Study On The Relationship Between Single Nucleotide Polymorphisms (SNPs) And MRNA Expression Of Lipid-susceptibility Genes And Lipid Levels In Children And Adolescents, Beijing

Backgrounds:Epidemiological evidences indicated that abnormal lipids are strong independent risk factor for cardiovascular disease. Genetic and environmental factors contribute to the lipid levels. It is known that multiple genes participate into the regulation of lipid metabolism, common and rare variants in lipid-susceptibility genes may collectively determine blood lipid levels. Genome-wide association studies have identified association signals containing both classically established genes and previously unknown genomic regions as determinants of lipid levels, which is helpful to understand the mechanism of lipid metabolism, and get diagnosis goals from these identified locus. Levels of lipids variables in childhood were associated with levels in adulthood, identifying genes and genetic variants associated with blood lipids will enrich our understanding of blood lipid metabolism.Objective:1. In the current study, we aimed to test the associations between seven variants (GALNT2(rs2144300), GCKR (rs1260326, rs1260333), LPL (rs10105606), TRIB1(rs2954029), ANGPTL3(rs1748195), and APOA5-A4-C3-A1cluster (rs964184)) and levels of triglycerides (TG), total cholesterol (TC), high density lipoprotein cholesterol (HDL-C), and low density lipoprotein cholesterol (LDL-C) as continuous traits in Chinese Han children population. Then we estimated the association of variants with risks of dyslipidemia as dichotomized traits.2. To test the relationship between genotypes of lipid-susceptibility loci and message RNA (mRNA).3. To investigate the effect of genes-environment interaction on dyslipidemia in Chinese Han children.Methods:1. Subjects:1) Association study:Subjects were recruited from the BCAMS study, a cross-sectional population-based survey carried out in2004. The survey included a questionnaire, medical examination, anthropometric measurement, and finger capillary blood tests among a representative sample (n=19593, boys accounted for50%of the sample) of Beijing school-age children aged6to18years from urban and rural districts. Anthropometric measurement included weight, height, waist circumference, and FMP by bioimpedance analysis. Birth weight was collected based on a self-report questionnaire, which was completed by parents or guardians. Within this large group of children,1229obese,655overweight and1620normal weight children were recruited and diagnosed by the Chinese age-and sex-specific BMI cutoffs, there were14un-grouped by the diagnosis criteria for missing data. Venipuncture blood samples were collected after a12-hour overnight fast.2) mRNA expression study: Children aged5to15years were recruited from patient wards in Capital Institute of Pediatrics from January to March in2013. The participants was diagnosed without chronic diseases of heart, lung, liver, kidney and other vital organs, without major physical disabilities, such as spinal and lower limb disability, and without endocrine diseases and drug-induced obesity. Anthropometric measurement included weight, height, waist circumference. The medical identification numbers were recorded and venipuncture blood samples were collected after a12-hour overnight fast.2. Genotyping:Genomic DNA was isolated from peripheral blood white cells using the salt fractionation method or commercial kit. Seven variants (GALNT2(rs2144300), GCKR (rs1260326, rs1260333), LPL (rs10105606), TRIB1(rs2954029), ANGPTL3(rs1748195), and APOA5-A4-C3-A1cluster (rs964184)) were selected from previous GWAS studies. All genotyping were performed by TaqMan probes Allelic Discrimination Assays with the GeneAmp7900Sequence Detection System.3. mRNA expression:Total RNA from venipuncture blood was extracted, and mRNA levels of GALNT2, GCKR, LPL, TRIB1, ANGPTL3and APOA5-A4-C3-A1cluster were detected by real time quantitative polymerase chain reaction (RTQ-PCR). The house-keeping gene