Type 2 diabetes mellitus: distribution of genetic markers in Kazakh population

Abstract

Background: Ethnic differences exist in the frequencies of genetic variations that contribute to the risk of common disease. This study aimed to analyse the distribution of several genes, previously associated with susceptibility to type 2 diabetes and obesity-related phenotypes, in a Kazakh population.

Methods: A total of 966 individuals belonging to the Kazakh ethnicity were recruited from an outpatient clinic. We genotyped 41 common single nucleotide polymorphisms (SNPs) previously associated with type 2 diabetes in other ethnic groups and 31 of these were in Hardy-Weinberg equilibrium. The obtained allele frequencies were further compared to publicly available data from other ethnic populations. Allele frequencies for other (compared) populations were pooled from the haplotype map (HapMap) database. Principal component analysis (PCA), cluster analysis, and multidimensional scaling (MDS) were used for the analysis of genetic relationship between the populations.

Results: Comparative analysis of allele frequencies of the studied SNPs showed significant differentiation among the studied populations. The Kazakh population was grouped with Asian populations according to the cluster analysis and with the Caucasian populations according to PCA. According to MDS, results of the current study show that the Kazakh population holds an intermediate position between Caucasian and Asian populations.

Conclusion: A high percentage of population differentiation was observed between Kazakh and world populations. The Kazakh population was clustered with Caucasian populations, and this result may indicate a significant Caucasian component in the Kazakh gene pool.

Keywords: Kazakh population; OpenArray; single-nucleotide polymorphism; type 2 diabetes.

Figure 1

PCA plot of the first two components, comparing the minor allele frequencies of 12 populations. The two ovals and one circle mean three obtained clusters, and the numbers represent vectors for these clusters. The numbers (SNPs) are provided in Table S2. Abbreviations: PC, principal component; PCA, principal component analysis; var., variance.

Figure 2

The dendrogram based on Nei’s genetic distance matrix. Abbreviations: ASW, Americans with African ancestry living in Southwest USA; CEU, Utah residents with Northern and Western European ancestry from the Centre d’Etude du Polymorphisme Humain collection; CHB, Han Chinese population in Beijing, China; CHD, Chinese population in metropolitan Denver, CO; GIH, Gujarati Indian population in Houston, TX; JPT, Japanese population in Tokyo, Japan; KZ, Kazakh population; LWK, Luhya population in Webuye, Kenya; MEX, Mexicans in Los Angeles, CA; MKK, Maasai in Kinayawa, Kenya; TSI, Tuscan population in Italy; YRI, Yoruban population in Ibadan, Nigeria.

Figure 3

MDS representation of the Nei’s genetic distance matrices between studied populations. Abbreviations: ASW, Americans with African ancestry living in Southwest USA; CEU, Utah residents with Northern and Western European ancestry from the Centre d’Etude du Polymorphisme Humain collection; CHB, Han Chinese population in Beijing, China; CHD, Chinese population in metropolitan Denver, CO; GIH, Gujarati Indian population in Houston, TX; JPT, Japanese population in Tokyo, Japan; KZ, Kazakh population; LWK, Luhya population in Webuye, Kenya; MDS, multidimensional scaling; MEX, Mexicans in Los Angeles, CA; MKK, Maasai in Kinayawa, Kenya; TSI, Tuscan population in Italy; YRI, Yoruban population in Ibadan, Nigeria.