研究確定乳腺癌風(fēng)險相關(guān)突變區(qū)域的靶基因
英國劍橋大學(xué)Alison M. Dunning、美國哈佛大學(xué)陳增熙公共衛(wèi)生學(xué)院Peter Kraft等研究人員合作對150個乳腺癌危險區(qū)域進(jìn)行了精細(xì)定位，從而確定了191個可能的靶基因。該項研究成果于2020年1月7日在線發(fā)表在《自然—遺傳學(xué)》上。
研究人員表示，全基因組關(guān)聯(lián)研究已在150多個基因組區(qū)域中發(fā)現(xiàn)了乳腺癌的風(fēng)險變異體，但潛在的風(fēng)險機(jī)制仍然未知。將關(guān)聯(lián)分析與計算機(jī)基因組特征注釋相結(jié)合可用來探索這些區(qū)域。
研究人員定義了205個獨立的風(fēng)險相關(guān)信號，每個信號中都有一組可靠的因果變體。同時，研究人員使用了貝葉斯方法（PAINTOR），該方法結(jié)合了遺傳關(guān)聯(lián)、連鎖不平衡和豐富的基因組特征，以確定具有較高因果關(guān)系的事后概率。潛在的因果變體在活性基因調(diào)節(jié)區(qū)和轉(zhuǎn)錄因子結(jié)合位點中明顯過量表達(dá)。研究人員利用基因表達(dá)（表達(dá)定量性狀位點）、染色質(zhì)相互作用和功能注釋，將INQUSIT流水線用于優(yōu)先考慮那些潛在因果變異的基因。已知的癌癥驅(qū)動因子、在發(fā)育、凋亡和免疫系統(tǒng)中的轉(zhuǎn)錄因子、以及DNA完整性檢查點途徑中的基因都被較高置信度的目標(biāo)基因所涵蓋。
附：英文原文
Title: Fine-mapping of 150 breast cancer risk regions identifies 191 likely target genes
Author: Laura Fachal, Hugues Aschard, Jonathan Beesley, Daniel R. Barnes, Jamie Allen, Siddhartha Kar, Karen A. Pooley, Joe Dennis, Kyriaki Michailidou, Constance Turman, Penny Soucy, Audrey Lemaon, Michael Lush, Jonathan P. Tyrer, Maya Ghoussaini, Mahdi Moradi Marjaneh, Xia Jiang, Simona Agata, Kristiina Aittomki, M. Rosario Alonso, Irene L. Andrulis, Hoda Anton-Culver, Natalia N. Antonenkova, Adalgeir Arason, Volker Arndt, Kristan J. Aronson, Banu K. Arun, Bernd Auber, Paul L. Auer, Jacopo Azzollini, Judith Balmaa, Rosa B. Barkardottir, Daniel Barrowdale, Alicia Beeghly-Fadiel, Javier Benitez, Marina Bermisheva, Katarzyna Biakowska, Amie M. Blanco, Carl Blomqvist, William Blot, Natalia V. Bogdanova, Stig E. Bojesen, Manjeet K. Bolla, Bernardo Bonanni, Ake Borg, Kristin Bosse, Hiltrud Brauch, Hermann Brenner, Ignacio Briceno, Ian W. Brock, Angela Brooks-Wilson, Thomas Brning, Barbara Burwinkel, Saundra S. Buys, Qiuyin Cai, Trinidad Calds, Maria A. Caligo, Nicola J. Camp, Ian Campbell, Federico Canzian, Jason S. Carroll, Brian D. Carter, Jose E. Castelao
Issue&Volume: 2020-01-07
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.
DOI: 10.1038/s41588-019-0537-1
Source: https://www.nature.com/articles/s41588-019-0537-1
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