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Mia M. Gaudet

Researcher at American Cancer Society

Publications -  195
Citations -  20310

Mia M. Gaudet is an academic researcher from American Cancer Society. The author has contributed to research in topics: Breast cancer & Cancer. The author has an hindex of 54, co-authored 185 publications receiving 14901 citations. Previous affiliations of Mia M. Gaudet include Memorial Sloan Kettering Cancer Center & Yeshiva University.

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Journal ArticleDOI

Ovarian cancer statistics, 2018.

TL;DR: Progress in reducing ovarian cancer incidence and mortality can be accelerated by reducing racial disparities and furthering knowledge of etiology and tumorigenesis to facilitate strategies for prevention and early detection.
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Breast cancer statistics, 2019.

TL;DR: Breast cancer was the leading cause of cancer death in women in four Southern and two Midwestern states among blacks and in Utah among whites during 2016‐2017, and could be accelerated by expanding access to high‐quality prevention, early detection, and treatment services to all women.
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Modeling Linkage Disequilibrium Increases Accuracy of Polygenic Risk Scores

Bjarni J. Vilhjálmsson, +394 more
TL;DR: LDpred is introduced, a method that infers the posterior mean effect size of each marker by using a prior on effect sizes and LD information from an external reference panel, and outperforms the approach of pruning followed by thresholding, particularly at large sample sizes.
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Association analysis identifies 65 new breast cancer risk loci

Kyriaki Michailidou, +396 more
- 02 Nov 2017 - 
TL;DR: A genome-wide association study of breast cancer in 122,977 cases and 105,974 controls of European ancestry and 14,068 cases and 13,104 controls of East Asian ancestry finds that heritability of Breast cancer due to all single-nucleotide polymorphisms in regulatory features was 2–5-fold enriched relative to the genome- wide average.
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Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes

Nasim Mavaddat, +310 more
TL;DR: This PRS, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset is developed and empirically validated and is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs.