Song Sunah, Shields Robert, Li Xin, Li Jing
Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, USA.
Department of Pathology, Stanford University, Stanford, CA 94305, USA.
BMC Proc. 2014 Jun 17;8(Suppl 1 Genetic Analysis Workshop 18Vanessa Olmo):S20. doi: 10.1186/1753-6561-8-S1-S20. eCollection 2014.
We developed a general framework for family-based imputation using single-nucleotide polymorphism data and sequence data distributed by Genetic Analysis Workshop 18. By using PedIBD, we first inferred haplotypes and inheritance patterns of each family from SNP data. Then new variants in unsequenced family members can be obtained from sequenced relatives through their shared haplotypes. We then compared the results of our method against the imputation results provided by Genetic Analysis Workshop organizers. The results showed that our strategy uncovered more variants for more unsequenced relatives. We also showed that recombination breakpoints inferred by PedIBD have much higher resolution than those inferred from previous studies.
我们使用遗传分析研讨会18分发的单核苷酸多态性数据和序列数据,开发了一种基于家系的插补通用框架。通过使用PedIBD,我们首先从SNP数据推断每个家系的单倍型和遗传模式。然后,未测序家庭成员中的新变异可以通过其共享的单倍型从已测序的亲属中获得。然后,我们将我们方法的结果与遗传分析研讨会组织者提供的插补结果进行了比较。结果表明,我们的策略为更多未测序亲属发现了更多变异。我们还表明,PedIBD推断的重组断点比以前研究推断的具有更高的分辨率。