An overview of the haplotype problems and algorithms

A single nucleotide polymorphism (SNP), as the most common form of genetic variation, has been widely studied to help analyze the possible association between diseases and genomes. To gain more information, SNPs on a single chromosome are usually studied together, which constitute a haplotype. Gaining haplotypes from biological experiments is usually very costly and time-consuming, which causes people to develop efficient methods to determine haplotypes from the computational angle. Many problems and algorithms about haplotypes have been proposed to reduce the cost of studies of disease association. In general, four categories of problems are widely researched: the haplotype assembly problem, the haplotype inference problem, the haplotype block partition problem, and the haplotype tagging SNP selection problem. The former two problems have been well reviewed by many researchers, whereas the latter two have not been comprehensively surveyed to our knowledge. In this paper, we try to make a detailed introduction to the four problems, especially the latter two.     

The Influence of PSCA Gene Variation on Its Expression and Gastric Adenocarcinoma Susceptibility in the Northwest Chinese Population

Gastric adenocarcinoma (GAC) imposes a considerable health burden around the world. Gene variation in prostate stem cell antigen gene (PSCA) has been identified to be associated with GAC risk, while the results showed regional variation. To explore the influence of PSCA gene variation on its expression and GAC risk in the Northwest Chinese population, four single nucleotide polymorphisms (SNPs) of PSCA were genotyped in 476 GAC cases and 481 controls using MassARRAY system. Two SNPs of rs2294008 (C>T) and rs2976392 (G>A) were identified to be associated with GAC risk. rs2294008, rs2976392 and rs10216533 made up two statistically significant haplotypes (Hap-CGG and Hap-TAG). Additionally, PSCA expression was analyzed by quantitative real time PCR, immunohistochemistry and tissue microarray. The results showed that PSCA expression was decreased in GAC tissues compared with adjacent normal tissues. For normal tissues, PSCA expression was higher with Hap-TA than that with Hap-CG. For GAC tissues, the differentiation degree of Hap-TA was higher than that of Hap-CG. The expression distribution of PSCA in multiple human organs showed disparity. These results suggest that PSCA gene variation has a potential effect on its expression and GAC risk in the Northwest Chinese population.     

改进粒子群算法在单体型重构问题中的应用

结合单体型装配问题的计算模型—最少错误纠正模型（MEC）的特定知识,提出了一种求解单体型装配问题的改进粒子群算法。应用改进粒子群算法对真实数据和模拟数据进行数值计算,并且与基础粒子群算法和遗传算法进行比较,数值结果表明所设计的改进粒子群算法在单体型重构率上优于基础粒子群算法和遗传算法。