancer cases included. The 8q24 breast cancer region also ranks highly and is supported by evidence from both early-onset and post-menopausal breast cancer samples. The PIK3AP1 gene region is highlighted in this analysis as a strong candidate for further study
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e-pub ahead of print date: 10 March 2011
Published date: May 2011
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Local EPrints ID: 177997
URI: http://eprints.soton.ac.uk/id/eprint/177997
ISSN: 1434-5161
PURE UUID: 4bc55ab3-573f-43d7-a630-386f7b014cd9
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Date deposited: 22 Mar 2011 15:05
Last modified: 15 Mar 2024 03:02
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Author:
Ioannis Politopoulos
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