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Tag SNP selection using similarity associations between SNPs

机译：使用SNP之间的相似性关联来标记SNP选择

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Genetic changes that may be associated with complex diseases are tried to be determined by means of many genome-wide association studies. Single Nucleotide Polymorphisms (SNPs) are used primarily in these studies since they comprise a large part of these genetic changes. Statistical importance of the genome-wide association study is directly related to the number of individuals and SNPs. However, it is still very costly and time-consuming to genotype all SNPs inside the candidate area for many individuals in very large-scale association studies. For this reason, with a small error, it is necessary to select an appropriate subset of all SNPs that will represent the rest of SNPs. These selected SNPs are called tag SNPs or haplotype tag SNPs (tag SNPs or htSNPs). It is essential in tag SNP selection to determine minimum tag SNP set with very good prediction accuracy. In this study, while Clonal Selection Algorithm (CLONALG) was used as tag SNP selection method, a new method named CLONSim, in which similarity association between SNPs was used as the prediction method for the rest of SNPs was proposed. The proposed method was compared with BPSO (Binary Particle Swarm Optimization) and CLONTagger methods with parameter optimization using datasets of different sizes. Experiment results showed that the proposed method could identify tag SNPs significantly faster.
机译：试图通过许多全基因组关联研究来确定可能与复杂疾病有关的遗传变化。单核苷酸多态性（SNP）主要用于这些研究中，因为它们构成了这些遗传变化的很大一部分。全基因组关联研究的统计重要性与个体和SNP的数量直接相关。但是，在非常大规模的关联研究中，对许多候选区域的所有SNP进行基因分型仍然非常昂贵且耗时。因此，在误差很小的情况下，有必要在所有SNP中选择一个合适的子集来代表其余的SNP。这些选定的SNP称为标签SNP或单倍型标签SNP（标签SNP或htSNP）。在选择标签SNP时，至关重要的是确定具有非常好的预测精度的最小标签SNP集。在这项研究中，虽然使用克隆选择算法（CLONALG）作为标记SNP选择方法，但提出了一种称为CLONSim的新方法，该方法将SNP之间的相似性关联用作其余SNP的预测方法。使用不同大小的数据集，将所提出的方法与BPSO（二进制粒子群优化）和具有参数优化的CLONTagger方法进行了比较。实验结果表明，该方法可以更快地识别标签SNP。

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