Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes
Authors
Fachal, LAschard, H
Beesley, J
Barnes, DR
Allen, J
Kar, S
Pooley, KA
Dennis, J
Michailidou, K
Turman, C
Soucy, P
Lemacon, A
Lush, M
Tyrer, JP
Ghoussaini, M
Marjaneh, MM
Jiang, X
Agata, S
Aittomaki, K
Alonso, MR
Andrulis, IL
Anton-Culver, H
Antonenkova, NN
Arason, A
Arndt, V
Aronson, KJ
Arun, BK
Auber, B
Auer, PL
Azzollini, J
Balmana, J
Barkardottir, RB
Barrowdale, D
Beeghly-Fadiel, A
Benitez, J
Bermisheva, M
Bialkowska, K
Blanco, AM
Blomqvist, C
Blot, W
Bogdanova, NV
Bojesen, SE
Bolla, MK
Bonanni, B
Borg, A
Bosse, K
Brauch, H
Brenner, H
Briceno, I
Brock, IW
Brooks-Wilson, A
Bruning, T
Burwinkel, B
Buys, SS
Cai, Q
Caldes, T
Caligo, MA
Camp, NJ
Campbell, I
Canzian, F
Carroll, JS
Carter, BD
Castelao, JE
Chiquette, J
Christiansen, H
Chung, WK
Claes, KBM
Clarke, CL
Collee, JM
Cornelissen, S
Couch, FJ
Cox, A
Cross, SS
Cybulski, C
Czene, K
Daly, MB
de la Hoya, M
Devilee, P
Diez, O
Ding, YC
Dite, GS
Domchek, SM
Dork, T
Dos-Santos-Silva, I
Droit, A
Dubois, S
Dumont, M
Duran, M
Durcan, L
Dwek, M
Eccles, DM
Engel, C
Eriksson, M
Evans, DG
Fasching, PA
Fletcher, O
Floris, G
Flyger, H
Foretova, L
Foulkes, WD
Friedman, E
Fritschi, L
Frost, D
Gabrielson, M
Gago-Dominguez, M
Gambino, G
Ganz, PA
Gapstur, SM
Garber, J
Garcia-Saenz, JA
Gaudet, MM
Georgoulias, V
Giles, GG
Glendon, G
Godwin, AK
Goldberg, MS
Goldgar, DE
Gonzalez-Neira, A
Tibiletti, MG
Greene, MH
Grip, M
Gronwald, J
Grundy, A
Guenel, P
Hahnen, E
Haiman, CA
Hakansson, N
Hall, P
Hamann, U
Harrington, PA
Hartikainen, JM
Hartman, M
He, W
Healey, CS
Heemskerk-Gerritsen, BAM
Heyworth, J
Hillemanns, P
Hogervorst, FBL
Hollestelle, A
Hooning, MJ
Hopper, JL
Howell, Anthony
Huang, G
Hulick, PJ
Imyanitov, EN
Isaacs, C
Iwasaki, M
Jager, A
Jakimovska, M
Jakubowska, A
James, PA
Janavicius, R
Jankowitz, RC
John, EM
Johnson, N
Jones, ME
Jukkola-Vuorinen, A
Jung, A
Kaaks, R
Kang, D
Kapoor, PM
Karlan, BY
Keeman, R
Kerin, MJ
Khusnutdinova, E
Kiiski, JI
Kirk, J
Kitahara, CM
Ko, YD
Konstantopoulou, I
Kosma, VM
Koutros, S
Kubelka-Sabit, K
Kwong, A
Kyriacou, K
Laitman, Y
Lambrechts, D
Lee, E
Leslie, G
Lester, J
Lesueur, F
Lindblom, A
Lo, WY
Long, J
Lophatananon, A
Loud, JT
Lubinski, J
MacInnis, RJ
Maishman, T
Makalic, E
Mannermaa, A
Manoochehri, M
Manoukian, S
Margolin, S
Martinez, ME
Matsuo, K
Maurer, T
Mavroudis, D
Mayes, R
McGuffog, L
McLean, C
Mebirouk, N
Meindl, A
Miller, A
Miller, N
Montagna, M
Moreno, F
Muir, K
Mulligan, AM
Munoz-Garzon, VM
Muranen, TA
Narod, SA
Nassir, R
Nathanson, KL
Neuhausen, SL
Nevanlinna, H
Neven, P
Nielsen, FC
Nikitina-Zake, L
Norman, A
Offit, K
Olah, E
Olopade, OI
Olsson, H
Orr, N
Osorio, A
Pankratz, VS
Papp, J
Park, SK
Park-Simon, TW
Parsons, MT
Paul, J
Pedersen, IS
Peissel, B
Peshkin, B
Peterlongo, P
Peto, J
Plaseska-Karanfilska, D
Prajzendanc, K
Prentice, R
Presneau, N
Prokofyeva, D
Pujana, MA
Pylkas, K
Radice, P
Ramus, SJ
Rantala, J
Rau-Murthy, R
Rennert, G
Risch, HA
Robson, M
Romero, A
Rossing, M
Saloustros, E
Sanchez-Herrero, E
Affiliation
Centre for Cancer Genetic Epidemiology, Department of Oncology, University of Cambridge, Cambridge, UKIssue Date
2020
Metadata
Show full item recordAbstract
Genome-wide association studies have identified breast cancer risk variants in over 150 genomic regions, but the mechanisms underlying risk remain largely unknown. These regions were explored by combining association analysis with in silico genomic feature annotations. We defined 205 independent risk-associated signals with the set of credible causal variants in each one. In parallel, we used a Bayesian approach (PAINTOR) that combines genetic association, linkage disequilibrium and enriched genomic features to determine variants with high posterior probabilities of being causal. Potentially causal variants were significantly over-represented in active gene regulatory regions and transcription factor binding sites. We applied our INQUSIT pipeline for prioritizing genes as targets of those potentially causal variants, using gene expression (expression quantitative trait loci), chromatin interaction and functional annotations. Known cancer drivers, transcription factors and genes in the developmental, apoptosis, immune system and DNA integrity checkpoint gene ontology pathways were over-represented among the highest-confidence target genes.Citation
Fachal L, Aschard H, Beesley J, Barnes DR, Allen J, Kar S, et al. Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes. Nat Genet. 2020;52(1):56-73.Journal
Nature GeneticsDOI
10.1038/s41588-019-0537-1PubMed ID
31911677Additional Links
https://dx.doi.org/10.1038/s41588-019-0537-1Type
ArticleLanguage
enae974a485f413a2113503eed53cd6c53
10.1038/s41588-019-0537-1
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