Programa de Pós-Graduação em Ciências Genômicas e Biotecnologia, Universidade Católica de Brasília, Brasília, DF, Brazil.
Autoimmunity. 2010 Jun;43(4):308-16. doi: 10.3109/08916930903405883.
The rs2476601-T allele at the protein tyrosine phosphatase non-receptor type 22 (PTPN22) gene has been consistently associated with several autoimmune diseases in European-derived populations. However, little is known about the allele and haplotype frequency distributions in PTPN22 among populations derived from other ethnic groups. In the present study, the allele and haplotype frequency distributions of six single nucleotide polymorphisms (SNPs) in the PTPN22 gene were compared among Brazilian populations and HapMap phase 3 dataset. A total of 10 different population samples were evaluated. Additionally, in admixed populations, individual genetic ancestries were estimated for Native American, African, and European contributions. Estimated individual ancestries were used as quantitative traits in a conditional approach for single-marker and haplotype-specific regression analyses. It was shown that several SNPs and haplotypes have different frequencies among different ethnic populations. Individual genetic ancestries were not associated with the rs2476601-T allele, but were associated with PTPN22 haplotypes in Brazilian, Mexican, and African-American admixed populations. Our results suggest caution in the interpretation of results found in association studies involving PTPN22 polymorphisms in admixed populations. Correction for stratification generated by admixture should be mandatory to minimize or avoid chances of spurious association.
蛋白酪氨酸磷酸酶非受体型 22(PTPN22)基因中的 rs2476601-T 等位基因与欧洲人群中的多种自身免疫性疾病一直相关。然而,关于其他种族人群中 PTPN22 基因的等位基因和单倍型频率分布知之甚少。在本研究中,比较了巴西人群和 HapMap 第三阶段数据集的 PTPN22 基因中六个单核苷酸多态性(SNP)的等位基因和单倍型频率分布。共评估了 10 个不同的人群样本。此外,在混合人群中,估计了美洲原住民、非洲和欧洲的个体遗传血统。估计的个体遗传血统被用作条件方法中单标记和单倍型特异性回归分析的定量特征。结果表明,不同的种族群体中存在几种 SNP 和单倍型的不同频率。个体遗传血统与 rs2476601-T 等位基因无关,但与巴西、墨西哥和非裔美国人混合人群中的 PTPN22 单倍型有关。我们的结果表明,在涉及混合人群中 PTPN22 多态性的关联研究中,对结果的解释应谨慎。为了最小化或避免虚假关联的可能性,必须对由混合引起的分层进行校正。