Trans-ethnic follow-up of breast cancer GWAS hits using the preferential linkage disequilibrium approach

Qianqian Zhu, Lori Shepherd, Kathryn L. Lunetta, Song Yao, Qian Liu, Qiang Hu, Stephen A. Haddad, Lara Sucheston-Campbell, Jeannette T. Bensen, Elisa V. Bandera, Lynn Rosenberg, Song Liu, Christopher A. Haiman, Andrew F. Olshan, Julie R. Palmer, Christine B. Ambrosone

Research output: Contribution to journalArticlepeer-review

8 Scopus citations


Leveraging population-distinct linkage equilibrium (LD) patterns, trans-ethnic follow-up of variants discovered from genome-wide association studies (GWAS) has proved to be useful in facilitating the identification of bona fide causal variants. We previously developed the preferential LD approach, a novel method that successfully identified causal variants driving the GWAS signals within European-descent populations even when the causal variants were only weakly linked with the GWASdiscovered variants. To evaluate the performance of our approach in a trans-ethnic setting, we applied it to follow up breast cancer GWAS hits identified mostly from populations of European ancestry in African Americans (AA). We evaluated 74 breast cancer GWAS variants in 8,315 AA women from the African American Breast Cancer Epidemiology and Risk (AMBER) consortium. Only 27% of them were associated with breast cancer risk at significance level a=0.05, suggesting race-specificity of the identified breast cancer risk loci. We followed up on those replicated GWAS hits in the AMBER consortium utilizing the preferential LD approach, to search for causal variants or better breast cancer markers from the 1000 Genomes variant catalog. Our approach identified stronger breast cancer markers for 80% of the GWAS hits with at least nominal breast cancer association, and in 81% of these cases, the marker identified was among the top 10 of all 1000 Genomes variants in the corresponding locus. The results support trans-ethnic application of the preferential LD approach in search for candidate causal variants, and may have implications for future genetic research of breast cancer in AA women.

Original languageEnglish (US)
Pages (from-to)83160-83176
Number of pages17
Issue number50
StatePublished - 2016

All Science Journal Classification (ASJC) codes

  • Oncology


  • Causal variant
  • Fine-mapping
  • Genome-wide association studies

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