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A genetic risk predictor for breast cancer using a combination of low-penetrance polymorphisms in a Japanese population

  • Epidemiology
  • Published:
Breast Cancer Research and Treatment Aims and scope Submit manuscript

Abstract

Genome-wide association studies (GWASs) have identified genetic variants associated with breast cancer. Most GWASs to date have been conducted in women of European descent, however, and the contribution of these variants as predictors in Japanese women is unknown. Here, we analyzed 23 genetic variants identified in previous GWASs and conducted a case–control study with 697 case subjects and 1,394 age- and menopausal status-matched controls. We fit conditional regression models with genetic variants and conventional risk factors. In addition, we created a polygenetic risk score, using those variants with a statistically significant association with breast cancer risk, and also evaluated the contribution of these genetic predictors using the c statistic. Eleven single-nucleotide polymorphisms (SNPs) revealed significant associations with breast cancer risk. A dose-dependent association was observed between the risk of breast cancer and the genetic risk score, which was an aggregate measure of alleles in seven selected variants, namely FGFR2-rs2981579, TOX3/TNRC9-rs3803662, C6orf97-rs2046210, 8q24-rs13281615, SLC4A7-rs4973768, LSP1-rs38137198, and CASP8-rs10931936. Compared to women with scores of 3 or less, odds ratios (ORs) for women with scores of 4–5, 6–7, 8–9, and 10 or more were 1.33 (95% confidence interval, 1.00–1.80), 1.71 (1.26–2.30), 3.01 (1.97–4.58), and 8.69 (2.75–27.5), respectively (P trend = 1.9 × 10−9). The c statistic for a model including the genetic risk score in addition to the conventional risk factors was 0.6933, versus 0.6652 with the conventional risk factors only (P = 1.3 × 10−4). Population-attributable fraction of the risk score was 33.0%. In conclusion, we identified a genetic risk predictor of breast cancer in a Japanese population. Risk models which include a genetic risk score are possibly useful in distinguishing women at high risk of breast cancer from those at low risk, particularly in the context of targeted prevention.

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Abbreviations

GWAS:

Genome-wide association studies

SNP:

Single-nucleotide polymorphism

ACCH:

Aichi Cancer Center Hospital

HERPACC:

Hospital-based Epidemiologic Research Program at Aichi Cancer Center

HWE:

Hardy–Weinberg equilibrium

LD:

Linkage disequilibrium

ROC:

Receiver-operating characteristic curve

AUC:

Area under the receiver operator characteristic curve

PAF:

Population-attributable fraction

OR:

Odds ratio

95%CI:

95% confidence interval

ER:

Estrogen receptor

PR:

Progesterone receptor

BMI:

Body mass index

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Acknowledgments

The authors gratefully acknowledge the efforts and contribution of the doctors, nurses, technical staff, and hospital administration staff of Aichi Cancer Hospital for their daily management of the HERPACC study. This study was supported by a Grant-in-Aid for Scientific Research from the Ministry of Education, Science, Sports, Culture and Technology of Japan and by a Grant-in-Aid for the Third Term Comprehensive 10-Year Strategy for Cancer Control from the Ministry of Health, Labour and Welfare of Japan.

Conflicts of interest

We declare that we have no conflict of interest.

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Authors and Affiliations

  1. Division of Epidemiology and Prevention, Aichi Cancer Center Research Institute, 1-1, Kanokoden, Chikusa-ku, Nagoya, 464-8681, Japan

    Aiko Sueta, Hidemi Ito, Takakazu Kawase, Satoyo Hosono, Hideo Tanaka & Keitaro Matsuo

  2. Department of Breast and Endocrine Surgery, Kumamoto University Graduate School of Medical Science, Kumamoto, Japan

    Aiko Sueta & Hirotaka Iwase

  3. Department of Planning and Information, Aichi Prefectural Institute of Public Health, Nagoya, Japan

    Kaoru Hirose

  4. Department of Pathology and Molecular Diagnostics, Aichi Cancer Center Central Hospital, Nagoya, Japan

    Yasushi Yatabe

  5. Aichi Cancer Center Research Institute, Nagoya, Japan

    Kazuo Tajima

  6. Department of Epidemiology, Nagoya University Graduate School of Medicine, Nagoya, Japan

    Hideo Tanaka & Keitaro Matsuo

  7. Department of Breast Oncology, Aichi Cancer Center Central Hospital, Nagoya, Japan

    Hiroji Iwata

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  1. Aiko Sueta
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  6. Yasushi Yatabe
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  11. Keitaro Matsuo
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Correspondence to Keitaro Matsuo.

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Sueta, A., Ito, H., Kawase, T. et al. A genetic risk predictor for breast cancer using a combination of low-penetrance polymorphisms in a Japanese population. Breast Cancer Res Treat 132, 711–721 (2012). https://doi.org/10.1007/s10549-011-1904-5

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