Performance of common genetic variants in breast-cancer risk models.
BACKGROUND
Genomewide association studies have identified multiple genetic variants associated with breast cancer. The extent to which these variants add to existing risk-assessment models is unknown.


METHODS
We used information on traditional risk factors and 10 common genetic variants associated with breast cancer in 5590 case subjects and 5998 control subjects, 50 to 79 years of age, from four U.S. cohort studies and one case-control study from Poland to fit models of the absolute risk of breast cancer. With the use of receiver-operating-characteristic curve analysis, we calculated the area under the curve (AUC) as a measure of discrimination. By definition, random classification of case and control subjects provides an AUC of 50%; perfect classification provides an AUC of 100%. We calculated the fraction of case subjects in quintiles of estimated absolute risk after the addition of genetic variants to the traditional risk model.


RESULTS
The AUC for a risk model with age, study and entry year, and four traditional risk factors was 58.0%; with the addition of 10 genetic variants, the AUC was 61.8%. About half the case subjects (47.2%) were in the same quintile of risk as in a model without genetic variants; 32.5% were in a higher quintile, and 20.4% were in a lower quintile.


CONCLUSIONS
The inclusion of newly discovered genetic factors modestly improved the performance of risk models for breast cancer. The level of predicted breast-cancer risk among most women changed little after the addition of currently available genetic information.
Division of Cancer Epidemiology and Genetics, National Cancer Institute, 6120 Executive Blvd., EPS 5050, MSC-7244, Bethesda, MD 20892, USA. wacholds@mail.nih.gov
Sholom
S
Wacholder
Patricia
P
Hartge
Ross
R
Prentice
Montserrat
M
Garcia-Closas
Heather Spencer
HS
Feigelson
W Ryan
WR
Diver
Michael J
MJ
Thun
David G
DG
Cox
Susan E
SE
Hankinson
Peter
P
Kraft
Bernard
B
Rosner
Christine D
CD
Berg
Louise A
LA
Brinton
Jolanta
J
Lissowska
Mark E
ME
Sherman
Rowan
R
Chlebowski
Charles
C
Kooperberg
Rebecca D
RD
Jackson
Dennis W
DW
Buckman
Peter
P
Hui
Ruth
R
Pfeiffer
Kevin B
KB
Jacobs
Gilles D
GD
Thomas
Robert N
RN
Hoover
Mitchell H
MH
Gail
Stephen J
SJ
Chanock
David J
DJ
Hunter
D000368:Aged; D019540:Area Under Curve; D001943:Breast Neoplasms / Q000235:genetics*; D016022:Case-Control Studies; D015331:Cohort Studies; D005260:Female; D020022:Genetic Predisposition to Disease; D055106:Genome-Wide Association Study; D006801:Humans; D016015:Logistic Models; D008875:Middle Aged; D015233:Models, Statistical; D020641:Polymorphism, Single Nucleotide; D012372:ROC Curve; D018570:Risk Assessment / Q000379:methods*; D012307:Risk Factors