Population structure analysis on 2504 individuals across 26 ancestries using bioinformatics approaches

Background

Characterizing genetic diversity is crucial for reconstructing human evolution and for understanding the genetic basis of complex diseases; however, human population genetics are very complicated. Previously, we proved that based on the Hardy-Weinberg equilibrium, the heterozygous vs. non-reference homozygous single nucleotide polymorphism (SNP) ratio (het/nonref-hom) is two[1]. Later, we found that this ratio is race dependent, with African being the most genetically diverse race and Asian being the most homozygous[2]. This observation prompted us to conduct further study to understand the reasoning behind this diversity.

Materials and methods

Using the 1000 Genomes Project (1000G) released genomic data of 2504 individuals (26 races from five major-races), we first computed the (het/nonref-hom) ratio which has been applied as a quality control parameter for sequencing data[1, 3].

Results

As expected, we found that the het/nonref-hom ratio is strongly associated with human ancestry. Africans had the highest het/nonref-hom ratios, followed by Americans and Europeans, and East Asians had the lowest (Figure 1). More interestingly, the het/nonref-hom ratios of South Asians are much higher than those of East Asians, and Americans showed the highest range (Figure 1). Thus we further quantitatively analyzed genetic variation in human populations on the 1000G dataset of 10¹¹ observed genotypes (2504 individuals at 13424776 SNPs) using Structure 2.3.4[4]. The resulting population structure is consistent with the major geographical regions. All races identified a dominate origin population, except Americans who had the most variation in the structure, represented by several populations including the dominant population of Europeans (Figure 2). Moreover, East Asians and South Asians were found to originate from different ancestries (Figure 2).

Figure 1

het/nonref-hom ratio across 26 ancestries.

Figure 2

Population structure inferred from the 1000G genetic data.

Conclusions

Using novel bioinformatics approach, we identified new insights into the history and geography of human evolution, and are valuable for tracking human migration and adaptation to local conditions.