Genome-wide association of breast cancer: composite likelihood with imputed genotypes
Genome-wide association of breast cancer: composite likelihood with imputed genotypes
We describe composite likelihood-based analysis of a genome-wide breast cancer case–control sample from the Cancer Genetic Markers of Susceptibility project. We determine 14?380 genome regions of fixed size on a linkage disequilibrium (LD) map, which delimit comparable levels of LD. Although the numbers of single-nucleotide polymorphisms (SNPs) are highly variable, each region contains an average of ~35 SNPs and an average of ~69 after imputation of missing genotypes.
Composite likelihood association mapping yields a single P-value for each region, established by a permutation test, along with a maximum likelihood disease location, SE and information weight. For single SNP analysis, the nominal P-value for the most significant SNP (msSNP) requires substantial correction given the number of SNPs in the region. Therefore, imputing genotypes may not always be advantageous for the msSNP test, in contrast to composite likelihood. For the region containing FGFR2 (a known breast cancer gene) the largest ?2 is obtained under composite likelihood with imputed genotypes (?22 increases from 20.6 to 22.7), and compares with a single SNP-based ?22 of 19.9 after correction. Imputation of additional genotypes in this region reduces the size of the 95% confidence interval for location of the disease gene by ~40%.
Among the highest ranked regions, SNPs in the NTSR1 gene would be worthy of examination in additional samples. Meta-analysis, which combines weighted evidence from composite likelihood in different samples, and refines putative disease locations, is facilitated through defining fixed regions on an underlying LD map.
194-199
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
February 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)
Genome-wide association of breast cancer: composite likelihood with imputed genotypes.
European Journal of Human Genetics, 19 (2), .
(doi:10.1038/ejhg.2010.157).
(PMID:20959865)
Abstract
We describe composite likelihood-based analysis of a genome-wide breast cancer case–control sample from the Cancer Genetic Markers of Susceptibility project. We determine 14?380 genome regions of fixed size on a linkage disequilibrium (LD) map, which delimit comparable levels of LD. Although the numbers of single-nucleotide polymorphisms (SNPs) are highly variable, each region contains an average of ~35 SNPs and an average of ~69 after imputation of missing genotypes.
Composite likelihood association mapping yields a single P-value for each region, established by a permutation test, along with a maximum likelihood disease location, SE and information weight. For single SNP analysis, the nominal P-value for the most significant SNP (msSNP) requires substantial correction given the number of SNPs in the region. Therefore, imputing genotypes may not always be advantageous for the msSNP test, in contrast to composite likelihood. For the region containing FGFR2 (a known breast cancer gene) the largest ?2 is obtained under composite likelihood with imputed genotypes (?22 increases from 20.6 to 22.7), and compares with a single SNP-based ?22 of 19.9 after correction. Imputation of additional genotypes in this region reduces the size of the 95% confidence interval for location of the disease gene by ~40%.
Among the highest ranked regions, SNPs in the NTSR1 gene would be worthy of examination in additional samples. Meta-analysis, which combines weighted evidence from composite likelihood in different samples, and refines putative disease locations, is facilitated through defining fixed regions on an underlying LD map.
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Published date: February 2011
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Local EPrints ID: 166437
URI: http://eprints.soton.ac.uk/id/eprint/166437
ISSN: 1018-4813
PURE UUID: fd7f3552-768e-4f15-866b-e110ec0c448b
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Date deposited: 28 Oct 2010 13:22
Last modified: 14 Mar 2024 02:48
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Author:
Ioannis Politopoulos
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