Evaluation of polygenic risk scores for predicting breast and prostate cancer risk

Mitchell J. Machiela, Chia Yen Chen, Constance Chen, Stephen J. Chanock, David J. Hunter, Peter Kraft

Research output: Contribution to journalArticle

Abstract

Recently, polygenic risk scores (PRS) have been shown to be associated with certain complex diseases. The approach has been based on the contribution of counting multiple alleles associated with disease across independent loci, without requiring compelling evidence that every locus had already achieved definitive genome-wide statistical significance. Whether PRS assist in the prediction of risk of common cancers is unknown. We built PRS from lists of genetic markers prioritized by their association with breast cancer (BCa) or prostate cancer (PCa) in a training data set and evaluated whether these scores could improve current genetic prediction of these specific cancers in independent test samples. We used genome-wide association data on 1,145 BCa cases and 1,142 controls from the Nurses' Health Study and 1,164 PCa cases and 1,113 controls from the Prostate Lung Colorectal and Ovarian Cancer Screening Trial. Ten-fold cross validation was used to build and evaluate PRS with 10 to 60,000 independent single nucleotide polymorphisms (SNPs). For both BCa and PCa, the models that included only published risk alleles maximized the cross-validation estimate of the area under the ROC curve (0.53 for breast and 0.57 for prostate). We found no significant evidence that PRS using common variants improved risk prediction for BCa and PCa over replicated SNP scores.

Original languageEnglish (US)
Pages (from-to)506-514
Number of pages9
JournalGenetic Epidemiology
Volume35
Issue number6
DOIs
StatePublished - Sep 2011
Externally publishedYes

Fingerprint

Prostatic Neoplasms
Breast Neoplasms
Single Nucleotide Polymorphism
Alleles
Genome
Genetic Markers
Early Detection of Cancer
ROC Curve
Ovarian Neoplasms
Area Under Curve
Prostate
Colorectal Neoplasms
Lung Neoplasms
Neoplasms
Breast
Nurses
Health

Keywords

  • Genome-wide association study
  • Human genetics
  • Single nucleotide polymorphisms

ASJC Scopus subject areas

  • Genetics(clinical)
  • Epidemiology

Cite this

Machiela, M. J., Chen, C. Y., Chen, C., Chanock, S. J., Hunter, D. J., & Kraft, P. (2011). Evaluation of polygenic risk scores for predicting breast and prostate cancer risk. Genetic Epidemiology, 35(6), 506-514. https://doi.org/10.1002/gepi.20600

Evaluation of polygenic risk scores for predicting breast and prostate cancer risk. / Machiela, Mitchell J.; Chen, Chia Yen; Chen, Constance; Chanock, Stephen J.; Hunter, David J.; Kraft, Peter.

In: Genetic Epidemiology, Vol. 35, No. 6, 09.2011, p. 506-514.

Research output: Contribution to journalArticle

Machiela, MJ, Chen, CY, Chen, C, Chanock, SJ, Hunter, DJ & Kraft, P 2011, 'Evaluation of polygenic risk scores for predicting breast and prostate cancer risk', Genetic Epidemiology, vol. 35, no. 6, pp. 506-514. https://doi.org/10.1002/gepi.20600
Machiela, Mitchell J. ; Chen, Chia Yen ; Chen, Constance ; Chanock, Stephen J. ; Hunter, David J. ; Kraft, Peter. / Evaluation of polygenic risk scores for predicting breast and prostate cancer risk. In: Genetic Epidemiology. 2011 ; Vol. 35, No. 6. pp. 506-514.
@article{b5b373c685ab448796aad8e97c144dc0,
title = "Evaluation of polygenic risk scores for predicting breast and prostate cancer risk",
abstract = "Recently, polygenic risk scores (PRS) have been shown to be associated with certain complex diseases. The approach has been based on the contribution of counting multiple alleles associated with disease across independent loci, without requiring compelling evidence that every locus had already achieved definitive genome-wide statistical significance. Whether PRS assist in the prediction of risk of common cancers is unknown. We built PRS from lists of genetic markers prioritized by their association with breast cancer (BCa) or prostate cancer (PCa) in a training data set and evaluated whether these scores could improve current genetic prediction of these specific cancers in independent test samples. We used genome-wide association data on 1,145 BCa cases and 1,142 controls from the Nurses' Health Study and 1,164 PCa cases and 1,113 controls from the Prostate Lung Colorectal and Ovarian Cancer Screening Trial. Ten-fold cross validation was used to build and evaluate PRS with 10 to 60,000 independent single nucleotide polymorphisms (SNPs). For both BCa and PCa, the models that included only published risk alleles maximized the cross-validation estimate of the area under the ROC curve (0.53 for breast and 0.57 for prostate). We found no significant evidence that PRS using common variants improved risk prediction for BCa and PCa over replicated SNP scores.",
keywords = "Genome-wide association study, Human genetics, Single nucleotide polymorphisms",
author = "Machiela, {Mitchell J.} and Chen, {Chia Yen} and Constance Chen and Chanock, {Stephen J.} and Hunter, {David J.} and Peter Kraft",
year = "2011",
month = "9",
doi = "10.1002/gepi.20600",
language = "English (US)",
volume = "35",
pages = "506--514",
journal = "Genetic Epidemiology",
issn = "0741-0395",
publisher = "Wiley-Liss Inc.",
number = "6",

