Estimating single nucleotide polymorphism associations using pedigree data: Applications to breast cancer

Barnes, D. R., Barrowdale, D., Beesley, J., Chen, X., James, P. A., Hopper, J. L., Goldgar, D., Chenevix-Trench, G., Antoniou, A. C. and Mitchell, G. (2013) Estimating single nucleotide polymorphism associations using pedigree data: Applications to breast cancer. British Journal of Cancer, 108 12: 2610-2622. doi:10.1038/bjc.2013.277

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Author Barnes, D. R.
Barrowdale, D.
Beesley, J.
Chen, X.
James, P. A.
Hopper, J. L.
Goldgar, D.
Chenevix-Trench, G.
Antoniou, A. C.
Mitchell, G.
Title Estimating single nucleotide polymorphism associations using pedigree data: Applications to breast cancer
Journal name British Journal of Cancer   Check publisher's open access policy
ISSN 0007-0920
1532-1827
Publication date 2013-06-25
Sub-type Article (original research)
DOI 10.1038/bjc.2013.277
Open Access Status DOI
Volume 108
Issue 12
Start page 2610
End page 2622
Total pages 13
Place of publication London, United Kingdom
Publisher Nature Publishing Group
Language eng
Abstract Background: Pedigrees with multiple genotyped family members have been underutilised in breast cancer (BC) geneticassociation studies. We developed a pedigree-based analytical framework to characterise single-nucleotide polymorphism (SNP) associations with BC risk using data from 736 BC families ascertained through multiple affected individuals. On average, eight family members had been genotyped for 24 SNPs previously associated with BC. Methods: Breast cancer incidence was modelled on the basis of SNP effects and residual polygenic effects. Relative risk (RR) estimates were obtained by maximising the retrospective likelihood (RL) of observing the family genotypes conditional on all disease phenotypes. Models were extended to assess parent-of-origin effects (POEs). Results: Thirteen SNPs were significantly associated with BC under the pedigree RL approach. This approach yielded estimates consistent with those from large population-based studies. Logistic regression models ignoring pedigree structure generally gave larger RRs and association P-values. SNP rs3817198 in LSP1, previously shown to exhibit POE, yielded maternal and paternal RR estimates that were similar to those previously reported (paternal RR=1.12 (95% confidence interval (CI): 0.99-1.27), P=0.081, one-sided P=0.04; maternal RR=0.94 (95% CI: 0.84-1.06), P=0.33). No other SNP exhibited POE. Conclusion: Our pedigree-based methods provide a valuable and efficient tool for characterising genetic associations with BC risk or other diseases and can complement population-based studies.
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Institutional Status UQ

Document type: Journal Article
Sub-type: Article (original research)
Collections: Official 2014 Collection
School of Medicine Publications
 
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Created: Wed, 02 Apr 2014, 00:25:32 EST by Matthew Lamb on behalf of School of Medicine