Diversity of killer cell immunoglobulin-like receptor genes in four ethnic groups in China

• DOI: 10.1007/s00251-011-0530-z
• Publication Date: 2011-05-09T10:07:48Z
• Authors: Yufeng Yao; Lei Shi; Yufen Tao; Keqin Lin; Shuyuan Liu; Liang Yu; Zhaoqing Yang; Wen Yi; Xiaoqin Huang; Hao Sun; Jiayou Chu; Li Shi
• Keywords: 0301 basic medicine; China; 03 medical and health sciences; Genetics, Population; 0302 clinical medicine; Genotype; Haplotypes; Receptors, KIR; Genetic Loci; Humans; 3. Good health

Killer cell immunoglobulin-like receptors (KIRs) show extensive variation in terms of gene content and allelic polymorphisms among different populations. The aim of this study was to analyze the distribution of KIR genes in the Bulang, Nu, Yugu, and Zhuang ethnic groups, which belong to four different language families in China, and thus to provide basic KIR gene and genotype data for these Chinese ethnic groups. Genotyping of 16 KIR genes was performed in 425 unrelated individuals using the polymerase chain reaction-sequence-specific oligonucleotide probe method with the Luminex MultiAnalyte Profiling System. The four framework KIR genes were detected in all four ethnic groups. The activating KIR genes as well as the inhibitory KIR genes showed extreme diversity among these four populations. A total of 35 distinct KIR genotypes were identified, one of which was previously unknown. The four most common genotypes were identified in all four populations and comprised 66.1~91.1% of all the genotypes. The group A haplotype occurred more frequently than the group B haplotype in the Nu, Yugu, and Zhuang populations, as in other East Asian populations. In contrast, the group A and group B haplotypes occurred equally in the Bulang population. The results of the present study suggested that the KIR genes and genotypes are diverse in these four ethnic groups, and each ethnic group has its own characteristic KIR distribution. The findings with respect to KIR gene diversity in these four populations should provide relevant genomic diversity data for the future study of viral infections, autoimmune diseases, and reproductive fitness.