}

TY - JOUR

T1 - Evaluation of polygenic risk scores for predicting breast and prostate cancer risk

AU - Machiela, Mitchell J.

AU - Chen, Chia Yen

AU - Chen, Constance

AU - Chanock, Stephen J.

AU - Hunter, David J.

AU - Kraft, Peter

PY - 2011/9

Y1 - 2011/9

N2 - Recently, polygenic risk scores (PRS) have been shown to be associated with certain complex diseases. The approach has been based on the contribution of counting multiple alleles associated with disease across independent loci, without requiring compelling evidence that every locus had already achieved definitive genome-wide statistical significance. Whether PRS assist in the prediction of risk of common cancers is unknown. We built PRS from lists of genetic markers prioritized by their association with breast cancer (BCa) or prostate cancer (PCa) in a training data set and evaluated whether these scores could improve current genetic prediction of these specific cancers in independent test samples. We used genome-wide association data on 1,145 BCa cases and 1,142 controls from the Nurses' Health Study and 1,164 PCa cases and 1,113 controls from the Prostate Lung Colorectal and Ovarian Cancer Screening Trial. Ten-fold cross validation was used to build and evaluate PRS with 10 to 60,000 independent single nucleotide polymorphisms (SNPs). For both BCa and PCa, the models that included only published risk alleles maximized the cross-validation estimate of the area under the ROC curve (0.53 for breast and 0.57 for prostate). We found no significant evidence that PRS using common variants improved risk prediction for BCa and PCa over replicated SNP scores.

AB - Recently, polygenic risk scores (PRS) have been shown to be associated with certain complex diseases. The approach has been based on the contribution of counting multiple alleles associated with disease across independent loci, without requiring compelling evidence that every locus had already achieved definitive genome-wide statistical significance. Whether PRS assist in the prediction of risk of common cancers is unknown. We built PRS from lists of genetic markers prioritized by their association with breast cancer (BCa) or prostate cancer (PCa) in a training data set and evaluated whether these scores could improve current genetic prediction of these specific cancers in independent test samples. We used genome-wide association data on 1,145 BCa cases and 1,142 controls from the Nurses' Health Study and 1,164 PCa cases and 1,113 controls from the Prostate Lung Colorectal and Ovarian Cancer Screening Trial. Ten-fold cross validation was used to build and evaluate PRS with 10 to 60,000 independent single nucleotide polymorphisms (SNPs). For both BCa and PCa, the models that included only published risk alleles maximized the cross-validation estimate of the area under the ROC curve (0.53 for breast and 0.57 for prostate). We found no significant evidence that PRS using common variants improved risk prediction for BCa and PCa over replicated SNP scores.

KW - Genome-wide association study

KW - Human genetics

KW - Single nucleotide polymorphisms

UR - http://www.scopus.com/inward/record.url?scp=80051817547&partnerID=8YFLogxK

UR - http://www.scopus.com/inward/citedby.url?scp=80051817547&partnerID=8YFLogxK

U2 - 10.1002/gepi.20600

DO - 10.1002/gepi.20600

M3 - Article

C2 - 21618606

AN - SCOPUS:80051817547

VL - 35

SP - 506

EP - 514

JO - Genetic Epidemiology

JF - Genetic Epidemiology

SN - 0741-0395

IS - 6

ER -