Cheng Hao, Xu Keyu, Li Jinghui, Abraham Kuruvilla Joseph
Department of Animal Science, University of California, Davis, Davis, CA, United States.
Department of Economics, FEARP, University of São-Paulo, Ribeirão Preto, Brazil.
Front Genet. 2021 Oct 22;12:740340. doi: 10.3389/fgene.2021.740340. eCollection 2021.
Low-cost genome-wide single-nucleotide polymorphisms (SNPs) are routinely used in animal breeding programs. Compared to SNP arrays, the use of whole-genome sequence data generated by the next-generation sequencing technologies (NGS) has great potential in livestock populations. However, sequencing a large number of animals to exploit the full potential of whole-genome sequence data is not feasible. Thus, novel strategies are required for the allocation of sequencing resources in genotyped livestock populations such that the entire population can be imputed, maximizing the efficiency of whole genome sequencing budgets. We present two applications of linear programming for the efficient allocation of sequencing resources. The first application is to identify the minimum number of animals for sequencing subject to the criterion that each haplotype in the population is contained in at least one of the animals selected for sequencing. The second application is the selection of animals whose haplotypes include the largest possible proportion of common haplotypes present in the population, assuming a limited sequencing budget. Both applications are available in an open source program LPChoose. In both applications, LPChoose has similar or better performance than some other methods suggesting that linear programming methods offer great potential for the efficient allocation of sequencing resources. The utility of these methods can be increased through the development of improved heuristics.
低成本的全基因组单核苷酸多态性（SNP）常用于动物育种计划。与SNP芯片相比，利用下一代测序技术（NGS）生成的全基因组序列数据在畜牧群体中具有巨大潜力。然而，对大量动物进行测序以充分发挥全基因组序列数据的潜力是不可行的。因此，需要新的策略来在已进行基因分型的畜牧群体中分配测序资源，以便能够对整个群体进行基因型填充，从而最大限度地提高全基因组测序预算的效率。我们提出了线性规划在测序资源高效分配方面的两种应用。第一种应用是确定测序所需的最少动物数量，条件是群体中的每个单倍型至少包含在所选测序动物中的一个中。第二种应用是在测序预算有限的情况下，选择其单倍型包含群体中尽可能大比例常见单倍型的动物。这两种应用都可在开源程序LPChoose中使用。在这两种应用中，LPChoose的性能与其他一些方法相似或更好，这表明线性规划方法在测序资源的高效分配方面具有巨大潜力。通过开发改进的启发式算法，可以提高这些方法的实用性。
