Polygenic Risk Scores for Prediction of Breast Cancer Risk in Women of African Ancestry: a Cross-Ancestry Approach

Guimin Gao; Fangyuan Zhao; Thomas U Ahearn; Kathryn L Lunetta; Melissa A Troester; Zhaohui Du; Temidayo O Ogundiran; Oladosu Ojengbede; William Blot; Katherine L Nathanson; Susan M Domchek; Barbara Nemesure; Anselm Hennis; Stefan Ambs; Julian Mcclellan; Mark Nie; Kimberly Bertrand; Gary Zirpoli; Song Yao; Andrew F Olshan; Jeannette T Bensen; Elisa V Bandera; Sarah Nyante; David V Conti; Michael F Press; Sue A Ingles; Esther M John; Leslie Bernstein; Jennifer J Hu; Sandra L Deming-halverson; Stephen J Chanock; Regina G Ziegler; Jorge L Rodriguez-gil; Lara E Sucheston-campbell; Dale P Sandler; Jack A Taylor; Cari M Kitahara; Katie M O’brien; Manjeet K Bolla; Joe Dennis; Alison M Dunning; Douglas F Easton; Kyriaki Michailidou; Paul D P Pharoah; Qin Wang; Jonine Figueroa; Richard Biritwum; Ernest Adjei; Seth Wiafe; Christine B Ambrosone; Wei Zheng; Olufunmilayo I Olopade; Montserrat García-closas; Julie R Palmer; Christopher A Haiman; Dezheng Huo

doi:10.1093/hmg/ddac102

Polygenic Risk Scores for Prediction of Breast Cancer Risk in Women of African Ancestry: a Cross-Ancestry Approach

Guimin Gao, Fangyuan Zhao, Thomas U Ahearn, Kathryn L Lunetta, Melissa A Troester, Zhaohui Du, Temidayo O Ogundiran, Oladosu Ojengbede, William Blot, Katherine L Nathanson, Susan M Domchek, Barbara Nemesure, Anselm Hennis, Stefan Ambs, Julian Mcclellan, Mark Nie, Kimberly Bertrand, Gary Zirpoli, Song Yao, Andrew F OlshanJeannette T Bensen, Elisa V Bandera, Sarah Nyante, David V Conti, Michael F Press, Sue A Ingles, Esther M John, Leslie Bernstein, Jennifer J Hu, Sandra L Deming-halverson, Stephen J Chanock, Regina G Ziegler, Jorge L Rodriguez-gil, Lara E Sucheston-campbell, Dale P Sandler, Jack A Taylor, Cari M Kitahara, Katie M O’brien, Manjeet K Bolla, Joe Dennis, Alison M Dunning, Douglas F Easton, Kyriaki Michailidou, Paul D P Pharoah, Qin Wang, Jonine Figueroa, Richard Biritwum, Ernest Adjei, Seth Wiafe, Christine B Ambrosone, Wei Zheng, Olufunmilayo I Olopade, Montserrat García-closas, Julie R Palmer, Christopher A Haiman, Dezheng Huo

Research output: Contribution to journal › Article › peer-review

Abstract

Polygenic risk scores (PRSs) are useful for predicting breast cancer risk, but the prediction accuracy of existing PRSs in women of African ancestry (AA) remains relatively low. We aim to develop optimal PRSs for the prediction of overall and estrogen receptor (ER) subtype-specific breast cancer risk in AA women. The AA dataset comprised 9235 cases and 10 184 controls from four genome-wide association study (GWAS) consortia and a GWAS study in Ghana. We randomly divided samples into training and validation sets. We built PRSs using individual-level AA data by a forward stepwise logistic regression and then developed joint PRSs that combined (1) the PRSs built in the AA training dataset and (2) a 313-variant PRS previously developed in women of European ancestry. PRSs were evaluated in the AA validation set. For overall breast cancer, the odds ratio per standard deviation of the joint PRS in the validation set was 1.34 [95% confidence interval (CI): 1.27-1.42] with the area under receiver operating characteristic curve (AUC) of 0.581. Compared with women with average risk (40th-60th PRS percentile), women in the top decile of the PRS had a 1.98-fold increased risk (95% CI: 1.63-2.39). For PRSs of ER-positive and ER-negative breast cancer, the AUCs were 0.608 and 0.576, respectively. Compared with existing methods, the proposed joint PRSs can improve prediction of breast cancer risk in AA women.

