Department of Computer Science, Biomedical Informatics Training Program, Stanford University School of Medicine, Stanford, CA 94305, USA.
Bioinformatics. 2011 Jul 1;27(13):i333-41. doi: 10.1093/bioinformatics/btr243.
Accurate inference of genealogical relationships between pairs of individuals is paramount in association studies, forensics and evolutionary analyses of wildlife populations. Current methods for relationship inference consider only a small set of close relationships and have limited to no power to distinguish between relationships with the same number of meioses separating the individuals under consideration (e.g. aunt-niece versus niece-aunt or first cousins versus great aunt-niece).
We present CARROT (ClAssification of Relationships with ROTations), a novel framework for relationship inference that leverages linkage information to differentiate between rotated relationships, that is, between relationships with the same number of common ancestors and the same number of meioses separating the individuals under consideration. We demonstrate that CARROT clearly outperforms existing methods on simulated data. We also applied CARROT on four populations from Phase III of the HapMap Project and detected previously unreported pairs of third- and fourth-degree relatives.
Source code for CARROT is freely available at http://carrot.stanford.edu.
在关联研究、法医学和野生动物种群的进化分析中,准确推断个体对之间的系谱关系至关重要。目前用于关系推断的方法仅考虑一小部分密切关系,并且没有能力区分具有相同减数分裂数的关系(例如,姑姑-侄女与侄女-姑姑或第一代堂亲-侄女与姑婆-侄女)。
我们提出了 CARROT(旋转关系分类),这是一种新颖的关系推断框架,利用连锁信息来区分旋转关系,即具有相同数量的共同祖先和减数分裂数的关系,减数分裂数分离了正在考虑的个体。我们证明 CARROT 在模拟数据上明显优于现有方法。我们还将 CARROT 应用于 HapMap 项目第三阶段的四个群体,并检测到以前未报告的第三和第四度亲属对。
CARROT 的源代码可在 http://carrot.stanford.edu 上免费获得。