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乳腺癌风险相关突变区域的靶基因
作者:小柯机器人 发布时间:2020/1/9 14:51:16

英国剑桥大学Alison M. Dunning、美国哈佛大学陈增熙公共卫生学院Peter Kraft等研究人员合作对150个乳腺癌危险区域进行了精细定位,从而确定了191个可能的靶基因。该项研究成果于2020年1月7日在线发表在《自然—遗传学》上。

研究人员表示,全基因组关联研究已在150多个基因组区域中发现了乳腺癌的风险变异体,但潜在的风险机制仍然未知。将关联分析与计算机基因组特征注释相结合可用来探索这些区域。

研究人员定义了205个独立的风险相关信号,每个信号中都有一组可靠的因果变体。同时,研究人员使用了贝叶斯方法(PAINTOR),该方法结合了遗传关联、连锁不平衡和丰富的基因组特征,以确定具有较高因果关系的事后概率。潜在的因果变体在活性基因调节区和转录因子结合位点中明显过量表达。研究人员利用基因表达(表达定量性状位点)、染色质相互作用和功能注释,将INQUSIT流水线用于优先考虑那些潜在因果变异的基因。已知的癌症驱动因子、在发育、凋亡和免疫系统中的转录因子、以及DNA完整性检查点途径中的基因都被最高置信度的目标基因所涵盖。

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

期刊信息

Nature Genetics:《自然—遗传学》,创刊于1992年。隶属于施普林格·自然出版集团,最新IF:25.455
官方网址:https://www.nature.com/ng/
投稿链接:https://mts-ng.nature.com/cgi-bin/main.plex