Please use this identifier to cite or link to this item: https://doi.org/10.1186/s12937-018-0340-3
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dc.titleGene-diet interaction effects on BMI levels in the Singapore Chinese population
dc.contributor.authorChang X.
dc.contributor.authorSun Y.
dc.contributor.authorHan Y.
dc.contributor.authorWang L.
dc.contributor.authorKhor C.-C.
dc.contributor.authorSim X.
dc.contributor.authorTai E.-S.
dc.contributor.authorLiu J.
dc.contributor.authorYuan J.-M
dc.contributor.authorKoh W.-P.
dc.contributor.authorVan Dam R.M.
dc.contributor.authorFriedlander Y.
dc.contributor.authorHeng C.-K.
dc.contributor.authorDorajoo R
dc.date.accessioned2020-09-09T10:30:53Z
dc.date.available2020-09-09T10:30:53Z
dc.date.issued2018
dc.identifier.citationChang X., Sun Y., Han Y., Wang L., Khor C.-C., Sim X., Tai E.-S., Liu J., Yuan J.-M, Koh W.-P., Van Dam R.M., Friedlander Y., Heng C.-K., Dorajoo R (2018). Gene-diet interaction effects on BMI levels in the Singapore Chinese population. Nutrition Journal 17 (1) : 31. ScholarBank@NUS Repository. https://doi.org/10.1186/s12937-018-0340-3
dc.identifier.issn1475-2891
dc.identifier.urihttps://scholarbank.nus.edu.sg/handle/10635/175401
dc.description.abstractBackground: Recent genome-wide association studies (GWAS) have identified 97 body-mass index (BMI) associated loci. We aimed to evaluate if dietary intake modifies BMI associations at these loci in the Singapore Chinese population. Methods: We utilized GWAS information from six data subsets from two adult Chinese population (N = 7817). Seventy-eight genotyped or imputed index BMI single nucleotide polymorphisms (SNPs) that passed quality control procedures were available in all datasets. Alternative Healthy Eating Index (AHEI)-2010 score and ten nutrient variables were evaluated. Linear regression analyses between z score transformed BMI (Z-BMI) and dietary factors were performed. Interaction analyses were performed by introducing the interaction term (diet x SNP) in the same regression model. Analysis was carried out in each cohort individually and subsequently meta-analyzed using the inverse-variance weighted method. Analyses were also evaluated with a weighted gene-risk score (wGRS) contructed by BMI index SNPs from recent large-scale GWAS studies. Results: Nominal associations between Z-BMI and AHEI-2010 and some dietary factors were identified (P = 0.047-0.010). The BMI wGRS was robustly associated with Z-BMI (P = 1.55 × 10- 15) but not with any dietary variables. Dietary variables did not significantly interact with the wGRS to modify BMI associations. When interaction analyses were repeated using individual SNPs, a significant association between cholesterol intake and rs4740619 (CCDC171) was identified (? = 0.077, adjPinteraction = 0.043). Conclusions: The CCDC171 gene locus may interact with cholesterol intake to increase BMI in the Singaporean Chinese population, however most known obesity risk loci were not associated with dietary intake and did not interact with diet to modify BMI levels. © 2018 The Author(s).
dc.sourceUnpaywall 20200831
dc.subjectadult
dc.subjectaged
dc.subjectAlternative Healthy Eating Index 2010 score
dc.subjectArticle
dc.subjectbody mass
dc.subjectcase control study
dc.subjectCCDC171 gene
dc.subjectChinese
dc.subjectcholesterol intake
dc.subjectcohort analysis
dc.subjectcontrolled study
dc.subjectdietary intake
dc.subjectfemale
dc.subjectgene
dc.subjectgene identification
dc.subjectgene interaction
dc.subjectgene locus
dc.subjectgenetic association
dc.subjectgenetic risk
dc.subjectgenome-wide association study
dc.subjectgenotype
dc.subjecthuman
dc.subjectmajor clinical study
dc.subjectmale
dc.subjectnutritional parameters
dc.subjectobesity
dc.subjectpopulation based case control study
dc.subjectpopulation genetics
dc.subjectpopulation research
dc.subjectprospective study
dc.subjectquality control procedures
dc.subjectrisk assessment
dc.subjectrisk factor
dc.subjectscoring system
dc.subjectSingapore
dc.subjectsingle nucleotide polymorphism
dc.subjectweighted gene risk score
dc.subjectadministration and dosage
dc.subjectAsian continental ancestry group
dc.subjectcross-sectional study
dc.subjectdiet
dc.subjectgenetics
dc.subjectgenotype
dc.subjectgenotype environment interaction
dc.subjectmeta analysis
dc.subjectmiddle aged
dc.subjectobesity
dc.subjectquantitative trait locus
dc.subjectred meat
dc.subjectvery elderly
dc.subjectAdult
dc.subjectAged
dc.subjectAged, 80 and over
dc.subjectAsian Continental Ancestry Group
dc.subjectBody Mass Index
dc.subjectCholesterol, Dietary
dc.subjectCross-Sectional Studies
dc.subjectDiet
dc.subjectFemale
dc.subjectGene-Environment Interaction
dc.subjectGenome-Wide Association Study
dc.subjectGenotype
dc.subjectHumans
dc.subjectMale
dc.subjectMiddle Aged
dc.subjectObesity
dc.subjectPolymorphism, Single Nucleotide
dc.subjectProspective Studies
dc.subjectQuantitative Trait Loci
dc.subjectRed Meat
dc.subjectSingapore
dc.typeArticle
dc.contributor.departmentDEAN'S OFFICE (DUKE-NUS MEDICAL SCHOOL)
dc.contributor.departmentDEPT OF MEDICINE
dc.contributor.departmentDEPT OF MICROBIOLOGY & IMMUNOLOGY
dc.contributor.departmentDEPT OF PAEDIATRICS
dc.contributor.departmentDEPT OF PHARMACY
dc.contributor.departmentDEPT OF PSYCHOLOGICAL MEDICINE
dc.contributor.departmentDUKE-NUS MEDICAL SCHOOL
dc.contributor.departmentSAW SWEE HOCK SCHOOL OF PUBLIC HEALTH
dc.description.doi10.1186/s12937-018-0340-3
dc.description.sourcetitleNutrition Journal
dc.description.volume17
dc.description.issue1
dc.description.page31
dc.published.statePublished
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