Microarray-Based Detection ofCYP1A1,CYP2C9,CYP2C19,CYP2D6,GSTT1,GSTM1,MTHFR,MTRR,NQO1,NAT2,HLA-DQA1, andAB0Allele Frequencies in Native Russians

Abstract

Xenobiotic-metabolizing genes (e.g., Cytochromes P450, GST, NAT2, and NQO1), folate metabolism genes (e.g., MTHFR and MTRR), and major histocompatibility complex genes (e.g., HLA-DQA1) play multiple roles in the organism functioning. In addition, AB0 is the most clinically significant high-polymorphic gene in transfusion and transplantation medicine. Epidemiological data show that allele frequencies of these genes exhibit ethnic and geographic diversity. Besides, little is known about frequency distribution of the major polymorphic variants in native Russians. We developed biological microchips that allow us to analyze a spectrum of allelic variants in 12 different genes: CYP1A1, CYP2D6, CYP2C9, CYP2C19, GSTT1, GSTM1, MTHFR, MTRR, NQO1, NAT2, HLA-DQA1, and AB0. Using this composite methodological platform we have studied 352 DNA samples from healthy native Russian volunteers. The allelic frequencies of gene polymorphisms obtained are close to allelic frequencies observed in some European populations, as published earlier. These data were used in comparative studies to determine predisposition to tuberculosis, lymphoma, and leukemia in adults and to childhood acute leukemia. The HLA-DQA1 and AB0 allele frequencies were used to estimate forensic population parameters for these loci.

Evidence that polymorphisms in detoxification genes modulate the susceptibility for sporadic medullary thyroid carcinoma

“…Therefore, definite conclusions depend on studies with larger sample sizes that determine the risk estimates associated with other variants, gene-gene and gene-environment interactions. Biological microchips designed to identify polymorphisms in a large series of xenobiotic-metabolizing, apoptotic and cell cycle control genes may allow the investigation of multiple contributing factors to the susceptibility to s-MTC and help understand their relationship (20). Figure 1 Graphic representation of the relative contribution of the investigated factors to sporadic medullary thyroid carcinoma susceptibility according to a stepwise regression analysis.…”

GST M1-T1 null Allele Frequency Patterns in Geographically Assorted Human Populations: A Phylogenetic Approach

“…On the basis of interest in allele frequency patterns and availability of GST M1-T1 null allele frequency data shared by all populations, we choose 45 representative geographically assorted human populations around the world from 38 investigations reported by various authors from Asia [10, 14, 15, 18, 21–38], Europe [4–7, 9, 10, 39–42], Africa [21, 37, 43–50] and South America [49, 50] as summarized in Table 1. Of these 45 GAHPs, 4 were chosen from Eastern Asia (Japan, Korea, China and Mongolia [10, 21]), 4 from South Eastern Asia (Vietnam, Philippines, Indonesia and Singapore-Malay [10, 22–25]), 8 from Southern Asia India (Tamilnadu, Kerala, Karnataka, Andhra Pradesh, Maharashtra, West Bengal, Uttar Pradesh and Gujarat [14, 15, 18, 26–35, present study]), 3 from Southern Asia (Afghanistan, Iran, Pakistan [36–38]), 4 from Northern Europe (Sweden, Finland, Denmark and UK [5, 10, 39]), 4 from Southern Europe (Italy, Spain, Slovenia and Greece [5–7, 10]), 5 from Eastern Europe (Czech Republic, Bulgaria, Poland, Slovakia and Russia [9, 10, 39–42]), 3 from Western Europe (Netherlands, Germany and France [4, 5, 10]), 8 from Africa (Egypt, Nigeria, Xhosa tribe, Namibia, Cameroon, Ethiopia, Somalia and Zimbabwe [21, 37, 43–50]), 1 from South American Brazil [49, 50] and Caucasian (Americans and Canadians [10]).…”

“…Of these 45 GAHPs, 4 were chosen from Eastern Asia (Japan, Korea, China and Mongolia [10, 21]), 4 from South Eastern Asia (Vietnam, Philippines, Indonesia and Singapore-Malay [10, 22–25]), 8 from Southern Asia India (Tamilnadu, Kerala, Karnataka, Andhra Pradesh, Maharashtra, West Bengal, Uttar Pradesh and Gujarat [14, 15, 18, 26–35, present study]), 3 from Southern Asia (Afghanistan, Iran, Pakistan [36–38]), 4 from Northern Europe (Sweden, Finland, Denmark and UK [5, 10, 39]), 4 from Southern Europe (Italy, Spain, Slovenia and Greece [5–7, 10]), 5 from Eastern Europe (Czech Republic, Bulgaria, Poland, Slovakia and Russia [9, 10, 39–42]), 3 from Western Europe (Netherlands, Germany and France [4, 5, 10]), 8 from Africa (Egypt, Nigeria, Xhosa tribe, Namibia, Cameroon, Ethiopia, Somalia and Zimbabwe [21, 37, 43–50]), 1 from South American Brazil [49, 50] and Caucasian (Americans and Canadians [10]). The “Caucasian” population used in this study was arbitrarily termed as “West Asian Caucasians” (wAs_Cau) to precise the geographical region and allele frequency patterns.…”

Comparison of <em>CYP2C9</em>, <em>CYP2C19</em>, <em>CYP2D6</em>, <em>ABCB1</em>, and <em>SLCO1B1</em> gene-polymorphism frequency in Russian and Nanai populations

“…Moreover, we did not find any publications devoted to studying these SNPs among Russian volunteers, so we tried to use only the data of the research with maximal patient numbers where distribution by Hardy–Weinberg equilibrium was not significant. Patients from Moscow with stomach ulcers were included by the CYP2C19*17 C806T polymorphism, with χ 2 by Hardy–Weinberg of 1.12 ( P =0.29) 28. Patients from Moscow with hyperlipidemia were included by the SLCO1B1*5 T521C polymorphism, with χ 2 by Hardy–Weinberg of 2.8 ( P =0.09) 15…”