Original language	English
Pages (from-to)	3133-3143
Journal	Human Molecular Genetics
Volume	31
Issue number	18
Early online date	12 May 2022
DOIs	https://doi.org/10.1093/hmg/ddac102
Publication status	Published - 15 Sept 2022

Keywords / Materials (for Non-textual outputs)

Breast Neoplasms/genetics
Female
Genetic Predisposition to Disease
Genome-Wide Association Study
Humans
Multifactorial Inheritance/genetics
Receptors, Estrogen/genetics
Risk Factors

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Polygenic Risk Scores for Prediction of Breast Cancer Risk in Women of African Ancestry_a Cross-Ancestry ApproachAccepted author manuscript, 567 KB

https://academic.oup.com/hmg/advance-article/doi/10.1093/hmg/ddac102/6584730

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Gao, G., Zhao, F., Ahearn, T. U., Lunetta, K. L., Troester, M. A., Du, Z., Ogundiran, T. O., Ojengbede, O., Blot, W., Nathanson, K. L., Domchek, S. M., Nemesure, B., Hennis, A., Ambs, S., Mcclellan, J., Nie, M., Bertrand, K., Zirpoli, G., Yao, S., ... Huo, D. (2022). Polygenic Risk Scores for Prediction of Breast Cancer Risk in Women of African Ancestry: a Cross-Ancestry Approach. Human Molecular Genetics, 31(18), 3133-3143. https://doi.org/10.1093/hmg/ddac102

@article{3c7f270da1a04e90994b2b7b3a5ce350,

title = "Polygenic Risk Scores for Prediction of Breast Cancer Risk in Women of African Ancestry: a Cross-Ancestry Approach",

abstract = "Polygenic risk scores (PRSs) are useful for predicting breast cancer risk, but the prediction accuracy of existing PRSs in women of African ancestry (AA) remains relatively low. We aim to develop optimal PRSs for the prediction of overall and estrogen receptor (ER) subtype-specific breast cancer risk in AA women. The AA dataset comprised 9235 cases and 10 184 controls from four genome-wide association study (GWAS) consortia and a GWAS study in Ghana. We randomly divided samples into training and validation sets. We built PRSs using individual-level AA data by a forward stepwise logistic regression and then developed joint PRSs that combined (1) the PRSs built in the AA training dataset and (2) a 313-variant PRS previously developed in women of European ancestry. PRSs were evaluated in the AA validation set. For overall breast cancer, the odds ratio per standard deviation of the joint PRS in the validation set was 1.34 [95\% confidence interval (CI): 1.27-1.42] with the area under receiver operating characteristic curve (AUC) of 0.581. Compared with women with average risk (40th-60th PRS percentile), women in the top decile of the PRS had a 1.98-fold increased risk (95\% CI: 1.63-2.39). For PRSs of ER-positive and ER-negative breast cancer, the AUCs were 0.608 and 0.576, respectively. Compared with existing methods, the proposed joint PRSs can improve prediction of breast cancer risk in AA women.",

keywords = "Breast Neoplasms/genetics, Female, Genetic Predisposition to Disease, Genome-Wide Association Study, Humans, Multifactorial Inheritance/genetics, Receptors, Estrogen/genetics, Risk Factors",

