LmTag: functional-enrichment and imputation-aware tag SNP selection for population-specific genotyping arrays

Abstract Despite the rapid development of sequencing technology, single-nucleotide polymorphism (SNP) array is still the most cost-effective genotyping solutions for large-scale genomic research and applications. Recent years have witnessed the rapid development of numerous genotyping platforms of different sizes and designs, but population-specific platforms are still lacking, especially for those in developing countries. We aim to develop methods to design SNP arrays for thse countries, so the arrays should be cost-effective (small size), yet can still generate key information needed to associate genotypes with traits. A key design principle for most current platforms is to improve genome-wide imputation so that more SNPs (imputed tag SNPs) not included in the array can be predicted. However, current tag SNP selection methods mostly focus on imputation accuracy and coverage, but not the functional content of the measured and imputed SNPs. It is those functional SNPs that are most likely associated to traits. Here, we propose LmTag, a novel method for tag SNP selection that not only improves imputation performance but also prioritizes highly functional SNP markers. We apply LmTag on a wide range of populations using both public and in-house whole genome sequencing databases. Our results showed that LmTag improved both functional marker prioritization and genome-wide imputation accuracy compared to existing methods. This novel approach could contribute to the next generation genotyping arrays that provide excellent imputation capability as well as facilitate array-based functional genetic studies. Such arrays are particularly suitable for under-represented populations in developing countries or non-model species, where little genomics data are available while investment in genome sequencing or high-density SNP arrays is limited.