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Polygenic risk scores for prediction of breast cancer and breast cancer subtypes

Polygenic risk scores for prediction of breast cancer and breast cancer subtypes
Polygenic risk scores for prediction of breast cancer and breast cancer subtypes
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.
0002-9297
21-34
Mavaddat, Nasim
bc5470a2-fb13-436d-ae0d-cc47bbedcc74
Durcan, Lorraine
bd059b41-9e77-4afe-b271-9ac4c91a05c6
Michailidou, Kyriaki
3998b901-962f-4233-b277-483ca6e195b6
Dennis, Joe
bd305c84-d968-4946-b154-a5bedb469210
Eccles, Diana
5b59bc73-11c9-4cf0-a9d5-7a8e523eee23
Maishman, Thom
cf4259a4-0eef-4975-9c9d-a2c3d594f989
Tapper, William
9d5ddc92-a8dd-4c78-ac67-c5867b62724c
et al.
Mavaddat, Nasim
bc5470a2-fb13-436d-ae0d-cc47bbedcc74
Durcan, Lorraine
bd059b41-9e77-4afe-b271-9ac4c91a05c6
Michailidou, Kyriaki
3998b901-962f-4233-b277-483ca6e195b6
Dennis, Joe
bd305c84-d968-4946-b154-a5bedb469210
Eccles, Diana
5b59bc73-11c9-4cf0-a9d5-7a8e523eee23
Maishman, Thom
cf4259a4-0eef-4975-9c9d-a2c3d594f989
Tapper, William
9d5ddc92-a8dd-4c78-ac67-c5867b62724c

Mavaddat, Nasim, Durcan, Lorraine and Michailidou, Kyriaki , et al. (2019) Polygenic risk scores for prediction of breast cancer and breast cancer subtypes. The American Journal of Human Genetics, 104 (1), 21-34. (doi:10.1016/j.ajhg.2018.11.002).

Record type: Article

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.

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Accepted/In Press date: 3 November 2018
e-pub ahead of print date: 13 December 2018
Published date: 3 January 2019

Identifiers

Local EPrints ID: 427479
URI: http://eprints.soton.ac.uk/id/eprint/427479
ISSN: 0002-9297
PURE UUID: 56e2d2c2-a77b-43e4-a7f3-c67fdd68e57e
ORCID for Diana Eccles: ORCID iD orcid.org/0000-0002-9935-3169
ORCID for William Tapper: ORCID iD orcid.org/0000-0002-5896-1889

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Date deposited: 18 Jan 2019 17:30
Last modified: 26 Nov 2021 02:42

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Contributors

Author: Nasim Mavaddat
Author: Lorraine Durcan
Author: Kyriaki Michailidou
Author: Joe Dennis
Author: Diana Eccles ORCID iD
Author: Thom Maishman
Author: William Tapper ORCID iD
Corporate Author: et al.

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