title: Effects of genetic variants on lipid parameters and dyslipidemia in a Chinese population.
abstract: A number of recent genome-wide association (GWA) studies have identified several novel genetic determinants of plasma lipid and lipoprotein concentrations in European populations. However, it is still unclear whether these loci identified in Caucasian GWA studies also exert the same effect on lipid and lipoprotein concentrations in a Chinese population. We genotyped 10 single-nucleotide polymorphisms (SNPs) in nine loci in a Chinese Han population sample (n = 4,192) and assessed the associations of these SNPs with metabolic traits, using linear regression adjusted for age, gender, diabetes status, and body mass index. Three variants (rs12654264, P ∼ 1.7 × 10(-6); rs3764261, P ∼ 7.1 × 10(-7); and rs4420638, P ∼ 1.1 × 10(-3)) showed strong evidence for association with total cholesterol; four variants (rs780094, P ∼ 1.8 × 10(-11); rs17145738, P ∼ 5.0 × 10(-7); rs326, P ∼ 2.3 × 10(-6); and rs439401, P ∼ 2.2 × 10(-5)) showed strong evidence for association with triglycerides, four variants (rs17145738, P ∼ 1.9 × 10(-4); rs326, P ∼ 9.7 × 10(-4); rs1800588, P ∼ 1.5 × 10(-7); and rs3764261, P ∼ 4.3 × 10(-14)) showed strong evidence for association with HDL-cholesterol (HDL-C), two variants (rs12654264, P ∼ 2.3 × 10(-5); and rs4420638, P ∼ 3.6 × 10(-4)) showed strong evidence for association with LDL-C, and four variants (rs326, P ∼ 2.8 × 10(-3); rs1800588, P ∼ 6.1 × 10(-4); rs3764261, P ∼ 2.0 × 10(-3); and rs4420638, P ∼ 9.4 × 10(-5)) showed strong evidence for association with total cholesterol-HDL-C-related ratio. These SNPs generated strong combined effects on lipid traits and dyslipidemia. Our findings indicate that the variants that associated with metabolic traits in Europeans may also play a role in a Chinese Han population.
authors.affiliation: Institutes of Biomedical Sciences, Fudan University, Shanghai, PR China.
authors.forename: Yun
authors.initials: Y
authors.lastname: Liu
authors.forename: Daizhan
authors.initials: D
authors.lastname: Zhou
authors.forename: Zhou
authors.initials: Z
authors.lastname: Zhang
authors.forename: Yiqing
authors.initials: Y
authors.lastname: Song
authors.forename: Di
authors.initials: D
authors.lastname: Zhang
authors.forename: Teng
authors.initials: T
authors.lastname: Zhao
authors.forename: Zhuo
authors.initials: Z
authors.lastname: Chen
authors.forename: Yun
authors.initials: Y
authors.lastname: Sun
authors.forename: Dabing
authors.initials: D
authors.lastname: Zhang
authors.forename: Yifeng
authors.initials: Y
authors.lastname: Yang
authors.forename: Qinghe
authors.initials: Q
authors.lastname: Xing
authors.forename: Xinzhi
authors.initials: X
authors.lastname: Zhao
authors.forename: He
authors.initials: H
authors.lastname: Xu
authors.forename: Lin
authors.initials: L
authors.lastname: He
mesh_terms: D000328:Adult; D000368:Aged; D000369:Aged, 80 and over; D044466:Asian People / Q000235:genetics*; D050171:Dyslipidemias / Q000235:genetics*; D005260:Female; D006801:Humans; D050356:Lipid Metabolism / Q000235:genetics*; D008055:Lipids / Q000097:blood; D008297:Male; D008875:Middle Aged; D020641:Polymorphism, Single Nucleotide
chemical_list: D008055:Lipids