author = "Guimin Gao and Fangyuan Zhao and Ahearn, \{Thomas U\} and Lunetta, \{Kathryn L\} and Troester, \{Melissa A\} and Zhaohui Du and Ogundiran, \{Temidayo O\} and Oladosu Ojengbede and William Blot and Nathanson, \{Katherine L\} and Domchek, \{Susan M\} and Barbara Nemesure and Anselm Hennis and Stefan Ambs and Julian Mcclellan and Mark Nie and Kimberly Bertrand and Gary Zirpoli and Song Yao and Olshan, \{Andrew F\} and Bensen, \{Jeannette T\} and Bandera, \{Elisa V\} and Sarah Nyante and Conti, \{David V\} and Press, \{Michael F\} and Ingles, \{Sue A\} and John, \{Esther M\} and Leslie Bernstein and Hu, \{Jennifer J\} and Deming-halverson, \{Sandra L\} and Chanock, \{Stephen J\} and Ziegler, \{Regina G\} and Rodriguez-gil, \{Jorge L\} and Sucheston-campbell, \{Lara E\} and Sandler, \{Dale P\} and Taylor, \{Jack A\} and Kitahara, \{Cari M\} and O{\textquoteright}brien, \{Katie M\} and Bolla, \{Manjeet K\} and Joe Dennis and Dunning, \{Alison M\} and Easton, \{Douglas F\} and Kyriaki Michailidou and Pharoah, \{Paul D P\} and Qin Wang and Jonine Figueroa and Richard Biritwum and Ernest Adjei and Seth Wiafe and Ambrosone, \{Christine B\} and Wei Zheng and Olopade, \{Olufunmilayo I\} and Montserrat Garc{\'i}a-closas and Palmer, \{Julie R\} and Haiman, \{Christopher A\} and Dezheng Huo",

year = "2022",

month = sep,

day = "15",

doi = "10.1093/hmg/ddac102",

language = "English",

volume = "31",

pages = "3133--3143",

journal = "Human Molecular Genetics",

issn = "0964-6906",

publisher = "Oxford University Press",

number = "18",

}

Gao, G, Zhao, F, Ahearn, TU, Lunetta, KL, Troester, MA, Du, Z, Ogundiran, TO, Ojengbede, O, Blot, W, Nathanson, KL, Domchek, SM, Nemesure, B, Hennis, A, Ambs, S, Mcclellan, J, Nie, M, Bertrand, K, Zirpoli, G, Yao, S, Olshan, AF, Bensen, JT, Bandera, EV, Nyante, S, Conti, DV, Press, MF, Ingles, SA, John, EM, Bernstein, L, Hu, JJ, Deming-halverson, SL, Chanock, SJ, Ziegler, RG, Rodriguez-gil, JL, Sucheston-campbell, LE, Sandler, DP, Taylor, JA, Kitahara, CM, O’brien, KM, Bolla, MK, Dennis, J, Dunning, AM, Easton, DF, Michailidou, K, Pharoah, PDP, Wang, Q, Figueroa, J, Biritwum, R, Adjei, E, Wiafe, S, Ambrosone, CB, Zheng, W, Olopade, OI, García-closas, M, Palmer, JR, Haiman, CA & Huo, D 2022, 'Polygenic Risk Scores for Prediction of Breast Cancer Risk in Women of African Ancestry: a Cross-Ancestry Approach', Human Molecular Genetics, vol. 31, no. 18, pp. 3133-3143. https://doi.org/10.1093/hmg/ddac102

TY - JOUR

T1 - Polygenic Risk Scores for Prediction of Breast Cancer Risk in Women of African Ancestry