is GAPDH.4. Statistical analysis:Access2003was used for data entry and SPSS, version13.0(SPSS Inc., Chicago, Illinois) was used for data analysis. The statistical methods included ANCOVA, multiple linear regression, multiple logistic regression.Results:With genetic additive model, after age, sex, BMI, and puberty stage, and correction with FDR (p<0.05), six SNPs(rs2144300,rs1260333,rs1260326,rs10105606, rs1748195and rs964184) associated with triglycerides (p<0.05). Two SNPs (rs2954029and rs964184) associated with total cholesterol (p<0.05). Two SNP (rs10105606and rs964184) associated with HDL-C(p<0.05). Four SNPs (rs1260333, rs1260326, rs2954029and rs964184) associated with LDL-C (p<0.05)).After adjustment for age, sex, BMI, and puberty stage, there were significant associations between rs1260333(OR=1.22,95%CI1.09-1.36), rs1260326(OR=1.21,95%CI1.09-1.39), rs964184(OR=1.37,95%CI1.20-1.55) and dyslipidemia. Five SNPs, rs2144300(OR=1.42,95%C11.12-1.80), rs1260333(OR=1.34,95%CI1.13-1.59), rs1260326(OR=1.31,95%CI1.11-1.55), rs1748195(OR=1.31,95%CI1.07-1.61), and rs964184(OR=1.71,95%CI1.43-2.06) associated with hypertriglyceridemia. Only rs964184associated with low HDL-C (OR=1.34,95%CI1.12-1.61).With the increased genetic risk scores (GRSs), there were a significant increasing in the lipid traits (TG, TC, HDL-C and LDL-C levels) with a significant stepwise manner (p<0.05). And with risk scores increased, the risks of dislipidemia, hypertriglyceridemia, hypercholesterolemia, low HDL-C and high LDL-C is increasing with17%,40%,9%,10%and11%.Under the genetic additive model of the loci genotyping, mRNA of GCKR gene decreased with the number of risk allele of rs1260333and rs1260326; mRNA of ANGPTL3increased with the number of risk allele of rs1748195; we didn’t found the RR homozygotes for the risk allele for APOA5-A4-C3-A1cluster SNP rs964184, mRNA level of heterozygotes with one risk allele (CR) was lower than non-risk allele homozygotes (CC).Using factor analysis methodology, five main factors, including lipid-protein, fruits&vegetable, smoke&drinking, sedentary behavior and physical exercise in spare time were extracted. We analyzed the interaction between gene and environment for dyslipidemia. The SNP rs1260333in GCKR interacted with smoke&drinking, sedentary behavior and physical exercise in spare time, and the attributable percent (AP) is2.61%,7.56%and3.89%, respectively. rs1260326interacted with smoke&drinking, sedentary behavior and physical exercise in spare time,too. And the AP is9.60%,6.95%and0.35%, respectively. APOA5-A4-C3-A1cluster SNP rs964184interacted with lipid-protein, vegetables&fruits, sedentary behavior and physical exercise in spare time, the AP is15.73%,6.82%,2.15%and26.07%.For hypertriglyceridemia, GALNT2SNP rs2144300interacted with smoke&drinking, fruits&vegetable, sedentary behavior and physical exercise in spare time, the AP is21.41%,18.88%,2.71%and25.58%, respectively. GCKR SNP rs1260333and rs1260326interacted with lipid-protein, the AP is8.30%and13.34%, respectively. ANGPTL3SNP rs1748195interacted with lipid-protein and sedentary behavior. The AP is4.04%and32.76%, respectively. APOA5-A4-C3-A1cluster SNP rs964184interacted with all five main factors, the AP is27.70%、5.39%、3.67%、8.35%and27.02%for lipid-protein, fruits&vegetable, smoke&drinking, sedentary behavior and physical exercise in spare time, respectively.Conclusions:Our study firstly validated the association of seven variants with blood lipids and risk of dyslipidemia among the Chinese Beijing children and adolescents.There were accumulative risk effects of these seven variants on lipid traits, risks of dislipidemia and hypertriglyceridemia.The trends between genotype and mRNA levels, implied that the genes expression might be influenced by the lipid-susceptibility SNPs.There were interactions between lipid-susceptible SNPs and environmental factors. Low vegetables&fruits, less physical exercise and smoke&drinking might co-mordify the effects of lipid-susceptibility genes on the risk of dyslipidemia.