Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes
Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes. / Fachal, Laura; Aschard, Hugues; Beesley, Jonathan; Barnes, Daniel R.; Allen, Jamie; Kar, Siddhartha; Pooley, Karen A.; Dennis, Joe; Michailidou, Kyriaki; Turman, Constance; Soucy, Penny; Lemaçon, Audrey; Lush, Michael; Tyrer, Jonathan P.; Ghoussaini, Maya; Marjaneh, Mahdi Moradi; Jiang, Xia; Agata, Simona; Aittomäki, Kristiina; Alonso, M. Rosario; Andrulis, Irene L.; Anton-Culver, Hoda; Antonenkova, Natalia N.; Arason, Adalgeir; Arndt, Volker; Aronson, Kristan J.; Arun, Banu K.; Auber, Bernd; Auer, Paul L.; Azzollini, Jacopo; Balmaña, Judith; Barkardottir, Rosa B.; Barrowdale, Daniel; Beeghly-Fadiel, Alicia; Benitez, Javier; Bermisheva, Marina; Białkowska, Katarzyna; Blanco, Amie M.; Blomqvist, Carl; Blot, William; Bogdanova, Natalia V.; Bojesen, Stig E.; Bolla, Manjeet K.; Bonanni, Bernardo; Borg, Ake; Bosse, Kristin; Brauch, Hiltrud; Flyger, Henrik; Nielsen, Finn C.; Wang, Qin; ABCTB Investigators; GEMO Study Collaborators; EMBRACE Collaborators; kConFab Investigators; HEBON Investigators.
I: Nature Genetics, Bind 52, Nr. 1, 2020, s. 56-73.Publikation: Bidrag til tidsskrift › Tidsskriftartikel › Forskning › fagfællebedømt
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TY - JOUR
T1 - Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes
AU - Fachal, Laura
AU - Aschard, Hugues
AU - Beesley, Jonathan
AU - Barnes, Daniel R.
AU - Allen, Jamie
AU - Kar, Siddhartha
AU - Pooley, Karen A.
AU - Dennis, Joe
AU - Michailidou, Kyriaki
AU - Turman, Constance
AU - Soucy, Penny
AU - Lemaçon, Audrey
AU - Lush, Michael
AU - Tyrer, Jonathan P.
AU - Ghoussaini, Maya
AU - Marjaneh, Mahdi Moradi
AU - Jiang, Xia
AU - Agata, Simona
AU - Aittomäki, Kristiina
AU - Alonso, M. Rosario
AU - Andrulis, Irene L.
AU - Anton-Culver, Hoda
AU - Antonenkova, Natalia N.
AU - Arason, Adalgeir
AU - Arndt, Volker
AU - Aronson, Kristan J.
AU - Arun, Banu K.
AU - Auber, Bernd
AU - Auer, Paul L.
AU - Azzollini, Jacopo
AU - Balmaña, Judith
AU - Barkardottir, Rosa B.
AU - Barrowdale, Daniel
AU - Beeghly-Fadiel, Alicia
AU - Benitez, Javier
AU - Bermisheva, Marina
AU - Białkowska, Katarzyna
AU - Blanco, Amie M.
AU - Blomqvist, Carl
AU - Blot, William
AU - Bogdanova, Natalia V.
AU - Bojesen, Stig E.
AU - Bolla, Manjeet K.
AU - Bonanni, Bernardo
AU - Borg, Ake
AU - Bosse, Kristin
AU - Brauch, Hiltrud
AU - Flyger, Henrik
AU - Nielsen, Finn C.
AU - Wang, Qin
AU - ABCTB Investigators
AU - GEMO Study Collaborators
AU - EMBRACE Collaborators
AU - kConFab Investigators
AU - HEBON Investigators
PY - 2020
Y1 - 2020
N2 - 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.
AB - 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.
U2 - 10.1038/s41588-019-0537-1
DO - 10.1038/s41588-019-0537-1
M3 - Journal article
C2 - 31911677
AN - SCOPUS:85077675544
VL - 52
SP - 56
EP - 73
JO - Nature Genetics
JF - Nature Genetics
SN - 1061-4036
IS - 1
ER -
ID: 235777262