A Simple Algorithm for Population Classification

A Hybrid Supervised Approach to Human Population Identification Using Genomics Data

Single nucleotide polymorphisms (SNPs) are one type of genetic variations and each SNP represents a difference in a single DNA building block, namely a nucleotide. Previous research demonstrated that SNPs can be used to identify the correct source population of an individual. In addition, variations in the DNA sequences have an influence on human diseases. In this regard, SNPs studies are helpful for personalized medicine and treatment. In the literature, unsupervised clustering methods especially principal component analysis (PCA) have been popular for studying population structure. In this study, we investigate supervised approaches, particularly the LASSO multinomial regression classification method, for recognizing individuals' origin genetic population. Then, we introduce PCA-LASSO as an extension of LASSO method that benefits from advantageous characteristics of both PCA and LASSO regression. The experimental results obtained on the 1,000 genome project dataset show PCA-LASSO's significantly high accuracy in prediction of individual's origin population.

Gut-Bioreactor and Human Health in Future

Gut-microbiome provides the complementary metabolic potential to the human system. To understand the active participation and the performance of the microbial community in human health, the concept of gut as a plug-flow reactor with the fed-batch mode of operation can provide better insight. The concept suggests the virtual compartmentalized gut with sequential stratification of the microbial community in response to a typical host genotype. It also provides the analysis plan for gut microbiome; and its relevance in developing health management options under the identified clinical conditions.