Liu Shijian, Wilson James G, Jiang Fan, Griswold Michael, Correa Adolfo, Mei Hao
Shanghai Children's Medical Center, School of Public Health, Shanghai Jiaotong University School of Medicine, Shanghai 200127, China.
Physiology & Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA.
Gene. 2016 Nov 30;593(2):315-21. doi: 10.1016/j.gene.2016.08.041. Epub 2016 Aug 26.
Genome-wide association study (GWAS) has been successful in identifying obesity risk genes by single-variant association analysis. For this study, we designed steps of analysis strategy and aimed to identify multi-variant effects on obesity risk among candidate genes.
Our analyses were focused on 2137 African American participants with body mass index measured in the Jackson Heart Study and 657 common single nucleotide polymorphisms (SNPs) genotyped at 8 GWAS-identified obesity risk genes.
Single-variant association test showed that no SNPs reached significance after multiple testing adjustment. The following gene-gene interaction analysis, which was focused on SNPs with unadjusted p-value<0.10, identified 6 significant multi-variant associations. Logistic regression showed that SNPs in these associations did not have significant linear interactions; examination of genetic risk score evidenced that 4 multi-variant associations had significant additive effects of risk SNPs; and haplotype association test presented that all multi-variant associations contained one or several combinations of particular alleles or haplotypes, associated with increased obesity risk.
Our study evidenced that obesity risk genes generated multi-variant effects, which can be additive or non-linear interactions, and multi-variant study is an important supplement to existing GWAS for understanding genetic effects of obesity risk genes.
全基因组关联研究(GWAS)已成功通过单变量关联分析鉴定肥胖风险基因。在本研究中,我们设计了分析策略步骤,旨在鉴定候选基因中对肥胖风险的多变量效应。
我们的分析聚焦于杰克逊心脏研究中2137名测量了体重指数的非裔美国参与者,以及在8个GWAS鉴定的肥胖风险基因处进行基因分型的657个常见单核苷酸多态性(SNP)。
单变量关联检验显示,经多重检验校正后,没有SNP达到显著水平。随后聚焦于未校正p值<0.10的SNP的基因-基因相互作用分析,鉴定出6个显著的多变量关联。逻辑回归显示,这些关联中的SNP没有显著的线性相互作用;遗传风险评分检验证明,4个多变量关联具有风险SNP的显著加性效应;单倍型关联检验表明,所有多变量关联都包含一个或几个与肥胖风险增加相关的特定等位基因或单倍型组合。
我们的研究证明,肥胖风险基因产生多变量效应,这种效应可以是加性的或非线性相互作用,多变量研究是现有GWAS的重要补充,有助于理解肥胖风险基因的遗传效应。