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Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes

Mavaddat, Nasim; Ellberg, Carolina LU ; Krüger, Ute LU ; Olsson, Håkan LU ; Försti, Asta LU ; Easton, Douglas F and , (2019) In American Journal of Human Genetics 104(1). p.21-34
Abstract
Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an... (More)
Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57–1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628–0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs. © 2018 The Authors (Less)
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author
organization
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type
Contribution to journal
publication status
published
subject
keywords
breast, cancer, epidemiology, genetic, polygenic, prediction, risk, score, screening, stratification
in
American Journal of Human Genetics
volume
104
issue
1
pages
14 pages
publisher
Cell Press
external identifiers
  • scopus:85059498503
ISSN
0002-9297
DOI
10.1016/j.ajhg.2018.11.002
language
English
LU publication?
yes
id
01438317-eb4b-4bdd-ac61-ba7735bf4deb
date added to LUP
2019-01-18 13:36:41
date last changed
2019-01-27 05:33:50
@article{01438317-eb4b-4bdd-ac61-ba7735bf4deb,
  abstract     = {Stratification of women according to their risk of breast cancer based on polygenic risk scores (PRSs) could improve screening and prevention strategies. Our aim was to develop PRSs, optimized for prediction of estrogen receptor (ER)-specific disease, from the largest available genome-wide association dataset and to empirically validate the PRSs in prospective studies. The development dataset comprised 94,075 case subjects and 75,017 control subjects of European ancestry from 69 studies, divided into training and validation sets. Samples were genotyped using genome-wide arrays, and single-nucleotide polymorphisms (SNPs) were selected by stepwise regression or lasso penalized regression. The best performing PRSs were validated in an independent test set comprising 11,428 case subjects and 18,323 control subjects from 10 prospective studies and 190,040 women from UK Biobank (3,215 incident breast cancers). For the best PRSs (313 SNPs), the odds ratio for overall disease per 1 standard deviation in ten prospective studies was 1.61 (95%CI: 1.57–1.65) with area under receiver-operator curve (AUC) = 0.630 (95%CI: 0.628–0.651). The lifetime risk of overall breast cancer in the top centile of the PRSs was 32.6%. Compared with women in the middle quintile, those in the highest 1% of risk had 4.37- and 2.78-fold risks, and those in the lowest 1% of risk had 0.16- and 0.27-fold risks, of developing ER-positive and ER-negative disease, respectively. Goodness-of-fit tests indicated that this PRS was well calibrated and predicts disease risk accurately in the tails of the distribution. This PRS is a powerful and reliable predictor of breast cancer risk that may improve breast cancer prevention programs. © 2018 The Authors},
  author       = {Mavaddat, Nasim and Ellberg, Carolina and Krüger, Ute and Olsson, Håkan and Försti, Asta and Easton, Douglas F and , },
  issn         = {0002-9297},
  keyword      = {breast,cancer,epidemiology,genetic,polygenic,prediction,risk,score,screening,stratification},
  language     = {eng},
  number       = {1},
  pages        = {21--34},
  publisher    = {Cell Press},
  series       = {American Journal of Human Genetics},
  title        = {Polygenic Risk Scores for Prediction of Breast Cancer and Breast Cancer Subtypes},
  url          = {http://dx.doi.org/10.1016/j.ajhg.2018.11.002},
  volume       = {104},
  year         = {2019},
}