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

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00209805%3A_____%2F20%3A00078372" target="_blank" >RIV/00209805:_____/20:00078372 - isvavai.cz</a>

Výsledek na webu

<a href="https://www.nature.com/articles/s41588-019-0537-1" target="_blank" >https://www.nature.com/articles/s41588-019-0537-1</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1038/s41588-019-0537-1" target="_blank" >10.1038/s41588-019-0537-1</a>

Jazyk výsledku

angličtina

Název v původním jazyce

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

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

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

Popis výsledku anglicky

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.

Druh

J_imp - Článek v periodiku v databázi Web of Science

CEP obor

—

OECD FORD obor

30204 - Oncology

Projekt

—

Návaznosti

I - Institucionalni podpora na dlouhodoby koncepcni rozvoj vyzkumne organizace

Rok uplatnění

2020

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název periodika

Nature genetics

ISSN

1061-4036

e-ISSN

—

Svazek periodika

52

Číslo periodika v rámci svazku

1

Stát vydavatele periodika

US - Spojené státy americké

Počet stran výsledku

25

Strana od-do

56-73

Kód UT WoS článku

000508163500002

EID výsledku v databázi Scopus

2-s2.0-85077675544