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.2020 Mar 27;10(1):5652.
doi: 10.1038/s41598-020-62645-0.

Genome-wide analyses disclose the distinctive HLA architecture and the pharmacogenetic landscape of the Somali population

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Genome-wide analyses disclose the distinctive HLA architecture and the pharmacogenetic landscape of the Somali population

Abshir A Ali et al. Sci Rep..

Abstract

African populations are underrepresented in medical genomics studies. For the Somali population, there is virtually no information on genomic markers with significance to precision medicine. Here, we analyzed nearly 900,000 genomic markers in samples collected from 95 unrelated individuals in the North Eastern Somalia. ADMIXTURE program for estimation of individual ancestries revealed a homogenous Somali population. Principal component analysis with PLINK software showed approximately 60% East African and 40% West Eurasian genes in the Somali population, with a close relation to the Cushitic and Semitic speaking Ethiopian populations. We report the unique features of human leukocyte antigens (HLA) in the Somali population, which seem to differentiate from all other neighboring regions compared. Current study identified high prevalence of the diabetes type 1 (T1D) predisposing HLA DR-DQ haplotypes in Somalia. This finding may explain the increased T1D risk observed among Somali children. In addition, ethnic Somalis were found to host the highest frequencies observed thus far for several pharmacogenetic variants, including UGT1A4*2. In conclusion, we report that the Somali population displays genetic traits of significance to health and disease. The Somali dataset is publicly available and will add more information to the few genomic datasets available for African populations.

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Conflict of interest statement

The authors declare no competing interests.

Figures

Figure 1
Figure 1
Maximum likelihood estimation of the ethnic descent of the 95 Somali individuals in this study. Data used comprised 849,267 markers in the Axiom PMRA multilocus SNP genotyping. PLINK 1.9 with a setting of MAF ≥ 0.4 was used along with analysis by the ADMIXTURE 1.3.0 program with K = 3 and K = 12, respectively (http://software.genetics.ucla.edu/admixture/). (A) K = 3 proportions represent East-African genes (blue), genes from West Asia (red), and North African genes (green), respectively. (B) K = 12 splits the supposedly non-African contributions into several minor bands (mixed colors), although the similarity of individuals is well preserved.
Figure 2
Figure 2
Principal Component Analysis (PCA) of the Axiom PMRA array data from 95 Somali study subjects. Somali data were projected onto African-West Eurasian PC1 and PC2 data from PGG population. The Luhya population in Kenya clustered with West Africans (far left on the diagram) rather than with East Africans.
Figure 3
Figure 3
Identity by descent (IBD) analysis performed on the Somali study participants (n = 95). The plot shows observed identity by descent for each individual. Each of the 2 outliers shown in the graph was supposedly a first cousin to another individual in the cohort.
Figure 4
Figure 4
Haplotype structure of theVKORC1 gene in the Somali population. (A) A region of 5805 base pairs in theVKORC1 gene is shown with tag SNPs for differentVKORC1 haplotypes. The SNPs rs2359612, rs8050894, and rs9934438 previously reported to be part ofVKORC*2 haplotype and found to be predictive of warfarin dose requirement in both European and Asian-descent individuals are in linkage disequilibrium in the Somali population. SNP rs61742245 representsVKORC1 Asp36Tyr associated with warfarin resistance. 16 of 95 Somali individuals were heterozygous to this variant. (B) Proportion ofVKORC1 haplotypes in the Somali population.
Figure 5
Figure 5
HLA Allele frequencies in different world populations, including Somalis. (A) Hierarchical population clustering of 61 HLA alleles in different world populations. Somalia clustered separately from neighboring countries/populations. (B) Principal component analysis (PCA) using the same set of 61 HLA alleles. Color and size of cos2 signify the quality of representation; the larger a circle is the greater is its contribution to the variables. It is worth to note the considerable variation between African populations compared to non-African populations who seem to be closer to one another. (C) Population contribution to the first two principal components. The Somali population has the largest contribution to the first two dimensions. The Ethiopian population in the analysis did not include Ethiopian Somalis, but rather comprised Oromo, Amhara and Ethiopian Jews. Populations compared were Somalia (SOM), Central Africa (CAF), Kenyan Luo tribe (KNL), Ethiopia (ETH), North Africa (NAF), South Arabia (SAR), North Europe (NEU), Han Chinese (CHH), and Native Americans (AMR).
Figure 6
Figure 6
Correlations of HLA (n = 61) and DMET (n = 15) alleles between Somali and Ethiopian populations. Horizontal and vertical axes represent allele frequencies in each population.
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