Over the years, ad-hoc procedures were used for designing SNP arrays, but the procedures and strategies varied considerably case by case. Recently, a multiple-objective, local optimization (MOLO) algorithm was proposed to select SNPs for SNP arrays, which maximizes the adjusted SNP information (E score) under multiple constraints, e.g. on MAF, uniformness of SNP locations (U score), the inclusion of obligatory SNPs and the number and size of gaps. In the MOLO, each chromosome is split into equally spaced segments and local optima are selected as the SNPs having the highest adjusted E score within each segment, conditional on the presence of obligatory SNPs. The computation of the adjusted E score, however, is empirical, and it does not scale well between the uniformness of SNP locations and SNP informativeness. In addition, the MOLO objective function does not accommodate the selection of uniformly distributed SNPs. In the present study, we proposed a unified local function for optimally selecting SNPs, as an amendment to the MOLO algorithm. This new local function takes scalable weights between the uniformness and informativeness of SNPs, which allows the selection of SNPs under varied scenarios. The results showed that the weighting between the U and the E scores led to a higher imputation concordance rate than the U score or E score alone. The results from the evaluation of six commercial bovine SNP chips further confirmed this conclusion.

中文翻译：

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统一的局部目标函数，用于在农业基因组学应用中优化选择阵列上的SNP。

多年来，临时程序被用于设计SNP阵列，但程序和策略因情况而异。最近，提出了一种多目标局部优化（MOLO）算法为SNP阵列选择SNP，从而在多个约束条件下（例如MAF，SNP位置的均匀性（U得分），包括强制性SNP以及缺口的数量和大小。在MOLO中，将每个染色体分成相等间隔的段，并根据存在强制性SNP的情况，选择局部最优值作为每个段内调整后E分数最高的SNP。但是，调整后的E分数的计算是经验性的，并且在SNP位置的均匀性和SNP信息量之间无法很好地缩放。此外，MOLO目标函数不适合选择均匀分布的SNP。在本研究中，我们提出了用于优化选择SNP的统一局部函数，作为对MOLO算法的修正。这个新的局部功能在SNP的均匀性和信息性之间具有可伸缩的权重，从而允许在各种情况下选择SNP。结果表明，U分数和E分数之间的权重导致的插补一致率比单独的U分数或E分数更高。对六种商用牛SNP芯片的评估结果进一步证实了这一结论。这种新的局部功能在SNP的均匀性和信息性之间具有可缩放的权重，从而允许在不同的场景下选择SNP。结果表明，U分数和E分数之间的权重导致的插补一致率比单独的U分数或E分数更高。对六种商用牛SNP芯片的评估结果进一步证实了这一结论。这种新的局部功能在SNP的均匀性和信息性之间具有可缩放的权重，从而允许在不同的场景下选择SNP。结果表明，U分数和E分数之间的权重导致的插补一致率比单独的U分数或E分数更高。对六种商用牛SNP芯片的评估结果进一步证实了这一结论。