Deep targeted sequencing of 12 breast cancer susceptibility regions in 4611 women across four different ethnicities View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-12

AUTHORS

Sara Lindström, Akweley Ablorh, Brad Chapman, Alexander Gusev, Gary Chen, Constance Turman, A. Heather Eliassen, Alkes L. Price, Brian E. Henderson, Loic Le Marchand, Oliver Hofmann, Christopher A. Haiman, Peter Kraft

ABSTRACT

BACKGROUND: Although genome-wide association studies (GWASs) have identified thousands of disease susceptibility regions, the underlying causal mechanism in these regions is not fully known. It is likely that the GWAS signal originates from one or many as yet unidentified causal variants. METHODS: Using next-generation sequencing, we characterized 12 breast cancer susceptibility regions identified by GWASs in 2288 breast cancer cases and 2323 controls across four populations of African American, European, Japanese, and Hispanic ancestry. RESULTS: After genotype calling and quality control, we identified 137,530 single-nucleotide variants (SNVs); of those, 87.2 % had a minor allele frequency (MAF) <0.005. For SNVs with MAF >0.005, we calculated the smallest number of SNVs needed to obtain a posterior probability set (PPS) such that there is 90 % probability that the causal SNV is included. We found that the PPS for two regions, 2q35 and 11q13, contained less than 5 % of the original SNVs, dramatically decreasing the number of potentially causal SNVs. However, we did not find strong evidence supporting a causal role for any individual SNV. In addition, there were no significant gene-based rare SNV associations after correcting for multiple testing. CONCLUSIONS: This study illustrates some of the challenges faced in fine-mapping studies in the post-GWAS era, most importantly the large sample sizes needed to identify rare-variant associations or to distinguish the effects of strongly correlated common SNVs. More... »

PAGES

109

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  • Identifiers

    URI

    http://scigraph.springernature.com/pub.10.1186/s13058-016-0772-7

    DOI

    http://dx.doi.org/10.1186/s13058-016-0772-7

    DIMENSIONS

    https://app.dimensions.ai/details/publication/pub.1042852523

    PUBMED

    https://www.ncbi.nlm.nih.gov/pubmed/27814745


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