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Tag SNP Selection Using Particle Swarm Optimization

机译：使用粒子群优化的标签SNP选择

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摘要

Single nucleotide polymorphisms (SNPs) are the most abundant form of genetic variations amongst species. With the genome-wide SNP discovery, many genome-wide association studies are likely to identify multiple genetic variants that are associated with complex diseases. However, genotyping all existing SNPs for a large number of samples is still challenging even though SNP arrays have been developed to facilitate the task. Therefore, it is essential to select only informative SNPs representing the original SNP distributions in the genome (tag SNP selection) for genome-wide association studies. These SNPs are usually chosen from haplotypes and called haplotype tag SNPs (htSNPs). Accordingly, the scale and cost of genotyping are expected to be largely reduced. We introduce binary particle swarm optimization (BPSO) with local search capability to improve the prediction accuracy of STAMP A. The proposed method does not rely on block partitioning of the genomic region, and consistently identified tag SNPs with higher prediction accuracy than either STAMP A or SVMISTSA. We compared the prediction accuracy and time complexity of BPSO to STAMP A and an SVM-based (SVMISTSA) method using publicly available data sets. For STAMPA and SVMISTSA, BPSO effective improved prediction accuracy for smaller and larger scale data sets. These results demonstrate that the BPSO method selects tag SNP with higher accuracy no matter the scale of data sets is used.
机译：单核苷酸多态性（SNP）是物种间遗传变异的最丰富形式。随着全基因组SNP的发现，许多全基因组关联研究很可能会发现与复杂疾病相关的多种遗传变异。然而，即使已经开发了SNP阵列来简化任务，对大量样品的所有现有SNP进行基因分型仍然具有挑战性。因此，对于全基因组关联研究，仅选择代表基因组中原始SNP分布的信息性SNP（标记SNP选择）至关重要。这些SNP通常选自单倍型，称为单倍型标签SNP（htSNP）。因此，预期基因分型的规模和成本将大大降低。我们引入具有局部搜索功能的二进制粒子群优化算法（BPSO），以提高STAMP A的预测精度。所提出的方法不依赖于基因组区域的块划分，并且始终如一地识别出具有比STAMP A或STAMP A或更高的预测精度的标签SNP。 SVMISTSA。我们使用公开可用的数据集将BPSO的预测准确性和时间复杂度与STAMP A和基于SVM的（SVMISTSA）方法进行了比较。对于STAMPA和SVMISTSA，BPSO有效地提高了对规模越来越大的数据集的预测精度。这些结果表明，无论使用何种数据集规模，BPSO方法都能以较高的准确性选择标签SNP。

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