Optimizing Sequencing Resources in Genotyped Livestock Populations Using Linear Programming

Abstract

AbstractBackgroundLow-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, a large number of animals are required to be sequenced to exploit the full potential of whole-genome sequence data. Thus, novel strategies are desired to allocate sequencing resources in genotyped livestock populations such that the entire population can be sequenced or imputed efficiently.MethodsWe present two applications of linear programming models called LPChoose for sequencing resources allocation. The first application is to identify the minimum number of animals for sequencing while meeting the criteria that each haplotype in the population is contained in at least one of the animals selected for sequencing. The second is to sequence a fixed number of animals whose haplotypes include as large a proportion as possible of the haplotypes present in the population given a limited sequencing budget.ResultsIn both applications LPChoose has similar or better performance than some other methods. The linear programming models we proposed are based on rigorous and well defined optimization techniques and easy and straightforward to implement. LPChoose is available as an open-source package.