Prediction of breast cancer risk based on profiling with common genetic variants
- Mavaddat, N
- Pharoah, P D P
- Michailidou, K
- Tyrer, J
- Brook, M N
- Bolla, M K
- Wang, Q
- Dennis, J
- Dunning, A M
- Shah, M
- Luben, R
- Brown, J
- Bojesen, S E
- Nordestgaard, B G
- Nielsen, S F
- Flyger, H
- Czene, K
- Darabi, H
- Eriksson, M
- Peto, J
- Dos-Santos-Silva, I
- Dudbridge, F
- Johnson, N
- Schmidt, M K
- Broeks, A
- Verhoef, S
- Rutgers, E J
- Swerdlow, A
- Ashworth, A
- Orr, N
- Schoemaker, M J
- Figueroa, J
- Chanock, S J
- Brinton, L
- Lissowska, J
- Couch, F J
- Olson, J E
- Vachon, C
- Pankratz, V S
- Lambrechts, D
- Wildiers, H
- Van Ongeval, C
- Van Limbergen, E
- Kristensen, V
- Grenaker Alnæs, G
- Nord, S
- Borresen-Dale, A-L
- Nevanlinna, H
- Muranen, TA
- Aittomäki, K
- Blomqvist, C
- Chang-Claude, J
- Rudolph, A
- Seibold, P
- Flesch-Janys, D
- Fasching, P A
- Haeberle, L
- Ekici, A B
- Beckmann, M W
- Burwinkel, B
- Marme, F
- Schneeweiss, A
- Sohn, C
- Trentham-Dietz, A
- Newcomb, P
- Titus, L
- Egan, K M
- Hunter, D J
- Lindstrom, S
- Tamimi, R M
- Kraft, P
- Rahman, N
- Turnbull, C
- Renwick, A
- Seal, S
- LI, J
- Liu, J
- Humphreys, K
- Benitez, J
- Pilar Zamora, M
- Arias Perez, J I
- Menéndez, P
- Jakubowska, A
- Lubinski, J
- Jaworska-Bieniek, K
- Durda, K
- Bogdanova, N V
- Antonenkova, N N
- Dörk, T
- Anton-Culver, H
- Neuhausen, S L
- Ziogas, A
- Bernstein, L
- Devilee, P
- Tollenaar, R A E M
- Seynaeve, C
- Van Asperen, C J
- Cox, A
- Cross, S S
- Reed, M
- Khusnutdinova, E
- Bermisheva, M
- Prokofyeva, D
- Takhirova, Z
- Meindl, A
- Schmutzler, R K
- Sutter, C
- Yang, R
- Schürmann, P
- Bremer, M
- Christiansen, H
- Park-Simon, T-W
- Hillemanns, P
- Guénel, P
- Truong, T
- Menegaux, F
- Sanchez, M
- Radice, P
- Peterlongo, P
- Manoukian, S
- Pensotti, V
- Hopper, J L
- Tsimiklis, H
- Apicella, C
- Southey, M C
- Brauch, H
- Brűning, T
- Ko, Y-D
- Sigurdson, A J
- Doody, M M
- Hamann, U
- Torres, D
- Ulmer, H-U
- Försti, A
- Sawyer, E J
- Tomlinson, I
- Kerin, M J
- Miller, N
- Andrulis, I L
- Knight, J A
- Glendon, G
- Marie Mulligan, A
- Chenevix-Trench, G
- Balleine, R
- Giles, G G
- Milne, R L
- McLean, C
- Lindblom, A
- Margolin, S
- Haiman, C A
- Henderson, B E
- Schumacher, F
- Le Marchand, L
- Eilber, U
- Wang-Gohrke, S
- Hooning, M J
- Hollestelle, A
- Van Den Ouweland, A M W
- Koppert, L B
- Carpenter, J
- Clarke, C
- Scott, R
- Mannermaa, A
- Kataja, V
- Kosma, V-M
- Hartikainen, J M
- Brenner, H
- Arndt, V
- Stegmaier, C
- Karina Dieffenbach, A
- Winqvist, R
- Pylkäs, K
- Jukkola-Vuorinen, A
- Grip, M
- Offit, K
- Vijai, J
- Robson, M
- Rau-Murthy, R
- Dwek, M
- Swann, R
- Annie Perkins, K
- Goldberg, M S
- Labrèche, F
- Dumont, M.
- Eccles, D M
- Tapper, W J
- Rafiq, S
- John, E M
- Whittemore, A S
- Slager, S
- Yannoukakos, D
- Toland, A E
- Yao, S
- Zheng, W
- Halverson, S L
- González-Neira, A
- Pita, G
- Rosario Alonso, M
- Álvarez, N
- Herrero, D
- Tessier, D C
- Vincent, D
- Bacot, F
- Luccarini, C
- Baynes, C
- Ahmed, S
- Maranian, M
- Healey, C S
- Simard, J
- Hall, P
- Easton, D F
- Garcia-Closas, M
DOIAbstract
Background: Data for multiple common susceptibility alleles for breast cancer may be combined to identify women at different levels of breast cancer risk. Such stratification could guide preventive and screening strategies. However, empirical evidence for genetic risk stratification is lacking. Methods: We investigated the value of using 77 breast cancer-associated single nucleotide polymorphisms (SNPs) for risk stratification, in a study of 33 673 breast cancer cases and 33 381 control women of European origin. We tested all possible pair-wise multiplicative interactions and constructed a 77-SNP polygenic risk score (PRS) for breast cancer overall and by estrogen receptor (ER) status. Absolute risks of breast cancer by PRS were derived fro...
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