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

Despite the rapid development of sequencing technology, single-nucleotide polymorphism (SNP) arrays are 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. SNP arrays designed for these countries should be cost-effective (small size), yet incorporate 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 not included in the array (imputed SNPs) can be predicted. However, current tag SNP selection methods mostly focus on imputation accuracy and coverage, but not the functional content of the array. It is those functional SNPs that are most likely associated with 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 show 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. $\textrm{LmTag}$ is available at: https://github.com/datngu/LmTag.

Analysis of Periodontal Conditions in the Provinces of Vietnam: Results From the National Dental Survey.

Nationwide dental health surveys are crucial for providing essential information on dental health and dental condition-related problems in the community. However, the relationship between periodontal conditions and sociodemographic data has not been well investigated in Vietnam. With data from the National Oral Health Survey in 2019, we performed several machine learning methods on this dataset to investigate the impacts of sociodemographic features on gingival bleeding, periodontal pockets, and Community Periodontal Index. From the experiments, LightGBM produced a maximum AUC (area under the curve) value of 0.744. The other models in descending order were logistic regression (0.705), logiboost (0.704), and random forest (0.684). All methods resulted in significantly high overall accuracies, all exceeding 90%. The results show that the gradient boosting model can predict well the relationship between periodontal conditions and sociodemographic data. The investigated model also reveals that the geographic region has the most significant influence on dental health, while the consumption of sweet foods/drinks is the second most crucial. These findings advocate for a region-specific approach for the dental care program and the implementation of a sugar-risk food reduction program.