Doan Duong D, Evans Patricia A
Faculty of Computer Science, University of New Brunswick, Fredericton, New Brunswick, Canada.
Algorithms Mol Biol. 2011 Apr 19;6:8. doi: 10.1186/1748-7188-6-8.
Genetic disease studies investigate relationships between changes in chromosomes and genetic diseases. Single haplotypes provide useful information for these studies but extracting single haplotypes directly by biochemical methods is expensive. A computational method to infer haplotypes from genotype data is therefore important. We investigate the problem of computing the minimum number of recombination events for general pedigrees with a small number of sites for all members.
We show that this NP-hard problem can be parametrically reduced to the Bipartization by Edge Removal problem with additional parity constraints. We solve this problem with an exact algorithm that runs in time, where n is the number of members, m is the number of sites, and k is the number of recombination events.
This algorithm infers haplotypes for a small number of sites, which can be useful for genetic disease studies to track down how changes in haplotypes such as recombinations relate to genetic disease.
遗传疾病研究探讨染色体变化与遗传疾病之间的关系。单倍型为这些研究提供了有用信息,但通过生化方法直接提取单倍型成本高昂。因此,一种从基因型数据推断单倍型的计算方法至关重要。我们研究了为所有成员位点数量较少的一般家系计算最小重组事件数的问题。
我们表明,这个NP难问题可以参数化地简化为带有附加奇偶性约束的通过边删除进行二分图问题。我们用一种精确算法解决了这个问题,该算法的运行时间为,其中n是成员数量,m是位点数量,k是重组事件数量。
该算法可推断少量位点的单倍型,这对于遗传疾病研究追踪单倍型变化(如重组)与遗传疾病的关系可能有用。