Li Xin, Chen Yixuan, Li Jing
Department of Electrical Engineering and Computer Science, Case Western Reserve University, Cleveland, OH 44106, United States.
Pac Symp Biocomput. 2010:348-58. doi: 10.1142/9789814295291_0037.
Data from current gene-disease association studies motivate changes to existing haplotype inference methodologies. Many datasets are now comprised of both pedigree and population data so it is desirable to incorporate both sources of information when inferring haplotypes. The availability of high-density SNP data also makes it possible to determine and use the precise locations of recombination events. Our proposed method reconstructs haplotype structure on a genome-wide level by jointly using the information from the Mendelian law of inheritance and local population structure. The method combines in one framework new techniques of recombination event detection, maximum likelihood optimization of population haplotype diversity and our previous algorithm of zero-recombinant haplotype reconstruction. Experiments on both real and simulated datasets prove the efficiency and accuracy of our approach in reconstructing the haplotype structure. Our method makes it possible to reveal the haplotypic variation on a genome-wide level.
当前基因与疾病关联研究的数据促使对现有的单倍型推断方法进行改进。现在许多数据集同时包含家系数据和群体数据,因此在推断单倍型时纳入这两种信息来源是很有必要的。高密度SNP数据的可用性也使得确定和使用重组事件的精确位置成为可能。我们提出的方法通过联合使用孟德尔遗传定律和局部群体结构的信息,在全基因组水平上重建单倍型结构。该方法在一个框架中结合了重组事件检测的新技术、群体单倍型多样性的最大似然优化以及我们之前的零重组单倍型重建算法。在真实数据集和模拟数据集上进行的实验证明了我们的方法在重建单倍型结构方面的效率和准确性。我们的方法使得在全基因组水平上揭示单倍型变异成为可能。