T2 - a Cross-Ancestry Approach

AU - Gao, Guimin

AU - Zhao, Fangyuan

AU - Ahearn, Thomas U

AU - Lunetta, Kathryn L

AU - Troester, Melissa A

AU - Du, Zhaohui

AU - Ogundiran, Temidayo O

AU - Ojengbede, Oladosu

AU - Blot, William

AU - Nathanson, Katherine L

AU - Domchek, Susan M

AU - Nemesure, Barbara

AU - Hennis, Anselm

AU - Ambs, Stefan

AU - Mcclellan, Julian

AU - Nie, Mark

AU - Bertrand, Kimberly

AU - Zirpoli, Gary

AU - Yao, Song

AU - Olshan, Andrew F

AU - Bensen, Jeannette T

AU - Bandera, Elisa V

AU - Nyante, Sarah

AU - Conti, David V

AU - Press, Michael F

AU - Ingles, Sue A

AU - John, Esther M

AU - Bernstein, Leslie

AU - Hu, Jennifer J

AU - Deming-halverson, Sandra L

AU - Chanock, Stephen J

AU - Ziegler, Regina G

AU - Rodriguez-gil, Jorge L

AU - Sucheston-campbell, Lara E

AU - Sandler, Dale P

AU - Taylor, Jack A

AU - Kitahara, Cari M

AU - O’brien, Katie M

AU - Bolla, Manjeet K

AU - Dennis, Joe

AU - Dunning, Alison M

AU - Easton, Douglas F

AU - Michailidou, Kyriaki

AU - Pharoah, Paul D P

AU - Wang, Qin

AU - Figueroa, Jonine

AU - Biritwum, Richard

AU - Adjei, Ernest

AU - Wiafe, Seth

AU - Ambrosone, Christine B

AU - Zheng, Wei

AU - Olopade, Olufunmilayo I

AU - García-closas, Montserrat

AU - Palmer, Julie R

AU - Haiman, Christopher A

AU - Huo, Dezheng

PY - 2022/9/15

Y1 - 2022/9/15

N2 - Polygenic risk scores (PRSs) are useful for predicting breast cancer risk, but the prediction accuracy of existing PRSs in women of African ancestry (AA) remains relatively low. We aim to develop optimal PRSs for the prediction of overall and estrogen receptor (ER) subtype-specific breast cancer risk in AA women. The AA dataset comprised 9235 cases and 10 184 controls from four genome-wide association study (GWAS) consortia and a GWAS study in Ghana. We randomly divided samples into training and validation sets. We built PRSs using individual-level AA data by a forward stepwise logistic regression and then developed joint PRSs that combined (1) the PRSs built in the AA training dataset and (2) a 313-variant PRS previously developed in women of European ancestry. PRSs were evaluated in the AA validation set. For overall breast cancer, the odds ratio per standard deviation of the joint PRS in the validation set was 1.34 [95% confidence interval (CI): 1.27-1.42] with the area under receiver operating characteristic curve (AUC) of 0.581. Compared with women with average risk (40th-60th PRS percentile), women in the top decile of the PRS had a 1.98-fold increased risk (95% CI: 1.63-2.39). For PRSs of ER-positive and ER-negative breast cancer, the AUCs were 0.608 and 0.576, respectively. Compared with existing methods, the proposed joint PRSs can improve prediction of breast cancer risk in AA women.

AB - Polygenic risk scores (PRSs) are useful for predicting breast cancer risk, but the prediction accuracy of existing PRSs in women of African ancestry (AA) remains relatively low. We aim to develop optimal PRSs for the prediction of overall and estrogen receptor (ER) subtype-specific breast cancer risk in AA women. The AA dataset comprised 9235 cases and 10 184 controls from four genome-wide association study (GWAS) consortia and a GWAS study in Ghana. We randomly divided samples into training and validation sets. We built PRSs using individual-level AA data by a forward stepwise logistic regression and then developed joint PRSs that combined (1) the PRSs built in the AA training dataset and (2) a 313-variant PRS previously developed in women of European ancestry. PRSs were evaluated in the AA validation set. For overall breast cancer, the odds ratio per standard deviation of the joint PRS in the validation set was 1.34 [95% confidence interval (CI): 1.27-1.42] with the area under receiver operating characteristic curve (AUC) of 0.581. Compared with women with average risk (40th-60th PRS percentile), women in the top decile of the PRS had a 1.98-fold increased risk (95% CI: 1.63-2.39). For PRSs of ER-positive and ER-negative breast cancer, the AUCs were 0.608 and 0.576, respectively. Compared with existing methods, the proposed joint PRSs can improve prediction of breast cancer risk in AA women.

KW - Breast Neoplasms/genetics

KW - Female

KW - Genetic Predisposition to Disease

KW - Genome-Wide Association Study

KW - Humans

KW - Multifactorial Inheritance/genetics

KW - Receptors, Estrogen/genetics

KW - Risk Factors

U2 - 10.1093/hmg/ddac102

DO - 10.1093/hmg/ddac102

M3 - Article

C2 - 35554533

SN - 0964-6906

VL - 31

SP - 3133

EP - 3143

JO - Human Molecular Genetics

JF - Human Molecular Genetics

IS - 18

ER -

Polygenic Risk Scores for Prediction of Breast Cancer Risk in Women of African Ancestry: a Cross-Ancestry Approach

Abstract

Keywords / Materials (for Non-textual outputs)

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