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Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes

Fachal, Laura ; Borg, Åke LU ; Olsson, Håkan LU orcid and Dunning, Alison M (2020) In Nature Genetics 52(1). p.56-73
Abstract
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... (More)
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. (Less)
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author
; ; and
author collaboration
organization
publishing date
type
Contribution to journal
publication status
published
subject
in
Nature Genetics
volume
52
issue
1
pages
18 pages
publisher
Nature Publishing Group
external identifiers
  • scopus:85077675544
  • pmid:31911677
ISSN
1546-1718
DOI
10.1038/s41588-019-0537-1
language
English
LU publication?
yes
id
34729d87-cba1-456f-be1d-17873f3e9415
date added to LUP
2020-01-29 13:28:22
date last changed
2022-04-18 20:05:16
@article{34729d87-cba1-456f-be1d-17873f3e9415,
  abstract     = {{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.}},
  author       = {{Fachal, Laura and Borg, Åke and Olsson, Håkan and Dunning, Alison M}},
  issn         = {{1546-1718}},
  language     = {{eng}},
  month        = {{01}},
  number       = {{1}},
  pages        = {{56--73}},
  publisher    = {{Nature Publishing Group}},
  series       = {{Nature Genetics}},
  title        = {{Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes}},
  url          = {{http://dx.doi.org/10.1038/s41588-019-0537-1}},
  doi          = {{10.1038/s41588-019-0537-1}},
  volume       = {{52}},
  year         = {{2020}},
}