Shijian Liu1, James G Wilson2, Fan Jiang3, Michael Griswold4, Adolfo Correa5, Hao Mei6. 1. Shanghai Children's Medical Center, School of Public Health, Shanghai Jiaotong University School of Medicine, Shanghai 200127, China. Electronic address: arrow64@163.com. 2. Physiology & Biophysics, University of Mississippi Medical Center, Jackson, MS 39216, USA. Electronic address: jgwilson2@umc.edu. 3. Shanghai Children's Medical Center, School of Public Health, Shanghai Jiaotong University School of Medicine, Shanghai 200127, China. Electronic address: fanjiang@shsmu.edu.cn. 4. Department of Data Science, University of Mississippi Medical Center, Jackson, MS 39216, USA. Electronic address: mgriswold@umc.edu. 5. Jackson Heart Study, University of Mississippi Medical Center, Jackson, MS 39216, USA. Electronic address: ACorrea@umc.edu. 6. Shanghai Children's Medical Center, School of Public Health, Shanghai Jiaotong University School of Medicine, Shanghai 200127, China; Department of Data Science, University of Mississippi Medical Center, Jackson, MS 39216, USA. Electronic address: hmei@umc.edu.
Abstract
OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
OBJECTIVE: 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. METHODS: 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. RESULTS: 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. CONCLUSIONS: 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.
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